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Green Engineering
and Technology
Green Engineering and Technology:
Concepts and Applications
Series Editors:
Brojo Kishore Mishra, GIET University, India
and Raghvendra Kumar, LNCT College, India
Environmental degradation is an important issue these days for the whole world.
Different strategies and technologies are used to save the environment. Technology is
the application of knowledge to practical requirements. Green technologies encom-
pass various aspects of technology that help us reduce the human impact on the envi-
ronment and create ways of sustainable development. This book series will enlighten
the green technology in different ways, aspects, and methods. This technology helps
people to understand the use of different resources to fulll their needs and demands.
Some points will be discussed as the combination of involuntary approaches and
government incentives, a comprehensive regulatory framework will encourage the
diffusion of green technology, and least developed countries and developing states of
small islands require unique support and measures to promote the green technologies.
Green Internet of Things for Smart Cities
Concepts, Implications, and Challenges
Edited by Surjeet Dalal, Vivek Jaglan, and Dac-Nhuong Le
Green Materials and Advanced Manufacturing Technology
Concepts and Applications
Edited by C. Samson Jerold Samuel, M. Suresh, Arunseeralan Balakrishnan,
andS. Gnansekaran
Cognitive Computing Using Green Technologies
Modeling Techniques and Applications
Edited by Asis Kumar Tripathy, Chiranji Lal Chowdhary, Mahasweta Sarkar,
andSanjaya Kumar Panda
Handbook of Green Engineering Technologies for Sustainable Smart Cities
Edited by Saravanan Krishnan and G. Sakthinathan
Green Engineering and Technology
Innovations, Design, and Architectural Implementation
Edited by Om Prakash Jena, Alok Ranjan Tripathy, and Zdzislaw Polkowski
For more information about this series, please visit: https://www.routledge.com/Green-
Engineering-and-Technology-Concepts-and-Applications/book-series/CRCGETCA
Green Engineering
and Technology
Innovations, Design, and Architectural
Implementation
Edited by
Om Prakash Jena
Alok Ranjan Tripathy
Zdzislaw Polkowski
MATLAB® is a trademark of e MathWorks, Inc. and is used with permission. e MathWorks
does not warrant the accuracy of the text or exercises in this book. is book’s use or discussion
of MATLAB® software or related products does not constitute endorsement or sponsorship by
eMathWorks of a particular pedagogical approach or a particular use of the MATLAB® software.
First edition published 2021
by CRC Press
6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742
and by CRC Press
2 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN
© 2021 selection and editorial matter, Om Prakash Jena, Alok Ranjan Tripathy, Zdzislaw Polkowski;
individual chapters, the contributors
CRC Press is an imprint of Taylor & Francis Group, LLC
e right of Om Prakash Jena, Alok Ranjan Tripathy, and Zdzislaw Polkowski to be identied as the
authors of the editorial material, and of the authors for their individual chapters, has been asserted
in accordance with sections 77 and 78 of the Copyright, Designs, and Patents Act 1988.
Reasonable eorts have been made to publish reliable data and information, but the author and
publisher cannot assume responsibility for the validity of all materials or the consequences of
their use. e authors and publishers have attempted to trace the copyright holders of all material
reproduced in this publication and apologize to copyright holders if permission to publish in this
form has not been obtained. If any copyright material has not been acknowledged, please write and
let us know so we may rectify in any future reprint.
Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced,
transmitted, or utilized in any form by any electronic, mechanical, or other means, now known
or hereafter invented, including photocopying, microlming, and recording, or in any information
storage or retrieval system, without written permission from the publishers.
For permission to photocopy or use material electronically from this work, access www.copyright.
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Trademark notice: Product or corporate names may be trademarks or registered trademarks and are
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Library of Congress Cataloging‑in‑Publication Data
Names: Jena, Om Prakash, editor. | Tripathy, Alok Ranjan, editor. |
Polkowski, Zdzislaw, editor.
Title: Green engineering and technology : innovations, design, and
architectural implementation / edited by Om Prakash Jena, Alok Ranjan
Tripathy, Zdzislaw Polkowski.
Description: First edition. | Boca Raton : CRC Press, 2021. | Series: Green
engineering and technology : concepts and applications | Includes
bibliographical references and index.
Identiers: LCCN 2020057631 (print) | LCCN 2020057632 (ebook) |
ISBN 9780367758059 (hbk) | ISBN 9781003176275 (ebk)
Subjects: LCSH: Sustainable engineering.
Classication: LCC TA163 .G44 2021 (print) | LCC TA163 (ebook) | DDC 628—dc23
LC record available at https://lccn.loc.gov/2020057631
LC ebook record available at https://lccn.loc.gov/2020057632
ISBN: 978-0-367-75805-9 (hbk)
ISBN: 978-1-032-00893-6 (pbk)
ISBN: 978-1-003-17627-5 (ebk)
Typeset in Times
by codeMantra
v
Contents
Preface.......................................................................................................................ix
Editors .................................................................................................................... xiii
Contributors ............................................................................................................. xv
Chapter 1 Cloud and Green IoT-based Technology for Sustainable
Smart Cities .......................................................................................... 1
Parthasarathi Pattnayak, Om Prakash Jena, and Saundarya Sinha
Chapter 2 Dynamic Models for Enhancing Sustainability in Automotive
Component Manufacturing Systems .................................................. 21
Jangam Ramesh, M. Mohan Ram, and Y.S. Varadarajan
Chapter 3 Internet of Agriculture Things (IoAT): A Novel Architecture
Design Approach for Open Research Issues ......................................35
K. Lova Raju and V. Vijayaraghavan
Chapter 4 E-Navigation: An Indoor System for Green City Sustainable
Development Using a UGU Engine Architecture .............................. 57
Ajay B. Gadicha, Vijay B. Gadicha, and Om Prakash Jena
Chapter 5 Biomass Waste-derived Electrode Material and Bio-based Solid
Electrolyte for Sustainable Energy Systems ......................................71
Sudhakar Y. N. and Anitha Varghese
Chapter 6 RF Energy Harvesting for WSNs: Overview, Design
Challenges,and Techniques ............................................................... 85
J. John Paul and A. Shobha Rekh
Chapter 7 Sustainable and Renewable Isolated Microhydropower
Generation Using a Variable Asynchronous Generator
Controlled by a Fuzzy PI AC–DC–AC Converter and
D-STATCOM ................................................................................... 103
P. Devachandra Singh and Sarsing Gao
vi Contents
Chapter 8 Phytoconstituents of Common Weeds of Uttarakhand Proposed
as Bio-pesticides or Green Pesticides with the Use of In-Silico
and In-Vitro Techniques ................................................................... 121
Somya Sinha, Kumud Pant, Manoj Pal, Devvret Verma,
and Ashutosh Mishra
Chapter 9 On Energy Harvesting in Green Cognitive Radio Networks ........... 137
Avik Banerjee and Santi P. Maity
Chapter 10 Mitigation on the Impact of Electric Vehicle Charging Stations
by Splitting the Capacity and Optimally Locating on a
Recongured RDS ........................................................................... 151
M. Satish Kumar Reddy and K. Selvajyothi
Chapter 11 Parameter Estimation of a Single Diode PV Cell Using
an Intelligent Computing Technique ................................................ 177
Shilpy Goyal, Parag Nijhawan, and Souvik Ganguli
Chapter 12 On the Dynamics of Cellular Automata-based Green Modeling
toward Job Processing with Group-based Industrial Wireless
Sensor Networks in Industry 4.0 ...................................................... 207
Arnab Mitra and Avishek Banerjee
Chapter 13 Green Cloud Computing: An Emerging Trend of GIT
in Cloud Computing ......................................................................... 225
Satya Sobhan Panigrahi, Bibhuprasad Sahu,
AmrutanshuPanigrahi, and Sachi Nandan Mohanty
Chapter 14 Internet of Things for Green Technology .........................................243
Saurabh Bhattacharya and Manju Pandey
Chapter 15 Green Health: Making Green Healthcare Using Reinforcement
Learning in Fog-assisted Cloud Environment .................................259
Saeed A. L. Amodi, Sudhansu Shekhar Patra,
OmPrakashJena, Suman Bhattacharya, Nitin S. Goje,
and Rabindra Kumar Barik
viiContents
Chapter 16 Smart Agricultural Robot ................................................................. 273
Vimal Kumar M. N., Aakash Ram S., and Bennet Nifn N.
Chapter 17 A Survey of Lightweight Cryptography for Power-constrained
IoT Devices: Security Challenges and Issues ................................... 293
Sunil Kumar and Dilip Kumar
Chapter 18 Nanogenerator-based Sensors for Human Pulse Measurement ....... 315
Ammu Anna Mathew, S. Vivekanandan, and
ArunkumarChandrasekhar
Chapter 19 Future Challenges and Applications in Green Technology..............327
Kali CharanRath, Ravindra N. Bulakhe, and
AnuradhaB.Bhalerao
Chapter 20 Implementation and Use of Green Computing in Polish
Companies versus Implementation of Features Characteristic
of Teal Organizations ....................................................................... 343
Agnieszka Rzepka, Maria Kocot, and Elżbieta Jędrych
Chapter 21 Design of a Pentagon Slot-based Multi-band Linear Antenna
Array for Energy-efcient Communication: Future Challenges
and Applications in Green Technologies..........................................357
Satheesh Kumar P. and Balakumaran T.
Index ...................................................................................................................... 369
ix
Preface
Without compromising on economic feasibility and productivity, green engineering
comprises the design, marketing, and use of processes and goods in a way that elimi-
nates waste, promotes sustainability, and minimizes risk to human health and the
environment. Green engineering promotes the idea to protect human health and the
environment, in the design and developmental phase of a process or product, when
applied early and may have the greatest impact and cost-effectiveness.
Green computing develops engineering strategies and applies them while becom-
ing mindful of local geography, aspirations, and cultures. It creates engineering strat-
egies beyond existing or dominant technologies, to achieve sustainability, enhance,
innovate, and invent (technologies).
Instead of being circumstantial, designers must aim to ensure that all inherently
non-hazardous materials and energy inputs and outputs are as practicable. The con-
cepts of Green engineering are inherent. Waste reduction is better than handling
or cleaning up waste after it has been made. Separation and purication activities
should be planned to minimize the consumption of energy and the use of materials.
This maximizes the efciency of products, processes, and procedures.
Green engineering approaches the design of goods and processes to achieve one
or more of the goals by applying nancially and technologically feasible principles as
follows: (1) reducing the amount of pollution generated by the construction or opera-
tion of a facility, (2) decreasing the human population’s exposure to potential hazards
(including reducing toxicity), and (3) decreasing the volume of pollution created by
the construction or operation of facility performance and viability.
Green architecture is active in many elds of engineering. This includes sustain-
able design, life cycle analysis (LCA), avoidance of waste, environmental design
(DfE), disassembly design (DfD), and recycling design (DfR). As such, green engi-
neering is a branch of sustainable engineering. Green engineering requires four fun-
damental approaches to improving processes and goods from an environmental point
of view in order to make them more effective.
From a holistic perspective that incorporates various technical disciplines, Green
engineering covers land use planning, architecture, landscape architecture, and other
design areas, as well as social sciences (e.g. to decide how different types of people
use goods and services), in addition to all engineering disciplines.
Green engineers are concerned with space, the sense of place, seeing the site map
as a collection of boundary-wide uxes, and considering these structures’ combina-
tions across larger regions, such as urban areas. Life cycle analysis is an important
method for Green engineering that provides a holistic view of the entirety of a prod-
uct, process, or service, including raw materials, manufacturing, transport, supply,
use, repair, recycling, and nal disposal.
By determining its life cycle, a full image of the product should be given.
Therst step in a life cycle assessment is to gather data on the movement of a
xPreface
product into a recognizable community. Once the amounts of various components
of such a ow are known, the important functions and impacts of each step in the
processing, manufacture, use, and recovery/disposal are estimated. In sustainable
design, engineers have to optimize performance for variables that offer the best
in temporal frames.
This book provides a comprehensive study of the use of Green computing in the
modern world by embedding it with IoT. It also presents several studies based on
Green IoT-based technology that will be helpful for readers of all levels. It consists of
a wide range of topics containing the use of Green computing in IoT.
Chapter 1 provides an overview of cloud and Green IoT-based technology for
sustainable smart cities. This chapter discusses the basic applications and services of
green IoT features in smart cities.
Chapter 2 gives a methodical treatment of dynamic models for enhancing sustain-
ability in automotive component manufacturing systems and a case study.
Chapter 3 presents the Internet of Agriculture Things (IoAT): a novel architecture
design approach for open research issues. It explains the functional blocks, charac-
teristics, and applications of IoT.
Chapter 4 explains e-navigation: an indoor system for Green city sustainable
development using a UGU engine architecture.
Chapter 5 explores the biomass waste-derived electrode material and bio-based
solid electrolyte for sustainable energy systems. RF energy harvesting for WSNs is
discussed in Chapter 6.
Chapter 7 presents an efcient and robust renewable small hydropower genera-
tion scheme for supplying remote areas. The proposed model is mostly suitable for
supplying household loads in remote areas located far away from the grid. CAG and
Hydro Turbine models controlled by a fuzzy PI-based AC-DC-AC converter and a
PI-based D-STATCOM are discussed.
Chapter 8 discusses the in-silico analysis, molecular interaction, and ADMET
studies of the phyto-compounds of Parthenium hysterophorus, Alternanthera
sessilis, and Lantana camara weeds with the essential proteins of beetles and
aphids to estimate the binding afnity or interaction of phytoconstituents with
proteins of the pests.
Chapter 9 suggests a simple energy harvesting EH-CRN model operated by a
typical frame structure. The frame structure consists of two non-overlapping time
slots, one for the spectrum sensing and the other slot performs energy harvesting
or secondary data transmission depending on the transmission or non-transmission
state of the PU, respectively.
Chapter 10 explores the mitigation on the impact of electric vehicle charging
stations (EVCS) by splitting the capacity and optimally locating on a recongured
RDS. Thebasic objective is to reduce the losses and to place EVCS at various locations.
Chapter 11 discusses the parameter estimation of single diode PV cells using an
intelligent computing technique. The aim is to identify the ve different parameters
of a single diode (SD) model depending on the parameters obtained from the com-
mercial PV modules in the datasheet.
xiPreface
Chapter 12 is titled On the Dynamics of Cellular Automata-based Green Modeling
toward Job Processing with Group-based Industrial Wireless Sensor Networks in
Industry 4.0. It provides energy-efcient scheduling and deployment with Industrial
Wireless Sensor Networks (WSNs). It discusses the power consumption with the
present CA-based approach.
Chapter 13 is about Green cloud computing: an emerging trend of GIT in cloud
computing. Green cloud computing has efcient energy consumption as compared to
the traditional cloud architecture and is highly environmentally sustainable. It basically
proposed a load-balancing algorithm using the basic rules of the genetic algorithm.
Chapter 14 reviews the Internet of Things for Green technology. IoT helps man-
kind and at the same time, it generates e-toxic waste, a large amount of heat. It can
be minimized by combining IoT with Green technology. This chapter discusses IoT,
steps to achieve Green IoT, different issues, and challenges to attain Green IoT.
Chapter 15 discusses Green health: making Green healthcare using reinforcement
learning in fog-assisted cloud environment. It uses the VM consolidation technique
using reinforcement learning to optimize the energy consumption in the fog-assisted
cloud data centers.
Automation of farm activities can lead to a transformation of the agricultural
domain from static to intelligent, which leads to higher production. The manual labor
on the eld for irrigation can be reduced by using a smart agricultural robot, mini-
mizing the time, cost, and power, as discussed in Chapter 16.
Due to the increase in IoT, data security becomes a primary concern in IoT devices.
Chapter 17 focuses on a survey of lightweight cryptographic algorithms that includes
lightweight block ciphers, hash functions, and stream ciphers for power-constrained
IoT devices. The cryptographic algorithms are evaluated based on the key size, block
size, round size, and structure.
Chapter 18, Nanogenerator-based Sensors for Human Pulse Measurement, focuses
on nanogenerators in the biomedical eld that combine medical principles with nano-
generators. Nanogenerators serve as an alternative supplier of power in healthcare
electronics.
The future challenges and applications in Green technology discussed in
Chapter19 state the green innovation without harming the assets of the world. Green
development includes energy effectiveness, reusability, security and w ell-being
concerns, and sustainable assets.
Chapter 20 discusses the implementation and use of Green computing in Polish
companies versus implementation of feature characteristics of Teal organiza-
tions. It analyzes the phenomenon of Green computing in companies that develop
characteristics of Teal organizations. The aim is to examine the characteristic features
of Teal organizations.
Chapter 21, Design of a Pentagon Slot-based Multi-band Linear Antenna Array
for Energy-efcient Communication: Future Challenges and Applications in Green
Technology explores a patch array antenna that provides different applications such
as mobile data communications to vehicles, machines, smart objects, and sensors and
handles a huge number of users to manage the future growth of trafc.
xiii
Editors
Dr. Om Prakash Jena i s an Assistant Professor in
the Department of Computer Science, Ravenshaw
University, Cuttack, Odisha. He has 10 years of
teaching and research experience in undergradu-
ate and postgraduate levels. He has published sev-
eral technical papers in international journals/
conferences and edited book chapters in reputed
publications. Dr. Jena is a member of IEEE, IETA,
IAAC, IRED, IAENG, and WACAMLDS. His cur-
rent research interest includes Database, Pattern
Recognition, Cryptography, Network Security,
Articial Intelligence, Machine Learning, Soft
Computing, Natural Language Processing, Data
Science, Compiler Design, Data Analytics, and Machine Automation. He has guided
many projects and thesis at the UG and PG levels. He has many edited books, pub-
lished by Wiley, Bentham Science Publication, and CRC Press to his credit, and he
is also the author of two textbooks under Kalyani Publisher. He is an editorial board
member and reviewer of several international journals.
Dr. Alok Ranjan Tripathy i s an Assistant Professor
in the Department of Computer Science, Ravenshaw
University, Cuttack, Odisha. He has more than
15 years of teaching experience in undergradu-
ate and postgraduate levels. He earned his MTech
degree and PhD in Computer Science from Utkal
University. Hehas published several technical papers
in international journals/conferences and edited book
chapters in reputed publications. He is a life member
of ISTE, CSI, and Odisha Information Technology
Society (OITS). His current research interests include
Algorithms, Pattern Recognition, Cloud Computing,
Network Security, Articial Intelligence, Machine
Learning, Quantum Computing, and Wireless Sensor
Networks.
xiv Editors
Dr. Zdzislaw Polkowski i s a Professor of UJW
at the Faculty of Technical Sciences and Rector’s
Representative for International Cooperation and
Erasmus+ Program at the Jan Wyzykowski University
Polkowice. He earned a PhD in Computer Science
and Management from the Wroclaw University of
Technology. He has published more than 75 papers
in journals, 25 conference proceedings, including
more than 20 papers in journals indexed in the Web
of Science, Scopus, IEEE. Dr. Polkowski served as
a member of the Technical Program Committee at
many international conferences in Poland, India, China, Iran, Romania, and Bulgaria.
To date, he has delivered 24 invited talks at different international conferences across
various countries. His areas of interest include IT in Business, IoT in Business, and
Education Technology. He has successfully completed a research project on devel-
oping the innovative methodology of teaching Business Informatics funded by the
European Commission. He also owns an IT SME consultancy company in Polkowice
and Lubin, Poland.
xv
Contributors
Saeed A. L. Amodi
School of Computer Engineering
KIITDeemed to be University
Bhubaneswar, Odisha, India
Balakumaran T.
Department of Electronics and
Communication Engineering
Coimbatore Institute of Technology
Coimbatore, Tamil Nadu, India
Avik Banerjee
Department of Electronics and
Communication Engineering
Madanapalle Institute of Technology
and Science
Angallu, Andhra Pradesh, India
Avishek Banerjee
Department of Information Technology
Asansol Engineering College
Asansol, West Bengal, India
Rabindra Kumar Barik
School of Computer Applications
KIIT Deemed to be University
Bhubaneswar, Odisha, India
Anuradha B. Bhalerao
Applied Science Department
K.K. Wagh Institute of Engineering
Education & Research
Nasik, Maharashtra, India
Saurabh Bhattacharya
National Institute of Technology
Raipur, Chhattisgarh, India
Suman Bhattacharya
CAAS
KIIT Deemed to be University
Bhubaneswar, Odisha, India
Ravindra N. Bulakhe
Korea National University
of Transportation
Chungju, South Korea
Arunkumar Chandrasekhar
Department of Sensors and Biomedical
Tech nology
Vellore Institute of Technology
Vellore, Tamil Nadu, India
Ajay B. Gadicha
Department of Computer Science and
Engineering
P.R. Pote College of Engineering and
Management
Amravati, Maharashtra, India
Vijay B. Gadicha
Department of Computer Science and
Engineering
G. H. Raisoni University
Amaravati, Maharashtra, India
Souvik Ganguli
Department of Electrical &
Instrumentation Engineering
Thapar Institute of Engineering &
Tech nology
Patiala, Punjab, India
xvi Contributors
Sarsing Gao
Department of Electrical Engineering
North Eastern Regional Institute of
Science and Technology
Nirjuli, Arunachal Pradesh, India
Nitin S. Goje
Faculty of Science, IT Department
Tishk International University
Erbil, Kurdistan Region, Iraq
Shilpy Goyal
Department of Electrical &
Instrumentation Engineering
Thapar Institute of Engineering &
Tech nology
Patiala, Punjab, India
Elżbieta Jędrych
Faculty of Business and International
Relations
Management Institute Academy of
Finance and Business Vistula
Warsaw, Poland
Om Prakash Jena
Department of Computer Science
Ravenshaw University
Bhubaneswar, Odisha, India
Maria Kocot
Department of Economic Informatics
University of Economics
in Katowice
Katowice, Poland
Dilip Kumar
Department of Computer Science &
Engineering
National Institute of Technology
Jamshedpur, Jharkhand, India
Satheesh Kumar P.
Department of Electronics and
Communication Engineering
Coimbatore Institute of Technology
Coimbatore, Tamil Nadu, India
Sunil Kumar
Department of Computer Science &
Engineering
National Institute of Technology
Jamshedpur, Jharkhand, India
Vimal Kumar M. N.
Department of Electronics and
Communication Engineering
R.M.D. Engineering College
Kavaraipettai, Tamil Nadu, India
Santi P. Maity
Department of Information Technology
Indian Institute of Engineering Science
and Technology
Shibpur, Howrah, West Bengal, India
Ammu Anna Mathew
School of Electrical Engineering
Vellore Institute of Technology
Vellore, Tamil Nadu, India
Ashutosh Mishra
Department of Research Ethics
Uttarakhand Council of Science
and Technology
Dehradun, Uttarakhand, India
Arnab Mitra
Department of Computer Science&
Engineering
Siksha ‘O’ Anusandhan
(Deemed to be University)
Bhubaneswar, Odisha, India
xviiContributors
Sachi Nandan Mohanty
Department of Computer Science &
Engineering
IC FAI Tech
Hyderabad, Telangana, India
Bennet Nifn N.
SciComm India
Greater Noida, Uttar Pradesh, India
Parag Nijhawan
Department of Electrical &
Instrumentation Engineering
Thapar Institute of Engineering &
Tech nology
Patiala, Punjab, India
Manoj Pal
Department of Life Sciences
Graphic Era (Deemed to be)
University
Dehradun, Uttarakhand, India
Manju Pandey
National Institute of Technology
Raipur, Chhattisgarh, India
Amrutanshu Panigrahi
Department of Computer Science &
Engineering
SOA University
Bhubaneswar, Odisha, India
Satya Sobhan Panigrahi
Department of Computer Science &
Engineering
BPUT
Bhubaneswar, Odisha, India
Kumud Pant
Department of Biotechnology
Graphic Era (Deemed to be) University
Dehradun, Uttarakhand, India
Sudhansu Shekhar Patra
School of Computer Applications
KIIT Deemed to be University
Bhubaneswar, Odisha, India
Parthasarathi Pattnayak
School of Computer Applications
KIIT Deemed to be University
Bhubaneswar, Odisha, India
J. John Paul
Karunya Institute of Technology and
Sciences
Karunya University
Coimbatore, Tamil Nadu, India
K. Lova Raju
Electronics and Communication
Engineering
Vignan’s Foundation for Science,
Technology & Research
(VFSTRUniversity)
Guntur, Andhra Pradesh, India
Aakash Ram S.
KPIT Technologies Limited
Pune, Maharashtra, India
M. Mohan Ram
Department of Industrial and
Production Engineering
The National Institute of Engineering
Mysuru, Karnataka, India
Jangam Ramesh
Department of Industrial and
Production Engineering
The National Institute of Engineering
Mysuru, Karnataka, India
Kali Charan Rath
Department of Mechanical Engineering
GIET University
Gunupur, Odisha, India
xviii Contributors
M. Satish Kumar Reddy
Department of ECE
IIITDM
Kancheepuram, Chennai,
Tamil Nadu, India
A. Shobha Rekh
Karunya Institute of Technology and
Sciences
Karunya University
Coimbatore, Tamil Nadu, India
Agnieszka Rzepka
Department of Economics and
Economic Management
Lublin University of Technology
Lublin, Poland
Bibhuprasad Sahu
Department of Computer Science &
Engineering
BPUT
Bhubaneswar, Odisha, India
K. Selvajyothi
Department of ECE
IIITDM
Kancheepuram, Chennai,
Tamil Nadu, India
P. Devachandra Singh
Department of Electrical
Engineering
North Eastern Regional Institute
of Science and Technology
Nirjuli, Arunachal Pradesh,
India
Saundarya Sinha
School of Computer Applications
KIIT Deemed to be University
Bhubaneswar, Odisha, India
Somya Sinha
Department of Biotechnology
Graphic Era (Deemed to be) University
Dehradun, Uttarakhand, India
Sudhakar Y. N.
Department of Chemistry
CHRIST (Deemed to be University)
Bengaluru, Karnataka, India
Y. S. Varadarajan
Department of Industrial and
Production Engineering
The National Institute of Engineering
Mysuru, Karnataka, India
Anitha Varghese
Department of Chemistry
CHRIST (Deemed to be University)
Bengaluru, Karnataka, India
Devvret Ver m a
Department of Biotechnology
Graphic Era (Deemed to be) University
Dehradun, Uttarakhand, India
V. Vijayaraghavan
Electronics and Communication
Engineering
Vignan’s Foundation for Science,
Technology & Research
(VFSTRUniversity)
Guntur, Andhra Pradesh, India
S. Vivekanandan
Department of Instrumentation,
Schoolof Electrical Engineering
Vellore Institute of Technology
Vellore, Tamil Nadu, India
1
1Cloud and Green
IoT-based Technology for
Sustainable Smart Cities
Parthasarathi Pattnayak
KIIT Deemed to be University
Om Prakash Jena
Ravenshaw University
Saundarya Sinha
KIIT Deemed to be University
CONTENTS
1.1 Introduction ......................................................................................................
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2
1.2 Smart City Applications and Services 3
1.2.1 Smart Waste Management 3
1.2.2 Smart Energy 3
1.2.3 Smart Transportation 3
1.2.4 Smart Water Management . 4
1.2.5 Smart Health Care 4
1.2.6 Smart Buildings and Lighting 4
1.2.7 Smart Public Safety 4
1.2.8 Smart Education 4
1.3 G-IoT Features in Smart Cities 5
1.3.1 Green Smart Homes 5
1.3.2 Green Smart Ofces 5
1.3.3 Green Smart Healthcare System 6
1.3.4 Green Smart Transport System 7
1.3.5 Green Smart Environment 7
1.3.6 Green Waste Management 8
1.4 Use of Algorithm and Software in G-IoT Smart Cities 8
1.4.1 Green Computing Eco-Friendly Technology 9
1.4.2 Design Green Data Center . 9
1.4.3 Virtualization for Going Green 9
1.4.4 Green Power Management 9
2Green Engineering and Technology
1.1 INTRODUCTION
The Internet of Things (IoT) has become one of the most common technologies
that we use in our day-to-day lives. From marketplaces to communication between
robots, it is everywhere and it shows its vast range of services by improving its
quality of service. This technology uses myriad sensors and sends data over
networks, which consumes a great deal of energy. The increasing energy utiliza-
tion causes emission of a vast amount of carbon dioxide (CO2) in the environment
leading to the increase in global warming [1]. It is already known that electronic
devices emit the gases that are harmful to the environment, and these increasing
trends of digitization and technological development will lead to more emission of
harmful gases leading to an increase in global warming. This p roblem demands
introducing more energy-efcient and environmentally friendly devices, which
provide the required technological facilities but also do not affect the already
compromised environment. Thisis Green technology.
Green IoT (G-IoT) is a term used to describe IoT-based devices that are more
energy-efcient and environmentally friendly. G-IoT has various applications in
industrial automation, improvement of health and living, habitat monitoring, smart
cities, energy, transportation, etc. This chapter presents a study that suggests building
IoT devices with more efciency and effectiveness but with less impact on the
environment.
G-IoT for smart cities allows providing various services such as smart build-
ings, smart street lights, smart waste management, smart water management,
and more. With increasing urbanization, the smart utilization of limited natural
resources such as water is vitally important and here, G-IoT can be very help-
ful for continuous and precise monitoring of the resources to minimize their
wastage. G-IoT, like other IoT devices, utilizes sensors, the Internet, tags, etc.,
for providing services. The main aim of G-IoT-enabled cities is to minimize the
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1.6 Smart Cities Green Index Indicators 10
1.7 Cloud-based G-IoT Architecture 12
1.7.1 Sensor Layer and Smart City Infrastructure 12
1.7.2 Network Layer 13
1.7.3 Analytic Big Data Layer . 13
1.7.4 Application Layer 14
1.7.5 Presentation Layer 14
1.8 Analytical Framework 15
1.8.1 Domains and Systems of Urban Areas 15
1.8.2 D ata Categories, Big Data Sources, and Storage
FacilitiesinUrban Areas 16
1.8.3 Cloud Computing or Fog/Edge Computing 16
1.8.4 Big Data Applications 17
1.9 Conclusion 17
References 18
3Cloud and Green IoT-based Technology
excessive harmful effects that these devices or their resources can impart to the
environment.
This chapter provides a brief overview of enabling G-IoT-based smart cities. We
describe the various characteristics of urban smart cities and how G-IoT can be
enabled in these cities to provide numerous technological functionalities. Here, we
have used a cloud-based framework for minimizing the use of hardware.
1.2 SMART CITY APPLICATIONS AND SERVICES
Smart cities rely on technological solutions such as IoT sensors, networks, and vari-
ous applications to improve the services pertaining to energy usage, air quality, and
trafc congestion in the cities, thereby enriching the quality of living of the residents.
The smart city market is growing at a rapid pace. In the year 2020, it is estimated to
be hundreds of billions of dollars. It is expected that the smart city market has huge
potential to grow in the future as well.
There are various types of services and applications such as transportation, pub-
lic utilities, education, health care, and public safety. Applications related to disas-
ter management, logistics, and smart buildings are also important for smart cities.
Following are some of the important applications and services worth mentioning here.
1.2.1 Smart WaSte management
The smart waste management mechanism includes activities such as waste sensing
and collection, sorting, recycling, and disposal. These sensors attached to waste bins
have the capability of notifying the status of the waste levels and upload data to the
Smart City Cloud. The ofcial in-charge can access the data from the cloud. Thedata
thus accessed by the management ofcial help redene the schedule to maximize
waste collection. It also helps locating vehicles and waste bins and redenes the short-
est routes for each waste-collection truck to become more fuel-efcient. Theentire
process can be centrally regulated and provides quality services to residents of the
sm a r t c ity.
1.2.2 Smart energy
Urban IoT will provide means to conserve and enhance energy efciency. The s olution
may provide a detailed account of the energy requirements of the city. For example,
management would be able to get a clear-cut idea about the different sources such
as transport, public lighting, trafc lights, and controlled cameras installed at vari-
ous important locations in the city. IoT can be used to create a smart grid system,
which will comprise a smart meter that will control the ow of electricity to meet the
demands of the citizens.
1.2.3 Smart tranSportation
IoT can help cities use technology to make commuting easy and hassle-free. The
smart features include parking, trafc regulation, and vehicle tracking. Passengers
4Green Engineering and Technology
will be provided with real-time information through a passenger information system.
For example, the expected arrival time is displayed on electronic signboards located
at railway stations, bus stations, and airports. Information related to the availability
of parking spaces can be provided through electronic signboards by real-time park-
ing management systems.
1.2.4 Smart Water management
Water management is the most critical aspect of a city. It broadly includes storage,
conservation, and efcient distribution. Smart water management by using IoT pro-
vides insights from data that help authorities regulate waste water treatment and
ood control measures and enable optimized water consumption. The IoT-based
water management solution can help transform the agricultural sector by using inputs
obtained from sensors for alerting farmers regarding various emergency situations
such as bad weather conditions and enables them to save their crops.
1.2.5 Smart HealtH Care
Smart health care is an integral part of smart cities. It comprises patients, doctors,
hospitals, and various research organizations. Smart health care includes disease
diagnosis, monitoring, prevention, hospital management, and medical research.
Information and communication technology (ICT) plays a very important role in this
regard. For instance, IoT, cloud computing, mobile Internet, and articial intelligence
(AI) form the core of smart health care that is required for smart cities.
1.2.6 Smart BuildingS and ligHting
Smart buildings constitute an important aspect in smart cities. They use cloud-based
computing. It combines and coordinates all aspects of the building and provides a
smart living experience. The important features include security and surveillance,
heating, ventilation and cooling, and lighting management. They can be combined
together and coordinated on the cloud through sensors and other IoT devices for bet-
ter and complete control on a single dashboard.
1.2.7 S mart puBliC Safety
Public safety in terms of minimum accidents, few trafc deaths, and reducing and
tracking crimes is the key feature of smart cities. IoT-enabled data-driven systems
can achieve all these objectives. The data on these items can be compiled and cap-
tured through sensors and can be processed in real time. It will help detect criminal
activities and enable timely policing.
1.2.8 Smart eduCation
Cloud can facilitate the process of eLearning. It will enable students to access all
study materials at any point in time through the Internet using a computer and
5Cloud and Green IoT-based Technology
other electronic devices. It will be able to solve the problems of Distance Learning.
The Smart City Cloud computing platform will help teachers to identify problem
areas of the students by examining students’ study records efciently.
1.3 G-IOT FEATURES IN SMART CITIES
A smart city has var ious components that are equa lly responsible for maintaining fu lly
equipped smart cities. This section discusses various components of smart c ities such
as green smart homes, green smart ofces, smart health care, smart transportation,
green smart environment, and waste management, and how G-IoT-based devices can
be used in these for providing the required service.
1.3.1 green Smart HomeS
A green smart home should be maintained to be environmentally sustainable.
Its main focus is the efcient utilization of resources such as energy, water, and
building materials. G-IoT devices having sensors, agents, motion detectors, etc.
can be used for designing such houses. The green smart home should be designed
to autonomously control appliances inside the house and also for utilizing energy
resources efciently.
Smart homes can react to the internal or external environment without the need
for any human interaction for providing comfort to the occupants. They improve
performance for reducing consumption of energy. The data captured from the
devices used in smart homes can be stored in the repositories to perform future
energy analyses.
To perform these functionalities, smart homes require high computational power.
Cloud computing can be the best-suited option for providing a higher level of com-
puting services with more reliability in smart homes. It is very useful in scenarios
where dynamic resources are required and smart homes require many dynamic
resources such as big data, distributed processing, web services databases, and stor-
age to operate correctly. For smart homes, cloud computing provides a cost- effective
and fault-tolerant environment for processing and storing data about resource usage
at a centralized point of control.
1.3.2 green Smart offiCeS
Smart ofces should have an intelligent, integrated, and context-sensitive envi-
ronment. The environment’s intelligence is based upon the context data that are
being collected from the connected systems such as sensors, microphones, cam-
eras, etc. Actuators including automatic door openers, displays, or speakers are
used for having an interactive environment with the users. Based on the collected
and analyzed data, the environment can exibly change the state of its integrated
systems.
Smart home and smart ofce scenarios both are made for indoor environments
but unlike smart home scenarios, smart ofces have a heterogeneous group of
users and require a sophisticated user access rights and security. Cost optimization,
6Green Engineering and Technology
time optimization, process optimization, security enhancement, and increasing
employee satisfaction are some of the main goals of a smart ofce [1].
Green smart ofces provide an environment that is techno-savvy, adaptive, and
interactive, and they are designed for multiple users with a high level of security.
They also aim for energy saving techniques with a smaller carbon footprint.
Connected IoT devices and sensors can be used for enhancing the efciency of
ofces and reducing carbon footprints. These devices can be used for controlling
appliances, environment, and lighting inside the ofce building, so that energy is not
expended when the space is not occupied. Renewable energy such as solar panels can
also be a good choice for having an energy-efcient scenario. It can be integrated into
the overall power management for an energy-efcient and environmentally friendly
solution for the ofce environment.
1.3.3 green Smart HealtHCare SyStem
Smart healthcare facilities combine the key concepts of safe and green hospitals to
make facilities to be climate-resilient and ensure health services to be provided at all
times. Green and smart healthcare facilities aim to:
• Ensure that healthcare facilities are environmentally friendly and disaster-
resilient.
• Reduce the impact of climate changes.
• Reduce operational cost.
• Enhance user comfort and performance.
• Empower decision-makers to select the most cost-effective green improve-
ments to be undertaken.
Increases in patient satisfaction, energy efciency, cost optimization, security
enhancements, and service optimization are the main goals of smart healthcare
places [1]. Green smart healthcare systems use devices that are eco-friendly and
have enhanced functionality. Accuracy is the main key concept that is required
for any healthcare device. Therefore, smart healthcare systems should always give
accurate results. It is one of the major features that cannot be compromised while
designing a healthcare system. Collected data from IoT devices along with other
connected devices can be stored and used for predicting the health status of patients.
Since these systems also require a high level of computational power for performing
analysis over the data stored during the interaction of devices with the healthcare
environment, cloud computing can perform a very vital role in this scenario for giv-
ing a reliable and cost-effective platform for storing and processing data over the
Internet, which can be accessed from anywhere giving convenience to both patients
and doctors.
7Cloud and Green IoT-based Technology
1.3.4 green Smart tranSport SyStem
Transportation is a very important and most used service of any city. With growing
urbanization and increasing population, the number of transports used by the
people is also increasing, leading to an alarming situation where transportation
is becoming a major environmental problem by increasing pollution caused by
burning fuels.
Green smart transport systems can use IoT devices for providing the users details
of congestion in a particular route and suggesting an alternative route. A model
proposed in a study [2] shows how an IoT-based intelligent transportation system can
be used in smart cities. An intelligent transport system should be able to perform the
following functionalities:
• Trafc incidents are detected and responded to promptly.
• Increase reliability and convenience of transportation services.
• Navigation systems are used for nding the best route based on real-time
conditions.
• Monitor the structural integrity of the infrastructures such as bridges.
• Coordinate speed limits and signal timing with real-time trafc situations.
• Real-time trafc and weather reports provided to the traveler.
• Inform drivers about potentially hazardous situations in time to avoid car
crashes.
• Manage fuel consumption.
1.3.5 green Smart environment
With massive damage inicted on the environment by humans throughout history,
the earth’s ability to support sustainability has been devastated.
A green smart environment focuses on the following points:
• Cleaning up the damage to the environment.
• Replacing the current way of using vital energy resources with new
technologies to conserve vital resources.
• Using various green technologies that restore and repair the environment in
the most energy-efcient and sustainable ways.
G-IoT can continuously monitor real-time environmental conditions. IoT-based
meters can measure and store air quality index to predict future environmental
conditions. Intelligent IoT devices should be established to measure the usage of non-
renewable resources to increase conservation and smart utilization. IoT devices are
very helpful in monitoring real-time changes to analyze environmental conditions.
8Green Engineering and Technology
Weather forecasting should also be performed using IoT devices by performing com-
putations on previously recorded data regarding humidity, precipitation, wind speed,
and so on.
1.3.6 green WaSte management
Waste is an inevitable byproduct of human life. Increasing population and urbaniza-
tion are contributing to increased waste products. Waste management is an important
aspect of any city’s management to keep the city clean and healthy. G-IoT can be very
benecial in waste management in smart cities. Following are the points that can
provide benets in waste management using an IoT-based system:
• It should monitor the amount of waste generated in a particular amount of time.
• Sensors can be attached to the public dustbins, which should alert nearby
garbage collectors to empty the dustbins.
• The data collected by the sensors should be analyzed to determine the time
pattern for emptying the dustbins.
• Efcient routes should be set for the garbage collecting vehicles based
on the position and location of the dustbins so that they can collect more
garbage in less time.
There are many more applications, which can be performed by the IoT devices for
maintaining well-designed waste management systems. For example, in a study [3], a
smart waste management system is proposed using IoT devices for having an efcient
system. It shows a model in which IoT devices are used for making smart waste
collecting systems based on the level of waste in the wastebin.
IoT sensing devices are very useful in these scenarios for sensing the conditions that
require some action and providing an efcient, effective, cleaner, and healthier city.
1.4 U SE OF ALGORITHM AND SOFTWARE
IN G-IOT SMART CITIES
G-IoT is dened by Murugesan as “the study and practice of designing, using, manu-
facturing, and disposing of servers, computers, and associated subsystems such as
monitors, storage devices, printers, and communication network systems efciently
and effectively with minimal or no impact on the environment.”
G-IoT becomes more efcient by green ICT through minimizing energy, unsafe
emissions, pollution, and consumption of resources. Minimizing the technology and
conserving articial resources have an impact on human health and environment and
minimize the cost signicantly. Consequently, G-IoT thus focuses on green activity,
green disposal, green design, and manufacturing [4].
1. Green use: minimizing computer’s power consumption and other informa-
tion systems as well as using them in a sound manner.
2. Green disposal: reusing, refurbishing, and recycling old unwanted elec-
tronic equipment and computers.
9Cloud and Green IoT-based Technology
3. Green design: creating power efciency for G-IoT healthy components,
computers, servers, and cooling equipment.
4. Green manufacturing: producing computers, electronic components and
other related subsystems with minimal or no impact on the environment.
The fundamental methodologies such as Green Computing Eco-Friendly Technology,
Design Green Data Center, Virtualization for Going Green, Green Power Management
[5] for algorithm and software utilized in G-IoT smart cities are as follows:
1.4.1 green Computing eCo-friendly teCHnology
As a sustainable approach, Green computing ensures a safe and healthy environment
while fullling the needs of the people. It offers solutions to involve IT in the business
process to bring in sustainability. Green computing aims at optimizing energy efciency
and promoting the recycling process of factory waste. Green computing is indispensable
for keeping smart cities clean by reusing old personal computers as they contain toxic
and harmful materials. It also favors the reuse of old machines.
1.4.2 deSign green data Center
The utility of a green data center is to store and manage data pertaining to smart
cities. It requires minimal power for operating and maintaining the whole system. Itis
capable of working using solar power, wind power, and hydropower. It is designed to
work in a low power system and to help reduce the carbon footprint.
1.4.3 virtualization for going green
The idea is that many operating systems can run on the same physical hardware at
the same time through virtualization. It paves the way for combining several physical
systems into a single virtual machine for smart cities. As a result, it minimizes the
hardware cost and controls power and cooling consumption.
1.4.4 green poWer management
It is usual to observe that during peak hours, there is a high energy consumption that
results in paying a high price for electricity. The smart power management efciently
manages the energy-related functions of the green smart cities.
1.5 BIG DATA AND IOT UTILIZATIONS:
SMARTSUSTAINABLE CITIES VERSUS SMART CITIES
Problem-solving on the IoT and related large information utilizations has been
dynamic in the domain of brilliant urban communities, managing generally with
monetary development and personal satisfaction. However, the IoT and related enor-
mous information utilized in progressing ecological supportability with regard to
brilliant reasonable urban areas, as a comprehensive metropolitan improvement
10 Green Engineering and Technology
approach, are scarcely investigated to date. Thus, another examination wave has
begun to zero in on the best way to upgrade shrewd city approaches too as economi-
cal city models by consolidating the two metropolitan improvement procedures try-
ing to accomplish the required degree of natural supportability through improving
metropolitan tasks, capacities, plans, and administrations utilizing progressed ICT
[6]. This integrated metropolitan advancement approach stresses the utilization of
large information examination as a lot of cutting-edge strategies, measures, stages,
frameworks, and applications, notwithstanding other progressed types of ICT like
setting mindful guring. Specically, the advancing information-driven methodol-
ogy supposedly holds incredible potential to address the test of natural supportability
under what is marked as “brilliant reasonable urban areas” of things to come [7]. The
route forward for future urban areas to progress ecological supportability is through
progressed ICT that guarantees the usage of enormous information examination [8].
1.6 SMART CITIES GREEN INDEX INDICATORS
The increasing global average temperature increases the need for having a low
carbon economy with sustainable and energy-efcient technologies. Green technol-
ogy helps in lowering the dependencies on fossil fuel energy resources and giving
the environment protection for unnecessarily increased carbon emission in the envi-
ronment. The G-IoT-based smart cities make use of natural environmental resources
as the sources of energy. These cities feature technologies to prevent the wastage
of non-renewable resources such as water, electricity, and so on. A smart city has
various features such as smart buildings, smart street lights, smart waste manage-
ment systems, and a smart environment. G-IoT helps in providing environmentally
friendly solutions for providing these facilities. For enabling G-IoT, it is necessary
to keep in mind the indicating factors that must be controlled for giving better green
solutions. These include controlled CO2 emission, providing energy-efcient solu-
tions, smart green transport for reducing the emission of harmful gases, and so on.
A green smart city must have the following green indicator factors that must be
followed strictly for maintaining the green index of the environment.
A high amount of CO2 is the main cause of growing global warming. Various
technological devices are equally responsible for adding the amount of CO2 into the
environment. It is estimated that IT industries account for almost 3% of CO2 emitted
into the environment that is nearly 80% more than it was in 2007 [9]. This increasing
percentage renders the need for making devices with less CO2 emission and inten-
sity that helps in the reduction of this harmful gas in nature. Since smart cities have
various numbers of electronic devices that increase the risk of high emission of CO2,
there is a need for having G-IoT devices that emit less amount of CO2 and maintain
the sustainability of the ecological balance of nature.
IoT devices are electronic devices and hence require energy (electricity) for
working. A large number of IoT devices in smart cities requires high energy for
working properly. Therefore, energy-efcient devices having high-energy intensi-
ties are needed for smart cities. Lighting and rooftop gardening are some of the
practices that can be used for providing green and energy-efcient concepts for
G-IoT [10].
11Cloud and Green IoT-based Technology
Smart buildings and smart ofces are some of the components of smart cities
where photosensors, occupancy sensors, and timers can be used for controlling the
indoor lights so that it can be switched ON and OFF whenever needed. They help
in minimizing the unnecessary utilization of power consumption. Motion detectors
can be used for turning off the lights of areas when they are empty, and no motion is
detected for a while. Dimmers can also be used for adjusting the intensity of the light
as per the user’s need. There are also many IoT devices that can be used efciently as
a helpful source for proper utilization of energy resources.
Energy-efcient buildings with proper environment-friendly occupancy of
devices should be settled. Various energy-saving techniques as discussed above like
rooftop gardening and lightning should be accepted for providing energy efciency.
The lights of the building can be controlled by AI devices such as sensors and motion
detectors as per the need. Rooftop gardening [10] can help in reducing high exposure
to sunlight. Widely adopted rooftop gardening can help in reducing the level of urban
heat island, which can lead to a reduction of smog episodes and heat stress leading to
lowering the energy consumption.
Transportation is another main component of a city. It is also one of the causes
that badly affect the environment. With the growing population, the means of
transportation is also increasing, which is becoming the cause of growing pollu-
tion. Green transportation can be the solution for the transportation-related pollu-
tion and problems in the city. It refers to using those transportation services that do
not diminish the natural resources such as fossil fuels. Electric bikes can be used
as a great way for having green transportation. Only light pedaling is needed for
riding the bike, and no harmful gas is emitted into the environment. Green vehicles
that are powered by clean energy [11] rather than non-renewable fossil fuels can be
used along with advanced vehicle technologies, resulting in less pressure of pol-
lution into the environment as compared to the conventional internal combustion
engine vehicles. Green trains are also coming into the picture and can be proved
to be a great initiative for having environmentally friendly transportation services.
Trains with hybrid locomotives having other green technologies are becoming part
of a greener urbanization.
Waste reduction policy and recycling are important aspects for providing
a greener and clean city. Various waste management techniques can be used for
empowering the waste management of the city. Dustbins can be set with the sensors
powered by the solar energies, which can sense the level of waste inside the dustbin
so that they can send the alert to the nearby waste-collecting dump-yard that the
dustbin should be emptied before it overows. Various graph algorithms can be
used for waste-collecting vehicles for making an efcient route that can help them
in collecting more garbage in less time. Agents can be used to determine the pattern
of the level of wastage on weekdays and weekends to setting an accurate timing for
waste collection. Recycling the recyclable waste is also encouraged for reducing
environmental pollution.
Water is a limited resource, and it needs to be conserved and used wisely. Water
consumption is also one of the main features of a smart city. Various IoT devices
can help in monitoring the level of water. Sensors can be used for monitoring the
level of water, and an alert will be generated whenever a very large volume of water
12 Green Engineering and Technology
is being used in less time. Water recycling policy should be adapted for its proper
use. Smart meters and monitoring systems can be used for measuring real-time
water consumption and identifying excessive usage and waste points, which helps
in making proper usage patterns and predicting future usage. These techniques sup-
port water consumption and water distribution mechanisms. For automated water
distribution, environmental sensors and self-learning algorithms help in supplying
water automatically to the end-users. Smart water management using G-IoT can be
used for reducing water wastage, improving the quality of water, and enhancing the
efciency of water.
IoT devices can be very useful in monitoring the air quality. Sensors and micro-
controllers can be used for making monitoring systems to measure the air quality
index. Wireless sensors can be used by placing those at a strategic location to sense
the level of dust particles, nitrogen dioxide, sulfur, carbon dioxide, and carbon mon-
oxide in the air. IoT-based air monitoring systems can be used for keeping these
data over the remote server and keeping those updated using the Internet. Various
predictive models can be used for predicting the future air quality and suggesting the
required measures for controlling the pollution.
IoT also has a broad application in environmental monitoring. The areas include
extreme weather monitoring, water management, commercial farming, endangered
species protection, and many more. Sensors can be used for monitoring the environ-
mental changes. IoT can help in collecting various samples, which can be used in
various predictive modeling for analyzing the patterns in the environment.
The features discussed above are very important for establishing a smart city with
fully equipped G-IoT devices. G-IoT in a smart city is a great initiative for having an
advanced techno-savvy city with environmentally friendly equipment.
1.7 CLOUD-BASED G-IOT ARCHITECTURE
Here, the proposed G-IoT framework for smart cities will attend correspondence,
normalization, and quality aspects. There are ve layers in the proposed G-IoT,
i.e., sensor layer and smart city infrastructure, network layer, analytic big data layer,
application layer, and presentation layer, as shown in Figure 1.1. This framework
characterizes the fundamental correspondence ideal models for the associating
elements. It gives a reference correspondence stack alongside an understanding
of the fundamental associations around the model. This depicts the approach of
correspondence plans, which can be applied to various kinds of G-IoT o rganizations.
It is signicant that different networks of sensors in various sorts of networks can
speak with one another.
1.7.1 SenSor layer and Smart City infraStruCture
In smart cities, different kinds of sensors installed are operating in different
systems with minimal power consumption, which is supported by this layer. Sensor
networks (WSN), crowdsourcing, and RFID are the sensing framework in this layer.
Thelabeled articles can be identied through RFID (automatic identication tech-
nique). These inactive RFID labels are not battery-worked. The power can take from
13Cloud and Green IoT-based Technology
the pursuer’s transmission sign to the RFID reader by imparting ID. WSN plays a
crucial part in urban-sensing utilization. The remote sensors are smaller in size, less
expensive, and widely used. The long-range interpersonal communication is blasting
another sort of detection of worldview, for example, savvy telephone innovation has
advanced by empowering the residents of the keen urban areas to contribute toward
the brilliant city the executives. It assumes a signicant function in the government–
resident communication. Therefore‚ this layer must have the option to help a gigantic
volume of IoT information created by remote sensors and brilliant gadgets.
1.7.2 netWork layer
To accept the ability across networks, the higher communication layer, the network,
and WSN preferably use common protocols in the lower communication layers.
The other correspondence advances such as Wireless Hart, Zig Bee, WIA-PA, and
ISA.100.11 are relying on their separations to convey [12]. The proposed framework
is masking extra recurrence groups, for example, television blank areas and territo-
rial groups, which work at ultra-low energy for various utilizations. Bluetooth such
as Bluetooth 4.0 is a low energy convention and a lightweight variation for low force
applications. A fundamental prerequisite of these correspondence advances is force
utilization and little computational impressions for remote sensor organization so
the IP convention suite is the principal contender for these layers. Indeed, even the
already explicit principles that characterize their own convention can be moved to
IP. Therefore, the WSN and IoT IPv6 are the attainable answers for brilliant urban
areas utilizations.
1.7.3 analytiC Big data layer
Periodic and aperiodic are the two types of data management and information
ow layer [13]. In intermittent information, the executives' IoT sensor informa-
tion requires sifting since the information is gathered intermittently and some
FIGURE 1.1 Green index indicators.
14 Green Engineering and Technology
information may not be required. Therefore, this information should be sifted
through. The information is an occasion to set off IoT sensor data, which may
require quick conveyance and reaction for model health-related crisis sensor infor-
mation. In these proposed engineering methods large information power through
ventures IoT and explanatory devices is explained. The G-IoT correspondence
advances, organizations, and administration movements ought to have the option
to help dynamic climate through web engineering advancement, conventions, and
remote framework access models, and developed security protection. The G-IoT
cloud stage and cloud measure the power efciency. They enable improving the
application layer. It controls the data investigation and security controlling mea-
sures and gadget control to the G-IoT cloud stage. It is likewise liable for opera-
tional help framework, security, business rule the board, and business measure
the executives. It has to offer support examination stage, for example, measurable
investigation, information mining and text mining, prescient examination, and
so on.
1.7.4 appliCation layer
In G-IoT through different communication techniques, this layer sets the most note-
worthy purpose among the batch and stack and is in charge of the transportation of
various utilization to various clients. Through fuzzy recognition, cloud computing
and other technologies analyze massive data and information. In this layer, all the
natural climate correspondence are a part such as comprehensive monitoring of
energy, water resources monitoring management, monitoring environment protec-
tion, smart air pollution observation, supply consumption monitoring, water quality
diagnostics monitoring, key pollution source, and automobile exhaust. Based on
these new services, there are increasing efciencies of urban management, real-time
physical world data, addressing environmental degradation, and improving infra-
structure integrity.
1.7.5 preSentation layer
In this layer, data are obtained from the application layer. This layer follows data
programming structure plans created for various dialects and gives the continu-
ous grammar required for correspondence between two articles such as layers,
frameworks, or organizations. The information organization ought to be satisfac-
tory by the following layers; in any case, the introduction layer may not perform
effectively. Different city frameworks such as water exibly framework, power
gracefully framework, contamination control framework, transport division, and
so on can share their data by utilizing web-based interfaces, web, versatile uses
that are based on this layer. Individuals and government departments could get
particular information as per their requirements through this layer, which can be
utilized in the services of the city.
15Cloud and Green IoT-based Technology
1.8 ANALYTICAL FRAMEWORK
1.8.1 domainS and SyStemS of urBan areaS
These should work and be overseen utilizing ICT of inescapable registering, in partic-
ular, the IoT and its fundamental enormous information examination as a lot of trend-
setting innovations together with their novel utilization. These ought to preferably be
joined with the typologies furthermore, plan ideas of feasible metropolitan structures
[13]. Typologies incorporate minimization, thickness, variety, and blended land use
as typologies relate to the manageable vehicle, greening, and detached sunlight-based
plan as plan ideas. These typologies and plan ideas establish key procedures to accom-
plish the necessary degree of supportability with regard to practical metropolitan
structures. These metropolitan segments are to be upheld by elevated requirements
of natural and metropolitan administration, the thought is that shrewd sustainable
FIGURE 1.2 G-IoT and cloud-based architecture.
16 Green Engineering and Technology
urban communities ought to be – as types of arranging standards and plan ideas of
maintainability – checked, comprehended, dissected, and intended to improve their
commitment to the objective of environmentally maintainable advancement based
on profoundly intelligent and inventive arrangements. Metropolitan frameworks and
areas establish the primary wellspring of metropolitan information, which are cre-
ated by different metropolitan substances regarding the physical resources related to
the IoT, including city specialists, metropolitan ofces, metropolitan administrators,
singular residents, and privately owned businesses. They provide heterogeneous and
epic measures of information as contributions for enormous information applications
empowered through the IoT. Metropolitan information in their assortment, scale, and
speed are constantly labeled with spatial and transient names, generally spilled from
different tactile sources and put away in information bases, created regularly and con-
sequently, and incorporated, what's more, mixed in information distribution centers.
Subsequently, this segment includes distinctive sectoral and cross-sectoral wellsprings
of metropolitan information of changed kinds and sizes that are to be collected, put
away, and recovered for later preparing, examination, visualization, sending, and
sharing all through the instructive scene to help metropolitan activities, capacities,
plans, what's more, administrations with regard to ecological manageability.
1.8.2 data CategorieS, Big data SourCeS, and Storage
faCilitieS in urBan
These include metropolitan large information sources, storerooms, and information
classes. This segment is dedicated to information assortment, stockpiling, and execu-
tives. It includes information storehouses, information stockrooms, and storehouses
of public information. For example, warehousing as a major information investigation
method utilized in the metropolitan area involves a combination of information from
a few information bases, which are kept up by different metropolitan units alongside
veriable and outline data. Database administration systems are utilized to keep up
metropolitan information of huge scope and various classications. Likewise, cloud-
based capacity can be completely virtu alized – PC produced varia nt of the storeroom –
and all gadgets are straightforward to the metropolitan components as clients of
the cloud that can interface with the distributed storage through the organization.
Theadditional benet of joining cloud capacity with clever pressure strategies lies
in, notwithstanding altogether decreasing capacity costs, giving the chance of effec-
tively putting away a wide range of huge information having a place with the spaces
of brilliant practical urban areas.
1.8.3 C loud Computing or fog/edge Computing
This segment is devoted to the cycle of information disclosure/information min-
ing. The sub-measures identied with information disclosure encompass choice,
prepossessing, change, mining, interpretation, and assessment [14]. As to the
17Cloud and Green IoT-based Technology
information mining, the sub-measures included incorporate information under-
standing, information planning, demonstrating, assessment, and organiza-
tion[15]. Thediscovered or separated information includes insight capacities and
results from information handling, which the executives carry out by Hadoop
MapReduce dependent on distributed computing. Such capacities are expected
for dynamic, choice help, and choice robotization. Insight capacities are utilized
for constant furthermore key choices, contingent upon the application area trafc
frameworks versus energy frameworks, as far as control, automation, advance-
ment, and the executives are concerned.
1.8.4 Big data appliCationS
This part involves the assorted information-centric applications empowered by
the IoT related to ecological manageability comparable to assorted metropolitan
spaces. One application typically includes a few arrangements related to various
sub-domains of every area, contingent upon the kind of natural supportability
issue that will be fathomed [16]. To put it in an unexpected way, information-
driven applications include framework conduct and administration conveyance
with regard to this chapter. At the center of this part is the result of the execu-
tion of improvement procedures and activity taking cycles. Along these lines,
it executes activities and offers types of assistance as per the sort of the choice
taken dependent on the extricated helpful information from the IoT information.
Figure 1.3 shows the work of a huge information investigation utilizing the center
empowering innovations on the cloud base IoT in the setting of brilliant reason-
able urban communities.
1.9 CONCLUSION
The proposed cloud administrations, visual specialized instruments utilizing fast
broadband correspondence networks in savvy urban communities, can improve
business in corporate and government divisions. Moreover, in the interim, sensor
networks using an assortment of remote advancements in green brilliant urban
areas offer admittance to data on the progression of merchandise and the status of
hardware and the climate. They additionally encourage the utilization of the con-
troller. This makes conceivable the execution of brilliant urban areas presuming
sheltered with environmentally cognizant. Collaboration between networks can
be energized as sensors and actuators, correspondence innovations, and control
frameworks are getting more crude and wise. Empowered by the IoT, as a type of
inescapable registering, large information applications are progressively getting
perpetually imperative to keen maintainable urban areas as for their operational
working and wanting to improve their commitment to the objectives of the natu-
rally maintainable turn of events.
18 Green Engineering and Technology
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21
2Dynamic Models
for Enhancing
Sustainability in
Automotive Component
Manufacturing Systems
Jangam Ramesh, M. Mohan Ram,
and Y.S. Varadarajan
The National Institute of Engineering
CONTENTS
2.1 Introduction ....................................................................................................22
2.2 Contextualization ............................................................................................24
2.2.1 Role of Energy in the Manufacturing Sector ...................................... 24
2.2.2 Role of Computational Tools in Enhancing Sustainability
in a Manufacturing System .................................................................25
2.2.3 Strategies Suggested by Researchers for Sustainable
Value Creation in the Manufacturing Sector ...................................... 26
2.3 Framework for the Optimization of Parameters for Achieving
Sustainability .................................................................................................. 26
2.4 Methodology of the Study ..............................................................................28
2.4.1 Outlining the Manufacturing System ................................................. 28
2.4.2 Modeling the Manufacturing System ................................................. 29
2.4.3 Data Acquisition ................................................................................. 29
2.4.4 Simulating Manufacturing System ..................................................... 29
2.4.5 Optimization of the Process Parameters ............................................ 29
2.4.6 Analyzing Results ...............................................................................30
2.5 Case Study ...................................................................................................... 30
2.6 Conclusions ..................................................................................................... 31
References ................................................................................................................ 32
22 Green Engineering and Technology
2.1 INTRODUCTION
The economic growth of the developed and developing countries relies on the man-
ufacturing sector. The manufacturing system is a transformation process (Davim
2013); it converts valuable resources into usable goods. It is a value-added system
with many factors that are directly and indirectly associated. It is a process of absorp-
tion of various inputs such as raw materials, man power, machine, money, land, and
information and conversion of them into a nished product, waste, and emissions
as outputs. Rapid growth in population and the resulting economic activities have
posed a real challenge in dealing with nite resources in the manufacturing sector.
This can be due to cut-throat market competition and a lack of better approaches
for effective resource utilization in the manufacturing sector. There is a need to
utilize resources optimally and reduce emission and waste, which is not occurring
effectively today. To overcome it, the manufacturing sector needs to precisely moni-
tor and control its production operations and the costs involved for raw materials,
energy, labor, and equipment (Ghani, Monfared, and Harrison 2012). This forces
the industries to scrutinize maintaining the same level of production with optimum
resource utilization (Billing 2016). This objective will not only strengthen the pro-
motion toward sustainable value creation but also create a positive approach for an
ever-growing economy.
Ever since the “Brundtland Report” of the World Commission on Environment
and Development (1987) was published, a growing social consciousness of industrial
production and its impact has been observable. From the sustainability perspective,
“an organization’s prime focus of prot maximization without appreciating stake-
holder concerns has become progressively less agreeable” (Kiel and Arnold 2017).
Though the sustainability perspective has diverse dimensions, yet, it is still prin-
cipally viewed only in ecological contexts. Sustainability is incorporated in three
dimensions, namely, economic, ecological/environmental, and social aspects.
To be competitive and for existence, every organization focuses on moving toward
economic prosperity through increased productivity, attaining prots, and return to
scale (Raluca Gh. Popescu and Popescu 2019). Hence, organizations make certain
long-run economic survival plans for the future. Therefore, organizations show less
attention to sustainable development compared to economic development.
According to the organization’s view, sustainable prosperity is achieved only with
resources that can be reproduced (Bakkari and Khatory 2017). These perspectives
determine an organization's environmental and social performance. Hence, the triple
bottom line (TBL) concept collaborates with organizational approaches to conserve
resources for value creation in enterprises. One of the major factors that cover the
three dimensions of sustainability is energy. Inefcient utilization of energy resources
tends to increase the production cost, and the emission is harmful to the ecosystem
(Linke et al. 2013).
Computational tools such as simulation, modeling, algorithms, and optimization
are the main drivers for enhancing sustainability in manufacturing systems. These
techniques combine technical operations along with interdependencies between
the entities in the manufacturing layout. These techniques analyze the huge data
and enable improving design and control parameters for achieving targets with less
23Dynamic Models in Enhancing Sustainability
investment. The simulation is used for getting an optimal solution that reduces the
time for planning and maintenance in the manufacturing system. The simulation
planning phase will give information about the capacity of the resource, the num-
ber of operators, buffer sizes, transport systems, and alternative planning scenarios
to produce goods (Seow, Rahimifard, and Woolley 2013). While in the operation
phase, it focuses on optimizing resource utilization, scheduling, analyzing failure
and interruptions, and material ow throughout the system. For an existing system,
a dynamic model is developed using the simulation tools for integrating the resource
efciency strategy for sustainable value creation. The dynamic model examines
diverse virtual manufacturing aspects and assesses parameters with the existing
system (Terkaj and Urgo 2015).
There are two types of simulation techniques: continuous simulation and discrete-
event simulation (DES) that are used for logistic and manufacturing operations.
These techniques are used based on the distribution of the random parameters that
are discrete, continuous, or both.
DES is a technique of simulation of the behavior and performance of an exist-
ing system. It models the system as a series of “events” that occur over time. In
DES, operations are modeled as independent units, each of which can be given
associated attribute information. It includes variables such as specic energy con-
sumption, setup time, processing time, and location of various stations in the man-
ufacturing system. It is the imitation of a system with its dynamic mechanism in
an experimental model to interpret the best ones compared to reality (Shao, Kibira,
and Lyons 2016).
There are many DES software packages available for modeling and simula-
tion, for example, Witness, Promodel, SIMUL8, ARENA, and plant simulation
Tecnomatix, a plant simulation package, developed by Siemens product lifecycle
management (PLM) software, is used for developing discrete event dynamic
models. It enables us to generate a hierarchical modeling approach and control
strategies for optimal utilization of resources and detect bottlenecks in material
handling systems. It is incorporated with key capabilities such as visualiza-
tion of 2D and 3D process layouts, genetic algorithms, and neural networks
(PLM2020).
Focusing on the potential users of this technique, automotive component man-
ufacturing systems (ACMS) are chosen for carrying out the case study. They are
categorized as small and medium enterprises, whose operations are carried out
predominantly with obsolete technologies. Figure 2.1 shows a pictorial overview of
the manufacturing system, depicting inputs, transformation processes, outputs, and
boundary interactions.
There is a need to incorporate sustainability aspects and improve energy ef-
ciency in manufacturing systems that are to be addressed through computational
tools. In the present work, a dynamic model has been developed and described
using the Tecnomatix plant simulation software. The motivation and principles
related to the technique are presented. This article also gives insights into cap-
turing the non-value-added energy consumption activities in the manufacturing
processes. These procedures can be enforced on the entire manufacturing system
or a unit of it.
24 Green Engineering and Technology
2.2 CONTEXTUALIZATION
2.2.1 role of energy in tHe manufaCturing SeCtor
The industrial sector in Brazil accounts for 32.5% of the energy, which is needed
for the manufacturing processes. In China, the industrial sector accounted for 68%
of the total electricity consumption in 2019. In India, the industrial sector accounts
for 41.16% of the energy utilization (IEA 2020). India is in the category of devel-
oping countries with a well-built manufacturing base, but it is not coping up with
developed countries due to inefcient resource utilization. Energy is an essential
factor for the growth of the economy in terms of improving living standards and
industrial development. Manufacturing enterprises have to focus on efcient utiliza-
tion of energy because energy costs have experienced a sharp rise, and reduction of
energy consumption is the agenda. However, there is a need for enhancing energy
efciency due to the availability of fewer energy resources that cannot meet the over-
all demand in countries like India due to the huge population and diverse sectors.
Inthe meantime, the higher price of energy has an impact on production costs.
The energy utilization behavior of the system is a prerequisite for adopting the
concept of enhancing sustainability. From a manufacturing perspective, the empha-
sis shall be on the energy analysis of equipment. Many factors such as processing
time, setup time, maintenance status, idleness of machine, and production seasonal-
ity are to be considered in evaluating energy consumption. Both value-added and
non-value-added consumption must be analyzed. From it, non-value-added activities
are eliminated, and the revision of the manufacturing system is to be carried out.
This results not only in the optimal usage of energy resources but also in reduc-
ing social and environmental impacts. A few researchers highlighted that energy
FIGURE 2.1 Pictorial overview of the manufacturing system.
25Dynamic Models in Enhancing Sustainability
consumption is not normally xed over time, but rather dynamic in nature, depend-
ing on the manufacturing activities and the actual state of the machines.
2.2.2 role of Computational toolS in enHanCing
SuStainaBility in a manufaCturing SyStem
Mass production dominated the global market in the last few decades, but the dynamic
situations in the current scenario shifted drastically to customized production due
to unprecedented resource cost, non-availability of resources, and regulatory pres-
sures. These challenges are overwhelmed by the utilization of computational tools in
the manufacturing sector for strategic benets by leveraging the production capac-
ity. The entire value chain process should be upgraded so that manufacturers real-
ize twofold benets as they strive to “build the right product and build the product
right.” Manufacturing organizations should constantly acclimate and enhance their
operation strategies to achieve more sustainable production and to hold competitive
global markets. This forces organizations to shift toward the environment-friendly
and quality products, at a faster production rate. Hence, optimal investments in inno-
vating technologies along with plant and machinery installed become the core of the
growth and prosperity of successful manufacturing organizations. Simulation helps
in decision-making for maximizing the overall production efciency and engag-
ing the manufacturing operations toward enhancing sustainability through optimal
resource utilization. It increases productivity, optimizes production capacity, and
effectively leverages capital investments through innovation and better strategies.
Modeling is an abstraction procedure where entities of a system and their behavior
are examined. The entities and their interdependencies are represented by logical and
mathematical relations.
DES models have experimental entities that resemble the physical system, which
is probabilistic. A dynamic model is built, which is associated with the underlying
probabilistic mechanism and interdependencies within the system. While generating
a dynamic model in DES, an input is considered based on the parameters of interest
in a physical system. Input data collected from appropriate entities of the manufac-
turing system of interest are important for modeling and are broadly classied into
two approaches in simulation. The classical approach includes data collection from
a designed experiment, and it is better in terms of control. On the other hand, the
exploratory approach includes problems that are answered through existing data; it is
better in terms of cost compared to the classical approach.
Entities in the simulation model require certain input parameters for assessing the
overall performance of a system. This should be incorporated by setting resources
such as the number of workers assigned to each work station. Each entity is assigned
with a specic setup time, cycle time, processing time, and type of probability dis-
tribution involved in processing the operation. The reliability of each entity is also
accounted for. Each production operation will highlight the status of resources such
as idle, busy, or down.
DES can be used to explore the current production scenarios to estimate
the energy consumption based on entities modeled from the existing system.
26 Green Engineering and Technology
Thesimulation captures the non-value added activities such as idle/underutilized
machinery in the layout, which shows inefcient energy utilization since they will
be running without use. This allows us to assess new scenarios with modied
data and come up with optimal scenarios that eliminate non-value-added activi-
ties. This is achieved either through checking and deactivating the machines or
through redesigning the manufacturing system layout/processes so as to use them
efciently, thereby increasing production.
The Tecnomatix plant simulation software provides a platform to integrate
various tools to improve productivity resources using resources efciently. It associ-
ates optimization tools with DES to enhance production efciency. The competence
of a dynamic model is that it is very close to a physical system.
2.2.3 StrategieS SuggeSted By reSearCHerS for SuStainaBle
value Creation in tHe manufaCturing SeCtor
Several works have been carried out for capturing the complementary areas of sus-
tainability research in manufacturing systems. Jayal et al. (2010) provided insights
on “modeling and assessment strategies mainly focusing on sustainable manufactur-
ing.” Duou et al. (2012) provided an exhaustive review of procedures to be adopted
to reduce energy consumption in the discrete part of manufacturing through the
integration of “unit process, manufacturing line, facility, manufacturing system, and
global supply chain.” This integration helped in cutting down energy utilization up
to 50% in the industrial sector.
Garetti and Taisch (2012) outlined the challenges in improving sustainability in
the manufacturing sector and the strategies required to enable sustainable manufac-
turing. The relationships among intellectual capital, corporate social responsibility,
and performance are addressed by Gallardo-Vázquez et al. (2019). They gave valu-
able insights into the Romanian business environment. The importance of setting up
metrics and designing a sustainability impact for monitoring projects was noted by
Lesic et al. (2019). They carried out subjective interviews about sustainability and the
impact of innovation in the process industries. Their work can be taken as a guideline
for introducing new impact metrics or evaluating existing ones.
2.3 FRAMEWORK FOR THE OPTIMIZATION
OF PARAMETERS FOR ACHIEVING SUSTAINABILITY
The total effort in manufacturing a product requires an assessment of the entire pro-
cess chain from the acquisition of raw materials to the nished product. The sequence
of individual manufacturing entities is arranged in the process chain to determine the
total effort, besides considering the efciency of an individual process. Focusing
on sustainability aspects, there is a need to examine the optimization techniques
through discrete event dynamic models that are practically applicable to overcome
inefcient process chains in the manufacturing system.
The simulation dynamic model allows the consideration of variations in different
scenarios in the system. Based on these inputs, strategies for designing, operation,
27Dynamic Models in Enhancing Sustainability
control, maintenance, and predictions on energy consumption can be developed in
the manufacturing sector (Rodrigues et al. 2018).
Developing a dynamic model and applying optimization techniques in simulation
tend to reduce energy consumption and help in enhancing sustainability parameters
in manufacturing layout considering machines, workers, raw materials, and energy
utilization in direct and indirect activities in an organization (Seow, Rahimifard, and
Woolley 2013).
The proposed procedure provides insights into actions to be incorporated for improv-
ing sustainability in the manufacturing system. The existing system is improvised using
a discrete event dynamic model and examines it in different scenarios through a simula-
tion tool. A ow chart depicting these steps is shown in Figure 2.2.
• Characterize the manufacturing system: The factors affecting sustain-
ability in the manufacturing system are analyzed, such as entities and pro-
cess parameters in the system.
• Generating the dynamic model: A DES dynamic model of the manufac-
turing system is built. It includes the model abstraction and implementation
of the proposed model in a Tecnomatix plant simulation software.
• Data acquisition from the physical system: The data required are acquired
from manufacturing histories and observation of the operations.
• Simulate the dynamic model: Use the dynamic model to simulate the
physical system. Different scenarios are evaluated to arrive at an optimal
scenario close to reality.
• Optimize the factors using algorithms: By using algorithms, the factors of
the manufacturing operations are optimized to enhance sustainability. The
results are assessed for objective functions to reduce energy consumption.
Characterize the manufacturing system
Generating the dynamic model
Data acquisition from the physical system
Simulate the dynamic model
Optimize the factors using algorithms
Analyze the sustainable indicators
Quantify the process parameters for
enhancing sustainability
FIGURE 2.2 Steps for optimizing the parameters to enhance sustainability.
28 Green Engineering and Technology
• Analyze sustainability indicators: Sustainable parameters can be deter-
mined for the operation, in addition to evaluating optimum scenarios for
minimal energy consumption. This is a relevant aspect of the system that
can cause the simulation activities to be re-executed with modied goals to
reach these metrics.
• Quantify the process parameters for enhancing sustainability: Once the
optimal parameters are found, it is important to check the feasibility of the
current implementation scenario to reduce energy consumption and satisfy
the sustainability aspects.
2.4 METHODOLOGY OF THE STUDY
In any system, execution is an important aspect; it involves connecting several activi-
ties to accomplish its objective. For every activity, many characteristics are involved
in planning and execution.
2.4.1 outlining tHe manufaCturing SyStem
The most important factor in every manufacturing system is acquiring data from var-
ious sources for the decision-making purpose. Interaction with the decision-making
teams is the initial point for system characterization. Details such as plant layout,
equipment specication, raw material acquisition, energy consumption patterns, and
registries help to accord an overview of the system in operation. The primary data are
captured by the knowledge and expertise of the worker through a eld study in the
manufacturing operations. Documents of plant layout, maintenance, and production
managers are the sources to determine an efcient manufacturing system.
The dynamic models focus on a specic manufacturing unit; it reduces the
computational demand for optimization and simulation. Yet, an entire system
model can help to identify the resource utilization loss that managers have not
detected in daily operations. This can be examined by labeling work shifts, work
hours, duration of machining operation, and the number of workers. It will allow
the determination of precise details of direct and indirect power utilization in the
manufacturing system. The manager outlines the utilization of various resources,
development/change of energy consumption patterns, and activities in the man-
ufacturing system. The information provided by the supervisory system also
includes electricity bills and alternative fuel consumption. One can evaluate the
correlation among operation, consumption, and idleness of machinery and work-
ers in a manufacturing system.
To ensure the analysis is compatible with the process, it is suggested that a subse-
quent interaction is organized with decision-makers to verify and validate the model.
Sustainability aspects are crucial to be observed in the TBL viewpoint, which cap-
tures the three variables in any dynamic model. The production cost, raw materials,
production volume, labor costs, and electricity consumption are some of the factors
that are considered in the economic dimension side. The social variables are incor-
porated in the dynamic model to assess the system performance; it includes labor
pool, environmental conditions, risks, and turnover rate. Environmental aspects are
29Dynamic Models in Enhancing Sustainability
examined by specic variables such as emission of pollutants, energy consump-
tion, and water consumption in the manufacturing system (Miller, Pawloski, and
Standridge 2010).
2.4.2 modeling tHe manufaCturing SyStem
In the abstraction phase, model integration has to be achieved. Physical plant activi-
ties are replicated in dynamic models through simulation techniques. Implementation
of simulation relies on the complexity of the production process; however, dynamic
models can be applied for a unit manufacturing process or for the whole manufactur-
ing system by utilizing simulation techniques. However, the researcher has to keep
his objective focused either on the complete production process or on a particular
line in a system based on problem identication.
Primarily, DES translates the abstraction model specications into a graphic or
textual language; 2D and 3D models can be built that illustrate the process entities
in terms of function/operations, their inputs/outputs, and how they are correlated in
the system. Yet, the exertion may not be reliable to the abstraction model due to the
limitation of the computational tool.
2.4.3 data aCquiSition
Before initiating data collection, process characterization helps to determine the pro-
cess parameters and the proven acceptable levels for efcient manufacturing. It reduces
the failure risks in the manufacturing system. Dynamic models are generated through
the data collected from the specication of equipment, plant operation routines, and
manufacturing cell layout. Lateral to the dynamic model, there is a need to collect rel-
evant data of the manufacturing systems such as electricity consumption, employment
of human resources, throughput, raw material utilization, and waste production.
2.4.4 Simulating manufaCturing SyStem
One can accomplish simulations of the manufacturing systems through the DES
modeling and data collected in the process. The rst validation of the simulation
model is signicant by feeding the input data observed in the physical system. By
comparing the real system and simulated results, one can validate the model ability
and conrm any further modications that are incorporated in the dynamic model to
correlate it to the real system.
After validating simulation results, different simulation observations can be ana-
lyzed. In each observation, the decision variable values can be distinct from the real
process ones. To optimize the process, input parameters play a key role in determin-
ing decision variables.
2.4.5 optimization of tHe proCeSS parameterS
Different output parameters of each observation are compared for optimization pur-
poses. However, there is a limitation in decision variables chosen due to operating
30 Green Engineering and Technology
restriction and the range of factors included in the activity. The TBL includes the
factors of production such as raw material availability for manufacturing, electricity
cost, and operation restrictions like shifts in a plant. Manual optimization techniques
are time-consuming, whereas, in simulation, these tools are integrated for faster pre-
diction of activities. For optimization purposes, computational tools are embedded
with several algorithms such as genetic algorithm, articial bee colony, and so on.
Hence, sustainable indicators are positively affected by optimal scenarios.
2.4.6 analyzing reSultS
It is to be noted that economic parameters impact the production cost. A non-feasible
solution should be carefully checked to nd the reason for its occurrence. Based on
the evaluated optimization solution, the procedure is re-executed from initial steps by
modeling, process characterization, simulation, and optimization. If the existing man-
ufacturing layout is utilizing minimum resources, then full modication is necessary.
2.5 CASE STUDY
The methodology for enhancing sustainability aspects is validated by the proposed
dynamic model of an ACMS, located in the Mysore region of Karnataka, India.
A owshop is taken for the case study. A Tecnomatix plant simulation v15 soft-
ware was used to develop the DES dynamic model of the owshop as shown in
Figure 2.3. In this case, the manufacturing process involves the movement of raw
materials, workers, and semi-nished goods to the next machining process. Along
with it, process time, setup time, recovery time, cycle time, assigning workers to the
work station, and other such operations are involved in the manufacturing process.
An examination has been carried out to nd barriers to sustainable value creation in
the existing system. This work is concerned with energy consumption, such as power
utilized by machines and accessories for the smooth running of the manufacturing
FIGURE 2.3 Discrete-event 2D and 3D dynamic models of the automotive component man-
ufacturing systems (ACMS).
31Dynamic Models in Enhancing Sustainability
system. Along with data acquired from the shop oor, production volume per cycle
and the duration of shifts have to be evaluated.
The dynamic model is incorporated with optimal settings of each entity process
parameters such as process time, setup time, cycle time, and idle time. The frame-
work proposed a detailed procedure to acquire the data for these settings. By associ-
ating entities and their interdependencies, dynamic models quantify the throughput
of the system at optimal settings. The main factor assessed in this dynamic model
is throughput; it is the output goods created in a day (24 h). The optimal process
parameters of all the entities are used to enhance the sustainability of the system.
The pool of workers is another factor to be incorporated; it impacts productivity
and cost. The basic assumption that the pool of workers have the same skill and
experience is taken while performing the simulation of an existing system. There is
a need to ensure the reliability of each entity of the dynamic model to capture the
failure rates during the manufacturing operation. The simulation output is impor-
tant to evaluate the minimum number of workers, energy consumption, raw materi-
als, and capital without compromising throughput. Due to economical constraints
for new investments and lack of available time to accomplish protracted breaks in
the production system, there is no scope for physical modication in the existing
plant layout. Hence, computational tools are the main drivers for enhancing sus-
tainability in the existing manufacturing system. DES incorporates optimization
tools to determine optimal scenarios by changing the variables of interest that are
dened by the user.
2.6 CONCLUSIONS
The objective of this research article was to develop a framework to enhance sus-
tainability. This paper addresses a barrier to implanting sustainability in the manu-
facturing system. There is a need to simplify the interdependencies in a complex
manufacturing system. The design of the manufacturing layout involves a compli-
cated procedure. It is a signicant activity encompassing designs for a long time
horizon and a major assurance of nancial stability. In a manufacturing system, mod-
eling and simulation involve the assessment of consistent activities for both design
and operational phases. This article gives insights into sustainable value creation
in the manufacturing system through computational tools such as simulation and
optimization. The primary aim of this article is to capture the inefcient utilization
of resources in a manufacturing layout through different scenarios. It helps to iden-
tify the non-value-added energy consumption by considering the idleness, processing
times, changing operation routines, varying the number of workers, and assigning
work stations to workers. It also aimed at identifying optimal process parameter
settings for an efcient manufacturing system. Once optimal parameters were found
for all entity processes, a digital manufacturing system was modeled, where every
activity operated at its optimal process parameters. This article throws light on the
signicance of computational tools to address the signicance of the dynamic model
to assess the performance of the manufacturing system.
32 Green Engineering and Technology
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35
3Internet of Agriculture
Things (IoAT)
A Novel Architecture
Design Approach for
Open Research Issues
K. Lova Raju and V. Vijayaraghavan
VFSTR University
CONTENTS
3.1 Introduction ....................................................................................................
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n ..................................................................................
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36
3.2 Functional Blocks of IoT 38
3.2.1 Device 39
3.2.2 Communicatio 40
3.2.3 Services 40
3.2.4 Management 40
3.2.5 Security 40
3.2.6 Application.40
3.3 Characteristics of IoT . 40
3.3.1 Self-Adapting and Dynamic 40
3.3.2 Self-Conguration 40
3.3.3 Interoperable Communication Protocols 41
3.3.4 Unique Identity . 41
3.3.5 Integrated into the Information Network 41
3.3.6 Context-Awareness 41
3.3.7 Intelligent Decision-Making Capability 41
3.4 IoT Protocol Stack 42
3.5 IoT Protocols . 42
3.5.1 MQTT 42
3.5.2 CoAP . 43
3.5.3 XMPP 43
3.5.4 AMQP 43
3.5.5 DDS 43
36 Green Engineering and Technology
3.1 INTRODUCTION
The term Internet of Things (IoT) was coined by Kevin Ashton in 1999. It aims to
connect anything at any time in any place (Harbi et al. 2019, Bharathi et al. 2019),
as shown in Figures 3.1 and 3.2. IoT techniques are a platform that can access and
3.5.6 REST HTTP .......................................................................................
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43
3.5.7 Web Sockets . 44
3.6 IoT Enabling Technologies . 44
3.7 IoT Applications 44
3.8 Existing Works 44
3.8.1 Internet of Things in Agriculture 46
3.9 IoAT Architecture ........ 46
3.9.1 Sensors Used in the IoAT Architecture 48
3.9.2 Wireless Technologies Used in the IoAT Architecture 48
3.9.3 Hardware Platforms Used in the IoAT Architecture 48
3.10 Data Analysis in the IoAT Architecture 48
3.10.1 Various IoT-Based Cloud Service Platforms 48
3.10.2 Big Data ....... 49
3.10.3 Machine Learning Techniques 49
3.10.4 Security Issues 53
3.11 IoAT Applications . 53
3.12 Conclusion 53
References 54
FIGURE 3.1 Connectivity of IoT device examples (Harbi 2019).
37Internet of Agriculture Things (IoAT)
control the devices remotely (Sangeetha et al. 2018) at any time. Things or objects
in the physical design of IoT refer to communicate with each other, for mutual data
exchanging over the Internet (Yan, Zhang, and Vasilakos 2014) without human inter-
vention. In the generation of IoT, any objects are embedded with sensors, actuators,
microcontrollers, and communication devices that can work together and make a
smatter world (Adelantado et al. 2017). Nearly 50 billion things will be connected
using the Internet by 2020 (da Cruz et al. 2018). It will increase exponentially as time
moves on.
The transformation of the Internet to the IoT consists of four (Khanna and Kaur
2019) phases. The rst phase was represented where communication is possible
through the xed telephone line and by the way of Short Message Service (SMS).
The second phase was focused on sending large size messages like e-mails in terms
of attachments, information, entertainment, and so on. The third phase was rec-
ommended for electronic applications such as e-productivity, e-commerce, and so
on. The fourth phase was associated with social media like Facebook, YouTube,
Skype, and so on. Finally, the ongoing emerging technology is IoT as shown in
Figure 3.3.
This chapter (Raju and Vijayaraghavan 2020) comprises ve parts. Part 1
addresses the introduction to IoT and Part 2 consists of fundamental concepts of
IoT such as IoT functional blocks, characteristics of IoT, IoT protocol stack, IoT
enabling technologies, and applications of IoT. Part 3 consists of IoAT architecture,
which consists of IoT in agriculture, sensors used in IoAT, wireless technologies in
IoAT, and hardware platforms in IoT. Part 4 represents the data analysis in IoAT
and it contains various cloud platforms, big data, machine learning, and security
issues. Part 5 describes the IoAT applications and conclusion of the chapter as
shown in Figure 3.4.
FIGURE 3.2 IoT view. (Adapted from Bharathi et al. 2019.)
38 Green Engineering and Technology
3.2 F UNCTIONAL BLOCKS OF IOT
The IoT system consists of various (Ray 2017, Al-Fuqaha et al. 2015) functional
blocks to simplify different characteristics of the system such as sensing, iden-
tication, actuation, communication, and management as shown in Figure 3.5.
Figure 3.6 highlights the functional blocks and Figure 3.7 shows the block dia-
gram of an IoT device.
FIGURE 3.3 Transformation of the Internet to IoT (Khanna and Kaur 2019).
FIGURE 3.4 Outline of the article. (Raju and Vijayaraghavan 2020.)
39Internet of Agriculture Things (IoAT)
3.2.1 deviCe
An IoT system consists of interrelated computing devices that are capable of perform-
ing a full-duplex mode of communication for the transfer and exchange of data with
the other devices over the Internet. The data are communicated to the centralized
servers or applications that involve the cloud for processing using local networks. For
communication purposes, IoT devices utilize both wired and wireless protocols that
are responsible for controlling the performed actions locally or remotely.
FIGURE 3.5 Elements in IoT (Al-Fuqaha 2015).
FIGURE 3.6 IoT functional blocks. (Adapted from Ray 2017.)
FIGURE 3.7 Block diagram of an IoT device (Ray 2017).
40 Green Engineering and Technology
3.2.2 C ommuniCation
The communication block in the functional diagram of the IoT device is mainly
pledged for transferring data from one device to another device. The communication
layers of the Open Systems Interconnection model, i.e., application, presentation, and
transport layers, are mainly responsible for maintaining protocols.
3.2.3 S erviCeS
A variety of services such as device modeling, controlling, recovering, data analyt-
ics, and publishing of data are provided by the services block of an IoT device.
3.2.4 management
For the governance of an IoT device, a management block plays a vital role. It pro-
vides different functionalities for an IoT device for device governance.
3.2.5 S eCurity
Providing security for an IoT device includes a huge task. To accomplish this task,
the functionalities such as privacy, authentication, content integrity, authorization,
data security, and message integrity are provided.
3.2.6 appliCation
The application layer is mainly responsible for data visualization and analyzing the
system status.
3.3 CHARACTERISTICS OF IoT
IoT can be dened as a container of essential service components (Ray 2018), as
shown in Figure 3.8.
3.3.1 Self-adapting and dynamiC
Sensors are equipped with special features called dynamic switching and self-adapt-
ing, which are able to take actions based on their operating conditions.
3.3.2 Self-Configuration
An additional feature is also equipped for IoT devices called self-conguration,
which can work with more number of devices to achieve some functionality, for
example, agriculture monitoring in elds.
41Internet of Agriculture Things (IoAT)
3.3.3 interoperaBle CommuniCation protoColS
To accomplish successful means of communication, an IoT device must be able to
allow other interoperable devices, protocols, and their infrastructures.
3.3.4 unique identity
To distinguish the IoT devices, each device is assigned a unique identier.
3.3.5 integrated into tHe information netWork
The IoT sensors are unied within the system by making necessary connections
among them and allowing the other devices to communicate by exchanging data.
These formed networks are dynamic.
3.3.6 Context-aWareneSS
The physical information is gathered by the sensor and the sensor node gains knowl-
edge about the neighboring context.
3.3.7 intelligent deCiSion-making CapaBility
To achieve long-distance communication, networks are multi-hop. After obtaining
the information from the sensor, they make intelligent decisions on their own and
improve their efciency.
FIGURE 3.8 Characteristics of IoT. (Ray 2018.)
42 Green Engineering and Technology
3.4 IoT PROTOCOL STACK
The most common protocols (Čolaković and Hadžialić 2018) shown in Figure 3.9
are based on the reference model of TCP/IP. It is essential to use suitable commu-
nication system architecture with various IoT protocols when they are needed to
interoperate.
The latest developments are the only way to develop for the future development
of IoT based on the standardized approach. Consider developing novel IoT protocols
and architectures that play a pivotal role in the present years.
3.5 IoT PROTOCOLS
IoT consists of signicant protocols (Glaroudis, Iossides, and Chatzimisios 2020),
which are present in the application layer. In this section, a few IoT protocols are
discussed.
3.5.1 mqtt
The Message Queue Telemetry Transport (MQTT) is a lightweight TCP/IP-based
messaging and publisher–subscriber network protocol that provides us a bi-
directional communication and lossless connections between the publisher and
subscriber. MQTT denes three QoS levels: QoS 0, 1, and 2. To provide more
security for the MQTT protocol, an additional feature S-MQTT (Secure MQTT)
is introduced. It supports only lightweight data packets; to overcome this, it uses
a UDP protocol for an effective way of transmission of higher data packets over
a long distance.
FIGURE 3.9 IoT Protocol Stack (Čolaković and Hadžialić 2018).
43Internet of Agriculture Things (IoAT)
3.5.2 Coap
The Constrained Application Protocol (CoAP) is a specialized Internet-based web
transfer protocol that is used for the devices that use the same constrained network
(i.e., low power and lossy networks) and is also used for different constrained
networks that are linked to the Internet. As opposed to HTTP, CoAP relies on a
non-connection-oriented transport protocol (UDP), and it supports unicast and
multicast.
3.5.3 xmpp
The Extensible Messaging and Presence Protocol (XMPP) is a message-oriented
communication protocol, which enables the exchange of real-time data between two
or more network entities. It is used for chatting, video, and voice calls by supporting
each one of these applications for providing authentication and encryption services.
For text messaging services, it uses Extensible Markup Language (XML). To provide
more security for our data, the XMPP has an inbuilt TLS mechanism to determine
more accuracy in terms of data integrity.
3.5.4 amqp
The Advanced Message Queuing Protocol (AMQP) is an open standard application
layer protocol that mainly denes for queuing, reliability, routing (including point-
to-point and publish-and-subscribe), security, and message orientation. It has built
two dissimilar versions like version 0.9.1 and version 1.0. The advanced AMQP ver-
sion is not exclusionarily associated with the publisher/subscriber mechanism. The
AMQP has been designed with reliability, security, and interoperability in mind by
providing an industrial-grade, open-source solution that works even in low-latency
environments.
3.5.5 ddS
The Data Distribution Service (DDS) for real-time systems are sophisticated by an
Object Management Group (OMG) that aims to get high performance, real-time,
enable dependable, interoperable, and scalable data exchanges using a publish–
subscribe pattern. It uses the UDP protocol by delinquency, but it can support the
TCP protocol also. It offers 23 wide ranges of QoS policies. However, the security
issue for this is still a pending question. Overall, it is backing both powerful and low
capacity devices that give solutions for a wide range of IoT applications.
3.5.6 reSt Http
The Representational State Transfer (REST) Hyper Text Transfer Protocol (HTTP)
is a basic web server protocol that uses the elemental client/server model. The HTTP
service is correlated with the architectural style recently to expedite to maintain the
interaction between different entities of web services.
44 Green Engineering and Technology
3.5.7 WeB SoCketS
The main usage of the web sockets protocol is to maintain a perfect interaction
between the web browsers and the web server with the lowest alternatives. It is an
independent single TCP-based full duplex computer communicating protocol. It does
not work based on the publisher/subscriber model or the response/request model.
They maintain an asynchronous connection. It is not suitable for devices with strong
constraints, but it can offer us real-time communication by minimizing the over-
head solutions for the applications of IoT by using a WAMP (Web socket-based
Application Messaging Protocol) sub-protocol.
3.6 IoT-ENABLING TECHNOLOGIES
Presently, IoT-enabling technologies play a prominent role in the IoT. Now, we will
discuss some of these IoT-enabling technologies (Bahga and Madisetti 2014) as
shown in Figure 3.10.
3.7 IoT APPLICATIONS
Figure 3.11 illustrates the taxonomy of applications of IoT such as environmental,
commercial, smart city, industrial, health care, general aspects, and so on (Asghari,
Rahmani, and Javadi 2019).
3.8 EXISTING WORKS
Nowadays, IoT is the emerging technology for providing better solutions to agricul-
tural applications full of the automation system and also some other emerging tech-
nologies are used like cloud computing, machine learning, articial intelligence,
and so forth. Different architecture designs are implemented for a smart agriculture
FIGURE 3.10 IoT enabling technologies. (Bahga and Madisetti 2014.)
45Internet of Agriculture Things (IoAT)
system using these technologies. Regarding this, innovative research is being done on
agricultural applications. Some of the existing works are discussed below.
Ayaz et al. (2019) suggested smart agriculture toward making the elds talk using
IoT. This article explained well the major role-play of the IoT and wireless sensors
in the agricultural applications such as preparation of the soil, status of the yield,
watering, insects, and detecting bugs. This review gives an insight into IoT-based
architectures, platforms, wireless technologies, and current and future challenges in
agriculture issues. But there is no explanation about the proper architecture regarding
the smart agriculture system in this article.
Muangprathub et al. (2019) explained the smart farm using IoT and data analy-
sis. This paper mainly focused on the trio areas of farms and deploying the sensors
in each farm for crop yield and water management in the way of real-time moni-
toring. By getting information regarding agricultural farms, it is sent to the former
via through web and mobile-based applications by using NodeMCU. The designed
structure is constituted by trio sensors such as soil moisture, DHT22, and ultrasonic
sensors along with a lesser price. In this work, there is no discussion regarding the
IoT-based architecture.
Chen and Yang (2019) proposed an IoT-based architecture for intelligent agri-
culture and emerging technologies. This article mainly concentrated on intelligence
added to the traditional agriculture methods by using the IoT. That term is called
smart agriculture. In addition, two types of analysis (data visualization analysis
and cluster analysis) are incorporated with the development of intelligent agriculture
and also the development of the IoT in the eld of agriculture in terms of techni-
cal functions like sensing, identication, transmission, and monitoring. This paper
article consists of layered architecture only.
FIGURE 3.11 IoT applications (Asghari, Rahmani, and Javadi 2019).
46 Green Engineering and Technology
Mekala and Viswanathan (2019) implemented a novel CMM measurement index for
a smart agriculture system. In this article, the authors described the thermal comfort
levels of crops or plants and also determined the temperature quotient that is based on
temperature (P), humidity (H), and soil moisture. By using layer architecture only, that
is used to send messages like E-mail alerts and SMS to the farmers regarding their
agriculture eld information. Here, the hardware section represents the DHT 11 sensor
that is interfacing with Arduino Uno, and the moisture sensor is also used in it.
Shi et al. (2019) presented the protected agriculture using IoT. Protected agriculture
is the method for the efcient development of new agriculture used to change climate
conditions such as temperature and humidity, and also it is suitable for the growth of
plants and animals. This review gives an insight into the IoT in the eld of protected
agriculture. In this article, the author explained the IoT-based layered architecture only.
Mekala and Viswanathan (2020) proposed a THAM index-based IoT system for
smart agriculture decision-making using sensor stipulation, a novel THAM index
for nding the comfort levels of the crops and plants. In this article, the authors
mainly focused on the sensor selection system that enables a sensor to impose the
process, and the entire agriculture eld is covered by the optimal number of sensors.
Moreover, the NPK fertilizer regulatory model is recommended for the proper nutri-
tion rate present in the soil. But there is no proper architectural explanation regarding
the agriculture monitoring in this article.
Zamora-Izquierdo et al. (2019) developed a smart farming IoT platform based on
edge and cloud computing. This article represents the advancement and design of a
system architecture along with a prototype that is used in Precision Agriculture with
automation. The IoT protocols like MQTT or CoAP are used to communicate with
Cyber-Physical Systems (CPS), while Next-Generation Service Interface (NGSI) for
northbound and southbound APIs (Application Programming Interfaces) gets access
to the cloud platform.
3.8.1 internet of tHingS in agriCulture
In the eld of agriculture area, to develop smart farming solutions using the IoT
technology, an important amount of work has been done. In smart farming, IoT has
transferred the enormous innovation in the agriculture environment by analyzing
the multiple issues and challenges (Elijah et al. 2018, Ojha, Misra, and Raghuwanshi
2015) in smart agriculture. In this present scenario, the growth of IoT technologies
provides the solutions for the problems faced by farmers such as water shortages,
productivity problems, and cost-effective management. Advanced IoT technologies
are identied and all these issues are provided with solutions to increase the produc-
tivity with cost-effectiveness. Wireless sensor networks enable the collection of data
from sensor devices and are sent to the main servers or clouds.
3.9 IoAT ARCHITECTURE
The proposed architecture of the Internet of Agriculture Things (IoAT) consists of
two subsystems (Singh, Chana, and Buyya 2020), namely, a User Subsystem (US)
and a Cloud Subsystem (CS), as shown in Figure 3.12.
47Internet of Agriculture Things (IoAT)
The US is responsible for monitoring the agriculture eld to get information
(agricultural parameters) from environmental sensors and deployment sensors
(agricultural sensors). Therefore, the physical layer comes into the picture and it
senses all agricultural parameters through sensors such as temperature, humid-
ity, moisture, and so on. It sends the digital signal to another above level. Data
acquisition is a collection of data from sensors deployed in the agriculture eld.
The data are processed after the completion of the data collection process and
this is called data processing, which comes under hardware-embedded platforms
(IoT gateway) along with wireless communication technologies for IoT, based on
the conceptual and communication layers. Through communication protocols like
MQTT and CoAP, only the messages are transmitted from the client to the server
based on a set of rules for data in formats such as XML, JSON, CSV, and so on.
These things have occurred while the Internet is available at farmers' place, and it
comes under the Internet layer.
The CS is to take care of the agriculture eld data that are stored in the IoT cloud
repository using IoT securities like APIs. Therefore, the data can be accessed from
FIGURE 3.12 Proposed Architecture of Internet of Agriculture Things (IoAT). (Singh,
Chana, and Buyya 2020.)
48 Green Engineering and Technology
the cloud repository through precongured devices such as mobile phones, laptops,
and so forth. The accessing layer is responsible for accessing the data from the cloud
through a farmer’s mobile phone regarding agriculture data. The application layer
is designed to perform graphical visualization, real-time monitoring, and statistical
analytics in a fully automated manner. IoT cloud itself is a data storage platform, and
data analysis is carried out in it. Some of the machine learning algorithms are used
in the IoAT applications for data analysis. Finally, IoT cloud platforms provide cloud
services for data storage and analysis. These cloud platforms are associated with the
architecture of IoAT for providing cloud services.
3.9.1 S enSorS uSed in tHe ioat arCHiteCture
The IoAT architecture consists of two types of sensors: environmental sensors and
deployment sensors. An environmental sensor describes the environmental param-
eters such as temperature (Raju et al. 2019) and humidity sensor (DHT11), light-
dependent resistor (LDR) sensor, and so forth. At the same time, a deployment sensor
describes the soil parameters like soil moisture, pH value, and so on. Table 3.1 shows
the details about the sensors that are used in the IoAT architecture.
3.9.2 WireleSS teCHnologieS uSed in tHe ioat arCHiteCture
Wireless communication technologies play a very signicant role in agricultural
applications. Table 3.2 shows the classication of different communication technolo-
gies (Bhoyar et al. 2019).
3.9.3 HardWare platformS uSed in tHe ioat arCHiteCture
Various hardware platforms or boards (Tzounis et al. 2017) are used for agricul-
tural applications, along with the parameters such as Operating Voltage (OV), Clock
Speed (CS), System Memory (SM), Programming Language (PL), and Integrated
Development Environment (IDE) as shown in Table 3.3 and that are equipped with
sensors and from the resources, the information is collected.
3.10 D ATA ANALYSIS IN THE IoAT ARCHITECTURE
Data analysis is a key part of the IoAT architecture.
3.10.1 variouS iot-BaSed Cloud ServiCe platformS
IoT-based cloud service platforms (Ray 2016) are used for the development of
advanced use cases with better solutions. Table 3.4 provides various kinds of cloud
platforms of IoT along with IoT-based cloud service platforms, IoT cloud service
type, application improvement, monitoring data, visualization, developer cost, and
research purpose.
49Internet of Agriculture Things (IoAT)
3.10.2 Big data
Big data shows the data properties (Wolfert et al. 2017) characterized by high vol-
ume, high velocity, and high variety that require specic technology and analytical
methods for their transformation into values.
3.10.3 maCHine learning teCHniqueS
Machine learning techniques are classied into three types, namely, supervised lear n-
ing, unsupervised learning, and reinforcement learning. Again, supervised learn-
ing is categorized into classication (SVM, KNN, NB, RF, and AR) and regression
TABLE 3.1
Different Sensors Related to Agriculture and Environmental Applications
(Raju 2019)
S. No. Sensor Image Sensor Name Technical Details Use Cases
1 DHT11:
Temperature
and
Humidity
Input voltage:
3.3–5.5 V, range
of humidity:
20%–90%,
range of
temperature
0°–50°,
Accurateness
±2, persistence
−1, and
exchangeability
Environmental
and
agricultural
2 LDR: Light
Dependent
Resistor
Input
voltage:3.3–5 V
dc,operating
current:15 mA,
digital
output:0–5 V,
analog
output:0–5 V
based on light
falling on LDR
Environmental
and
agricultural
3 FC-28:Soil
moisture
Operating
voltage: 3.3–5 V,
output voltage:
0–4.2 V, input
current: 35 mA,
output signal:
both analog and
digital
Smart
gardening
and
agricultural
50 Green Engineering and Technology
TABLE 3.2
Taxonomy of Different Wireless Communication Technologies
S.
No.
Communication
Technology
Frequency
Operated (Hz)
Distance of
Communication
Power
UtilizationNetwork Type Data Rate (b/s) Cost Use Cases
1 Radio Frequency
Identication
Proximity 13 M 424 K 10 m Very low Very low Environmental and
agricultural
2 Bluetooth Personal Area
Network
2.4 G 25 M Below 10 m Low Low Environmental and
agricultural
3 ZigBee/IPv6 over Low
Power Wireless
Personal Area Networks
Local Area
Network
Between 868
and 915 M,
2.4 G
250 K Between 10 and
50 m
Low Low Environmental and
agricultural
4 Wireless Fidelity Local Area
Network
Between 2.4
and 5 G
Between 54
and 600 M
100 m High High Environmental,
agricultural, and
waste management
5 Long Range Local Area
Network
Between 433
and 915 M
100 K Between 3 and
5 km
Very Low High Environmental,
agricultural, and
waste management
6 Global Mobile
Communications/
General Packet Radio
Service
Local Area
Network
Between 850
and 1900 M
Between 80
and 384 K
Between 5 and
30 km
High Low Environmental,
agricultural, and
waste management
7 Extended coverage-
GSM
Wide Area
Network
Between 750
and 900 M
240 K Extended
distance
Low Low Environmental and
smart agricultural
8 Narrow Band Internet of
Things
Wide Area
Network
Licensed LTE
bands
250 K Below 35 km Low High Smart irrigation and
environmental
9 Sigfox Low Power
Wide Area
Network
200 k Between 100
and 600
Between 30 and
50 km
Low Low Precision agricultural
10 Cellular Metropolitan
Area Network
Between 700
and 2500 M
Between 500 M
and 1 G
Within the
coverage area
High Medium Environmental and
agricultural
Source: Information from Bhoyaret al. (2019).
51Internet of Agriculture Things (IoAT)
TABLE 3.3
Classication of Hardware Platforms Used for Agricultural Applications
Name of the
Hardware Platform
Processor/
Controller Used
I/O
Connectivity
Communication
Technologies UsedS. No. OV CS SM PL IDE
1 Arduino Nano ATmega328P 5 V 16MHz 2KB I2C, SPI,
UART
Wi-Fi, Zigbee,
Bluetooth,
Ethernet, Serial
Wiring,
C/C++
Arduino
2 Arduino Mega ATmega2560 5 V 16MHz 8KB I2C, SPI,
UART
Wi-Fi, Zigbee,
Bluetooth,
Ethernet, Serial
Wiring,
C/C++
Arduino
3 Arduino Uno ATmega328P 5 V, 3 V 16MHz 2KB I2C, SPI,
UART
Wi-Fi, Zigbee,
Bluetooth,
Ethernet, Serial
Wiring,
C/C++
Arduino
4 ESP8266
Wi-Fi Module
RISC CPU 3.3 V, 80 MHz 32KB SPI, UART, Wi-Fi, Serial Wiring, Lua and AT
3.6 V GPIO C/C++ commands
5 CC3200
SimpleLink
Wi-Fi LaunchPad
ARM Cortex -M4
Core
5 V 80 MHz 256KB UART Wi-Fi, Bluetooth,
Ethernet, Serial
Wiring,
C/C++
Code
Composer
Studio™
Cloud
6 Node
MCU
ESP8266, LX106 5 V 80 MHz 128 KB I2C, SPI,
UART,
GPIO
Wi-Fi, Bluetooth,
Serial
Wiring,
C/C++
Arduino
7 Raspberry Pi 3 quad-core
processor
5 V 1.2 GHz 256 MB I2C, SPI,
SERIALto 1 GB
Wi-Fi, Zigbee,
Bluetooth,
Ethernet, Serial
Python,
C/C++,
Java,
Ruby
NOOBS,
Raspbian OS
8 ARM Processor 32-bit and 64-bit
RISC multi-core
processors
5 V Above
1 GHz
8KB UART Wi-Fi, Zigbee,
Bluetooth,
Ethernet, Serial
C/C++ Keil MDK,
Arm Online
Compiler
Source: Data from Tzounis(2017)
52 Green Engineering and Technology
TABLE 3.4
Classication of Different IoT-Based Cloud Service Platforms Used in Agricultural Applications (Ray 2016)
IoT Cloud Service
Platforms
Application
Improvement
Monitoring
Data
Research
PurposeS. No. IoT Cloud Service Type Visualization Developer Cost
1 AMEE Private Π Π Π Use through pay Ο
2 Arkessa Platform as a Service (Private) Π Π Π Use through pay Ο
3 Axeda Platform as a Service (Private) Π Π Π Use through pay Ο
4 Carriots Platform as a Service (Private) Π Π Π Free of cost up to
10 devices
Π
5 Connecterra Private Π Π Π Pay per access Π
6 Exosite IoT Software as a Service Π Π Π Free of cost up to
2 devices
Π
7 GroveStreams Private Π Π Π Free of cost up to
20 stream, 5
SMS, 500 Email
Π
8 Nimbits Platform as a service (Hybrid) Π Π Π Free Π
9 Phytech Software as a Service (Private) Π Π Π Use through pay Π
10 Plotly Public Π Π Π Free Π
11 Thethings.iO Public Π Π Π Free Π
12 ThingsBoard Public Π Π Π Low Π
13 ThingSpeak Public Π Π Π Free Π
14 ThingWorx Infrastructure as a Service
(Private)
Π Π Π Use through pay Π
15 Ubidots Public Π Π Π Free Π
16 Xively Software as a Service (Public) Π Π Π Free Π
17 Yaler Infrastructure as a Service
(Private)
Π Π Π Use through pay Π
53Internet of Agriculture Things (IoAT)
(DT, NN, and ET), and k-mean clustering and principal component analysis (PCA)
come under unsupervised learning.
Machine learning techniques (Liakos et al. 2018) are used for various applications
in the eld of agriculture such as agriculture yield prediction, managing diseases,
soil maintenance, detection of weed plants in the agriculture eld, and managing the
water requirement. These techniques are developed for the qualitative and quantita-
tive analyses regarding the yield of agriculture production.
3.10.4 SeCurity iSSueS
IoT security is the main concern while preserving (HaddadPajouh et al. 2019) and
observing things and environments. Environmental monitoring by IoT sensors or
things needs a reliable security mechanism for stopping the data gap and maintaining
data condentiality on the devices.
3.11 IoAT APPLICATIONS
IoT has many applications in the eld of agriculture (Farooq et al. 2019), as shown
in Figure 3.13.
3.12 CONCLUSION
IoT looks into the propriety of the agriculture eld to improve crop yields, improve
quality, and reduce costs. IoT can provide better solutions in the agriculture sector
by deploying the sensors and other devices that have been discussed. This chapter
FIGURE 3.13 IoAT applications. (Farooq 2019.)
54 Green Engineering and Technology
examines the above-mentioned aspects and recommends the role of different tech-
nologies, particularly IoT, to build a smarter and cost-effective IoAT to meet future
anticipation. This is why smart sensors and cloud computing communication technol-
ogies are discussed systematically. Moreover, passionate insight into current research
works is provided. In addition, different kinds of cloud platforms are provided for
agricultural applications. We proposed that the architecture of IoAT supports the
contemporary research developments in the eld of agricultural applications.
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57
4E-Navigation: An
Indoor System for
Green City Sustainable
Development Using a
UGU Engine Architecture
Ajay B. Gadicha
P.R.Pote College of Engineering and Management
Vijay B. Gadicha
G H Raisoni University
Om Prakash Jena
Ravenshaw University
CONTENTS
4.1 Introduction ....................................................................................................
..............................................................................
...........................................................................
............................................................................................
..................................................................................................
...................................................................................................
..............................................................................
...........................................................................................
.......................................................................................
......................................................................................................
........................................
...............................................................................................
.......................................................................................
.............................................................
............................................................................
58
4.1.1 Problem Statement 58
4.1.2 Purpose of the Study 58
4.2 Literature Survey 59
4.2.1 Tango 60
4.3 Methodology 60
4.3.1 Interior Modeling .. 60
4.3.2 Navigation 61
4.3 System Architecture 62
4.4 Flow Chart 62
4.5 Progression System of Execution in E-Navigation . 62
4.6 How the UGU Architecture is Useful in E-Navigation
for Smart Cities 64
4.7 Proposed Algorithm 64
4.7.1 Player Controller Mechanism 64
4.7.2 Obstacle Animation 65
58 Green Engineering and Technology
4.1 INTRODUCTION
Today, in this evolving time-centric world, people are so much into themselves that
they cannot even waste a single second to ask and navigate in a huge institute or
building. Rather than just roaming and asking, they want something, something cool
and self-efcient, which can not only guide them but also provide a better navigation
experience. Due to such large construction, many consumers visiting an institute or
mall or super-market for the rst time just cannot nd exactly what they are looking
for and then many a time, customers/users leave unsatised leading to a reduction in
the popularity and the mark that a user-friendly organization must leave.
Enormous development in navigating systems, 3D technologies, and the lean-
ing of the world toward augmented reality (AR) and virtual reality (VR) has made
the development of such indoor navigation systems possible, in many developed
countries. There are 3D navigable super-markets where you can navigate directly
to the item you want to search. As a citizen of one of the most rapidly developing
countries, why not we provide such indoor navigation capabilities to our users and
consumers to help them in saving their crucial time. Hence, this is the prime time
to hit the metal.
4.1.1 proBlem Statement
To develop an AR-based system for indoor navigation for the users to help them navi-
gate in indoor conditions and nd their path more accurately and quickly.
4.1.2 purpoSe of tHe Study
The purpose of the study is to keep the crucial element time at the main focus for
users while navigating in a huge building and nding the path to where they need to
be. In this project, we have researched about many hardware and software to provide
such facility; we have analyzed the problem faced by the consumers and users and
then designed a robust system successfully for the implementation of indoor naviga-
tion. We have created our own 3D model of a building with all the components to
provide indoor navigation in it by using a rst-person camera system.
Due to the lack of reach to the buildings and permission of the organizations,
we have developed the system only for our college, but in the near future, this can
be extended to every building. The main focus of this device is to provide the indoor
navigation capability. This will not only be in the rst person but also in the t hird
person where you can traverse a building without even getting there.
4.7.3 Level Generator Algorithm.................................................................
...................................................
...................................................................................
......................................................................................................
..................................................................................................
................................................................................................................
65
4.7.4 E-Navigation Movement Algorithm 65
4.8 Results and Discussion 67
4.9 Conclusion 68
4.10 Further Scope 69
References 70
59E-Navigation
4.2 LITERATURE SURVEY
This section discusses the brief concept about the present strategies or methods asso-
ciated with harmony, Godot and unreal engine (UGU) mechanism and one-of-a-kind
modeling correlated to a 3D view of any item and its signicance in an inexperienced
metropolis. The 20th century has become characterized by means of fast and fre-
quently out of control urban increase essential to the emergence of large dispersed
or decomposed towns no longer like the small usage town of the 19th century. The
rapid industrialization, innovations that incorporate e-vehicles, and the ease of use of
reasonably priced terra rma and less expensive fossil fuels represent a quantity of
the use of forces for city enhancement [1].
In the 1980s, the assessment “The restrictions to development” introduced the
concept of sustainable nancial growth [3]; “Our general expectations” tested that
economic boom, environmental safety, and social improvement could be reconciled
[4]; and the New Urbanism lobby group endorsed strategies to limit the detached
built-up boom of cities through the use of more environmentally friendly metropolis
layout practices [5].
The concept of sustainability in the 19th century resigned community impartiality,
nancial expansion, and ecological protection with the upgrading of the conurbation
[6, pp. 296–312] and opened the way for the improvement of different ideas alongside
sustainable cities [7], inexperienced urbanism, habitable metropolis [8–11], and com-
pact metropolis [12, 13], which is probably modern and architectural, among others.
In the 2000s, the inclusion of climate trade issues in the global political timetable
put power [14] and the useful resources of overall performance [15] at the heart of
the Sustainable Development and City Sustainability Dialogue. Discussions on city
FIGURE 4.1 Green city framework [2].
60 Green Engineering and Technology
paperwork including the strength, overall performance of assets, and usual overall
environmental performance have become the number one element in the search for
new ideas and techniques for dening and smart city sustainability.
These contemporary-day inclinations delivered about the improvement of the term
“inexperienced.” “Green” approaches numerous subjects to remarkable humans. The
time period is nowadays extensively utilized by non-private and private agencies as
an emblem for sustainability and eco-friendliness. “Greening” is over again duration
associated with the term green. In this text, “inexperienced” and “greening” are used
synonymously for sustainability and associated issues in which power and benecial
resource are of primary concern. As a give up cease end result of the progressed
interest given to electricity, benecial aid performance, and concrete shape close to
weather change, questions already formulated earlier such as “Are superb metropolis
ofce work and metropolis designs more sustainable than others in terms of pollu-
tion, environmental impact and strength use?”; “What techniques and movements
can correctly make a contribution to make towns extra sustainable (greener)?”; and,
extra nowadays, “How are we able to manipulate the modern city increase manner
underneath the outcomes of climate change, and on the identical time make this
manner greener?” have regained signicance.
Although actively being studied to date, there can be no vital consensus regarding
the exceptional answers to those questions. Scholars [12, 13, 16–19] stated the com-
pact town form as one that could strongly contribute to city sustainability, speci-
cally nearly about the inuences of the metropolis increase technique and the usage
of strength, belongings performance, infrastructure, and environmental ordinary
regular average overall performance-related problems.
4.2.1 tango
In recent architecture and planning phases of smart cities, the majority of program-
mers concentrate on the latest software, which not only helps to generate the 2D or
3D view of the object but also gives rise to the planning of the smart city.
In this context, Tango software is widely used in an AR or VR to visualize the
object in depth and nd out various cons of the building plan to convert into a smart
version. This tango device is capable of capturing the position without GPS and gives
the experience to visualize the object using AR or VR.
4.3 METHODOLOGY
Our proposed system consists of two modules.
4.3.1 interior modeling
Any interior model consists of several isolated objects such as rooms, doors, walls,
pillars, and many more. To design these models, we are using Unity 3d. Unity 3d
provides various 3D models, which can be used as it is, and some of the models are
needed to be developed by own due to cost issues. We also need to provide an accu-
rate structure to those models and place them accurately according to the structure
61E-Navigation
of the building. We have created the ground oor model of our college premises with
more than eight rooms and have applied textures to each one of them.
These textures need to be created and are useful to provide the feel of the actual
aura. This enables the user to get the exact feel that he/she is traveling in the same
building. We have provided a marker to every room so that our camera/3D model
can move towards the marker following the shortest path provided by the NavMesh.
Every room is having a door and the name on it so that the user can conrm that he/
she has reached the right location.
4.3.2 navigation
The most important part of the app is navigation on the shortest path. In the model,
we have provided the user with a dropdown menu where he/she can select a destina-
tion and as soon as they click on submit, the NavMesh tool will nd the shortest path
to the destination and will guide the user to the location. The tool will take care of all
the paths and will only guide on the shortest path. This will save the time of the user
and increase the efciency of the product.
The navigation system allows you to create characters that can intelligently ow
around the sports world, the use of navigation meshes, which might be created rou-
tinely from your scene geometry. Dynamic limitations can help you regulate the
navigation of the characters at runtime, while off-mesh links permit you to con-
struct precise moves like starting doors or jumping down from a ledge. This section
describes Unity’s navigation and routes locating systems in the element.
Navigation requires the use of a simplied geometrical aircraft, frequently
known as a NavMesh. The NavMesh allows characters to plan a direction across
the numerous complex items in a scene. In this video, we can observe the way to
create a map using NavMesh (often known as “baking”) with the use of Unity’s
Navigation view.
FIGURE 4.2 Academic factors of sustainable development.
62 Green Engineering and Technology
4.3 SYSTEM ARCHITECTURE
The major focus of the proposed work is to provide accurate indoor navigation even in
the low network area. For this purpose, we are using algorithms to nd out the shortest
distance and baked the path according to the structure to avoid any type of collision.
Unity provided a tool NavMesh from which we can provide the shortest path and make
the movement accordingly. To test this, we have deployed the program on a Pie-based
Android device where we have done the third-person navigation. As the user selects the
location, the NavMesh will guide the user to the destination through the shortest path
and if the user wants to go to another location, then he/she can add another location.
4.4 FLOW CHART
Here the user will rst start the app and will see the dropdown menu from where
he/she can select the destination. As soon as the user conrms the destination, the
request will be given to the NavMesh tool and then the NavMesh tool will provide
the shortest distance to that location. After the path is conrmed, the navigation will
start, and the user will be guided to the end location.
4.5 P ROGRESSION SYSTEM OF EXECUTION IN E-NAVIGATION
Step I: Select destination
As the start-up screen, the user will be provided with a pre-dened dropdown
menu from where the user can select the destination where he/she wants to navigate.
Step II: Send the shortest path
As soon as the user selects and conrms the destination from the dropdown menu,
the input of the destination will be given to the NavMesh agent and the shortest path
of the destination will be searched, and as the path is conrmed, the path is sent back.
FIGURE 4.3 System architecture.
63E-Navigation
FIGURE 4.4 Flow working of the proposed modeling.
FIGURE 4.5 Progression ow.
64 Green Engineering and Technology
Step III: Navigate to the destination
As the shortest path is received, the navigation will start, and the user will be
navigated to the destination.
Step IV: End navigation
After the navigation, if the user wants to update the destination, then he/she can
select another location and the process will be repeated. If the user wants to end the
navigation, then he/she can just close the app.
4.6 HOW THE UGU ARCHITECTURE IS USEFUL
IN E-NAVIGATION FOR SMART CITIES
With the proliferation of technologies and the tremendous development, sensible
navigation structures have emerged as one of the maximum promising solutions
for developing better and extra sustainable towns, smart cities as they are normally
called. The following article addresses six future dispositions in the utilization of
Smart Navigation for smart cities.
I. Smart Navigation Systems: How it is far green?
The time period navigation system refers to a positive machine that
assists in navigation and is placed generally on board on an automobile
or a vessel, or somewhere else and talked through indicators, or even inte-
grated all the above. Smart Navigation resulted from the evolution inside the
technological abilities of navigation systems through the years, additionally
known as clever navigation technologies. Depending on their use, naviga-
tion structures might also incorporate maps in a human-readable format,
decide an automobile or tool’s area, provide guidelines to a human through
textual content or speech, offer commands straight away to a self-enough
tool, which encompasses a robot, share statistics on nearby motors, gadgets,
or even on-site visitors conditions, and recommend alternative routes.
II. Smart navigation for smart towns?
Urbanization is thought of as one of the maximum tremendous demand-
ing situations for current societies (Suzuki et al., 2010). The expectation is
that 80% of the area’s population will live in urban environments through
using 2050 (Ordnance Survey, 2015). Achieving sustainable urbanization
is a destiny pursue through organizing smart towns. The time period smart
cities describe a technique through the use of facts and communication era
designed to address metropolis demanding situations consisting of over-
crowding, transport, and power.
4.7 PROPOSED ALGORITHM
4.7.1 player Controller meCHaniSm
i. Declaration of Unity_Engine
ii. Declaration of UnityEngine.AI
iii. Declaration of Player_Controller class as MonoBehaviour
65E-Navigation
iv. Declaration of Cam
v. Declaration of NavMesh_Agent
vi. Call update() once per frame
vii. if
viii. mousebutton down
iX. then
X. set mouse position
Xi. Else
Xii. set_destination
4.7.2 oBStaCle animation
i. Declaration of Unity_Engine
ii. Declaration of UnityEngine.AI
iii. Declaration of System.Collections;
iv. Declaration of System.Collections.Generic;
v. Declaration of Obstacle_Animation as MonoBehaviour
vi. inialize drift pace =.2f
vii. inialize drift power=9f
viii. initialization of Random_Range to 0f and 2f
ix. call update method
x. check vecot3 position using remode function
xi. use the formula Time.Time * pace + randomOffset) * power
xii. calculate the transform.Role = pos;
4.7.3 level generator algoritHm
i. inialization of width = 10;
ii. inialization of peak = 10;
iii. Use this for initialization
iv. void Start ()
v. GenerateLevel();
vi. floor.BuildNavMesh();
vii. For Loop over the grid (width and height)
viii. assign x=0
ix. if x<=width then x+2
x. assign y=0
xi. if y<=width then y+2
xii. If any random value > .7f
xiii. then Spawn a wall through Vector3(x - width / 2f, 1f,
y - peak / 2f);
xiv. else if no obstacle is observed then Should we spawn
a participant?
Xv. Spawn the participant Vector3(x - width / 2f, 1.25f,
y - peak / 2f);
4.7.4 e-navigation movement algoritHm
using Unity_Engine;
using System.Collections;
66 Green Engineering and Technology
namespace Complete_Project
{
private class ClickTo_Move :Mono_Behaviour {
private float shootDistance = 10f;
private float shootRate = .5f;
private PlayerShootingshootingScript;
private Animator anim;
private NavMeshAgentnavMeshAgent;
private Transform targetedEnemy;
private Ray shootRay;
private RaycastHitshootHit;
private bool walking;
private bool enemyClicked;
private float nextFire;
// Use this for initialization
void Awake ()
{
anim = GetComponent<Animator> ();
navMeshAgent = GetComponent<NavMeshAgent> ();
}
// Update is called once per frame
void Update ()
{
Ray ray = Camera.main.ScreenPointToRay (Input.mousePosition);
RaycastHit hit;
if (Input.GetButtonDown ("Fire2"))
{
if (Physics.Raycast(ray, out hit, 100))
{
if (hit.collider.CompareTag("Enemy"))
{
targetedEnemy = hit.transform;
enemyClicked = true;
}
else
{
walking = true;
enemyClicked = false;
navMeshAgent.destination = hit.point;
navMeshAgent.Resume();
}
}
}
if (enemyClicked) {
MoveAndShoot();
}
if (navMeshAgent.remainingDistance<= navMeshAgent.
stoppingDistance) {
if (!navMeshAgent.hasPath || Mathf.Abs (navMeshAgent.velocity.
sqrMagnitude) <float.Epsilon)
walking = false;
67E-Navigation
} else {
walking = true;
}
anim.SetBool ("IsWalking", walking);
}
private void MoveAndShoot()
{
if (targetedEnemy == null)
return;
navMeshAgent.destination = targetedEnemy.position;
if (navMeshAgent.remainingDistance>= shootDistance) {
navMeshAgent.Resume();
walking = true;
} if (navMeshAgent.remainingDistance<= shootDistance) {
transform.LookAt(targetedEnemy);
Vector3 dirToShoot = targetedEnemy.transform.position -
transform.position;
if (Time.time>nextFire)
{
nextFire = Time.time + shootRate;
shootingScript.Shoot(dirToShoot);
}
navMeshAgent.Stop();
walking = false;
}
}
}
}
4.8 RESULTS AND DISCUSSION
This section illustrates the consequences and results associated with the UGU engine
and its executions in the 3D environment so that the entire city will be covered in the
application and that must show on the map.
FIGURE 4.6 Map conguration of the application.
68 Green Engineering and Technology
4.9 CONCLUSION
By providing indoor navigation facilities, we are opening new doors for the develop-
ment of mapping system and removing the dependencies on hardware, that is, we
need not be dependent on others to develop some software implementation and then
we can use it. This will change the face of the navigation system, as this will not only
provide you the navigation but also the VR experience that you can never get in any
navigation tool presently available. For the development of such an indoor navigation
tool, we have performed a lot of research on the engines that can provide us the facil-
ity we need. We have used specialized and fundamental engineering for investigating
and designing the complex tool.
We have identied and analyzed the problem by referring to different research
papers. This project will fulll the need of the society and will boast the use of
FIGURE 4.7 Integration of various locations in the application.
TABLE 4.1
Inuence of Various Major Parameters on Smart Cities
Parameters for
Improving the Quality
of Smart City
Detection
Using the
UGU EngineSr. No.
Scope of Future
Enhancement
(a) 1 Smart health Ye s Ye s
2
3
Smart grid/energy
Smart retails
Yes
Yes
Yes
Yes
4
5IoT Yes Yes
Smart mobility Yes Yes
6
7Education Yes Yes
Smart agriculture Ye s Yes
8Smart infrastructure Ye s Ye s
69E-Navigation
advanced tools like Unity, NevMesh, and so on. The application is one of its kind and
is AR implementation of various buildings. We have applied engineering knowledge
to analyze the societal problem of people for indoor navigation whenever they want
to traverse in a huge institute. We have analyzed the present solution and technolo-
gies to design the latest and user-friendly products. We have used the modern tools
Unity and ARCore to implement the project. During this project, we have applied
professional ethics and understood the importance of teamwork and communication
while presenting the project in various seminars for the project, which lead us to
engage ourselves in lifelong learning. This solution can be developed at a generalized
level for multiple sectors for life-long learning.
4.10 FURTHER SCOPE
In the future, this can be used as a promotional activity where companies can provide
their VR-enabled apks at their websites, and users before going to the place can check
the place by using this app in the third-person view. This can be used with Google to
provide a live imaging system of roads so that whole roads will be in a VR mode and
we can traverse to our destination easily and fast. Currently, companies like ArcGIS
and Google are providing indoor navigation facilities in various developed cities of
the USA. This can be a game changer for our country.
FIGURE 4.8 Inuence of major parameters.
70 Green Engineering and Technology
REFERENCES
1. Westerink, J.; Haase, D.; Bauer, A.; Ravetz, J.; Jarrige, F.; Aalbers, C.B.E.M. 2013,
Compact city strategies in Europe compared for trade-offs. Science for environment.
Eur. Plan. Stud. 21: 473–497.
2. Brilhante, O; Klaas, J. 2018, Green city concept and a method to measure green city
performance over time applied to fty cities globally: Inuence of GDP, population size
and energy efciency. Sustainability. 10: 2031; doi:10.3390/su10 062031.
3. Matsumoto, T. 2011, Compact City Policies: Comparative Assessment. In 47th
ISOCARP Congress; ISOCARP: The Hague, The Netherlands.
4. Brilhante, O. 2018, The compact city versus making room for future city expansion in
the context of Nampula, Mozambique. Trialog. 128:1–13.
5. Kahn, M.E. 2007, Green Cities: Urban Growth and the Environment; Brookings
Institution Press: Washington, DC, USA.
6. Lindeld, M.; Steinberg, F. (Eds.) 2012, Green Cities; ADB: Manila, Philippines.
7. Pace, R.; Churkina, G.; Rivera, M. 2016, How Green Is a Green City? A Review of
Existing Indicators and Approaches; IASS: Potsdam, Germany.
8. Siemens. 2010, Economist Intelligence Unit. African, Asian and European Green
City Index—Assessing the Environmental Performance of Major Cities; Siemens:
Munich, Germany.
9. Inter-American Development Bank (IDB). 2016, Evaluation of IDB’s Emerging and
Sustainable Cities Initiative; Inter-American Development Bank: Washington, DC, USA.
10. Zoeteman, K.; Zande, M.V.D. 2015, Integrated Sustainability Monitoring of 58 EU Cities:
A Study of European Green Capital Award Applicant Cities; Tilburg University: Tilburg.
11. European Bank for Reconstruction and Development (EBRD). 2016, Green Cit y Program
Methodology; European Bank for Reconstruction and Development: London, UK.
12. European Union. European Green Capital 2018, Good Practice Report; European
Commission: Brussels, Belgium.
13. OECD. 2014, Green Growth Indicators 2014; OECD Publishing: Paris, France.
14. Tamanini, J.; Bassi, A.; Hoffman, C.; Valenciano, J. 2016, The Global Green Economy
Index. Measuring National Performance in the Green Economy; Dual Citizen Inc.:
Washington, DC, USA.
15. Arcadis. 2016, Sustaina ble City Index . Putting People at the Hear t of City Susta inability;
Arcadis: Rotterdam, The Netherlands.
16. Parilla, J.; Trujillo, J.L.; Berube, A.; Ran, T. 2015, Global Metro Monitor. An Uncertain
Recovery; Metropolitan Policy Program at Brookings: Washington, DC, USA.
17. Asian Development Bank (ADB). 2015, Green City Development Tool Kit; Asian
Development Bank: Manila, Philippines.
18. Sandhu, S.C.; Singru, R.N.; Bachmann, J.; Sankaran, V.; Arnoux, P. 2016, GrEEEn
Solutions for Livable Cities; Asian Development Bank: Manila, Philippines.
19. Ayik, C.; Ayatac, H.; Sertyesilisik, B. 2017, A gap analysis on urban sustainability
studies and urban sustainability assessment tools. Archit. Res. 7:1–15.
71
5Biomass Waste-derived
Electrode Material
and Bio-based Solid
Electrolyte for Sustainable
Energy Systems
Sudhakar Y. N. and Anitha Varghese
CHRIST (Deemed to be University)
5.1 INTRODUCTION
Rechargeable, sustainable, and environmentally friendly energy devices lay a sig-
nicant growth in the future energy eld. Nonetheless, the increasing population and
ever-growing demand will exhaust the fossil fuel and other rare-earth ions present
in the planet. Incorporation of all the aspects of sustainability, eco-friendly nature,
greener manufacturing, and cell development has been the hotspot in the eld of
electrochemical energy devices. Herein, the development of a complete solid-state
energy device has been discussed in detail, which consists of a biomass-waste-derived
CONTENTS
5.1 Introduction ....................................................................................................
........
..........................................................................................
.................................................................................
......................................................
.................................................................................
..........................................................................................
...............................................
........................................................................
...................................................................................
..........................................................................
..........................................................................
................................................................................................................
71
5.2 Biomass Waste Materials and Their Suitability As Bio-Energy Materials 72
5.2.1 Preparation 73
5.3 Biopolymer Electrolytes 74
5.3.1 Types of Biopolymer Electrolytes 75
5.3.2 Sodium Dopants 76
5.3.3 Preparation 76
5.3.4 Plausible Ion Conduction Mechanism . 77
5.4 Application of Biowaste-Derived Carbon Electrode and Biopolymer
Electrolyte in Energy Devices 78
5.4.1 Supercapacitors 78
5.4.2 Sodium-Ion Batteries 81
5.5 Summary and Future Scope . 82
References 83
72 Green Engineering and Technology
carbon-based electrode material, sodium salt-based biodegradable/biopolymer as an
electrolyte and a separator. This device fully consists of biodegradable materials
except for the current collector (Al or stainless steel) underpinning the future devel-
opment of state-of-art all-solid-state energy devices.
5.2 B IOMASS WASTE MATERIALS AND THEIR
SUITABILITY AS BIO-ENERGY MATERIALS
Biomass and bio-waste are mainly referred to as “biodegradable materials” and
“renewable sources of energy” Biomass is dened as living or dead organisms, plant
or animal, and their by-products. Biomass grows by making use of carbon from the
surroundings and has chemical energy obtained from sunlight; hence materials such
as coal, fossil fuels, soil, and oils can be excluded from the term biomass. Biomass
when consumed for generating energy releases carbon back to the surrounding envi-
ronment. This results in a “carbon-neutral cycle” and the process does not cause the
increase of greenhouse gases, which can solve the issue of global warming.
The term “bio-waste material” means biodegradable materials, which are elimi-
nated or discarded as no longer useful or required after the completion of a process.
Bio-waste materials can be modied biologically, physically, or chemically into
market value-based reused end products using emerging technologies. The correla-
tion between bio-waste feedstock and its market value is based on the technological
costs and sophistication of the process involved in it. Bio-waste is limited to domes-
tic wastes such as waste from gardens, kitchens, restaurants, and food industries.
Biomass waste involves wastes from agriculture and forest sectors such as bagasse,
rice husk, rice straw, cotton stalk, coconut shells, soya husk, de-oiled cakes, cof-
fee waste, jute wastes, peanut shells, wood shavings, bark, and sawdust. The other
wastes such as paper, cardboard, sewage sludge, natural textiles, cosmetics, dyes,
gels, biomedical and pharmaceutical waste can be grouped under “biodegradable
waste”, which on reuse in the energy sector can cause more environmental prob-
lems. As biomass waste are inexpensive and are generated in thousands of tons per
year, this can be considered as a potential source for the production of carbon-based
energy devices.
Electrochemical devices like batteries and supercapacitors are in huge demand
for many commercial applications. Demand is not only for numbers but also for
increased energy and power density, accessibility, fast regeneration, reduced size,
weight, cost, enhanced cycle life, and sustainability. Biomass waste is a potential
source to produce activated carbon, which can be used as the anode material in lith-
ium/sodium-ion batteries and the electrode material in supercapacitors (Figure 5.1).
The morphology, structure, and elemental composition of the bio-waste-derived
carbon-based material play a key role in the high performance of the energy device.
The unique physicochemical property of biowaste carbon materials can be corre-
lated with their structural properties. The presence of organic and nitrogen matter
helps in creating an N-doped porous carbon structure (Xin, Guo, and Wan 2012), and
inorganic matters when washed systematically help in creating nano, micro, or meso-
porous structures. This overall network favors rapid mass diffusion of electrons and
73Electrode Material and Electrolyte
sodium ions. In the case of sodium ion as the dopant, the presence of soft carbon
decreases the charge/discharge cycle capacity. Soft carbon further decreases the
interlayer distance and breaks easily due to continuous movement of sodium ions.
The prepared biomass-waste carbon must be hard and able to withstand the impact
of sodium ion movement.
5.2.1 preparation
Biomass and biowaste resources from agricultural, domestic, and industrial waste are
used as precursors in the preparation of activated carbon materials for use as anode
electrodes in batteries and symmetric electrodes (same material on the cathode and
anode) in supercapacitors. Biomass possesses a delicate porous structure in nature that
incorporates water, air, and other organic and inorganic materials. These porous struc-
tures are essential for energy application as they provide high surface area and ordered/
tunable dimensions. Nevertheless, the sodium ion-based energy devices require hard
FIGURE 5.1 Schematic diagram showing the main topics from the cellulose resources,
nanocellulose fabrication, structural and functional design to the energy storage applications.
(Reprinted with permission from Moon et al. 2011.)
74 Green Engineering and Technology
carbon, which can be prepared by drying the biomass followed by pyrolysis in a con-
trolled manner. Using a template method, the structure of carbon can be controlled to
get one-, two-, and three-dimensional structures. The raw carbon present in the bio-
mass needs to be processed to obtain a high-value functional product. The availability
of biomass resources and their diversity help the researchers to explore further their
utility in the eld of energy devices. Various preparation methods to obtain activated
carbon and tunable structures based on the sources are shown in Figure 5.2.
5.3 BIOPOLYMER ELECTROLYTES
Polymer electrolytes are emerging as potential materials that can provide enhanced
energy density in electrochemical devices. Polymer electrolytes are dened as a lin-
ear/network structure of macromolecules with charged or chargeable groups. They act
as a host for ionic dopants or they themselves bear ionic/ionizable functional groups.
Polymer hosts are preferred mainly on three character istics (Sudhakar, Selvakumar,
and Krishna 2018):
i. There must be functional atoms or groups that can coordinate with ions.
ii. Segmental motion of a polymer must have low barrier resistance so that it
can move/vibrate easily.
iii. The distance between the chains must be sufciently far to avoid intrapolymer
bonding.
FIGURE 5.2 Biomass sources at the center from which on the right side, arrows show
pyrolysis, hydrothermal, microwave, multistep calcination, microemulsion, and air-expansion
methods followed by KOH activation and drying. On the left side, arrows show 1D, 2D, and
3D structures of prepared activated carbon.
75Electrode Material and Electrolyte
A biopolymer electrolyte is a category of polymer electrolytes, which undergoes bio-
degradation upon exposure to the environment. It acts as an ionic conductor and a
separator between the two electrodes. They are also considered excellent leak-proof
exible solid electrolytes. The prolonged usage of any polymer material in an energy
device will face wear and tear in its matrix causing a reduction in the charge/dis-
charge efciency. The conventional polymer electrolytes, which are mainly synthetic
non-biodegradable polymer electrolytes, are of major concern as they cannot be recy-
cled and must be discarded under specied areas. Biopolymer electrolytes have a
cutting edge over this non-biodegradable polymer electrolyte, which degrades easily.
Moreover, the presence of dopants like inorganic salts enhances the rate of degrada-
tion as it breaks the inter/intra-molecular bonding in the polymer matrix (Sudhakar,
Selvakumar, and Krishna 2018). The major limitations of biopolymer electrolytes are
the lack of accurate molecular mass of biopolymers, proper packing of biopolymer
electrolytes is required when using in energy devices, tedious and time-consuming
preparation methods are required when natural polymers are used as a host, and the
mass/power ratio is less compared to conventional liquid electrolyte based-energy
devices.
A good biopolymer electrolyte should have the following characteristic proper-
ties: ionic conductivity should be near 10−3 S cm−1 at ambient temperature and mobil-
ity, diffusion coefcient, solvation, radius of hydrated spheres, dielectric constant,
viscosity, thermal stability, electrochemical stability, compatible electrolyte, disper-
sion interaction, achievable capacitance (amount of charge stored), voltage window,
charge storage time constants, rate performance characteristic resistances, minimize
concentration polarization, wide electrochemical stability window, cyclability, non-
ammability, safety, moisture tolerance, and last but not least environmental friend-
liness. It is desired that biopolymer electrolytes should moisten the electrode material
to enhance the fast movement of sodium ions and not to corrode them. To match
between the redox potentials of an electrode material and biopolymer electrolytes,
computer-simulated software are suitably employed to increase the performance of
the energy device (Zhang et al. 2018).
5.3.1 typeS of Biopolymer eleCtrolyteS
Based on the source of the biopolymer, there are two types of biopolymer electro-
lytes: (i) natural biopolymer-based electrolytes and (ii) synthetic biopolymer-based
electrolytes. The biopolymer electrolytes are further classied into three types based
on their physical state: (i) solid, (ii) blend, and (iii) gel.
Solid biopolymer electrolytes are a liquid-free, charged/chargeable long-chain
single-type of macromolecules. The ease of processing, shape exibility, and lower
cell weight are the key advantages of these electrolytes. Blend biopolymer electro-
lytes are also liquid-free, charged/chargeable long-chain macromolecules, but they
are mixtures of two different compatible/non-compatible biopolymers at a specic
ratio. Mechanical stability and chemical/electrochemical stability are the key advan-
tages of these electrolytes. Gel biopolymer electrolytes have liquid content in their
matrix, which boosts the ionic conduction and avoids crystallization of electrolytes
during operation.
76 Green Engineering and Technology
5.3.2 S odium dopantS
Dissolution and stability of sodium salts in a biopolymer solution before the formation
of a free-standing lm are major factors, which need to be interplayed for achiev-
ing higher performance in supercapacitors. In sodium batteries, the transportation of
sodium ions is facilitated through this polymer electrolyte. The volumetric expansion is
greater in the sodium ion-based electrode and electrolyte than in lithium-ion batteries.
Moreover, the use of different solvents and the interaction of anion and cation varying
from strong to weak have inuences on the conductivity. Hence, optimization of both
the components of the device needs more effort. The factors such as breaking of lattice
enthalpy of salt, electrostatic interactions between the ions, and solvent–ion interaction
must be considered during the optimization of the salt concentration. Sodium salts
that are commonly used as dopants are sodium iodide (NaI), NaBr, NaCl, NaClO3,
NaIO4, NaAsF6, NaNO3, NaSCN, Na2SO4, NaClO4, NaBF4, NaPF6, NaTf, NaTFSI,
Na3PS4, Na2HPO4, NaHCO3, Na2B4O7, sodium bis(uorosulfonyl)imide (NaFSI),
sodium yttrium tetrauoride (NaYF4), sodium (uorosulfonyl)(nonauorobutanesulfo-
nyl)imide (NaFNFSI), NaPA, polymeric sodium tartaric acid borate (PSTB), sodium
(di-methyl ammonio)bis(butane-1-sulfonate) (NaMM4411), SiO2-sodium salt, and
SiO2-PEG-sodium salt. In addition, an inorganic ller may be required to reduce the
crystallinity and glass transition temperature of polymer electrolytes. Inorganic llers/
nanomaterials such as SiO2, Al2O3, TiO2, γ-LiAlO2, and BaTiO3 interact with doped
salt and biopolymer chains and enhance the ionic conductivity by providing channels.
5.3.3 preparation
The amorphous, semicrystalline, or crystalline nature of the biopolymer electro-
lyte depends on the methods of preparation. Mainly, the type of solvent, the rate
of removal of the solvent, and residual solvent remaining after drying are deciding
factors during preparation.
The solution casting method of preparation of biopolymer electrolytes involves dis-
solving a suitable biopolymer in a solvent and a known amount of sodium salts along
with llers are sonicated and mixed thoroughly for several hours. The solution is kept
for 1–2 h to get stabilized and is ltered. The clean solution obtained is cast to form
lms of the required shape and size. Melt casting methods involve grinding the raw
biopolymer granules or pellets into a ne powder and then mixing it well with a known
quantity of sodium salt. It is then subjected to a hot press, which melts the ingredients
to form a lm when cooled to room temperature. This method is utilized to minimize
the effect of residual solvent, which may form a small cluster of pools in the polymer
matrix. To obtain thin lms of biopolymer electrolytes, the spin coating method and
plasma polymerization method are implemented. In the spin coating method, a drop
of the prepared polymer and salt mixture solution is placed on a substrate and spun at
a desired rate. The thickness of the lm depends on the rpm of rotation and solution
texture/viscosity. This method of preparation is not applicable for the preparation of
gel biopolymer electrolytes. In the plasma polymerization method, ultrathin lms are
obtained, which involves spraying the biopolymer solution onto a substrate such as
stainless steel, glass, gold, or nickel. Another layer containing sodium salt dissolved in
77Electrode Material and Electrolyte
a solvent is sprayed, which is further followed by spraying a multilayer of biopolymers
as per the requirement of thickness and property. This layered lm is subjected to
70°C–75°C overnight under a vacuum oven, which results in the formation of a homo-
geneous mixture after cooling. This method helps in optimizing the conductivity and
strength of the lm. Techniques are required for the preparation of a gel polymer
electrolyte that holds solvent or plasticizers in its matrix. The normal methods are
solution casting, bulk polymerization, solution polymerization, suspension polymer-
ization, irradiation polymerization, and phase inversion methods.
5.3.4 plauSiBle ion ConduCtion meCHaniSm
The conductivity of sodium ions in the biopolymer electrolyte matrix determines
the capacity of energy and power output of an energy device. The interaction of the
biopolymer chain with sodium ions determines the solubility, dissociation, ion–ion
interaction, and movement of free ions. Most often, a slightly higher temperature
than room temperature is needed to achieve the solvation energy of the solvent. The
presence of solvent will behave in two ways depending on the interaction with the
anion and cation of the salt. One mode is that solvent molecules solvate the ions and
move like a vehicle through the biopolymer network (Figure 5.3a) and another is
FIGURE 5.3 (a) Interaction between solvent molecules with ions. (b) Interaction between
the polymer chain and ion in the presence of a solvent. (c) Interconnected network of ller
sodium ion chain within the polymer matrix and the movement of ion occurring in this inter-
connected channel is represented in path 3, that due to sodium ion clusters is represented in
path 1, and that through solvent is represented in path 2. (d) Solvation and desolvation of ion
during hopping transit in the polymer chain.
78 Green Engineering and Technology
that solvent and biopolymer chains act as a supporting medium for the movement
of ions (Figure 5.3b). In addition to sodium salts used as dopants, super sodium ion
conductors such as Na3Zr2Si2PO12 and Na3.4Zr1.8Mg0.2Si2PO12 may be used as llers.
The inclusion of llers decreases the degree of crystallinity, which increases the
movement of sodium ions. Moreover, llers provide surfaces having a continuous
network, wherein the sodium ion transportation on this dedicated pathway also con-
tributes to the overall conductivity of the biopolymer electrolyte (Figure 5.3c) (Lixin
et al. 2020). The transportation of ions can occur by the percolation effect, wherein
the sodium ion hops from one interaction site of the polymer to another. Due to
segmental motion/lateral displacement of the polymer chains, the ions get solvated
and desolvated during transit toward the electrode (Figure 5.3d). With an increase
in salt concentration, there will be the formation of triplet ions or more to form a
cluster of ions chain, which avoids access to biopolymer chains to penetrate them.
Nevertheless, charge transfer is comparatively faster than the hopping mechanism as
it involves cation-functional biopolymer bond exchange, but these clusters are con-
ned to a small area in the matrix, which induces the conductance to a small range
within the matrix as the below charge transfer reaction.
Na+−
XNaX
+−
+→Na++
Na XN
−+
+aX
−+
Na
5.4 APPLICATION OF BIOWASTE-DERIVED CARBON ELECTRODE
AND BIOPOLYMER ELECTROLYTE IN ENERGY DEVICES
5.4.1 SuperCapaCitorS
Energy devices such as sodium-ion supercapacitors and sodium-ion batteries are
promising devices in the near future as evident in signicant advancement in stability
and energy storage properties. Moreover, all-solid-state eco-friendly energy devices
consisting of biomass-derived carbon-based electrodes and biopolymer electrolytes
satisfy the exibility and leak-proof properties of the devices. A supercapacitor is a
device that bridges the gap between a capacitor and a battery. Based on the charge
storage mechanism, it is classied into three types: (i) electric double-layer capaci-
tors (EDLCs), (ii) pseudocapacitors, and (iii) hybrid electrodes. A biowaste-derived
carbon material is used as an electrode material symmetrically (parallel) or asym-
metrically (hybrid) and the mechanism of energy storage is shown in Figure 5.4a and
b. The movement of ions during charge/discharge is illustrated in Figure 5.4c. The
fast diffusion of ions will lead to high power density, but lacks energy density, hence
the presence of biopolymer electrolytes compared to conventional liquid electrolyte-
based devices has moderate energy and power densities in between that of battery and
capacitor. The energy density can be tuned based on the plasticizers used or blending
with other polymers. The interface regions of the above-mentioned supercapacitor are
shown in Figure 5.4d. The EDLC occurs due to the formation of a layer of ions at the
electrode/electrolyte interface, which generates equal and opposite charges on the elec-
trode surface (Figure 5.4d(i)). The material being porous facilitates more number of ion
accumulations on the electrode surface leading to a high power density (Figure5.4d(ii)).
Biomass waste-derived carbon along with metal oxides/conducting polymers leads to
79Electrode Material and Electrolyte
FIGURE 5.4 (a) Illustration of the three-electrode system and two-electrode system, (b) three different types of supercapacitors, EDLC, pseudocapacitor,
and hybrid, (c) the mechanism of charge storage during charging and discharging of EDLC and (d) (i–iv) EDLC mechanism in different carbon-based elec-
trodes (i) activated carbon and (ii) porous carbon, (iii) mechanism of pseudocapacitance arising from the redox reaction in hydrous metal oxides, and (iv)
hybrid EDLC and pseudocapacitance occurring due to intercalation of Na+ ions on to the electrode surface. (Reprinted with permission from Pal et al. 2019.)
80 Green Engineering and Technology
intercalated pseudocapacitance, which has both EDL and redox charge transfer mecha-
nisms (Figure 5.4d(iii)). In the pseudocapacitive type of electrodes, the charge interca-
lation is surface limited and diffusion limited to electrodes of battery, which constitutes
the highest energy density (Figure 5.4d(iv)) (Pal et al. 2019).
Supercapacitors fabricated using a biomass-derived carbon material enable
clean-reversible energy storage, high conductivity, reduced pollution, high volumet-
ric and gravimetric energy, and power density. A complete eco-friendly all-solid-
state supercapacitor was developed by Zeng, Wei, and Guo(2017), which consists
of biopolymer hydrogel electrolyte, binder, and electrode materials obtained from
the biomass material (Figure 5.5). Due to the hydrophilicity of sodium alginate and
low interface resistance, this supercapacitor showed a high specic capacitance of
277 Fg−1, good rate capability, and cyclability.
The nanober and nanoplate str uctures of a highly conductive cellulose-derived car-
bon aerogel matrix with intercalated multilayer FeOCl as the anode material and 1 M
Na2SO4 electrolyte exhibited a superior specic capacitance of 647 Fg−1 at 2 mA cm2.
The capacitance loss after 10,000 cycles was less than 10%, which indicates improved
mechanical stability and charge/discharge storage kinetics (Wan et al. 2019). Lignin-
rich biomass waste materials such as tea waste, bamboo waste, and enzymatic hydro-
lyzed lignin give high-performance porous carbon and high graphitization degree via
carbonization and KOH activation. Tea waste-derived porous carbon showed capaci-
tance retention of 96% up to 16,000 cycles at 4 Ag−1 and an ultrahigh power density
of 33,494 Wkg−1 with an energy density of 19.45 Whkg−1(Song et al. 2019). Enzymatic
hydrolysis of lignin-rich wood and microwave treatment, followed by KOH activa-
tion, showed a high specic capacitance of 338 Fg−1 at 1 Ag−1 and 86% of capacitance
retention at 10 Ag−1. Using the Na2SO4 electrolyte, the supercapacitor gave an energy
density of 17.1 Whkg−1(Wang et al. 2019). Lin, Wang, and Shao (2020) and Zhiwei
FIGURE 5.5 Illustration of the synthesis of activated carbon, binder, electrolyte, and separator
from Kelp plant. The construction of a supercapacitor using these materials forms individual
components in supercapacitors. (Reprinted with permission from Zeng, Wei, and Guo 2017.)
81Electrode Material and Electrolyte
et al. (2015) reviewed biomass materials applied in supercapacitors; the availability,
synthetic strategies, and improvement in supercapacitor applications were discussed.
The starch and gelatin biopolymer electrolytes doped with NaCl salt showed a desired
mechanical property suitable for supercapacitors when the concentration of gelatin
was higher than that of starch. Although the conductivity values were 84.6 mS cm−1
at 1.5 mol L−1 and 71.5 mS cm−1 at 2 mol L−1 for gelatin and starch, respectively, the
lack of free-standing lm of starch made it unsuitable for this application (Railanmaa,
Lehtimäki, and Lupo 2017). The corn starch crosslinked with glutaraldehyde found
to be having enhanced mechanical strength and doping with NaClO4 showed a con-
ductivity of 10−2S cm−1 and an electrochemical stability window of 2.4 V (Chauhan
et al. 2017). Tunable chitosan and sodium alginate electrolyte by sol–gel transition
and uniform composition has been successfully applied as the hydrogel electrolyte in
solid-state supercapacitors. A tensile strength of 0.29 MPa and an ionic conductivity of
5.1 × 10−2 S cm−1 were recorded for the gel biopolymer electrolyte, which is suitable for
energy devices. The fabricated supercapacitor showed a higher specic capacitance of
234 Fg−1 at 5 mVs−1 and performed well during 1000 cycles by retaining capacitance
up to 95.3% (Zhao et al. 2018).
5.4.2 Sodium-ion BatterieS
Batteries store energy via electrochemical reactions and depending upon the mate-
rial, they are either rechargeable or non-rechargeable. Lithium-ion batteries have
occupied almost every eld of energy applications due to advancements in technolo-
gies. Nevertheless, lithium being a rare earth metal, there is a major shift in research
toward the development of sodium-air, sodium-ion batteries.
Biomass-derived carbon-based electrode materials are gaining momentum because
of their low cost, ease of availability, and preparation methods. Flame-deposited car-
bon nanoparticles from coconut oil were used as the anode material for a sodium-
ion battery and compared with a lithium-ion battery. The discharge capacity of the
sodium-ion battery was 277 mA hg−1 at a current density of 100 mA g−1, which was
less by 400 mA hg−1 than the lithium-ion battery having the same material except for
lithium salt (Rohit et al. 2016). The importance of porous hard carbon was signicant
as described in pomelo peel extracted carbon material for the sodium-ion battery. The
hard carbon had a 3D porous structure and a specic area of 1272 m2g−1. The Na+ in
nanoscale pores intercalated into disordered layers undergoes electron exchange with
O-containing functional groups to deliver a capacity of 181 mA h g−1 at 200 mA g−1
after 220 cycles (Hong et al. 2014). The biomass of waste tea powder was also subjected
to sodium-ion battery application as an anode material. The hard carbon prepared by
simple pyrolysis exhibited an enhanced reversible specic capacity of 282.4 mAh g−1
with 83% of capacity retention after 200 cycles (Pei et al. 2020). Finally, the use of bio-
mass in the preparation of a negative electrode from a suitable precursor based on their
structure, composition, and texture inuencing the performance of sodium-ion bat-
teries must be evaluated (Górka, Vix-Guterl and MateiGhimbeu2016). Understanding
the charge storage mechanism with relation to structure is critical in biomass-derived
carbon, wherein Zhu et al. (2020) demonstrated the potential of carbonaceous materi-
als for next-generation sodium-ion capacitors and sodium-ion batteries.
82 Green Engineering and Technology
The use of biopolymer electrolytes in the sodium-ion battery is yet to be extensively
explored as only a few studies are found to be reported in recent times. Polyethylene
oxide (PEO) and NaFSI polymer electrolyte showed a conductivity of 4.1 × 10 −4 S cm−1
with good electrochemical and thermal stability. The dendrite formation was avoided
at the metallic electrode/electrolyte interface, which was promoted by Na+ enhancing
the long charge/discharging cycle and capacity retention ability of the battery (Qi et al.
2016). An easy, low-cost, and aqueous solvent sodium-ion biopolymer electrolyte-based
battery was fabricated using a blend of PEO and biomass-derived carboxymethyl cellu-
lose (CMC) with NaClO4 as the dopant (Colo, Bella and Nair 2015). PEO:NaClO4:Na-
CMC showed ionic conductivity around 10−3 Scm−1. The glass transition temperature
of the PEO-Na lm was found to be greater than that of the PEO-Li+ lm, which is
attributed to the fact that Na salt improves the transition temperature of this blend sys-
tem. The inclusion of a gel biopolymer electrolyte facilitates sodium-ion battery with
increased charge/discharge stability, even at increased temperature. For large-scale
energy applications, the gel biopolymer electrolyte provides low-cost support for cross-
linking with other synthetic polymers like poly(methyl methacrylate) (PMMA). The
PMMA units crosslinked on the cellulose membrane are highly compatible and showed
an ionic conductivity of 6.2 × 10 −3 S cm−1 at 25°C (Gao et al. 2016). TheArrhenius
behavior of linear increase in conductivity was observed on increasing the temperature.
This further supports that sodium ion transfers by hopping throughthe polymer chains
rather than the segmental motion of the polymer electrolyte.
5.5 S UMMARY AND FUTURE SCOPE
Biomass-derived carbon-based electrodes and biopolymer electrolytes are a
reliable option for satisfying safer, renewable, and eco-friendly tag of energy
devices such as supercapacitors and batteries. Being prepared from a vast diver-
sity of resources, the biomass-derived carbon shows that tunable surface mor-
phology can be achieved based on the requirements of energy and power output.
Exploring the capability of biopolymer electrolytes for use in sodium batteries
is yet to be made signicantly, hence leading to a large scope for further studies.
Biopolymer electrolyte and sodium metal electrode interfacial properties need
to have a deep understanding to control rechargeability and stability. A better
understanding of the interaction of Na+ ions with a polymer host is required as
the transition temperature of the polymer host has a striking difference from
that of a lithium ion-based polymer electrolyte. Moreover, lithium-ion capacitors
and lithium-ion batteries have far superior electrochemical performance than
sodium-ion capacitors and batteries. This performance needs to be extensively
investigated to maximize the efciency of sodium-ion capacitors and batteries.
Dependence on solar energy storage is necessary in the near future through elec-
tricity grid systems wherein the size of batteries does not matter much. This
provides a solution to low-cost storage and helps to maintain the balance between
the cost of raw material and electricity pricing during the time of peak demand.
83Electrode Material and Electrolyte
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85
6RF Energy Harvesting
for WSNs
Overview, Design
Challenges, and Techniques
J. John Paul and A. Shobha Rekh
Karunya Institute of Technology and Sciences,
Karunya University.
6.1 INTRODUCTION
Advancement in technologies has brought about a demand for instant access to
everything. Engineers are bound to revamp the existing technologies such as IoT [1],
5G technologies [2], articial intelligence (AI) [3], wearable electronics [4], and so
on. These technologies involve a huge measure of sensors, which are deployed in
remote places, but these sensors require batteries to satisfy their power require-
ment. However, replacing these batteries periodically is quite a herculean task from
these ubiquitous sensors, which makes the environment increasingly more hazard-
ous. Energy harvesting or green energy can be an alternate or a feasible solution to
this environmental challenge, which involves extracting energy from the readily
available renewable energy sources. Figure 6.1 illustrates various energy harvesting
methods for wireless sensor networks (WSNs). The ambient energy from various
CONTENTS
6.1 Introduction 85 ....................................................................................................
6.2 Design Considerations for Antenna 88................................................................
6.2.1 Antenna Miniaturization 88....................................................................
6.2.2 Antenna Polarization 90 ..........................................................................
6.2.3 Recongurability 92................................................................................
6.2.4 Harmonic Rejection 93 ............................................................................
6.3 Design Considerations for Matching Circuits 94 ................................................
6.4 Rectier Circuits 95.............................................................................................
6.4.1 Diode-Based Rectier Circuits 96...........................................................
6.4.2 MOSFET-Based Rectier Circuits 97 .....................................................
6.5 Conclusion 98 ......................................................................................................
References 98................................................................................................................
86 Green Engineering and Technology
sources such as solar power, wind power, thermal power, and vibration power and
dedicated RF energy sources such as Wi-Fi, WLAN, Bluetooth, AM/FM, and so on
are converted into electrical energy. Advantages of energy harvesting help in reduc-
ing the complexity of end devices (WSN/IoT nodes) by avoiding the power circuits,
reducing the overall cost, and improving the overall efciency of devices/system.
Figure 6.2 shows the architecture of a wireless sensor node with energy harvest-
ing capability. A typical energy harvesting system consists of an energy harvest-
ing device, a power management unit, and a storage device. An energy harvesting
device converts the incident ambient energy from various energy sources such as
FIGURE 6.1 Various Energy harvesting methods in the wireless networks.
FIGURE 6.2 Architecture of a WSN node powered by various energy sources.
87RF Energy Harvesting for WSNs
solar, wind, thermal, vibration, and RF wireless energy into DC voltage. The power
management unit is responsible for either storing the harvested energy or regulating
the power to the WSN node (end device). The storage device conserves the har-
vested power, which helps to use them in the future.
In the past, several reviews were reported by the researchers on different energy
harvesting techniques. Leonov et al. [5] demonstrated energy harvesting from body
heat (thermal energy) to power WSN node. Guo et al. [6] demonstrated piezoelectric
techniques for energy harvesting. Zhao et al. [7], Invernizzi et al. [8], and Albrni et al.
[9] focused on small-scale windmills for powering low-power WSN nodes. RF-energy
harvesting (RFEH) is an emerging technology that can drive the generation of WSNs
without the requirement of batteries. Though battery is the main source of power,
replacing and discarding them remain a challenge for uninterrupted data and also
remain hazardous to the environment [10]. Therefore, a green energy or environment-
friendly technology is required to avoid the toxic chemicals and metals, which pollute
the environment. Energy harvesting methods include near eld methods [15–18] and
far-eld methods [19–22], but more of our focus will be on the (far-eld) RFEH.
Figure 6.3 shows the general block diagram of an RFEH system, which consists
of an antenna, a matching circuit, a voltage multiplier, and the load (WSN node).
The antenna helps in absorbing the ambient RF energy and the matching circuit
helps in matching the antenna impedance with rectifying circuit and the load, which
improves the efciency of the overall circuit [11–13]. The rectifying circuit converts
the RF signals into DC signals. The voltage multiplier circuit boosts the converted
DC voltage to the required level. Power conversion efciency (PCE) determines the
overall efciency of the system.
The overall conversion efciency of the RFEH system is given by [14]
I=P
PCE() DC (6.1)
P
RF
where
PDC =>DC power
PRF =>RF power
The PCE depends on the following factors:
1. Uncertainty in the availability of the RF power,
2. Associated component losses,
3. Insensibility of the sensor circuit,
FIGURE 6.3 General block diagram of an RFEH system.
88 Green Engineering and Technology
4. Measuring distance between the transmitter and the receiver,
5. Loss due to impedance mismatch, and
6. Limitation of the radiated power.
This chapter is ar ranged to give the reader an i nsight view of various energy har vesting
antenna designs and their related rectication and matching circuits. Section 6.2
presents an overview of various antennas and metamaterial-based absorber designs
for energy harvesting applications. Section 6.3 discusses the rectier and matching
circuits for energy harvesting. Section 4 closes with the conclusion.
6.2 D ESIGN CONSIDERATIONS FOR ANTENNA
The soaring demand for handheld communicating devices with a small form factor
urges the need for compact antennas whose size, weight, ease of assembling, and low
maintenance are major constraints. A compact antenna is the prerequisite of wireless
network interface controllers, Wi-Fi devices, and many other gadgets. The extensive
use of PCB led to the idea of microstrip antennas.
6.2.1 antenna miniaturization
The microstrip technology has become renowned over the years, due to its ubiqui-
tous accessibility, lightweight nature, ease of construction, low fabrication cost, and
convenient mounting on the ground plane due to its thin layer of substrate prole.
Figure 6.4 illustrates a three-layered microstrip architecture with the patch (top
layer), ground (bottom layer), and dielectric substrate (middle layer) sandwiched
between them. However, the elevation of substrate height introduces surface waves;
therefore, the height of the copper patch is kept as small as possible, and it varies
from 0.035 mm to 0.07 mm. The thin layer of substrate in the microstrip antenna
paves for a high Q-factor (25–100) that conversely reduces the bandwidth, power,
and efciency.
f
Q=r (6.2)
BW
FIGURE 6.4 Geometry of a microstrip patch antenna.
89RF Energy Harvesting for WSNs
In Eq. (6.2), the value of Q is inversely proportional to the bandwidth, which results
in the bandwidth reduction. For a miniaturized antenna with a minimum loss, Chu
suggested Eq. (6.3) for Q- factor,
11
Q≥+
33 (6.3)
ka ka
where k is 2π/λ and “a” represents the antenna space.
Thick substrates of lower dielectric range (2.2 ≤ ∈r ≤ 7) are desirable due to their
better efciency and performance, but it increases the cost of the material used
for radiation. In addition, size reduction should be obtained with thin substrates of
higher dielectric constants (7 ≤ ∈r ≤ 12), which are desirable for microwave circuitry
to minimize undesired radiation.
Other methods of miniaturization include shorted posts, slots, meandered slits,
defective ground structures (DGS), and metamaterials. A cross-shaped slot [23] is
etched from a square patch antenna, which reduces the size to about 32.5% com-
pared with the conventional approach. A square patch antenna [24] with ten mean-
dered slits each on its periphery reduces the size to 48%. Slots and slits help in patch
size reduction compensating the shift in the resonant frequency and impedance
mismatch. Figure 6.5 illustrates a square patch with (a) a cross-shaped etch and (b)
meandered slits.
Therefore, additional matching circuits are needed, which makes the design more
complex and increases the overall form factor.
DGS [25] is an alternate method of miniaturization, modeled using an LC or RLC
equivalent circuit. A defective ground plane increases the dielectric strength and
hence decreases the size of the patch. The size reduction of about 42% is achieved
through circular slots on the ground plane [26]. Using H-shaped slots at the ground
plane, a size reduction of 43% was achieved [27]. H-shaped slots achieve size
FIGURE 6.5 Miniaturized patch antenna designs: (a) square patch with cross-shaped etched
[23] and (b) square patch with meandered slits [24].
90 Green Engineering and Technology
FIGURE 6.6 Plus-shaped patch H-shaped defective ground structures at the ground plane.
reduction but with decreased efciency due to back radiation. Figure 6.6 illustrates a
plus-shaped patch with an H-shaped DGS structure at the ground plane.
Metamaterials are articially engineered structures suggested by V. G. Veselago [28]
in 1968, who proposed that if a material has both permeability and permittivity values
lesser than zero (ε < 0 & µ < 0), the refractive index of such materials would be negative
(n < 0). Split-ring resonator (SRR) and complementary split-ring resonator (CSRR)-based
metamaterials exhibit a negative refractive index, which improves the overall efciency
in the applications such as antenna [29], absorber [30], amplier [31], and RF oscillator
[32]. The advantage of SRR is its compact size, impedance matching, and advantage of
sensing a wide frequency band. Almoneef [33] in his experiment with a 12 × 12 SRR
array provided an output power of more than 60% per footprint with wide bandwidth
compared to a conventional patch structure. Fowler [34] in his work proposed an SRR-
based metamaterial structure with very high efciencies of 230% and 130% at power
densities of 10µWcm−2 and 1µWcm−2, respectively. Almutairi [35] proposed a compact
CSRR-based structure with a unit cell size of 5.5 × 5.5 mm 2 with an effective medium
ratio of 8.0 for energy harvesting applications.
Figure 6.7 shows that electromagnetic propagation can be controlled by a special
class of metamaterials known as the electromagnetic band gap (EBG) [36]. A patch
antenna [37] achieved a size reduction of 22.38% and a bandwidth improvement of
39.3% with a square-shaped EBG structure at the ground plane. Various antenna size
reduction techniques are compared in Table 6.1.
6.2.2 antenna polarization
Antenna polarization is an important parameter that inuences the conversion ef-
ciency of a harvesting system. Various polarization techniques include horizontal
polarization, vertical polarization, slant polarization, and circular polarization.
91RF Energy Harvesting for WSNs
Out of those, the circular polarization is considered the best type for the RFEH
system since it does not result in energy loss like the other types of polarization.
The “minimum loss” property of a circularly polarized antenna makes it the most
efcient in the RFEH system [39]. The circular polarization can be achieved by
truncating the corners, loading stubs, cross dipoles, slits, spur lines, and y-shaped
slots in the patch [40]. The simplest of all to achieve circular polarization is to
truncate the corners of the patch. The single feed circular polarized structures
FIGURE 6.7 Metamaterial-based structures: (a) CSRR-based structure [35] and (b) periodic
EBG structure [37].
TABLE 6.1
Antenna Size Reduction Techniques
Size
Antenna Substrate Polarization
Frequency
(GHz)
Size
(mm)
Reduction
(%)
Patch antenna with a Arlon A25N & Circular 2.45 34 × 34 32.5
cross-shaped slot [23] Rohacell 51
foam
Planar antenna with
meandered slits [24]
Taconic(TLY-
5lamiate
Linear,
orthogonal
2.36 41 × 0.5 48
Patch with H-shaped
split rings (SRR) [26]
High dielectric
material of
3.2 mm
Circular 2.865 34 × 20 27
Patch antenna with
DGS [27]
Argon material Linear 2.4–2.49 56 × 66 46
Metamaterial-based FR-4 Circular 4–8 5.5 × 5.5 50
CSRR structure [35]
EBG-based printed
patch antenna [38]
RT Duroid
5880
Linear 10 25 × 25 89
92 Green Engineering and Technology
FIGURE 6.8 Single-feed CP microstrip patches: (a) square patch and (b) square patch with
truncated corners.
are fed at 45° with respect to perturbation. Typical congurations [41] of a single
point feed patch capable of producing linear and circular polarization are shown in
Figure 6.8. The single feed conguration sets up asymmetrical current paths with
two different resonant frequencies at a 90° phase difference between them. The
disadvantages are reduced bandwidth and less efciency.
Limitations of bandwidth and efciency are improved when using stacked patch
structures [42,43], metamaterials, and magnetic conductors. In the stacked etched
structures [42], the overall height of the antenna measures a thickness of about
4.8 mm. These structures improve the axial ratio and bandwidth, with an overall
increase in the size of the antenna. From the comparisons of Table 6.1, metamaterial-
based structures show improved performance for energy harvesting as they do not
degrade the performance.
6.2.3 reConfiguraBility
Recongurable antennas are highly attractive and preferable in energy harvest-
ing due to their advantage in selecting the operating frequency and polarization.
Recongurability aids to choose from the wideband frequencies to select the
desired frequency. Recongurability is achieved by using switching devices such
as RF electromechanical systems, PIN diodes, or photoconductive switches with
biasing elements for switching ON and OFF the respective switching elements. By
turning the switch ON and OFF accordingly, the antenna is switched between two
different frequency bands. Pal H et al. [44] proposed a frequency-recongurable
antenna by using diodes to resonate at two different frequencies (2.4 GHz when
the diodes are OFF and 1.6 GHz when the diodes are ON). Figure 6.9 illustrates
antenna with frequency recongurability using diodes for switching between two
different frequencies. S. W. Cheung et al. [45] proposed a recongurable antenna
93RF Energy Harvesting for WSNs
FIGURE 6.9 Recongurable antenna with diodes for switching between frequencies.
using slots and pin diodes and achieved an axial ratio bandwidth of 7.2% and a
wide impedance bandwidth of 19.5%.
6.2.4 HarmoniC rejeCtion
The rectifying circuits convert the incoming RF signals to DC signals [46]. However,
these circuits have nonlinear components such as Schottky diodes and PIN diodes,
which induce harmonics and hence reduce the overall performance of the harvesting
system. These harmonics create a mismatch in the impedance between the antenna
and the rectifying circuits and results in a poor PCE. Hence, low pass lters (LPFs)
should be added between the rectifying diodes and antenna to suppress and reject
the harmonics, thereby increasing the efciency of the system. Numerous antenna
designs incorporate harmonic rejection capability for RFEH systems. To improvise
the maximum power to be received by the antenna, several structural modications
such as stub, meandered slits, and DGS are adopted to reject the harmonics and to
improve the PCE.
An example of harmonic rejection [47] is the circular slot antenna with a DGS
structure inscribed at the ground plane, which performs the ltering function in
rejecting higher-order harmonics (stop band) and omits the need for an additional l-
ter. The rectifying diode [48] with the microstrip patch induces harmonics of 4.8 and
7.2 GHz at the resonating frequency of 2.4 GHz. However, with a simple circular sec-
tor angle of 240° and a feed angle of 30°, it blocks the second and third harmonics,
rejecting re-radiation and eliminating the need for a lter. The harmonic rejection
feature by these antennas also helps in improving the antenna gain and overall PCE.
94 Green Engineering and Technology
6.3 DESIGN CONSIDERATIONS FOR MATCHING CIRCUITS
This section briefs the importance of matching circuits and their design consider-
ations for RFEH systems. The degradation of the efciency lies in the mismatching
of the impedance between the antenna and the non-linear components in the rectify-
ing circuits. Impedance mismatch echoes the incident wave back to the source and
results in poor efciency. Impedance matching improves the efciency in converting
RF power into the desired DC voltage resulting in the maximum PCE. Matching cir-
cuits can also be called as LPFs, which help in rejecting the higher-order harmonics
that is twice or thrice the fundamental resonant frequency [49].
A good matching circuit should possess the following characteristics:
2. The matching circuit should be relatively small, because it should not
increase the overall form factor.
1. The impedance matching must be attained between the antenna and the
load impedance (rectier circuit along with the load) over a wide frequency
range and input power level.
Usually, matching circuits are designed using lumped circuits such as T-network,
PI-network, L-network, shunt inductor, gamma matching network, band pass lter, or
distributed microstrip lines. A simplied design of an L-shaped matching network is
discussed followed by a PI-type matching network. Figure 6.10a shows an L-shaped
matching network for low power range. L-shaped matching networks provide good
impedance matching with minimum loss [50]. However, this type of network suffers
from narrow bandwidth due to the high value of Q.
Assuming source impedance as 50Ω, Lm and Cm values are calculated as
Lm=R
in
()
(6.4)
ωω
00
QC+in Rin
R1
Cm=in (6.5)
LR
ms
()
in −R
ω
21
0
−
LC
min
The L-section matching network has its limitations on tuning both Lm and Cm. The
limitations of the above matching network can be overcome by a PI-network or
T-network. Figure 6.10b illustrates a PI-shaped matching network.
FIGURE 6.10 (a) L-section matching network and (b) PI-section matching network.
95RF Energy Harvesting for WSNs
FIGURE 6.11 Comparison of L and PI networks: (a) output voltage as a function of fre-
quency and (b) variation of L and C with Q. (Adapted from Sachin Agrawal, Jawar Singh
and Manoj S. Parihar. 2015. Performance Analysis of RF Energy Harvesting Circuit with
Varying Matching Network Elements and Diode Parameters, IET Microwaves, Antennas
& Propagation, vol. 1, pp. 6–18)
The design equation for a PI network is given as
1
1
|ZR
in =
()
LL−jX |+
()
jL
ω
|| (6 6)
jC
ω
21
jC
()
.
ω
Figure 6.11 compares L and PI matching networks with 6.11a illustrating the output
voltage as a function of frequency. The parasitic losses allied with the passive ele-
ments uctuate with frequency. Figure 6.11b depicts the frequency-dependent nature,
as the frequency increasing the capacitive behavior is changed into inductance. As
Q increases, the value of L decreases drastically, so high Q circuits can be designed
with low values of L [14]. However, for frequencies greater than 1 GHz, lumped ele-
ments due to parasitic losses are not suitable. Hence, at high frequencies, microstrip-
based lines are designed to match the complex input impedance of the antenna and
the rectier circuit at a particular input power level [51]. Song et al. [52] designed an
impedance matching network suitable for a load range of 1–10 kΩ. A broad band
impedance matching circuit [53] with its upper branch consists of a radial stub and
a shorted stub and a 6nH chip inductor is presented for frequency ranges from 1.8 to
2.5 GHz, and its lower branch consists of a shorted bent shaped stub and a 1.8nH chip
inductor for matching frequency range at 2.1 GHz.
6.4 RECTIFIER CIRCUITS
Rectier circuits, typically with one or more diodes, must have a high PCE. The
selection of diode is of primary importance as it directly relates to the conversion
efciency of the rectier circuits. A typical diode with poor performance leads to a
poor conversion efciency. The PCE [54] is inuenced by the (i) series resistance of
96 Green Engineering and Technology
FIGURE 6.12 (a) Single-stage voltage doubler circuit and (b) Dickson charge pump.
the diode (Rs), (ii) zero-bias junction capacitance (Cj0), (iii) diode breakdown voltage
(Vbr), (iv) high switching frequency of the rectifying diode, and (v) low threshold
voltage (VT). Figure 6.12 shows the parameters such as harmonics, parasitic effects,
reverse breakdown voltage (Vbr), and threshold voltage (VT) that affects the conver-
sion efciency [55].
Rectier circuits are classied based on the components used in the rectier cir-
cuits. They are (i) diode-based rectier circuits and (ii) MOSFET-based rectier
circuits.
6.4.1 diode-BaSed reCtifier CirCuitS
Diodes prove to be an efcient candidate for rectier circuits for a high PCE.
Schottky diodes are widely used for rectenna applications [56]. Rectiers can be clas-
sied into two types: (i) a simple rectier and (ii) a voltage doubler rectier. Figure
6.12a illustrates a single-stage voltage doubler circuit, which is widely used to double
the output voltage for low-and medium-power applications. A two-stage Cockcroft
Walton voltage doubler [57] is capable of generating output voltage three times the
input voltage but results in a low voltage gain of the circuit due to its high-coupling
voltage drop. Figure 6.12b shows an n-stage Dickson charge pump [58] for doubling
97RF Energy Harvesting for WSNs
the input voltage. The important requirement of the charge pump is the need for the
clock pulses in every stage, which limits its applications for high voltages.
6.4.2 moSfet-BaSed reCtifier CirCuitS
The advantage of MOSFET lies in its fast switching speed. However, MOSFETS
require a high threshold voltage, which limits the efciency of the RFEH system
[59]. Also, the high voltage drop across the device and the reverse leakage current
further degrade the efciency of the system [60]. To compensate for the high thresh-
old voltage, the cross-coupled voltage multiplier [61] technique is used, as illustrated
in Figure 6.13. This circuit complexity improves efciency at the cost of making the
multiplier circuit more bulky during the increase in the number of stages. Another
method used to compensate the threshold voltage is by implementing cascaded cross-
coupled multipliers. This circuit combines a voltage doubler with a cross-coupled
voltage multiplier circuit. As the number of stages gets increased, voltage ripples
increase, which degrade the efciency of the system [62].
Various rectier topologies are reviewed in Table 6.2. Schottky-based diodes
show a better efciency compared to CMOS and MOSFET-based rectiers. HSMS-
based diodes from Agilent and SMS-based diodes from Skyworks are preferred
in most of the works compared to the MOSFET-based rectiers. In the HSMS-
based diodes, HSMS8101 is preferred due to its high-power handling capability, low
threshold voltage, and wide input frequency range. In the range of SMS diodes, SMS
7630–061 is preferred due to its low threshold voltage, low junction capacitance, and
high series resistance [63].
FIGURE 6.13 Single-stage cross-coupled voltage multiplier [61].
98 Green Engineering and Technology
6.5 CONCLUSION
Energy harvesting from RF energy for WSN–IoT nodes is an attractive strategy,
which is challenging in the design aspects such as low power output levels, less con-
version efciency, and broadband matching, creating keenness among the researchers
to ponder in this research area. IoT era, where billions of devices are to be added to
the Internet energy harvesting, will play a keen role in the near future. This chapter
has given an overview of the selection and designing compact antenna by applying
various miniaturization techniques, designing of the matching circuits, various recti-
er topologies built with diodes, and MOSFETS to build an efcient RFEH system
for real-time applications.
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103
7Sustainable and
Renewable Isolated
Microhydropower
Generation Using a
Variable Asynchronous
Generator Controlled
by a Fuzzy PI
AC–DC–AC Converter
and D-STATCOM
P. Devachandra Singh and Sarsing Gao
North Eastern Regional Institute of Science and Technology
CONTENTS
7.1 Introduction 104 ..................................................................................................
7.2 System Description 105 .......................................................................................
7.3 CAG and Variable Turbine Model for the Proposed VMHPG System 106........
7.3.1 Hydro Turbine Model 106........................................................................
7.3.2 CAG Model 107.......................................................................................
7.4 AC–DC–AC Converter Control 108....................................................................
7.5 Fuzzy PI D-STATCOM Control 110...................................................................
7.6 Simulation Results and Discussion 110...............................................................
7.6.1 Case I: Performance of the Proposed VMHPG Model
under R-Load and RL-Load 112.............................................................
7.6.2 Case II: Performance of the Proposed MHPG Model under
a Non-linear Load . 115............................................................................
7.7 Conclusion 117 ....................................................................................................
Appendix 118................................................................................................................
References 119..............................................................................................................
104 Green Engineering and Technology
7.1 INTRODUCTION
Every effort to increase penetration of renewable energy resources can contribute to
the reduction in the consumption of fossil fuels. In the last two decades, the contribu-
tion of energy generation from hydropower is about 17% [1]. A large amount of share
among the renewable energy generation is seen from small-scale hydropower genera-
tion in the last many years. Until the last few years, the paradigm of renewable energy
generation is shifted toward wind and solar sources [1]. Most studies, at present, are
focused on these two technologies; however, the scope of microhydropower genera-
tion (MHPG) and small hydropower is high, provided a suitable technology is being
implemented. The continuous monitoring of operation and maintenance required for
conventional microhydropower plants is the major setback [2]. This chapter focuses
on the variable MHPG (VMHPG) technology for extracting energy from hydro
potential, which is similar to the extraction of wind power from its variable wind.
Hence, the study considers an MHPG system using three-phase capacitor-excited
asynchronous generators (CAG) operating at variable speeds fed by an uncontrolled
hydro turbine. The primary advantage of CAG is their rugged brushless construction,
and no DC eld supply is required. They are reliable, economical, and available in a
wide range of capacity [3]. The application of such generators in wind power systems
requires gearbox arrangement to cope with high wind speed, which will increase the
maintenance cost [3]. However, in microhydro applications, CAG without a gearbox
arrangement may be used as the velocity of water is much less than that of wind. It
also has an inherent capability to operate at variable speeds and the ability to take any
type of load [3]. Another advantage includes natural protection against short circuit
fault, which makes it perform better than Permanent Magnet Synchronous Generator
(PMSG) at medium-speed operation [4]. Furthermore, the proposed scheme considers
variable voltage constant frequency by the implementation of an active front-end con-
verter for efcient extraction of power. The technology implemented in the proposed
model is closely related to that of the wind energy generation system as reported by
many researchers [5–7]. Different classications of hydropower based on capacity are
pico (<5 kW), micro (5–100 kW), small (101–2000 kW), mini (2001–25,000 kW), and
large (>25,000 kW) [8]. However, different countries may have different classica-
tions. The study considers a 7.5 kW capacity VMHPG system. It can also be classied
based on the type of installation such as impoundment, diversion, and pump storage,
and based on the type of water turbine, it may be a reaction or impulse type [8]. The
proposed model of MHPG is designed considering a diversion type of installation,
which is also sometimes known as the “run of the river” type. This installation allows
water to be diverted from the mainstream and only the required amount of water can
be fed to the turbine. To avoid the complicacy of controlling the diverted water to the
turbine, the proposed system is designed to accept free-owing water. This will run
the turbine as well as the generator at variable speeds. Hence, the generator will give
variable voltage and frequency in its output terminal. To control this output voltage,
the controllers such as static VAR compensator, neural network technique, and so on
have been proposed; however, the frequency is not controlled [9,10]. Some topolo-
gies and architecture are popular for DFIG, PMSG, and so on [3–10], but these are
105Isolated Microhydropower Generation
limited to application for very large wind variation and highly unpredictable systems.
In this article, two types of controls have been implemented in the proposed VMHPG
system. First, a fuzzy PI-based PWM AC–DC–AC converter is used to convert the
generated variable AC output to DC output, which is further converted to controlled
AC output and thereby maintaining frequency constant. A two-level IGBT-based
VSC converter with a DC-link capacitor is used to obtain constant frequency and
voltage output. Such control offers more exibility in power ow control and hence,
its efciency is improved. The harmonics generated during the conversions are duly
taken care of by determining the optimum modulation index required [11]. Second, a
fuzzy PI-based D-STATCOM is connected in the load terminal to regulate the volt-
age uctuations due to non-linear and unbalanced loadings. D-STATCOM is power-
ful in eliminating harmonics, which otherwise will affect connected loads and cause
heating of generator windings [12]. Its operation requires properly designed control
algorithms such as synchronous reference frame theory, instantaneous reactive power
theory, Icos Ø algorithm, Adaline algorithm, and so on [13–15]. A fuzzy PI-based one
has shown superior performance as compared to other conventional types in terms of
better undershoot, overshoot, and robustness [16–18]. The subsequent sections pres-
ent the modeling and simulation of the proposed model and analysis of performances
based on simulation results under (i) resistive and inductive loads and (ii) non-linear
and unbalanced loadings.
7.2 SYSTEM DESCRIPTION
The proposed VMHPG system consists of a three-phase 7.5 kW CAG fed by a variable
turbine, a capacitor bank to provide reactive power required by CAG, a controllable
IGBT-based fuzzy PI-PWM 2-level AC–DC–AC converter with a DC-link capacitor
to maintain constant frequency at AC output and lters, and fuzzy PI D-STATCOM
for controlling voltage and load as shown in Figure 7.1. The excitation capacitors of
CAG are selected to obtain rated voltage at its output. The saturation parameters of
the CAG are obtained from its open circuit test [19]. The detailed parameters are
given in the Appendix. The detailed working of the AC–DC–AC converter is also
presented in the subsequent sections.
Hydro
turbine
Excitation
capacitors
CAG LC
Filter
Generator-side
converter
Load-side
converter
Vdc
Fuzzy PI-based
PWM control 1
Fuzzy PI-based
PWM control 2
Variable
input
LOAD
LC
Filter
Fuzzy PI
D-STATCOM
Rs
Ls
FIGURE 7.1 Schematic of the proposed VMHPG.
106 Green Engineering and Technology
7.3 C AG AND VARIABLE TURBINE MODEL FOR
THE PROPOSED VMHPG SYSTEM
7.3.1 Hydro turBine model
The turbine is modeled considering a run-of-the-river scheme MHPG with a
velocity comparatively smaller than that of the wind turbine. By determining the
torque-speed characteristics of the turbine with the changing ow rate (m3s−1) of
water, the turbine model can be developed. If Pm (Nm) denotes the mechanical
power output of the turbine or prime mover, Tm is the torque in Nm, and ωr (rads−1)
is the rotor speed, then
PT
mm
=
ω
r (7.1)
In terms of coefcients of torque (
τ
0), speed (n0), angular speeds (
ω
0), and angular
synchronous speed,
ω
s in rads−1, Tm can be rewritten as
ω
Tn=−
τ
r
m00 (7.2)
ω
s
Tmr
=−
τω
00
ω
(7. 3)
where
ωω
00
=ns (7.4)
For run-of-the-river, if
η
,,
ρ
Q, and v denote the efciency of turbine, density of water
(kgm−3), ow rate (m3s−1), and velocity of water (ms−1), re spect ively,
1
PQ
m=
ηρ
v2 (7. 5 )
2
Combining Eqs. (7.1) and (7.5), the mechanical torque can be written as
1
TQ
m=
ηρ
v2 (7.6)
2
ω
r
Here vQ=A (7.7)
where “A” is the swept area of the turbine blades in m2.
Combining Eqs. (7.4) and (7.5) and solving the quadratic equation of angular
speed, it can be seen that
ω
0 is a function of ow rate (Q). Therefore, the variable
turbine is modeled with respect to varying ‘Q’. Now, Eq. (7.4) can rewritten as
Tf
mr
=−
τω
0()Q⋅ (7. 8)
This equation gives turbine torque-speed characteristics with respect to varying ‘Q’
and the same is used to model the variable turbine as shown in Figure 7.2.
107Isolated Microhydropower Generation
FIGURE 7.2 CAG and variable turbine model.
7.3.2 Cag model
The dynamic model of a three-phase CAG can be obtained using stationary
d–q axes references frame, where the voltage and current equations are given
by [19]
[]vR=+[][]iL[]pi[]+
ω
r[]Gi[] ( 7.9)
Taking current derivatives, Eq. (7.9) can be written as
pi[]=−[]Lv
−1{[ ][Ri][ ][−
ω
rGi][ ]} (7.10)
where
LL
sm
+00L
m
00LL
Lsm L
[] +m
=LL
mr
00+Lm
00
LL
mr
+Lm
00 00
00 00
[]G= (7.11)
00−+
LL
mr
Lm
LL
mr
00+L
m
108 Green Engineering and Technology
T
[]
v=vvvvi
=iiii
ds qs dr qr ;[ ];
ds qs dr qr
[]
R=diag[
RRR
ssrr
R]
The shaft torque of a CAG is given by [17]
TT
shaft=+
er
JP(2 /)p
ω
(7.12)
Rearranging Eq. (7.12),
pP
ω
re
=−
{}
()
2JT
()
Tshaft (7.13)
The shaft torque or the mechanical torque of the prime mover is given in Eq. (7.11).
Since the CAG operates in the saturation region and it has non-linear magnetizing
characteristics, the magnetizing current can be calculated in integral steps of stator
and rotor currents as given by:
2
Imd
=+
()
ii
2
rdsq
++
()
ii
sqr (7.14)
The synchronous speed test of the 7.5 kW CAG gives the relationship between Lm and
Im as given below [20]:
LI
mm
=<0.134 (for 2.8A)
Le=−9−52
mm
TI0.0087 mm
+<0.1643 (for 2.8AI<14.2A) (7.15)
LI
mm
=>0.068 (for 14.2A)
7.4 AC–DC–AC CONVERTER CONTROL
As shown in Figure 7.1, the AC–DC–AC converter is placed in between the CAG and
the load along with LC lters on both sides. The generator-side IGBT-controlled rec-
tier bridge with the LC lter changes the variable AC voltage to DC. The dc voltage
changes with changing the speed of the CAG and is adapted to a DC-link capacitor.
The switching pulses of 2 kHz are utilized to trigger the switches as required by
the control signal. The high switching frequency is selected to have a much higher
bandwidth than that of the generator. The DC-link capacitor stores energy and is
released to the dc link bus instantaneously at the time of requirement [21]. The IBGT-
based VSI installed at the load side converts the DC voltage to AC voltage at rated
frequency. It requires six pulses to trigger the bridge switches synchronously, which
is generated by a PWM generator. The output AC is a rectangular shaped wave and
hence, LC lters with suitable values of L and C are used to smoothen the waveforms.
The control algorithms of the generator-side converter and the load-side convert-
ers are developed using the most popular d–q decoupling techniques [22] and are
depicted in Figures 7.3 and 7.4.
109Isolated Microhydropower Generation
To make the generator and load side converters robust and perform better, fuzzy PI
controllers are implemented [22], as shown in Figure 7.5. Triangular membership func-
tions of seven scales are implemented for input and output variables. The comparisons
of each of the d-axis and q-axis components are fed as input to fuzzy PI controllers,
PLL
PWM
Generator
abc
dq0
PI/Fuzzy PI
Current
Regulator
PI
Controller
V
abc
-
-
-
+
+
+
V
dc
V
dc-ref
id
iq
Iabc
ΔViq
ΔVid
θ
iq-ref
id-ref
VSC
Reference
voltage
generation
dq0 to abc
Vdc
Vac_nom
abc
Vdq_mes
dq0
FIGURE 7.3 Control scheme of a generator-side converter.
Discrete
Virtual PLL
50HZ
PWM
Generator
abc
dq0
Controller
PI/Fuzzy PI
Controller
-
+
+
Vd-ref(pu)
Vd-
Vq
∆Vq
∆Vd
θ
Vq-ref(pu
)
VSC
Reference
voltage
generation
dq0 to abc
V
dc
Vac_nom
Vabc(pu)
Sin_Cos
PI/Fuzzy PI
FIGURE 7.4 Control scheme of a converter at the load side.
FIGURE 7.5 (a) Fuzzy PI control structure applied to GSC and LSC. (b) Fuzzy PI control
structure.
110 Green Engineering and Technology
as shown in Figures 7.3 and 7.4. IF-THEN Mamdani rule of size 7 × 7 is used for the
control system, as shown in Table 7.1 and Figure 7.6.
7.5 F UZZY PI D-STATCOM CONTROL
D-STATCOM maintains the load voltage constant by feeding any additional reactive
power requirement due to changes in generations, loads, faults, and so on by injecting
a leading or lagging current. It consists of a voltage source converter (VSC), DC-link
capacitor, and AC inductors connected in shunt with the line. The detailed operation
of a D-STATCOM is depicted in Figure 7.7. It requires the decoupling of sensed volt-
age into d–q components. To maintain load voltage, control of the q-axis component
is required. The d-axis components help to maintain the voltage across the DC-link
capacitor. The proposed fuzzy PI controller aims at optimizing the fuzzy PI control-
lers each for the terminal voltage regulator and the current regulators, as shown in
Figure 7.7. The design strategies of the fuzzy PI controller remain the same as that of
the GSC and LSC. The error and change in the error of input voltages or currents are
taken as inputs to the fuzzy PI controller.
7.6 SIMULATION RESULTS AND DISCUSSION
The MATLAB/Simulink model of the proposed MHPG scheme with the vari-
able-CAG system is shown in Figure 7.8. A three-phase 7.5 kW CAG excited with
a star-connected capacitor bank and a turbine continuously varying over time is
used. The turbine speed and torque vary as the ow rate of the water varies over a
minimum discharge of 0.45 m3s−1 and a maximum discharge of 0.88 m3s−1 as shown
in Figure 7.9.
Two cases of loading conditions are considered each for R/RL load and non-lin-
ear/unbalanced load. In both the cases, the waveforms of frequency, generator speed,
various voltages, and current are analyzed. In addition, the THD analysis is carried
out for voltage waveforms.
TABLE 7.1
Fuzzy Rule Base
Δe
A. e NB NM NS Z PS PM PB
NB NB NB NB NB NM NS Z
NM NB NB NB NM NS Z PS
NS NB NB NM NS Z PS PM
Z NB NM NS Z PS PM PB
PS NM NS Z PS PM PB PB
PM NS Z PS PM PB PB PB
PB Z PS PM PB PB PB PB
111Isolated Microhydropower Generation
FIGURE 7.6 (a) Membership functions for input and output variables. (b) IF-THEN
Mamdani rule and (c) surface view of the IF-THEN Mamdani rule.
112 Green Engineering and Technology
PLL
PWM
Generator
abc
dq
Fuzzy PI
Current
Regulator
PI
Controller
V
abc
(load)
-
-
-
+
+
+
V
dc
V
dc-ref
i
d
i
q
Iabc(load)
ΔV
iq
ΔV
id
θ
i
q-ref
i
d-ref
VSC
Reference
voltage
generation
dq0 to abc
V
dc
V
ac_nom
abc
dq
V
dq_mes
Voltage
Amplitude
Computation
Vt
Vt-ref
+
-
Fuzzy PI
Controller
FIGURE 7.7 Block diagram of the D-STATCOM control algorithm.
FIGURE 7.8 Simulink model of the proposed MHPG with variable turbine-CAG.
0 1 2 3 4 5 6 7 8 9 10
0.4
0.5
0.6
0.7
0.8
Time (sec)
Flow Rate (cubic m/sec)
FIGURE 7.9 Variation of water ow to the turbine.
7.6.1 CaSe i: performanCe of tHe propoSed vmHpg
model under r-load and rl-load
In this case, the proposed model is tested for R-load (5 kW) and RL (5 kW and 2
kVAR) at different simulation times. When the R-load is connected to the system
at 1 s, the D-STATCOM is switched ON at 1.2 s and again switched OFF at 1.4 s.
113Isolated Microhydropower Generation
Second, the RL-load is connected at 1.5 s and D-STATCOM at 1.6 s, and discon-
nected at 1.8 s. Figures 7.10–7.13 show the transient waveforms of three-phase
VMHPG speed, frequency and load frequency, generator voltage, load voltage, and
dc-link voltage, generator current, load current, and D-STATCOM currents, respec-
tively. In Figure 7.10, the generator frequency and speed are deviated from the rated
44
46
48
50
Generator frequency
(Hz)
44
46
48
50
Load frequency
(Hz)
00.2 0.40.6 0.811.21.4 1.61.8 2
1000
1200
1400
1600
CAG Speed (rpm)
Time (Sec)
FIGURE 7.10 Proles of generator frequency, load frequency, and generator speed for Case-I.
FIGURE 7.11 Waveforms of generated voltage, load terminal voltage, and DC link voltage
for Case-I.
-800
-500
0
500
800
CAG Voltage (V )
-800
-500
0
500
800
Load Voltage (V)
00.20.40.60.8 1 1.21.41.61.8 2
0
200
400
600
800
Time (Sec)
DC Link Voltage (V)
114 Green Engineering and Technology
-20
0
20
Generator current
(A)
-20
0
20
Load current
(A)
-20
0
20
D-STATCOM
current-R ph (A)
-20
0
20
D-STATCOM
current-Y ph (A)
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
-20
0
20
Time (Sec)
D-STATCOM
current-B ph (A)
FIGURE 7.12 Waveforms of various currents during different phases of operation for Case-I.
0100 200 300400 500600 700 8009001000
0
1
2
3
Frequency (Hz)
Fundamental (50Hz) = 586.6 , THD= 3.86%
Mag (% of Fundamental)
0100 200 300400500 600 7008009001000
0
1
2
3
Frequency (Hz)
Fundamental (50Hz) = 557.9 , THD= 3.84%
FFT Analysis at RL load
FFT Analysis at R load
Mag (% of Fundamental)
FIGURE 7.13 FFT analysis of load terminal voltage at different cases for R and RL-load.
115Isolated Microhydropower Generation
values due to the variable nature of the turbine input, whereas the load frequency
is found to be maintained around the rated frequency except during switching of
D-STATCOM. Figure 7.11 shows that the D-STATCOM can regulate the terminal
voltage of the generator near the rated value. Charging and discharging actions of
the DC-link capacitor is observed, which shows the compensating aspect of the
D-STATCOM. The injection of D-STATCOM and generator currents for reactive
and active power compensation at different operating points are also depicted in
Figure 7.12. The %THDs for R load and RL-load with D-STATCOM are found to be
3.84% and 3.86%, respectively, as shown in Figure 7.13.
7.6.2 CaSe ii: performanCe of tHe propoSed mHpg
model under a non-linear load
In this case, the proposed VMHPG is tested for non-linear and unbalanced loads.
The transient waveforms of all the performance parameters with three-phase rec-
tier DC load and single-phase rectier load are shown in Figures 7.14–7.17. At
1.0 s, the three-phase rectier load with DC resistive load is connected, while the
D-STATCOM is switched ON from 1.2 to 1.4 s. The unbalanced rectier load is
connected at 1.5 s, while the D-STATCOM is switched ON again at 1.6 s. Figure 7.14
shows that the generator frequency and speed vary as per the turbine’s variation
while the load frequency is an observer to be maintained at around rated frequency
with slight uctuation during switching of D-STATCOM. As seen from Figure 7.15,
the voltage waveforms of the generator, load, and DC-link capacitance are found to
be quite stable. Slight overshoots and undershoots are observed in load voltage and
DC link voltage during switching of D-STATCOM, which get settled in a few cycles.
40
45
50
CAG frequency (Hz)
40
45
50
Load frequency (Hz)
00.20.40.60.8 1 1.21.41.61. 8 2
1000
1200
1400
1600
Time (Sec )
CAG Speed (rpm)
FIGURE 7.14 Proles of generator frequency, load frequency, and generator speed for Case-II.
116 Green Engineering and Technology
FIGURE 7.15 Waveforms of generated voltage, load terminal voltage, and DC-link voltage
for Case-II.
-20
0
20
Generator
current (A)
-20
0
20
Load Current
(A)
-20
0
20
D-STATCOM
R-ph current (A)
-20
0
20
D-STATCOM
Y-ph current (A)
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
-20
0
20
Time(Sec)
D-STATCOM
B-ph current (A)
FIGURE 7.16 Waveforms of various currents during different phases of operation for Case-II.
-1000
-500
0
500
1000
Generator Voltage (V)
-1000
-500
0
500
1000
Load Voltage (V)
0
200
400
600
800
1000
Time (sec)
DC link Voltage (V)
117Isolated Microhydropower Generation
The DC-link capacitor tries to maintain the DC load voltage constant, which leads
to changing D-STATCOM and generator currents to adjust the reactive and active
power requirements respectively as depicted in Figure 7.16. The %THDs of the load
voltage during three-phase rectier and unbalanced loads without D-STATCOM are
found to be 7.51% and 15.76% respectively whereas those with D-STATCOM are
found to be 4.12% and 4.61% respectively as shown in Figure 7.17. This shows that
the D-STATCOM also helps to improve the harmonics.
7.7 CONCLUSION
As depicted from the simulation results and Table 7.2, the developed three-phase
VMHPG model is capable of operating with satisfactory performance while feeding
linear and non-linear loads under transient conditions. The load frequency can be
maintained at the rated frequency by using a fuzzy PI-based AC–DC–AC converter.
The terminal voltage of the generator is compensated by fuzzy PI D-STATCOM
during different loading conditions and is maintained constant at rated values as
observed from the simulated results. When rectier loads are connected, harmon-
ics are generated, which are able to maintain within the permissible limits by using
fuzzy PI D-STATCOM. It is also observed that the fuzzy PI D-STATCOM helps in
balancing load but sensing all the generator currents, load currents, and the fuzzy
PI D-STATCOM currents. The use of fuzzy PI controllers limits the overshoot and
0 200
400 600 800 1000
0
2
4
6
8
Frequency (Hz)
Fundamental (50Hz) = 558.1 , THD = 4.61%
Mag (% of Fundamental)
0 200 400 600 800 1000
0
5
10
15
Frequency (Hz)
Fundamental (50Hz) = 480.9 , THD = 15.76%
FFT analysis for Single phase rectifier load without D-STATCOM FFT analysis for Single phase rectifier load without D-STATCOM
FFT analysis of 3-ph rectifier load without D-STATCO
MF
FT analysis of 3-ph rectifier load without D-STATCOM
Mag (% of Fundamental)
0 200 400 600 800 1000
0
1
2
3
4
5
Frequency (Hz)
Fundamental (50Hz) = 337.6 , THD = 7.56%
Mag (% of Fundamental)
0 200 400 600 800 1000
0
0.5
1
1.5
Frequency (Hz)
Fundamental (50Hz) = 532.5 , THD = 4.12%
Mag (% of Fundamental
)
FIGURE 7.17 FFT analysis of load terminal voltage at different cases for three-phase and
single-phase rectier loads.
118 Green Engineering and Technology
undershoot of the waveforms and also makes the system more robust. Hence, it can
be concluded that the fuzzy PI D-STATCOM acts as a voltage compensator, load
balancer, and harmonic reducer. Similar studies can be extended for a multimachine
system and other intelligent techniques may be implemented to optimize its perfor-
mance. Therefore, in all, the proposed VMHPG system performs satisfactorily as
depicted from the simulation results.
APPENDIX
MACHINE PARAMETERS:
Squirrel Case Induction Machine: 7.5 kW, 3 phase, 415 V, 29 A, 50 Hz, Y-Connected,
4-Pole, Rs = 0 .9 Ω, Rr = 0.6 6 Ω, X = X = 0.00 457 H and J = 0.138 4 k gm 2
ls lr
PRIME MOVER CHARACTERISTICS:
Tf
mr
=−
τω
0()Q⋅
where
τ
0 = 1242,
f(Q) varies with the varying ow rate.
GENERATOR SIDE CONTROLLER PARAMETERS:
DC voltage regulator: Kp = 0 .15, Ki = 0.0002
Current regulator: Kp = 0.6, Ki= 0.009
LOAD SIDE CONTROLLER PARAMETERS:
Kp = 0.38, KI = 0.0002
TABLE 7. 2
Summary of Performance Analysis of the VMHPG System
Load RMS Voltage (V) %THD of Load Voltage
Frequency Without With Without With
Type of Load (Hz) D-STATCOM D-STATCOM D-STATCOM D-STATCOM
R 50 398.2 582.7 5.46 3.86
RL 50 387.4 586.3 5.37 3.84
3-ph rectier 50 407.4 586.4 7.56 4.12
1-ph rectier 50 401.7 587.2 15.76 4.71
Performance Parameters during Loading
119Isolated Microhydropower Generation
FILTER PARAMETERS:
Generator side: R = 5 Ω, L = 40 mH, C = 160 μF
Load side: R = 1.1 3 Ω, L = 22 mH, C = 30 0 μF
DC-LINK CAPACITOR:
C = 10,000 μF, Vdc = 38 0 V
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121
8Phytoconstituents
of Common Weeds
of Uttarakhand Proposed
as Bio-pesticides
or Green Pesticides with
the Use of In-Silico and
In-Vitro Techniques
Somya Sinha, Kumud Pant,
Manoj Pal, and Devvret Verma
Graphic Era (Deemed to be) University
Ashutosh Mishra
Uttarakhand Council of Science and Technology
CONTENTS
8.1 Introduction .................................................................................................. 122
8.2 In-Vitro Methodology ................................................................................... 123
8.2.1 Plant Sample Collection.................................................................... 123
8.2.2 Aqueous Extract Preparation ............................................................ 123
8.2.3 Phytoconstituent Analysis ................................................................123
8.2.4 Assessment for Alkaloids ................................................................. 123
8.2.5 Test for Steroids and Sterols .............................................................124
8.2.6 Assessment of Anthraquinones ........................................................124
8.2.7 Assessment of Flavonoids .................................................................124
8.2.8 Assessment of Saponins ................................................................... 124
8.3 In-Silico Methodology .................................................................................. 124
8.3.1 Sequence Retrieval ...........................................................................124
8.3.2 Comparative Modeling ..................................................................... 124
8.3.3 Evaluation of the Model ....................................................................125
122 Green Engineering and Technology
8.1 INTRODUCTION
The biochemical pesticides are the major outcome of human activities and agriculture
that generally remains as the disseminated ecological contaminants and stay open to
strict regulations, which helps in safeguarding the biome and well-being of humans
in India, USA, Europe, and other countries of the world.1 Disparity in the biochemi-
cal structures of these insect repellents illustrates the sub-families of the pesticides,
insecticides, herbicides, and antifungal agents that are clustered together according to
the disastrous action.2 The pesticides, herbicides, insecticides, and fungicides have an
enormous possibility of exercising detrimental impacts on humans, animals, birds,
and other living organisms through different modes of inhalation, consumption of
food, breathing, and skin contact.3 They are of the origin for the turmoil of the bio-
diversity of the atmosphere. Subsequently, dened realizable variations may take
place on exposure such as hematological disorders, malignancy, respiration problem,
and impairment of the reproductive body parts. Diverse modes of entrance points
and the efcacy of interaction of pesticides with the actual target protein have been
taken into reection. Pesticides, chiey organophosphorus, are the utmost used pes-
ticides in the world with applications extending from marketable to home-grown use
and agronomic use for governing disagreeable bug’s population.4 Organophosphate
pesticides stay lethal as they possess the capability of xing to inhibit physiological
enzymes like glutathione S-transferases, acetylcholinesterase (AChE), protein kinase
C, and cytochrome P450, triggering neurotoxicity among the population.5,6 Existence
of AChE protein in innumerable mammals, insects, ora and fauna could give a clas-
sication of pesticides noxiousness in the direction of the unintended target for the
dislocation of the metabolic gastral system in anthropoid.5,7 Likewise, insecticides,
namely pyrethroids,6 are commonly utilized as they are formed from the owers of
Chrysanthemum cinerariaefolium that shows their deadly properties in contrast to the
bed bugs.8 These insecticides consist of cypermethrin, α-cyano-3-phenoxybenzyl, and
cyuthrin9 that are lethal for gadies, bees, and the other craniates. They pose a huge
riddle to the marine organism (for instance, shes) at fairly minuscule levels.
Wildowers are principally the plants that generally grow in erroneous areas like
farm elds, pools, lawns, and so on.10 Most of the bellicose species or non-native
classes are identied that show insecticidal and pesticidal effects against numerous
pest inhabitants. Among the non-native kinds of species of Uttarakhand Parthenium
hysterophorus,11 Alternanthera sessilis,12 and Lantana camara,13 shown in Figure1.1,
and several more are revealed to exhibit insect repellent properties in contradiction of
various insect inhabitants.14,15 Various reports propose that L. camara leaves turn to
8.3.4 Construction of a Ligand 126 ..................................................................
8.3.5 Preparation of Protein 126.......................................................................
8.3.6 Molecular Interaction 129 .......................................................................
8.3.7 Toxicity Examination 129........................................................................
8.3.8 Docking Analysis 129 .............................................................................
8.4 Conclusion 133....................................................................................................
Acknowledgment 133...................................................................................................
References 134..............................................................................................................
123Phytoconstituents of Common Weeds
be a possible insect repellent for the supervision of the stored grain insect pests.16,17,18
Assessment of the inherent oil properties of the leaf of L. camara has been achieved
that showed its insecticidal part.12,19, 20 P. hysterophorus has also been used against
Callosobruchus chinensis on chickpea.21,22
This chapter focusses on the target AChE, which is a signicant enzyme exist-
ing in the bug’s nervous structure that will pass the neurotransmitter into acetic acid
and choline, which results in the ending of nerve impulse;23,24 numerous reports and
papers we endowed says that this AChE will turn out to be a target for the carba-
mates and organophosphates.25 Hence, these insect repellents are the source of major
menace to insects as they have the aptness to disable the enzyme’s catalytic center.5
Consequently, these insect repellents x to the active site of the protein and cause
the suppression of its activity by phosphorylating serine, which is a polar amino acid
residue in the bug’s catalytic center important in plant development in addition to
directing into the swift quavering of the muscles and recurrent dismissal of the elec-
tric signals ensuing in the death.26
Moreover, the computational approach employs interaction studies of the pesti-
cides with the target AChE protein along with the phytochemical constituents taken
from the common weeds.
8.2 IN-VITRO METHODOLOGY
8.2.1 plant Sample ColleCtion
Leaves from the given plants P. hysterophorus, A. sessilis, and L. camara should be
collected. The collected leaves need to be thoroughly cleaned to shed dry and pulver-
ized to a powdered form using a blender for additional use.
8.2.2 aqueouS extraCt preparation
The aqueous extract was prepared by dissolving powdered leaves in distilled water.
The mixture was heated on a hot plate with continuous agitation. Then the water
extract was sieved through lter paper. The ltrate was kept in a beaker and allowed
to dry by heating in a water bath. The adhesive residue obtained was used for the
evaluation of the percentage yield, the remaining marc left and the behavior of the
leaf powder was extracted with water and used for the qualitative analysis.
8.2.3 pHytoConStituent analySiS
The extracts are examined for the occurrence of biologically active complexes by
implementing standardized measures and actions of the drug powder with dissimilar
chemical reagents.
8.2.4 aSSeSSment for alkaloidS
Wagner’s test is performed for the extract by adding a few drops of iodine solution
in potassium iodide. The appearance of a reddish-brown precipitate will show the
presence of alkaloids.
124 Green Engineering and Technology
8.2.5 teSt for SteroidS and SterolS
The Salkowski test is performed by adding a few drops of chloroform and concen-
trated sulphuric acid to the extract. The turning of the bluish red color to cherry red
will indicate the presence of steroids and sterol.
8.2.6 aSSeSSment of antHraquinoneS
Borntrager’s test is carried out in which the extract is boiled upon addition of dilute
sulphuric acid and is ltered and to the benzene that is being ltered, chloroform is
added and is properly shaken, after which the biological layer is detached to which
ammonia is cautiously added.
8.2.7 aSSeSSment of flavonoidS
To the crude stock extract, a few drops of dilute sodium hydroxide are added. The
yellow color that appears in the plant extract will become colorless after the addition
of a few drops of diluted acid, which will indicate the existence of avonoids.
8.2.8 aSSeSSment of SaponinS
For testing the presence of saponins, the extract is taken in a test tube and then
diluted with distilled water after which shaking is performed; after shaking, the
appearance of a foam layer on the top of the test tube will indicate the presence
of saponins.
8.3 IN-SILICO METHODOLOGY
8.3.1 S equenCe retrieval
A three-dimensional structure of the studied pesticides was obtained from
Research Collaboratory through structure Bioinformatics, Protein Data Bank
(PDB) accessible at www.rcsb.org.27 Owing to the inaccessibility of the three-
dimensional structure for the target protein AChE of the beetles and aphids in
the protein data bank, FASTA sequences containing AChE of Rhopalosiphum
padi padi, Myzus persicae, Acyrthosiphon pisum aphids and Onthophagus tau-
rus, Leptinotarsa decemlineata, Agrilus planipennis beetles remained stood
from protein database of NCBI that is accessible at www.ncbi.nlm.nih.gov as seen
in Table 8.3.1.
8.3.2 C omparative modeling
AChE sequences of the pests for beetles and aphids were taken in FASTA format
from the protein database of NCBI accessible at www.ncbi.nlm.nih.gov.in, and the
three-dimensional structure of these pests was made as specied in Table 8.3.2
125Phytoconstituents of Common Weeds
with the aid of SWISS-MODEL server28,29 for the forecasting of the formation of
protein structure, as shown in Figure 8.3.1,30,31 which depicts the structure of pests.
8.3.3 evaluation of tHe model
A comprehensive model of the AChE protein of beetles and aphids was generated
by means of SWISS-MODEL.32 Various parameters and algorithms were used to
access the models. Validation was performed to check the conformation through
a Raman plot obtained by a structure analysis and verication server. Evaluation
of the stereochemical quality of the protein structure was performed through
TABLE 8.3.1
Description of the Reviewed Protein
Group Organism Entry ID Enormousness
Coleopteran Beetle Onthophagus taurus XP_022919632 630 aa
Agrilus planipennis XP_018327670 633 aa
Leptinotarsa decemlineata AAB00466 629 aa
Aphidoidea Aphids Myzus persicae XP_022160417 822 aa
Acyrthosiphon pisum XP_029344443 663 aa
Rhopalosiphum padi AII01418 672aa
TABLE 8.3.2
SCF-BIO Toxicological Evaluation of Phytoconstituents
S. No. Phytoconstituents
Molecular
Mass
Hydrogen
Bond
Donor
Hydrogen
Bond
Acceptor LOGP Refractivity
1. Lantadene A 552 1 5 7.9297 156.375931
2. γ-Gurjenone 204 0 0 4.5811 66.672981
3. β-Sitosterol 414 1 1 8.0248 128.216751
4. Parthenin 262 1 4 1.3904 68.111786
5. Caffeic acid 312 5 6 −0.0531 77.145782
6. β-Caryophyllene 204 0 0 4.7252 66.742981
7. Kaempferol 286 4 6 2.3053 72.385681
8. Campesterol 400 1 1 7.6347 123.599747
9. Stigmasterol 426 1 1 8.0248 130.64975
10. Lupeol 426 1 1 8.0248 130.64975
11. Germacrene D 204 0 0 4.7252 66.742981
12. Valencene 204 0 0 4.7252 66.742981
LOGP, lipophilicity
126 Green Engineering and Technology
FIGURE 8.3.1 Three-dimensional structure of the modeled protein structure.
PROCHECK by validating and analyzing the geometry of the structure residue by
residue available at https://servicesn. mbi.ucla.edu/SAVES/33 as illustrated in Table
8.3.3 that shows the identity of the protein.
8.3.4 ConStruCtion of a ligand
Phytoconstituents from the communal weeds were designed in the ligand form.
PubChem was used to download phytocompounds in the .sdf format and they are
changed to the Protein Data Bank format by Open Bable.34 Open Bable is a software
that converts the le form in regard to the phytocompounds.34 The synthetic pes-
ticides that are recognized diazinon, methamidophos, and cypermethrin remained
for the study.35,36 Lipinski’s lter was used to test the toxicity of the compounds as
specied in Table 8.3.4 that depicts the toxicity potential of the pesticides and the
phytoconstituents.
8.3.5 preparation of protein
FASTA sequences for the pest’s AChE protein have been obtained from the National
Centre for Biotechnology Information protein database (https://www.ncbi.nlm.nih.gov/
protein) and those were taken in SWISS-MODEL, which is a web server for homology
modeling and protein structure.28,31 The protein that is modeled was taken in the .pdb
format from SWISS-MODEL, which was further taken for supplementary work.
127Phytoconstituents of Common Weeds
TABLE 8.3.3
Pesticides and Phytoconstituent Analysis Through ADMET
Complexes
Aqueous
Solubility
Estimation
Class Value of log S
Silicos-IT
Class
Gastrointestinal
Absorption
Blood–Brain
Barrier
Pgp
Substrate
Cytochrome
P450 3A4
Inhibitor
Protox-II Toxicity
and Class
Diazinon Soluble Moderately
soluble
Soluble Extortionate Not at all Not at all Not at all Lethal dose 50–17 mg kg−1,
Class-2
Cypermethrin Poorly
soluble
Poorly soluble Poorly
soluble
Extortionate Not at all Not at all Absolutely
yes
Lethal dose 50–25 mg kg−1,
Class-2
Methamidophos Very soluble Very soluble Soluble Extortionate Not at all Not at all Not at all Lethal dose 50–8 mg kg−1,
Class-2
Lantadene A Poorly
soluble
Poorly soluble Poorly
soluble
Low-slung Not at all Yes Not at all Lethal dose 50–79 mg kg−1,
Class-2
Gamma
gurjenone
Moderately
soluble
Moderately
soluble
Soluble Low-slung Not at all Not at all Not at all Lethal dose 50–5000 mg
kg−1, Class-5
Parthenin Very soluble Very soluble Soluble Extortionate Absolutely
yes
Not at all Not at all Lethal dose 50–125 mg
kg−1, Class-3
Βeta
Caryophyllene
Soluble Moderately
soluble
Soluble Low-slung Not at all Not at all Not at all Lethal dose 50–5000 mg
kg−1, Class-5
Kaempferol Soluble Soluble Soluble Extortionate Not at all Not at all Absolutely
yes
Lethal dose 50–3919 mg
kg−1, Class-4
(Continued )
128 Green Engineering and Technology
TABLE 8.3.3 (Continued )
Pesticides and Phytoconstituent Analysis Through ADMET
Complexes
Aqueous
Solubility
Estimation
Class Value of log S
Silicos-IT
Class
Gastrointestinal
Absorption
Blood–Brain
Barrier
Pgp
Substrate
Cytochrome
P450 3A4
Inhibitor
Protox-II Toxicity
and Class
Beta-Sitosterol Poorly
soluble
Poorly soluble Poorly
soluble
Low-slung Not at all Not at all Not at all Lethal dose 50–890 mg
kg−1, Class-4
Valencene Moderately
soluble
Moderately
soluble
Soluble Low-slung Not at all Not at all Not at all Lethal dose 50–5000 mg
kg−1, Class-5
Stigmasterol Poorly
soluble
Poorly soluble Moderately
soluble
Low-slung Not at all Not at all Not at all Lethal dose 50–890 mg
kg−1, Class-4
Lupeol Poorly
soluble
Insoluble Poorly
soluble
Low-slung Not at all Not at all Not at all Lethal dose 50–2000 mg
kg−1, Class-4
Caffeic acid Very soluble Soluble Soluble Extortionate Not at all Not at all Not at all Lethal dose 50–2980 mg
kg−1, Class-5
Campesterol Poorly
soluble
Poorly soluble Moderately
soluble
Low-slung Not at all Not at all Not at all Lethal dose 50–890 mg
kg−1, Class-4
Germacrene-D Moderately
soluble
Moderately
soluble
Soluble Low-slung Not at all Not at all Not at all Lethal dose 50–5000 mg
kg−1, Class-5
129Phytoconstituents of Common Weeds
8.3.6 moleCular interaCtion
Studies stood xed out with iGEMDOCK, which exists as an automatic system for
drugs basically pre-owned for docking, broadcasting, and analysis of the positions
that are docked.37 This software permits the uploading of the ligands and the protein
target that will generate the interaction prole.38 The exploration table comprises
detailed evidence portraying the free Energy as illustrated in Table 8.3.5. LigPlus and
PyMol were used to create the most favorable docked pose with the target protein as
illustrated in Figure 8.3.239,40 that shows the interaction of the phytoconstituents with
the receptor protein.
8.3.7 toxiCity examination
SWISS-ADME is used to check the “dispersal” “absorption” “excretion” and char-
acteristics in humans. It usually measures the pharmacology and pharmacokinet-
ics and also denes the nature of a drug or a chemical inside a plant, virus, or an
animal. This is gained using http:// www.swissadme.ch/. ProTox server is used to
check the toxicity of the phytoconstituents accessible at http://tox.charite.de/ pro-
tox_II/ given in Table 8.3.441 aimed for the investigation of the toxicity potential of
the phytoconstituents.
8.3.8 doCking analySiS
The studies were conducted considering the phytoconstituents as the ligand fragments
and the bugs taken as a constructive switch for learning. The phytoconstituent present
in P. hysterophorus was kaempferol, which showed the least binding attraction with
AChE (M. persicae), AChE (R. padi), and AChE (A. pisum), i.e., −92.4516, −102.38,
and −98.64 kilocalories per mol. Therefore, the synthetic pesticides comprising meth-
amidophos, diazinon, and cypermethrin were cast-off contrary to the phytoconstitu-
ents that exhibited lesser binding afnity when distinguished with kaempferol and
the additional phytoconstituents, which were −90.9694, −91.3296, and −90.9694 kcal
mol−1, as shown in Table 8.3.5.
The principle phytoconstituent present in L. camara is Lantadene A, which on
interaction with the target protein AChE of O. taurus and L. decemlineata showed
minimum energies of −87.4555 kcal mol−1 and −98.6 kcal mol−1.42 The key compo-
nent present in A. sessilis is stigmasterol that has shown the least binding afnity with
TABLE 8.3.4
SAVESv5.0 Server Protein Analysis
AChE AChE AChE AChE AChE AChE
A. planipennis O. taurus L. decemlineata M. persicae R. padi A. pisum
Template 1qo9.1. A 5hcu.1. A 1qo9.1. A 1qo9.1. A 6ary.1. A 6arx.1. A
Sequence
Identity
64.84 % 43.05 % 61.27 % 57.33 % 64.38 % 42.86 %
130 Green Engineering and Technology
TABLE 8.3.5
Interaction Energy of the Phytoconstituents and Synthetic Pesticides with AChE Protein in kcal mol−1
AChE AChE AChE AChE AChE AChE
S. No. Phytoconstituents (A. planipennis) (O. taurus) (L. decemlineata) (M. persicae) (R. padi ) (A. pisum)
1. Methamidophos −57.9 −48.3491 −58.6 −48.5813 −54.6947 −52.4783
2. Diazinon −80.6 −77.7962 −84 −77.3719 −81.3328 −88.0724
3. Cypermethrin −91.7 −87.4555 −98.6 −90.9694 −91.3296 −96.959
4. Lupeol −93.45 −84.82 −81.7 −89.41 −95.4344 −75.69
5. Gamma gurjenone −67.0385 −64.1266 −63.8858 −66.18 −64.5 −66.32
6. Lantadene A −86.92 −90.9896 −99.3161 −85.61 −90.65 −85.74
7. Germacrene-D −77.6 −76.17 −75.5528 −73.7 −64.66 −76.6688
8. Parthenin −85.17 −75.3631 −83.9 −79.08 −89.9 −80.53
9. Caffeic acid −75.732 −79.59 −74.8889 −76.3779 −75.4162 −80.28
10. Beta-caryophyllene −65.4154 −65.43 −65.4517 −63.1982 −62.3067 −66.79
11. Kaempferol −97.45 −88.89 −89.4697 −92.4516 −102.38 −98.64
12. Stigmasterol −100.47 −87.09 −92.1 −87.58 −82.3 −77.4695
13. Beta sitosterol −97.7277 −86.9 −98.0451 −82.1903 −83.56 −94.8527
14. Valencene −71.04 −69.61 −69.1 −67.68 −71.03 −66.0873
15. Campestrol −86.3941 −83.83 −78.1968 −80.93 −80.9 −81.01
131Phytoconstituents of Common Weeds
FIGURE 8.3.2 Interaction poses of the possible herbicides with the known target protein.
132 Green Engineering and Technology
FIGURE 8.3.3 Structural quality of the studied proteins through Ramachandran plot.
(Continued )
133Phytoconstituents of Common Weeds
FIGURE 8.3.3 (CONTINUED) Structural quality of the studied proteins through
Ramachandran plot.
AChE protein of A. planipennis, i.e., −100.47 kcal mol−1. However, the pesticides that
were considered for the study showed very little interaction energy in contradiction
with the phytoconstituents on interactivity by the AChE protein of A. planipennis as
given in Table 8.3.5 illustrating the docking energies with the receptor protein.
8.4 CONCLUSION
Comparative modeled structures, interaction studies, pernicious analysis, and ADMET
have revealed good outcomes. Among the known phytoconstituents, the weed phy-
toconstituents can prove to be an effective bio-pesticide. Similar to Lantadene A,
kaempferol and stigmasterol showed minimal interaction afnity with AChE protein
of the identied coleopteran beetles and sap-sucking insects. Hence, the aforemen-
tioned phytoconstituents could be well-thought-out means for the forthcoming studies
that are intended for the complete eradication of the noxious pests.
ACKNOWLEDGMENT
The authors are grateful to the Department of Biotechnology and Department of
Life Sciences Graphic Era Deemed to be University for giving us proper direction in
completing this study.
134 Green Engineering and Technology
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137
9On Energy Harvesting
in Green Cognitive
Radio Networks
Avik Banerjee
Madanapalle Institute of Technology and Science
Santi P. Maity
Indian Institute of Engineering Science and Technology
9.1 INTRODUCTION
Recently, green communications emerge as an innovative research area with several
radio networking solutions to cater to the diverse applications in 5G (Khoshabi Nobar
et al.2016). An increase in the demand for wireless broadband services and data-
intensive applications meeting the diverse quality of service (QoS) requirements lead
to the scarcity of the traditional two communication resources, spectrum bandwidth
and power (Khoshabi Nobar et al. 2016, Li et al. 2018, Li et al. 2015, Zou et al. 2016).
Recent reports state that an estimate of 2% of global carbon dioxide (CO2) emission
and 3% of the global energy consumption is generated from the information and
communication technology. It was also predicted that by the year 2020, about 20
billion wireless nodes are to be connected worldwide and approximately 320 million
tons of carbon emission would be from the mobile networks and connected infra-
structure, of which 50% is expected to be emitted from mobile transmission (Cao et
al. 2018). This growing concern in energy consumption is due to the unprecedented
usage of wireless devices leading to the scarcity in the radio spectrum and its pol-
lution as data congestion while creating an impact on the global heating and carbon
emission in the physical environment.
CONTENTS
9.1 Introduction 137 ..................................................................................................
9.2 Literature Review 139.........................................................................................
9.3 System Model 140...............................................................................................
9.4 Energy Harvesting and Secondary Data Transmission 143 ................................
9.5 Mathematical Solution to Throughput Maximization 145..................................
9.6 Numerical Results 146.........................................................................................
9.7 Conclusions and the Scope of Future Work 149..................................................
References 149..............................................................................................................
138 Green Engineering and Technology
The primary cause of radiofrequency spectrum scarcity is the current static mode
of its allocation showing its inefcient and underutilization reported by the studies of
the several research groups and regulatory bodies like the Federal Communications
Commission (FCC 2002) and Ofce of Communications (OFCOM 2007). To address
the spectrum scarcity issue, a concept called the “cognitive radio” (CR) emerges where
a wireless node, before data transmission, senses the surrounding radio environment
and accordingly changes its data transmission and reception to make it adaptive to
the available radio resources (Haykin 2005). In other words, a secondary user (SU)
or a CR node senses the licensed/primary user (PU) spectrum (bandwidth), nds the
spectrum hole, and transmits data opportunistically without creating any interference
to PU data transmission. This mode of spectrum access is called “interweave” and
was the origination of the CR concept. In due time, there have been other modes of
spectrum sharing reported, namely, underlay where PU and SU share the spectrum
simultaneously with a compulsion from SUs to meet a target interference threshold
to the PU receiver. An overlay mode is one where the PU and SU share the spectrum
in time slots, i.e., SU avails an exclusive data transmission slot in exchange of its co-
operation in the PU data transmission.
Wireless nodes are mostly battery-driven and they need provisioning of recharg-
ing or replacement leading to the problem of limited network lifetime. The issues of
the network lifetime and mutual multiple access interferences among the wireless
nodes are the driving forces for the low-power energy-efcient wireless communica-
tion system design over the last two decades. The trend in reduction in energy con-
sumption motivates to design an energy-efcient “Green” communication (Khoshabi
Nobar et al. 2016). Thus, on one hand, cognitive radio networks (CRNs) (Khoshabi
Nobar et al. 2016, Li et al. 2018, Li et al. 2015, Zou et al. 2016) promise to overcome
the spectrum scarcity problem, and, on the other hand, the issue of energy crisis can
be addressed through energy harvesting (EH). Hence, EH-CRNs are promising for
future wireless communications wherein a SU or CR node opportunistically accesses
the spectrum holes of the PU, with the enhanced network lifetime through EH lead-
ing to green communication technologies (Zou et al. 2016, Pratibha et al. 2015, Lu
et al. 2014, Yin et al. 2013). CR is green because it manages the scarce resource “radio
spectrum” in the consumer society and addresses the problems of having an impact
on the environment: energy efciency, energy savings, electromagnetic radiation,
pollution, and so on.
Spectrum sensing (SS) and SU data transmission are the two key operations
in CRN. Cooperative spectrum sensing (CSS) involves several SUs to participate
in the PU sample acquisition and then either forwards their local sensing deci-
sion (hard decision) or sends the sensed samples of the PU signals to the fusion
center (FC) (Banerjee et al. 2019). CSS overcomes data collision with PU and
fast spectrum access opportunity by the secondary nodes at the cost of the higher
energy consumption (Bhowmick et al. 2016) through the involvement of the sev-
eral SU nodes in SS operation. Energy consumption also occurs due to SU data
transmission. As mentioned earlier, these wireless nodes, in general, are battery-
driven. Hence, one critical design issue is to conserve the energy for enhancing
the network lifetime (Banerjee and Maity 2019). In this practical scenario, EH
enhances the battery lifetime of the wireless nodes (minimizes hardware failure)
139Energy Harvesting in Green Radio Networks
and overcomes the problem of periodic battery replacement offering a sustain-
able green communication system (Zou et al. 2016, Pratibha et al. 2015, Lu et al.
2014, Yin et al. 2013). EH can be accomplished either from RF signals (Pratibha
et al. 2015, Lu et al. 2014, Yin etal. 2013, Banerjee et al. 2019, Bhowmick et al.
2016) or renewable sources such as solar, thermal, wind, and so on (Dondi et al.
2008). Since electromagnetic waves have the potential to carry both power and
information, recent literature explores the simultaneous wireless information and
power transfer (SWIPT) technology (Liet al. 2015) as a potential means of EH.
SWIPT-enabled CR node operating in power splitting (PS) mode (Banerjee et al.
2019, Banerjee and Maity 2019) splits the received RF signal with an adjustable
power to enable SS (to know about the PU spectrum state) and EH operation to
work simultaneously. Another mode of EH operation in CRN is the time switch-
ing (Banerjee and Maity 2019). Here the SS and EH operations are carried out
in non-overlapping slots with a full proportion of the received RF signal used
in individual work and is the research issue of this chapter. This study aimed
to maximize the SU throughput using the harvested energy. The organization
of the chapter is as follows: literature review on EH-CRN is made in Section
9.2. Section 9.3 then briey introduces an “EH-CRN” system model. Section 9.4
discusses EH and secondary data transmission while the mathematical analysis
in throughput maximization is performed in Section 9.5. Simulation results are
presented in Section 9.6 while conclusions and scope of future works are reported
in Section 9.7.
9.2 LITERATURE REVIEW
The literature on EH-CRN is rich (Li et al. 2015, Zou et al. 2016, Pratibha et al. 2015,
Lu et al. 2014, Yin et al. 2013, Banerjee et al. 2019, Bhowmick et al. 2016). Li et al.
(2015) developed the SWIPT technology in CRNs, and the optimal power allocation
and PS ratio are calculated analytically to maximize the energy efciency of the SUs.
In the study of Zou et al. (2016), a priority-based sleeping scheduling algorithm for
the sensing nodes is reported in the framework of EH-CSS while maintaining the
primary and secondary data collision and energy causality constraints. An optimal
SS policy was developed in the work of Pratibha et al. (2015) along with the through-
put maximization of the SU under PU collision and energy causality constraints. Lu
et al. (2014) considered a two-way relay-based CRN using the SWIPT technology
where the relays harvest energy from the received signals for assisting the informa-
tion exchange process between the two secondary nodes. Yin et al. (2013) consid-
ered a typical frame structure that consists of three non-overlapping time frames for
sensing, harvesting, and data transmission purposes. An optimization framework is
developed to maintain a target throughput in a mixed-integer non-linear program-
ming framework and a differential evolution algorithm is used to derive an optimal
sensing strategy. In the study of Banerjee et al. (2019), a sum secondary throughput
maximization problem is formulated under the constraints of SS reliability, energy
causality, PU cooperation data rate, SU outage probability, the sum, and the indi-
vidual SU secrecy outage probability. An optimal SS time is found in the study of
Bhowmick et al. (2016) to maximize the harvested energy and the performance is
140 Green Engineering and Technology
investigated in terms of harvested energy by the SU, SU data outage, and throughput
of the CRN. The optimization problem also considers the SS time duration, the num-
ber of time frames, and data collision probability as design variables.
The above literature review highlights the importance of EH in SS, maintaining
the target throughput during data transmission and enhancing network lifetime to
develop a sustainable green CRN. To this aim, this work proposes a typical frame
structure for green CRN consisting of two non-overlapping time slots, one for CSS
and the other for data transmission or EH (based on the CSS decision) purposes. The
system model includes a PU, K number of SU nodes, a single secondary receiver, and
an FC. In the rst time slot, i.e., during SS, each SU senses the signal samples of PU
and after that amplies the individual received signals and forwards them to the FC.
FC then uses the energy detection (ED) technique to identify the transmission state
of the PU. Secondary data transmission between a single selected SU transmitter
and receiver pair occurs if the CSS decision goes in favor of the non-transmission
state of the PU. Now if PU is found to be transmitted in that particular time slot, then
all the SUs harvest energy from the interfering signal of the PU. An optimization
framework to maximize the sum SU throughput is formulated while maintaining
SS reliability and energy causality constraints. The energy causality implies that the
required energy for SS, reporting, and data transmission by the SU be met from the
harvested energy. The optimal values of SS duration and the fraction of the EH used
for data transmission are derived. The research outcomes of this work can be sum-
marized as follows:
• SU throughput is shown to attain a global maximum value at some values
of the sensing duration and fraction of the energy harvested used in data
transmission.
• Increase in the number of cooperative users enhances the secondary
throughput value.
• A performance gain on ~12.68% in secondary throughput over the existing
work is shown through simulation results.
9.3 SYSTEM MODEL
Figure 9.1 shows a simple CR system model that consists of a primary transmitter
(PUT)–receiver pair (PUR), K number of SUs, and an FC. Depending on the demand
and the instantaneous channel fading condition, a selected secondary transmitter
(SUT) (among the K number of SUs) communicates with its intended destination
(SUR) as shown in Figure 9.1. The data transmission between one SUT and SUR pair
is considered in our analysis. A list of symbols are given in Table 9.1.
The frame structure, shown in Figure 9.2, consists of three non-overlapping time
slots, i.e., sensing, reporting, and data transmission or EH. During the rst slot of
duration ατ, all SUs sense the received signal of PU and forward the same to the
FC using amplifying gain β. The duration of the reporting slot is kept (1 − α) τ.
Intentionally, the durations of the sensing and the reporting are kept unequal and
is dependent on α (α can be a design parameter; however, its optimal value is not
determined here). FC combines all the received signals and makes a resultant signal
141Energy Harvesting in Green Radio Networks
TABLE 9.1
List of Symbols
Symbols Description
NTotal number of samples
• KTotal number of Secondary users
• TfTotal frame duration
• ΑFraction of the time slot for spectrum sensing
• ΤSensing time duration
• fsSampling frequency
ΦBinary indicator variable for PU’s transmission and non-transmission phase
P(H1), P(H0) Probability for PU’s transmission and non-transmission state
Pdet, Pfa Detection, false alarm probability of PU
xpr(n) PU transmitted signal
• PpPU transmitted power
• βAmplifying the gain factor of SUi for reporting PU signal sample to the fusion center
ΛFraction of the harvested energy used for SU data transmission
• ηEnergy conversion efciency of the harvester circuit
hpri, hrci, hsd Fading gains of the channels between PU and SUi, SUi and FC, and SUT and SUR
dpri, drci, dsd Distance between PU and SUi, SUi and FC, and SUT and SUR
ψ
pri
ψψ
,
rci ,
sd
Link loss exponent between PU and SUi, SUi and FC, and SUT and SUR
vri(n), vc(n) Circularly symmetric complex Gaussian noise at SUi and noise at FC
2 2 2
σns , σc, σnd Noise variance at the receiver of SUT, FC, and SUR
γ Detection threshold for cooperative spectrum sensing
based on which a global decision of the transmission or non-transmission state of PU
is determined using ED. Depending on the CSS decision, SUs perform EH (if PU is
found to be in the transmitting state) or a selected SU transmits its own data to its
intended destination (if CSS decision goes in favor of the non-transmission state of
FIGURE 9.1 System model: green CRN model.
142 Green Engineering and Technology
FIGURE 9.2 Frame structure.
the PU). Although all SUs perform EH from the interference signal of the PU, yet
the amount of energy harvested by that selected SU is considered in the calculation
of the residual energy. Since other SUs are not participating in the data transmission
process, energy causality by the selected SU is maintained only if the harvested
energy of the latter is sufcient enough to meet the power required for its own data
transmission.
a. Received signal and channel modeling: During ατ, the signal received by
the ith SU is mentioned below
yn
ri ()=+
φ
hx
priprr
()nv
i()n (9.1)
where n = 1 to N = ατfs. The symbol fs denotes the sampling frequency. The
symbol ‘N’ denotes the total number of the samples and α denotes the frac-
tion of duration for SS. The parameters P (H1) = P(ϕ = 1) and P(H0) = P(ϕ = 0)
denote the stationary probabilities of PU’s transmission and non-transmis-
sion states. The PU signal is circularly symmetric complex phase-shift key-
ing modulated and is represented as xpr(n) with 0 mean and variance Pp, i.e.,
E[|xpr(n)|2] = Pp. PU transmitted power is denoted by the symbol ‘Pp’. The
symbol vri(n) denotes the circularly symmetric complex Gaussian (CSCG)
noise at SUi and is assumed to be independent and identically distributed
random process with zero (0) mean and variance E[|vri(n)|2] = σ2
ns .
The combined signal at FC, during (1 − α)τ, can be represented as follows:
K
Yci
()nh=+
∑
β
rcir
yn
ic
() vn() (9.2)
i=1
vc(n) denotes the CSCG noise at the receiver of FC with zero mean and
variance E[|v(n)|2] = σ2
c c . The wireless channels (i.e., both the sensing and
the reporting channels) are modeled as CSCG Rayleigh at fading chan-
nels, where the symbols hpri and hrci denote the at fading coefcients of the
links between PU to SUi and SUi to FC, respectively. The fading coefcients
statistics are given as hpri ~ CN(0, d−
ψ
pri
pri ) and hrc ~ CN ,
ψ
i(0 d−rci
rci). The path loss
exponents for the links between PU to SUi and SUi to FC are denoted by
ψ
pri
and
ψ
rci. The symbols dpri and drci indicate the distance from PU to SUi and
SUi to FC, respectively.
143Energy Harvesting in Green Radio Networks
b. Cooperative SS: FC uses an energy detector to perform CSS on the com-
bined signal Yc(n). The test statistics (Tst) is given by
ατ
=∑
fs
TY()n2
st c (9.3)
n=1
Using the central limit theorem, for a sufciently large number of N, ‘Tst’
follows a Gaussian distribution under both the hypotheses H1 and H0 (Banerjee
et al. 2019). The STs being co-located, the assumption dpri = dpr, drci = drc,
ψpri = ψpr, ψrci = ψrc, βi = β looks reasonable for all i = 1 to K (Banerjee and Maity
2019, Huang et al. 2012). The mean values of Tst under H1 and H0 are expressed
as E(Tst1) = ατfsµ1 and E(Tst0) = ατfsµ0, respectively. Here, µ1 = Kβr1 + σ2
c,
µ0 = Kβr0 + σ2
c, r1 = d−
ψ
pr dP
−
ψ
rc
pr rc p + r0, and r0 =
d−
ψ
rc
rc
σ
2
ns. The variances of Tst
under H1 and H0 are expressed as Var(Tst1) ≈ ατfsµ2
1and Va r(Tst0) ≈ ατfsµ2
0
(Banerjee and Maity 2019, Huang et al. 2012).
The cooperative detection and false alarm probabilities can be expressed as
γα
−µf
τ
γα
−µf
τ
PQ=1s0s
det ,PQ
fa = (9.4)
µf
1
ατ
s µf
0
ατ
s
where
γ
denotes the detection threshold for CSS. Some amount of energy is
consumed by each SU for the sensing and the reporting operations, which
can be expressed as Es = Psατ and Ec = βSατ, respectively, where S = P(H1)
(d−
ψ
pr
pr Pp +
σ
2
ns). Ps is the power consumed for SS. The duration for the broad-
casting CSS decision, which seems to be negligible compared to the sample
acquisition and reporting slots, is not considered in the mathematical analy-
sis (Banerjee et al. 2019).
9.4 ENERGY HARVESTING AND SECONDARY
DATA TRANSMISSION
This section presents EH and SU data transmission briey.
a. Linear energy harvesting model: At the end of sensing time (τ), all SUs
harvest energy when CSS decision goes in favor of PU’s transmission state.
However, since only one SU is selected for data transmission/harvesting
purposes, the amount of harvested energy by that particular SUT during
(Tf − τ) can be expressed as mentioned below.
ET=−
ητ
()
PP
()
Hd
()
−
ψ
pr
fp
PP
2 2
harvestdet 1rp
++
σσ
ns fa PH
()
0ns
=−
ητ
()
TB
f (9.5)
where B = PdetP(H)(d−
ψ
pr P2
1pr p +
σ
ns) + PfaP(H0)
σ
2
ns and ‘η’ denotes the energy
conversion efciency of the harvester circuit (0 < η < 1). It is worth mentioning
144 Green Engineering and Technology
that the linea r EH model (Bhowmick et al. 2016) is considered here; however,
the non-linear EH model can also be considered (Banerjee et al. 2019).
Furthermore, the choice of particular SUT − SUR in participating in the
data transmission process is governed by some priority algorithm.
b. SU data transmission: Some amount of power, say Pst, is required (a por-
tion of the harvested power) by the SUT to perform its own data transmis-
sion, and it can be expressed as
λ
E
λη
()
TB
−
τ
P
st =ha f
()
rvest=
−
()
=
λη
B (9.6)
Tf
τ
Tf−
τ
The power required for the SUT to run the harvester unit, being small, is not
considered in the mathematical analysis. The entire portion of the harvested
energy is not utilized by SUT, rather some amount of the latter, say (1 −
λ
)
portion, is kept to support the energy required for SS and reporting signal
samples to the FC. Here, the symbol
λ
(01<<
λ
) denotes the portion of the
harvested energy used for SU data transmission.
c. Secondary throughput calculation: The instantaneous spectrum ef-
ciency (SE) of SUT while transmitting data to SUR can be expressed as
given below.
R
()
Tf−
τ
()
()
||hP
2
sr =PH
02
1l−+Psd st
fog 12 (9.7)
Tf
σ
nd
The instantaneous channel-fading coefcient of the link between SUT and
SUR is expressed as hsd. The modeling of a channel is similar as discussed
above. The noise variance at the receiver of SU 2
R is expressed as
σ
nd.
The frame structure is repetitive, which leads to the individual SE of SUT
averaged on a large set of instantaneous channel gains. SE averaged over
large frames (10,000) can be expressed as
−
ψ
sd
Ravg
()
Tf−
τ
sr =
()
0f
()
dP
PH 1l−+Pa2
og 1
st
sd
T2
f
σ
nd
()
TC−
τ
dB
−
ψ
sd
=f
λη
log1 sd
2+
2 (9.8)
Tf
σ
nd
where the mean channel power gain of ||h2
sd averaged over 10,000 frames
is calculated as E[h2
sd ] = d−
ψ
sd, since, h ~ CN(0, d−
ψ
sd
sd sd sd ), C = 1PH
()
0f
()
−Pa,
and PB
st =
λη
. Here dsd and
ψ
sd denote the distance and the path loss expo-
nent between the link SUT and SUR, r espectively.
145Energy Harvesting in Green Radio Networks
9.5 MATHEMATICAL SOLUTION TO
THROUGHPUT MAXIMIZATION
In this section, the problem formulation and its mathematical solution are mentioned.
The mathematical analysis as an optimization problem is given below.
−
avg
()
TC−
τ
dB
ψ
sd
f
λη
max Rsr ≡maxl
og2+
τλ
,
τ
1sd (9.9)
Tf
σ
2
nd
s.t.(i) PP
det ≥≤
det ,PP
fafa ⇒+KR
βα
()
Qs
()
rr
τσ
fB
2
01
−+
cQ
=0
(ii) EE
harvests
≥+
sc
EP+−
t
()
TB
ff
τη
⇒−
()
TP
τα
≥+
s
τβ
SB
ατ
+−
λη
()
Tf
τ
⇒−
()
1
λη
BT
()
fs
−−
τβ
()
PS+≥
ατ
0,
where B = P
detP(H1)(d−
ψ
pr
pr Pp +
σ
2
ns) + P
faP(H0)
σ
2
ns and C =1 PH
()
0f
()
−Pa
Constraint (i) in Eq. (9.9) represents the SS reliability, where P
det and P
fa denote the
detection and the false alarm probability thresholds, respectively. Here, RQ = (r0Q−1
(
P−1
fa
)
− r1Q(P
det
)) and BQ = (Q−1(P
fa
) − Q−1(P
det
)) in constraint (i) in Eq. (9.9). To nd
out the optimal value of
τ
that maximizes the throughput, the SS reliability con-
straints are set to equality, i.e., PP
det ==,
det PP
fafa. The detailed steps for the modi-
cation of this constraint are similar to those of Banerjee and Maity 2019 and are not
mentioned here due to space constraint. The energy causality constraint mentioned in
Eq. (9.9) (ii) ensures that the total harvested energy of SUT is sufcient to provide the
necessary energy required for SS, reporting, and data transmission. Lagrange multi-
plier and Karush–Kuhn–Tucker (KKT) are used to solve this optimization problem.
The Lagrangian of this optimization problem can be expressed as
L
()
TC
f−
τ
dB
−
ψ
sd
λη
=log1
2+
sd
2
Tf
σ
nd
++
εβ
10
KR
()
Qs
()
rr
−+
1
ατ
fB
σ
2
cQ
+−
ελ
21
ητ
()
BT
()
fs
−−
()
PS+
βατ
=
(9.10)
0
where
ε
1 and
ε
2 are the Lagrange multipliers and
ε
12
,
ε
>0.
KKT is used to solve the optimization problem mentioned in Eq. (9.10). It is found
∂LL∂ ∂LL∂
that putting = == = 0 (partial derivatives of L w.r.t
τλ
,,,
εε
∂
τλ
∂ ∂
εε
12
∂12
) and
considering the case
ε
12
≠≠0,
ε
0, one feasible optimal solution can be found as
mentioned below.
146 Green Engineering and Technology
*ACdP
−
ψ
sd
()
+
βα
S
λ
=−111
ησ
BT
()
sd s
fn
−A2 (9.11)
11 d
*1
τ
= (9.12
2C2)
1 1 dB
−
ψ
sd
λη
*
−−
2
BA
11 −2
11 log1
2+sd
2−
1
Tf
σ
nd
1KR
βσ
2
2
Qc
+BQ
()
1−
λη
*BT
where A11 = and f
B=.
α
fsKr
β
()
−r11
()
1
λη
*
10 −+BP
()
s+
βα
S
The detailed mathematical analysis is not shown due to the space constraint.
9.6 NUMERICAL RESULTS
Numerical results are presented that show the performance of the secondary
throughput
(
Ravg
sr
)
with the variation of different system parameters. Monte Carlo
simulations over 10,000 runs consider the randomness in channel characteristics
while numerical results are obtained. The simulation parameters are set as follows:
PP
ps
==1W, 0.1 W, PP
det ==0.95, fa1
0.05, PH
()
==0.7, PH
()
00.3,1Tf=0ms,
fK
sp
==10 kHz, 10,
βη
==1.4, 0.6, dd
rp
==1.2 m,
ψψ
rr
3.5, cr
==1.3 m, c3.8,
dW==
ψσ
sd 4, 22
σσ
0.6 W, 2
sd 1.4 m, ns ==
cnd=0.5 to haveafaircomparison.
Figures 9.3 and 9.4 show the variation on the secondary throughput
(
Ravg
sr
)
with
the fraction of EH used by SUT for its data transmission (
λ
) and SS duration (
τ
),
respectively for α = 0.3 and 0.4. We see that the throughput increases initially as the
λ
value increases since the increase in the latter enhances the power requirement
(Pst given in Eq. (9.6)) for data transmission. However, when the
λ
value is further
increased, the Ravg
sr value decreases after reaching a maximum point. This is because
an increment in the
λ
value leads to a decrease in the data transmission time slot
(Tf − τ) to maintain the energy causality and the xed data rate requirement of SUT.
It is worth mentioning that a xed data transmission rate is maintained by SUT; how-
ever, data rate constraint in the form of outage probability is not included in the opti-
mization problem, which may be considered as an extended work. Furthermore, it is
observed that Ravg
()
α
=0.4 > Ravg
sr sr
()
α
=0.3 . An increase in the
α
value decreases
τ
(for maintaining a target SS reliability constraint) and enhances the transmission
time slot (Tf − τ), which not only increases the required time slot for data transmission
but also enhances the slope of EH during that enhanced slot. This in overall increases
the secondary throughput of SUT. Figure 9.3 shows that the simulation and the ana-
lytical values match well.
Figure 9.4 shows that with the initial increment in the sensing duration
(
τ
)
,
the secondary throughput of SUT
(
Ravg
sr
)
increases. This is because an increase in
the
τ
value decreases the required data transmission slot (Tf − τ) and increases the
fraction of harvested energy
λ
. An increase in the
λ
value increases the secondary
throughput. With further increase in the value, Ravg
τ
sr decreases after attaining a
maximum point since the (Tf − τ) slot reduces signicantly and the scope of EH
147Energy Harvesting in Green Radio Networks
FIGURE 9.3 Secondary throughput
(
Ravg
sr
)
versus the fraction of EH for data transmission (
λ
).
FIGURE 9.4 Secondary throughput Ravg
sr versus SS duration (
τ
).
( )
148 Green Engineering and Technology
also diminishes with this fact. It is found that the maximum values of Ravg
sr are
0.2457 MbpsHz−1 and 0.2312 MbpsHz−1 (at
λτ
**
==0.2045,0.04 s) at α = 0.4 and
α = 0.3, r espectively.
A comparative performance analysis of the secondary throughput between the
proposed approach and that of the work reported by Banerjee et al. 2019 is shown
in Figure 9.4. Since the work reported by Banerjee et al. 2019 analyszes the sum
secondary throughput consisting of K number of cooperative transmit–receive
pairs (unlike the present system model, which consists of a single transmit–receive
pair to be active at a time in data transmission), the value of the former is averaged
(total throughput divided by K number of users) and is compared with the pres-
ent work to have fairness in the analysis. It is found that the value of Ravg
sr for the
present work is ~12.68% greater than the corresponding value of Ravg
sr reported by
Banerjee et al. 2019 at α = 0.4. It is also observed that the value of
τ
* obtained by
Banerjee et al. 2019 is greater than the same for the present work. An increase in
the
τ
* value reduces both the data transmission slot and the scope of EH for the
work reported by Banerjee et al. 2019, which decreases the overall value of Ravg
sr
compared to the present work.
Figure 9.5 shows the variation on Ravg
sr with the fraction of time slot for SS (
α
). It is
observed that with the increase in
α
, the secondary throughput value increases. This
is because an increase in the
α
value leads to a decrease in the τ value (lessamount
of τ is required to meet the SS reliability constraint) and an increase in the data
transmission slot (Tf − τ) value. This increased (Tf − τ) value increases the overall
FIGURE 9.5 Secondary throughput
(
Ravg
sr
)
versus the fraction of time slot for SS (
α
).
149Energy Harvesting in Green Radio Networks
throughput of SUT. However, after attaining a maximum point when the value of
α
is further increased, the Ravg
sr value decreases since the (Tf − τ) value increases sig-
nicantly, which reduces the
λ
value (for maintaining the target data rate and energy
causality constraint). The maximum value of Ravg is observed to be 0.3816 MbpsHz−1
sr
for
λτ
**
==0.3525, 0.03251 s at
α
=0.5. Thus,
α
may be a design parameter in
this optimization problem, however, not explored to keep mathematical analysis sim-
ple. It is also observed that RK
avga
sr
()
=>12 RK
vg
sr
()
=8 (of about ~ 21.15% more) at
α
=0.5. An increase in the value of cooperative SUs (K) decreases the sensing dura-
tion τ (less amount of τ required to meet the SS reliability constraint) and increases
the data transmission slot (Tf − τ). An increase in (Tf − τ) also enhances the chance of
EH and increases the secondary throughput.
9.7 C ONCLUSIONS AND THE SCOPE OF FUTURE WORK
In this chapter, a simple EH-CR model is proposed that maximizes the throughput of
the selected secondary transmitter while maintaining SS reliability and energy cau-
sality constraints. The main ndings of the work reported in this chapter can be sum-
marized as follows: (i) a maximum value of the secondary throughput (Ravg
sr ) is found
for both the optimal values of the sensing duration (τ) and the fraction of the har-
vested energy for the data transmission (λ), (ii) the maximum value of Ravg
sr is found
to be ~ 12.68% greater over the value obtained in the study of Banerjee et al. 2019 at a
xed value of the fraction of the time slot for SS (α) = 0.4, (ii i) Ravg
sr is found to attain a
maximum value for a xed value of α, and (iv) an increase in the value of cooperative
users (K) is found to have benet in secondary throughput (about ~ 21.15% more for
K = 12 over K = 8) at
α
=0.5.
• The work reported in this chapter may be extended as the same system
model involving multiple pairs of secondary transmitter and receiver with
the inclusion of secondary outage probability as a constraint in the same
optimization framework.
• The issue of security (another important research issue in present-day wireless
communication) can be included considering the presence of an eavesdropper
and may be counter measured through the presence of jamming signals.
• The proposed system model and its various extended models can be made
or tuned to have an application-specic design like the Internet of Things,
unnamed aerial vehicles, and intelligent transport systems.
• The issue of time and energy consumption in SS operation can be avoided
through PU spectrum prediction using a machine learning scheme.
REFERENCES
Banerjee, A. and Maity, S. P. 2019. “On residual energy maximization in cooperative spec-
trum sensing with PUEA.” IEEE Wireless Communications Letters 8(6): 1563–1566,
(December 2019). DOI 10.1109/LWC.2019.2927482.
Banerjee, A., Maity, S. P., and Das, R. K. 2019. “On throughput maximization in cooperative
cognitive radio networks with eavesdropping.” IEEE Communications Letters 23 (1):
120–123, (January 2019). DOI 10.1109/LCOMM.2018.2875749.
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Bhowmick, A., Roy, S. D., and Kundu, S. 2016. “Throughput of a cognitive radio network
with energy-harvesting based on primary user signal.” IEEE Wireless Communications
Letters 5 (2): 136–139, (April 2016). DOI 10.1109/LWC.2015.2508806.
Cao, X., Liu, L., Cheng, Y., et al. 2018. “Towards energy-efcient wireless networking in the
big data era: A survey.” IEEE Communications Surveys & Tutorials 20 (1): 303–332,
(Firstquarter 2018). DOI 10.1109/COMST.2017.2771534.
Dondi, D., Bertacchini, A., Larcher, L., et al. 2008. “A solar energy harvesting circuit for low
power applications.” IEEE International Conference on Sustainable Energy Technologies,
Singapore: 945–949. DOI 10.1109/ICSET.2008.4747143.
Federal Communications Commission. 2002. “Spectrum policy task force.” Rep. ET Docket
no.02–135: (November 2002).
Haykin, S. 2005. “Cognitive radio: Brain-empowered wireless communications.” IEEE Journal
on Selected Areas in Communications 23 (2): 201–220, (February 2005). DOI 10.1109/
JSAC.2004.839380.
Huang, S., Chen, H., Zhang, Y., et al. “Energy-efcient cooperative spectrum sensing with
amplify-and-forward relaying.” IEEE Communications Letters 16 (4): 450–453, (February
2012). DOI 10.1109/LCOMM.2012.021612.112143.
Khoshabi Nobar, S., AdliMehr, K., and MuseviNiya, J. 2016. “RF-Powered Green Cognitive
Radio Networks: Architecture and Performance Analysis.” IEEE Communications
Letters 20(2): 296–299. DOI 10.1109/LCOMM.2015.2500897.
Li, B., Xu, W., and Gao, X. 2015. “Energy-efcient simultaneous information and power trans-
fer in OFDM-based CRNs.” IEEE 81st Vehicular Technology Conference (VTC Spring):
1–5, (May 2015). DOI 10.1109/VTCSpring.2015.7145819.
Li, S., Liu, Y., Lin, L., et al. 2018. “Millimeter-wave channel simulation and statistical channel
model in the cross-corridor environment at 28 GHz for 5G wireless system.” International
Conference on Microwave and Millimeter Wave Technology (ICMMT), Chengdu: 1–3.
DOI 10.1109/ICMMT.2018.8563957.
Lu, X., Xu, W., Li, S., et al. 2014. “Simultaneous wireless information and power transfer for
cognitive two-way relaying networks.” IEEE 25th Annual International Symposium on
Personal, Indoor, and Mobile Radio Communication (PIMRC): 748–752, (September
2014). DOI 10.1109/PIMRC.2014.7136264.
OFCOM 2007. “Digital dividend review, a statement on our approach towards awarding the
digital dividend.”: (December 2007).
Pratibha, M., Li, K. H., and Teh, K. C. 2015. “Energy-harvesting cognitive radio systems
cooperating for spectrum sensing and utilization.” IEEE Global Communications Con-
ference (GLOBECOM): 1–6, (December 2015). DOI 10.1109/GLOCOM.2015.7417161.
Yin, S., Zhang, E., Yin, L., et al. 2013. “Saving-sensing-throughput tradeoff in cognitive radio
systems with wireless energy harvesting.” IEEE Global Communications Conference
(GLOBECOM): 1032–1037, (December 2013). DOI 10.1109/GLOCOM.2013.6831210.
Zou, Y., Peng, J., Liu, K., et al. 2016. “Energy-efcient cooperative spectrum sensing for cogni-
tive sensor networks with energy harvesting.” Chinese Control and Decision Conference
(CCDC): 2373– 2378, (May 2016). DOI 10.1109/CCDC.2016.7531382.
151
10 Mitigation on the Impact
of Electric Vehicle
Charging Stations by
Splitting the Capacity
and Optimally Locating
on a Recongured RDS
M. Satish Kumar Reddy and K. Selvajyothi
IIITDM
CONTENTS
10.1 Introduction 152 ..................................................................................................
10.2 Load Flows 152...................................................................................................
10.2.1 Modied NR Load Flow 153 ..................................................................
10.2.2 F/B Load Flow 153 ..................................................................................
10.2.3 Branch Incidence Matrix Load Flow 153................................................
10.3 Analysis of a Sample Distribution System 156...................................................
10.4 IEEE 16 Bus RDS 160.........................................................................................
10.4.1 Bus Voltage Prole 161...........................................................................
10.4.2 Branch Current Prole 162 ......................................................................
10.4.3 Reconguration of 16 Bus RDS 162 .......................................................
10.4.4 Modeling of Loads at RDS 163...............................................................
10.5 Optimization Techniques 164..............................................................................
10.6 Optimal Placement of EVCS Using PSO 165 .....................................................
10.6.1 Optimal Reconguration of 16 Bus RDS without EVCS 166.................
10.6.2 Optimal Placement of EVCS without Reconguration
(Scena r io 1) 167.......................................................................................
10.6.3 Optimal Placement of EVCS before Reconguration
(Scenar io 2) 170.......................................................................................
152 Green Engineering and Technology
10.1 INTRODUCTION
Conventional transportation is the second culprit for global warming, which leads to
unseasonal rains, unpredictable climatic changes, forest res, and so on. However,
sudden replacement of vehicles with electric vehicles (EVs) (Ahmadi, et al. 2019)
based on internal combustion engines is not recommended without an infrastruc-
ture for them. The low specic energy of EVs could not motivate people for choos-
ing EVs. Therefore, the electric vehicle charging stations (EVCS) erected at optimal
locations with the latest charging technologies would change the mindset of custom-
ers choosing the EVs (Alsabbagh, et al. 2019). Here, a major problem is welcoming
the power system engineers for the sudden implementation of EVCS (Catalbas, et al.
2017) at the domestic and commercial levels. The incorporation of EVCS (Hatef &
Ghaffarzadeh 2020, Huiling, et al. 2018, Yazdi, et al. 2019) to the existing radial
distribution systems (RDS) adds an extra burden to the power system, affecting the
stability, as well as increasing the losses of the system. The problem can be eas-
ily solved with the erection of additional generating stations at a cost of additional
investment. Even then, with the RDS reconguration system, losses can be reduced
with EVCS in the system. The analysis of the RDS is needed to determine its per-
formance with and without reconguration through load ow studies. The load ow
studies can be implemented in the distribution system by modeling its components
like lines, transformers, loads, and so on. The EVCS are located in such a way that
they must have the least impact and are minimized further with their proper recon-
guration. Optimization strategies accomplish the proper reconguration and mini-
mal impact of EVCS placement. In this chapter, particle swarm optimization (PSO)
(Yenchamchalit, et al. 2008) is applied for the optimal location of EVCS and recon-
guration for minimizing the losses of the RDS.
10.2 LOAD FLOWS
Load ow studies are used to analyze either transmission or distribution system for
calculating the bus voltages, branch currents, and losses. These are formulated using
numerical methods that start with assumed initialized values for the variables chosen.
Load ows are also used for power system planning, short circuit studies, and so on.
Load ows that are used for analyzing the transmission system are not directly used
for analyzing the distribution system due to their radial nature and low X/R. Hence,
distribution load ows are formulated with some modications in conventional load
ows like modied Newton-Raphson (NR) load ow, modied Gauss-Siedel load
ow, and modied fast decoupled load ows. The load ows that are formulated with
forward current and backward voltages of the RDS are called forward/backward
10.6.4 Optimal Placement of EVCS after Reconguration
(Scenario 3) 171.......................................................................................
10.6.5 Optimal Placement of EVCS along with Reconguration
(Scenario 4) 172.......................................................................................
10.7 Conclusion 175 ....................................................................................................
References 175..............................................................................................................
153Electric Vehicle Charging Stations Impact
(F/B) load ow. The load ows that are formulated with branch incidence matrix
(BIM) to determine the performance of RDS are called as BIM load ow.
10.2.1 modified nr load floW
The commercial NR load ow is unable to analyze the RDS because of its radial
nature and low X/R ratio. Therefore, Xu et al., in 2009, proposed modications in
the conventional NR method to study the performance of RDS. The Jacobian matrix
in the process of solving increases its complexity. The complexity further increases
with unbalanced RDS.
10.2.2 f/B load floW
The F/B load ow is formulated for studying the RDS (Hajimiragha, et al. 2010).
This method is started with a at voltage prole for all buses. The current of each
branch is calculated as per the bus voltage and bus powers in the forward direction
and from the end bus, the voltage is calculated to the starting bus. Thus, it is called
F/B load ow.
10.2.3 BranCH inCidenCe matrix load floW
In this chapter, a new load ow algorithm is proposed, which handles the distribution
system accurately, efciently, and adequately. All the conventional load-ows that
are used to solve the distribution system are started with a uniform voltage prole of
1.0 pu, which took one additional iteration for converging the algorithm. However,
the proposed load ow converges the system without a at voltage stage. It is formu-
lated using simple vector algebra and a minimum number of equations inferior to the
commercial load ows. BIM is the backbone of this load ow, so it is named BIM
load ow. The ow chart of BIM is shown in Figure 10.1.
The load ow is further solved after the formation of BIM of the RDS. The start-
ing step of any load ow is to convert the connected loads to pu values using the
base voltage and base power of the system. The base voltage can be taken as an
operating voltage at the distribution level, whereas the base power can be taken as the
maximum load of the RDS. However, IEEE has prescribed different base values for
different bus systems. An important step in this load ow is introducing a new vari-
able ‘
β
’, which is used to calculate the receiving end voltages of the system.
K is used to evaluate the branch current Ibr from the bus current Ib (Huiling et al.
2018) as mentioned below.
I=KI
−1
br b (10.1)
The complex branch powers and bus powers are represented in terms of the K matrix,
and losses via (10.2) and (10.3). The system’s Lbr branch power losses produce a volt-
age difference between the sending and receiving end buses.
SK
b=−
[]
SL
br br (10.2)
154 Green Engineering and Technology
FIGURE 10.1 Flow chart representing formulation of BIM (K).
Electric Vehicle Charging Stations Impact 155
SK=+
−1
br SL
bbr (10.3)
Considering the power ow between lth and mth buses, the complex power in branch
‘lm’ can be written as:
*VV*
SP
lm =+jQ ==VI llm***
lm lm llm=−VV()VY
Z*ll mlm
lm
SP
lm =+
lm jQ =
β
*
lm lmYlm (10.4)
where
β
lm =−VV
**
ll
()
Vm (10.5)
From (10.4)
β
*
lm =SZ
lm lm (10.6)
From (10.5)
β
*
Vml
=−Vlm
* (10.7)
Vl
Taking conjugate on either side of (10.4), the branch current can be calculated as:
β
*
lm
Ilm = Y
V*lm (10.8)
l
The total losses of the system can be calculated as:
LL=∑r
lm lm (10.9)
Hence, the apparent power of the receiving end branch is obtained as:
SS
recsend
br =−
br Lloss (10.10)
where
LL
rspecr−1r−1*
loss ==
lm SV
mm
−Im (10.11)
Max€
()
Lr
lm ≤ (10.12)
156 Green Engineering and Technology
where
r is the iteration count.
Llm is lm branch losses.
Sspec
m is specied apparent power at the mth bus.
Vm is the mth bus voltage.
Im is the mth bus current.
Sbr is the branch apparent power.
€ is the very small positive value.
As per (10.12), the maximum losses in the system are considered to be nite and
small. The algorithm will perform faster for less complex systems.
10.3 ANALYSIS OF A SAMPLE DISTRIBUTION SYSTEM
The formation of BIM is explained using a sample 7 bus system, as shown in Figure 10.2.
According to the algorithm explained in Section 10.2, the branches ‘2b’ and ‘5b’
are augmented with ‘1b’ through the 2nd bus, and the rest of the branches are not
augmented with branch ‘1b’. So, a ‘−1’ and ‘0’ are lled at the corresponding space
in the matrix as shown in Table 10.1. Similarly, the remaining rows of the matrix
are also lled. The diagonal elements of the matrix are generated by the augmented
self-branches and are represented by ‘1’.
Assumed values of line data and load data required for solving the load ow
problem of sample 7 bus system are shown in Table 10.2.
FIGURE 10.2 Single line diagram of sample 7 bus system.
TABLE 10.1
BIM of Sample 7 Bus System
Branch 1b 2b 3b 4b 5b 6b
1b 1 −10 0 −10
2b 0 1 −10 0 −1
3b 0 0 1 −10 0
4b 0 0 0 1 0 0
5b 0 0 0 0 1 0
6b 0 0 0 0 0 1
157Electric Vehicle Charging Stations Impact
Step 1: Convert the loads to pu choosing a base value of 100 MVA.
Step 2: Assuming an 11 kV distribution system, convert the branch impedances to
pu choosing a base impedance of 1.21 Ω.
Step 3: Form the BIM (K) using the procedure given in Section 10.3
Bus no. P (pu) Q (pu)
2 0.006 0.006
3 0.004 0.003
4 0.005 0.0055
5 0.003 0.003
6 0.002 0.0015
7 0.0055 0.0055
TABLE 10.2
Line Data and Load Data of the Sample Bus System
Sending End
Bus
Receiving
End Bus
Resistance
(Ω)
Reactance
(Ω)
Real Power
(kW)
Reactive
Power (kVAR)
1 2 1.093 0.455 600 600
2 3 1.184 0.494 400 300
3 4 2.095 0.873 500 550
4 5 3.188 1.329 300 300
2 6 1.093 0.455 200 150
3 7 1.002 0.417 550 550
Branch R (pu) X (pu)
1 0.903 0.376
2 0.978 0.408
3 1.731 0.721
4 2.634 1.098
5 0.903 0.376
6 0.828 0.346
Branch 1b 2b 3b 4b 5b 6b
1b 1 −10 0 −10
2b 0 1 −10 0 −1
3b 0 0 1 −10 0
4b 0 0 0 1 0 0
5b 0 0 0 0 1 0
6b 0 0 0 0 0 1
158 Green Engineering and Technology
Step 4: Calculate pu apparent power.
Iteration 0:
Let the total losses TL of the system be initialized as zero. Now, calculate the
branch power using
SK=[]
−1
branch Sbu
Step 5: Calculate the new variable
β
branch using
β
branch =ZS
*branch
Step 6: Calculate the bus voltages and bus currents using the equations
Vb = Va − (
β
/Va)* and Ib = Y(
β
/Va)* respectively.
where ‘a’ and ‘b’ are the sending end bus and receiving end bus of branch ‘ab’.
Branch Apparent Power (pu)
10.042 + j0.033
20.017 + j0.013
30.008 + j0.008
40.003 + j0.003
50.019 + j0.014
60.005 + j0.005
Branch
β
branch
10.0504 + j0.0104
20.022 + j0.0058
30.0196 + j0.0081
40.0112 + j0.0046
50.0224 + j0.0055
60.0059 + j0.0024
Bus Bus Voltages (pu) Bus Current (pu)
20.949 − j0.0140 0.042 + j0.033
30.926 − j0.0019 0.0181 + j0.0135
40.905 − j0.028 0.0088 + j0.0085
50.892 − j0.032 0.0034 + j0.0032
60.925 − j0.0195 0.0218 + j0.0149
70.920 − j0.022 0.0055 + 0.0053i
159Electric Vehicle Charging Stations Impact
Step 7: Calculate the branch currents from the bus currents and K using equation
Ibranch = KIbus.
Step 8: Calculate the sum of the branch losses using the equation TI
L1b
=∑rb
Vr.
This is valid only for zero iteration.
Step 9: Calculate the difference between the losses TL = TL1 − TL0. Since TL0 is
zero at the beginning of the iteration, the difference is TL1 only.
Step 10: Check the convergence of TL such that it satises less than or equal to
0.0001 and displays the bus voltages, bus currents, and losses, else go to step 4.
With this detailed explanation of the procedure for solving the sample 7 bus sys-
tem, let us see the simulation studies for a standard IEEE 16 bus RDS in the succeed-
ing section, which has three feeders connected to the substation.
Branch Current (pu)
10.0021 − 0.0047i
20.0038 + 0.0003i
30.0054 − 0.0052i
40.0034 − 0.0032i
50.0037 − 0.0015i
60.0055 − 0.0053i
Branch Branch Losses
10.0019 + 0.0045i
20.0035 − 0.0002i
30.0047 + 0.0049i
40.0029 + 0.0030i
50.0034 + 0.0014i
60.0050 + 0.0050i
Branch Loss Difference
10.0019 + 0.0045i
20.0035 − 0.0002i
30.0047 + 0.0049i
40.0029 + 0.0030i
50.0034 + 0.0014i
60.0050 + 0.0050i
160 Green Engineering and Technology
TABLE 10.3
The Load Data of Buses Connected to Feeders 1, 2, and 3 of the 16 Bus System
Feeder 1 Feeder 2 Feeder 3
Active Reactive Active Reactive Active Reactive
Power Power Power Power Power Power
Bus No. (MW) (MVAR) Bus No. (MW) (MVAR) Bus No. (MW) (MVAR)
4 2 1.6 8 4 2.7 13 1 0.9
5 3 1.5 9 5 3 14 1 −1.1
6 2 0.8 10 1 0.9 15 1 0.9
7 1.5 1.2 11 0.6 0.1 16 2.1 -0.8
12 4.5 2
Total 7.5 6.1 Total 15.1 8.7 Total 5.1 −0.1
FIGURE 10.3 Single line diagram of IEEE 16 bus RDS.
10.4 IEEE 16 BUS RDS
The single line diagram of IEEE 16 bus RDS is shown in Figure 10.3, which consists
of 16 buses with three feeders each maintaining 1.0 pu due to direct connection with
the substation (zero load buses). The line and load data for the system are given in
Tables 10.3 and 10.4 respectively. The base values of voltage and MVA for the buses
are assumed to be 23 kV and 100 MVA respectively. The rm line represents section-
alized switches and the tie line is represented with dotted lines. The system has a real
power load of 28.7 MW and a reactive power load of 14.8 MVAR. The switches to be
opened for basic load ow are 5, 11, and 16.
The system has three feeders named feeder-1, feeder-2, and feeder-3. The total loads
at feeder-1, feeder-2, and feeder-3 are 7.5 + j6.1 MVA, 15.1 + j8.7 MVA, and 5.1 − j0 .1
161Electric Vehicle Charging Stations Impact
MVA, respectively. Buses 4, 5, 6, and 7 are connected to feeder-1, buses 8, 9, 10, 11,
and 12 are connected to feeder-2, and buses 13, 14, 15, and 16 are connected to feeder-3.
10.4.1 BuS voltage profile
Figure 10.4 shows the bus voltage prole of the system using the proposed method.
It can be observed that the voltage prole of feeders 1, 2, and 3 are 1.0 pu as they
are the starting buses directly connected to the substation. The voltage prole is
dropped from 1.0 pu to 0.97 pu at the 9th bus. The buses 4, 5, 6, and 7 are con-
nected to the 1st feeder so the voltage prole is dropped to 0.985 at the 7th bus and
the buses 8, 9, 10, and 11 are connected to the 2nd feeder, where also the voltage
prole is dropped to 0.97 pu at the 9th bus and raised to 0.976 at the 10th bus as
024681012141618
0.965
0.970
0.975
0.980
0.985
0.990
0.995
1.000
Voltage profile (p.u)
Bus numbers
without EVCS
FIGURE 10.4 Voltage prole of 16 bus RDS.
TABLE 10.4
The Line Data of Branches Connected to Feeders 1, 2, and 3 of the 16
Bus System
Feeder 1 Feeder 2 Feeder 3
(R + jX) (R + jX) (R + jX)
Br No. Seb-Reb Ωkm−1 Br No. Seb-Reb Ωkm−1 Br No. Seb-Reb Ωkm−1
1 1–4 0.075 + j0.1 6 2–8 0.11 + j0.11 12 3–13 0.11 + j0.11
2 4–5 0.08 + j0.11 7 8–10 0.11 + j0.11 13 13–15 0.08 + j0.11
3 4–6 0.09 + j0.18 8 8–9 0.08 + j0.11 14 13–14 0.09 + j0.12
4 6–7 0.04 + j0.04 9 9–11 0.11 + j0.11 15 15–16 0.04 + j0.04
5 5–11 0.04 + j0.04 10 9–12 0.08 + j0.11 16 7–16 0.09 + j0.12
Red belongs to tie-lines 11 10–14 0.04 + j0.04 Black belongs to main lines
162 Green Engineering and Technology
12345678910 11 12 13 14 15 16
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
Branch Current profile (p.u)
Branch numbers
FIGURE 10.5 Branch current prole of 16 bus RDS.
it has less load. The buses 11 and 12 are connected to the 9th bus so the voltage
prole is further dropped from 0.976 pu to 0.969 at the 12th bus. Finally, at the 3rd
feeder, buses 13, 14, 15, and 16 are connected, which has the least loads among
these three feeders. So, the voltage prole is improved compared to the 12th bus.
The 15th bus and 16th bus voltage proles are dropped from that of the 14th bus as
they have more loads in feeder-3.
10.4.2 BranCH Current profile
The current through the branches of RDS is shown in Figure 10.5. The branches 5,
11, and 16 are open in the RDS. Hence, the current through those branches is zero as
shown in Figure 10.5. The current through the 6th branch is the maximum because it
is directly connected to the 2nd feeder having a pu voltage of 10. The 8th bus load is
the highest among the buses 4, 8, and 13, which are directly connected to the feeders.
The losses of the 16th bus RDS are calculated using the bus voltage and branch cur-
rent prole. The systems’ real power losses are obtained to be 511.43 kW.
10.4.3 reConfiguration of 16 BuS rdS
Reconguration is the method of changing topology without disrupting its radial
nature. The major challenge for reconguration of RDS is the selection of tie lines.
The number of tie line switches depends on the number of loops formed by the tie
line switches. In the case of 16, 33, 69, and 119 bus RDS, 3, 5, 5, and 15 loops,
respectively, are formed according to their layout.
If the reconguration gives minimum losses among all switching combinations,
then that reconguration is called optimum reconguration. The losses of the system
for various switching combination of opening of tie and sectionalized switches of
16 bus RDS are given in Table 10.5.
163Electric Vehicle Charging Stations Impact
The losses are found to be minimum when switches 5, 11, and 16 are opened. Hence,
an optimization algorithm is necessary to optimize the switching conguration of tie
line and sectionalized switches to reduce the total losses of RDS and hence to improve
the voltage prole. In this chapter, PSO is used to optimize the 16 bus RDS.
10.4.4 modeling of loadS at rdS
The loads are classied into three types based on their behavior. They are as follows:
Constant power load: The constant power load is the load that draws variable
current for maintaining the constant power for its high performance. For example,
the motor draws current from the grid to maintain its power as constant.
The constant power loads are modeled as (14) for analyzing the distribution sys-
tem via load ows substituting n = 0.
Constant current load: The constant current load is the load that draws con-
stant current from the grid. For example, the battery draws constant current from the
mains during its charging.
The constant current loads are modeled as (14) for analyzing the distribution sys-
tem via load ows substituting n = 1.
Constant impedance load: The constant impedance load is the load that draws
the current from the mains to maintain its impedance as constant.
The constant impedance loads are modeled as (14) for analyzing the distribution
system via load ows substituting n = 2.
Pj+Q
Ik=n
kk
()
V (10.13)
Vkk
The EV battery behaves as a constant current load until it reaches 80% of its rated
power, and then, it acts as a constant power load during the constant current constant
TABLE 10.5
Real Power Losses of 16 Bus RDS
with Different Recongurations
Opened Switches Losses (kW)
5 11 16 511.4352
9 14 3 560.5099
9 11 3 547.6767
5 11 13 574.7994
9 7 13 524.9117
9 11 13 559.3434
2 11 13 724.6371
9 14 15 548.5673
2 14 13 758.8787
8 14 15 775.4750
5 11 3 568.6186
164 Green Engineering and Technology
FIGURE 10.6 Modeling of EVCS at a connected bus.
voltage (CCCV) method of charging (Moghaddam et al. 2018). However, in this
chapter, the EVCS is considered as an additional constant power load connected to
RDS as is shown in Figure 10.6.
10.5 OPTIMIZATION TECHNIQUES
The need for an optimization algorithm is described in Section 10.4 to reduce the
losses through optimal reconguration and placement of EVCS. There are various
techniques such as genetic algorithm (GA), game-theoretical framework, fuzzy logic,
and so on available in the literature to optimally place the EVCS. In the study of
Sanchari Deb et al. 2017, the optimum position of the EVCS is determined by a GA
that does not analyze losses and conductor thermal limits, affecting the reliability
of the grid connectivity distribution system. Including device parameter limits, such
as voltage, current, and temperature rise in the conducts, can regulate the effect of
EVCS. The EVCS optimal placement not only inuences system performance but also
adds extra cost to the distribution grid through charging devices (Karmaker 2019),
land, and extra conductors, and so on. A game theoretical framework (Sanchari Deb,
et al 2018) is used for nding the optimal location of EVCS. The power quality issue is
also one of the problems faced by the distribution grid with the incorporation of EVCS
without proper planning. Charging methodologies, vehicle density at EVCS, and so on
are the inuencing factors for EVCS to impact the distribution grid. In this chapter,
the PSO optimization algorithm is used for placement and reconguration of RDS.
The initialization plays a key role in formulating and further processing the algo-
rithm. The size of the initialization particle matrix depends on the number of par-
ticles and dimensions of the problem. In the case of EVCS placement in RDS, the
number of EVCS locations resemble the dimensions of the algorithm, whereas in
the case of reconguration of RDS, tie line switches resemble the dimensions of the
algorithm. The algorithm for PSO to solve the optimization problem is as follows:
Step 1: The particles are initialized with a random number by considering the
constraints
xx
11 12
xx
21 22
X=−
−
−−
xx
np12np
165Electric Vehicle Charging Stations Impact
Step 2: Initialize the PSO parameters.
Step 3: Determine the tness for every particle at each iteration.
Step 4: Verify the minimum tness for every generation.
Step 5: Particles at every iteration is updated with (10.14).
X(Update)1
=×WX+×CPrand(1)r×−
()
best XC+×
2b
and(1) ×−
()
GX
est (10.14)
where
()
WW−
WW=−
max min
max×−(iter1)
(ng1−)
Step 6: Set the updated positions within its constraints.
Step 7: is iter ngif yes display optimum values else go to step 3 with iter it er 1.== = +
10.6 OP TIMAL PLACEMENT OF EVCS USING PSO
The placement of EVCS using PSO for IEEE 16 bus RDS system is analyzed by
dividing the problem formulation into four scenarios and at each scenario, the impact
of EVCS is studied by choosing three different cases as mentioned below:
Scenario 1: Placement of EVCS on an existing RDS (single-step optimization
process)
Scenario 2: Placement of EVCS before reconguration on an existing RDS
(two-step optimization process)
Scenario 3: Placement of EVCS after reconguration on an existing RDS
(two-step optimization process)
Scenario 4: Placement of EVCS along with reconguration on an existing RDS
(simultaneous two-step optimization process)
The particle matrix (X) chosen for each scenario is illustrated in Table 10.6.
The cases at each scenario are as follows:
Case 1: Single charging station of 3 MW placed at the optimal location.
Case 2: Single charging station of 4.5 MW placed at the optimal location.
Case 3: Two charging stations of 3 and 1.5 MW are placed at optimal locations.
The formulated algorithms for load ow and reconguration of RDS are used as objec-
tive function with desired current and voltage constraints, which is mentioned as follows.
n
f==mi ∑∑
branch nbranch
obj nTL VI
*
lm lm lm (10.15)
lm==1lm 1
where lm is the branch number
0.95 < Ibranch < 1.05
0.95 < Vbus < 1.05
166 Green Engineering and Technology
10.6.1 optimal reConfiguration of 16 BuS rdS WitHout evCS
Section 5 outlines the reconguration of the 16-bus RDS. Using PSO to mitigate
losses, optimal reconguration is achieved in this section. As explained earlier,
choosing the number of switches based on the number of loops in the system is
the major challenge in RDS reconguration. The major requirements for recon-
guration of RDS are that the switches that are supposed to be opened should not
be part of other loops without disturbing the systems’ radial nature. The above
requirements can be easily met by using a loop matrix (LM), which has the ele-
ments as the branch numbers. Thus, the derived loop matrix for the IEEE 16 bus
RDS is as follows:
TABLE 10.6
Initialization of Particles at PSO for Each Scenario
Scenario Type
Scenario 1
Particle Initialization at the
First Step
x
11 x
12
x
21 x
22
x
31 x
X=32
−−
−−
x
np1
x
np2
Particle Initialization at the
Second Step
Scenario 2 x
11 x
12
x
21 x
22
xx
X=31 32
−−
−−
x
np1
x
np2
xx
11 12 x13
x
21 x
22 x23
xxx
X=31 32 33
−−−
−−−
x
np1
x
np2
xnp3
Scenario 3 x
11 x
12 x13
x
21 x
22 x23
x
31 x
32 x33
X=
−−−
−−−
x
np1
x
np2
xnp3
x
11 x
12
x
21 x
22
xx
X=31 32
−−
−−
x
np1
x
np2
xx x
11 12 x
13 14 x15
x
21 x
22 x
23 x
24 x25
x
X=31 xx
32 x
33 x
34 35
−−−−−
−−−−−
x
np1x
np x
2
x
np3np4
xnp5
Scenario 4
167Electric Vehicle Charging Stations Impact
25890
LM=3413 15 16
7111400
In the LM, each row indicates the formation of the loop with distribution branches. The
rst loop is formed between feeder-1 and feeder-2. Branches 1 and 6 are not selected
since the substation is disconnected while opening these branches. The second loop is
formulated with feeder-1 and feeder-3. In this loop, also branch 12 is not selected as it
disconnects the substation from the other part of the RDS. The third loop is formed
between feeder-2 and feeder-3. Finally, by observing the LM, branch 10 is also not
selected because it makes the bus 12 idle. Therefore among 16 branches, 12 branches are
effectively used for formulating optimal reconguration of RDS. In other words, 12C3
combinations need to be analyzed to optimize the reconguration of RDS. Thus, there
is a necessity of a heuristic algorithm to optimize the reconguration of RDS.
The PSO is successfully implemented for the optimal reconguration of RDS select
branches 7, 9, and 16, reducing the losses from 511.43 to 466.12 kW. The voltage pro-
le was found to be improved, which resembles that at a minimum voltage bus. The
minimum voltage is improved from 0.969 to 0.971 pu, which is shown in Figure 10.7.
The current through the optimized branches is also reduced. By reconguration, the
load is shifted from feeder 2 to feeder 1 and feeder 3. Therefore, the current through
the branches 1 and 2 increases from the value of the existing RDS (Figure 10.8).
10.6.2 optimal plaCement of evCS WitHout reConfiguration (SCenario 1)
The optimization algorithm begins with the initialization of the parameters of PSO
like the number of particles, initial velocity, nal velocity, minimum inertia, maxi-
mum inertia, and a maximum number of generations. The particle matrix dimension
0246810 12 14 16 18
0.965
0.970
0.975
0.980
0.985
0.990
0.995
1.000
Voltage Profile(p.u)
Bus number
Without EVCS
With Reconfig
FIGURE 10.7 Voltage prole of 16 bus RDS with and without reconguration.
168 Green Engineering and Technology
12345678910111213141516
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
Branch Current profile(p.u)
Branch numbers
Without Reconfig
WithReconfig
FIGURE 10.8 Comparative analysis of branch currents of 16 bus RDS with and without
reconguration.
is equal to the number of variables of the objective function, which have lower
and higher limits. In this case, the number of tie line switches is the variables of
the system. The particles that are initialized earlier are updated at the end of each
generation by using inertia. At every generation, a minimum value of the objective
function is determined, and nally, the minimum value of the objective function is
determined among all generations. The dimensions for the existing problem are the
number of EVCS.
Here, in this scenario, the placement of EVCS on an existing RDS is planned
such as to reduce the losses. The EVCS optimal location is obtained as bus 13
corresponding to cases 1 and 2 using PSO. While in case 3, the additional EVCS
is assigned at bus 4. The voltage prole of 16 bus RDS with and without charging
station is shown in Figure 10.9. By connecting the 3.0 MW charging station at bus
13, the real power losses increase from 511.4 to 556.14 kW. In comparison to feed-
ers 1 and 2, feeder 3 has a lesser load as shown in Table 10.7. Therefore, the PSO
algorithm chooses a bus nearer to feeder-3. The same is true under case 2 where
4.5 MW EVCS is to be optimally located but with more losses. The losses further
rise to 586.320 kW. To reduce the losses in the system, case 3 is introduced, which
divides large EVCS between the two feeders 1 and 3 locating them at bus numbers
4 and 13. The load among the three buses is balanced after incorporating charg-
ing stations (CS) at less sensitive buses (Hajimiragha et al. 2010). The real power
loss is 577.65 kW in case 3, which is higher than case 1 and lower than case 2. The
bus voltages of RDS are given in Figure 10.9. Figure 10.10 illustrates the branch
currents in the above-mentioned three cases. It can be observed that the 1st (under
case 3) and 12th (under cases 1, 2, and 3) branch currents are showing a higher
value, which enhances the losses of the system since the EVCS are connected to
buses closer to these branches.
169Electric Vehicle Charging Stations Impact
0246810121416
0.965
0.970
0.975
0.980
0.985
0.990
0.995
1.000
Voltage profile(p.u)
Bus number
Without EVCS
With 3.0 MW EVCS
with 4.5 MW EVCS
with 3.0 MW and 1.5 MW EVCS
FIGURE 10.9 Voltage prole of 16 bus RDS under various cases of scenario 1.
TABLE 10.7
Comparative Analysis of 16 Bus RDS with EVCS and without Reconguration
Open Real Power Minimum Optimal EVCS
Cases Switches Losses (kW) Voltage (bus) Bus Location
Without EVCS 5, 11, 16 511.43 0.9693(12) -
Case 1: 3 MW EVCS 5, 11, 16 556.14 0.9693(12) 13
Case 2: 4.5 MW EVCS 5, 11, 16 586.30 0.9693(12) 13
Case 3: 3 MW and 1.5 MW EVCS 5, 11, 16 577.64 0.9693(12) 13 and 4
024681012141618
-0.02
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
Branch current profile (p.u)
Branch numbers
Without EVCS
With 3.0 MW EVCS
With 4.5 MW EVCS
With 3.0 MW and 1.5 MW EVCS
FIGURE 10.10 Comparative analysis of branch currents of 16 bus RDS under various cases
of scenario 1.
170 Green Engineering and Technology
10.6.3 optimal plaCement of evCS Before reConfiguration (SCenario 2)
The EVCS that are optimally placed in the system act as additional loads to the
system during charging of EV that ultimately hikes current through the branches.
The increase in current through the branches increases the voltage drop across the
branch and increases the losses of the system. It draws additional current from the
grid, which affects the performance. Hence, after including the EVCS and other
additional compensating devices, the system should improve its performance in
terms of voltage prole and reduction of losses. Therefore, in this scenario, RDSs
are examined further and incorporated reconguration to reduce the losses after
optimally locating the EVCS.
In the previous scenario, EVCS are incorporated optimally without recon-
guration. In this scenario, EVCS are optimally located before reconguration of
IEEE 16 bus RDS. The voltage prole corresponding to different cases is shown
in Figure 10.11. The real power losses of the RDS can be reduced even after locat-
ing the EVCS optimally through optimal switching or reconguration. Compared
to the previous scenario, it is observed that the voltage prole is improved, and real
power losses are reduced with reconguration. Similarly, the branch currents also
are adjusted with reconguration as illustrated in Figure 10.12 for various cases con-
sidered for this study. As shown in Figure 10.12, the 1st and 2nd branch currents are
increased after reconguration of the RDS. Branches 5, 11, and 16 are not carrying
any current while placing the EVCS. However, during reconguration, the branches
7, 9, and 16 do not carry any current as these branches contribute to the optimally
selected path. This change in topology reduces the real power losses and improves
the bus voltages of RDS. The variation in the losses, minimum voltage of the system,
and optimal opened switches for each case are shown in Table 10.8.
024681012141618
0.965
0.970
0.975
0.980
0.985
0.990
0.995
1.000
Bus Voltage Profile (p.u)
Bus number
Without EVCS
With 3.0 MW EVCS
With 3.0 MW EVCS and Reconfig
With 4.5 MW EVCS
With 4.5 MW EVCS and Reconfig
With 3.0 MW EVCS and 1.5 MW EVCS
With 3.0 MW EVCS and 1.5 MW EVCS and Reconfig
FIGURE 10.11 Bus voltages for various cases at scenario 2.
171Electric Vehicle Charging Stations Impact
10.6.4 optimal plaCement of evCS after reConfiguration (SCenario 3)
The system performance can be improved by reconguration, a cost-effective solu-
tion, without using any additional compensating devices like distributed generations
(DGs). This optimizes the available resources. The performance of the distribution
system is thoroughly investigated under this scenario.
The bus voltages are reduced with increased losses with the inclusion of EVCS
at optimal locations in the previous scenario. Hence, the voltage prole has to be
enhanced through reconguration in the existing topology of the RDS. Here, PSO
nds the optimal location of EVCS and the optimal reconguration path to reduce
the losses thereby improving the voltage prole of the buses in the balanced radial
distribution systems (BRDS). The voltage prole and the branch currents of IEEE
024681012141618
0.00
0.05
0.10
0.15
0.20
Branch current profile (p.u)
Branch number
Without EVCS
With 3.0 MW EVCS
With 3.0 MW EVCS and Reconfig
With 4.5 MW EVCS
With 4.5 MW EVCS and Reconfig
With 3.0 MW and 1.5 MW EVCS
With 3.0 MW EVCS and 1.5 MW EVCS and Reconfig
FIGURE 10.12 Branch currents for various cases under scenario 2.
TABLE 10.8
Comparative Analysis of Losses at Scenario 2
Open Real Power Minimum Optimal EVCS
Cases Switches Losses (kW) Voltage (Bus) Bus Location
Without EVCS 5, 11, 16 511.43 0.9693(12) -
Case 1: 3 MW EVCS 5, 11, 16 556.14 0.9693(12) 13
Case 1: Reconguration with EVCS 7, 9, 16 517.90 0.9716(12) 13
Case 2: 4.5 MW EVCS 5, 11, 16 586.30 0.9693(12) 13
Case 2: Reconguration with EVCS 7, 9, 16 551.68 0.9716(12) 13
Case 3: 3 MW and 1.5 MW EVCS 5, 11, 16 577.64 0.9693(12) 13 and 4
Case 3: Reconguration with EVCS 7, 9, 16 540.81 0.9716(12) 13 and 4
172 Green Engineering and Technology
0246810 12 14 16 18
0.965
0.970
0.975
0.980
0.985
0.990
0.995
1.000
Voltage Profile(p.u)
Bus number
Without EVCS
With Reconfig
With 3.0 MW EVCS
With 4.5 MW EVCS
With 3.0 MW and 1.5 MW EVCS
FIGURE 10.13 Bus voltage prole of IEEE 16 bus RDS for various cases.
0246810 12 14 16 18
-0.02
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
Branch Current profile (p.u)
Branch number
Without EVCS
With Reconfig
With 3.0 MW EVCS
With 4.5 MW EVCS
With 3.0 MW and 1.5 MW EVCS
FIGURE 10.14 Branch currents for various cases.
16 bus RDS corresponding to the three cases are shown in Figures 10.13 and 10.14,
respectively. The voltage prole is improved as shown in Figure 10.13, and the losses
are reduced to 466.12 kW with reconguration (Table 10.9).
10.6.5 optimal plaCement of evCS along WitH
reConfiguration (SCenario 4)
To study the performance of the RDS, the optimal placement of EVCS is furnished
along with the reconguration. The algorithm for placing EVCS along with optimal
reconguration of RDS is given in Table 10.10.
173Electric Vehicle Charging Stations Impact
In this scenario, optimal placement of EVCS and reconguration are carried out
simultaneously. In the previous scenarios 2 and 3, the optimal placement of EVCS
and optimal combination of open switches for reconguration are operating sequen-
tially, which consumes more computation time for predicting the correct result.
Hence, the major focus here is not only on optimal placement of EVCS but also on
optimal reconguration to reduce the computation time of the system using a proper
algorithm. The voltage prole corresponding to each case of increasing capacity of
EVCS is shown in Figure 10.15. The change in branch currents at every case is shown
in Figure 10.16. The currents through branches 5, 11, and 16 are zero, which is the
condition in the existing RDS. Similarly, the currents in branches 7, 9, and 16 are
zero as they are an optimal combination of reconguration of the system. The current
in branches 1 and 2 have increased as they are directly connected to the bus 4, which
is the optimal bus location for EVCS. The impact of EVCS on the RDS in terms of
real power losses and minimum voltage is shown in Table 10.10. The losses of the
system are controlled by optimal placement of EVCS along with reconguration.
The real power loss for the existing RDS is 511.43 kW, which is raised to 515.50 kW
with the simultaneous placement of EVCS and reconguration. But the selection of
the optimal location is similar to scenario 2 and is different from scenarios 1 and 3.
Therefore, the real power losses are lesser compared to scenario 2.
TABLE 10.9
Optimal Placement of EVS before Reconguration
Open Real Power Minimum Optimal EVCS
Cases Switches Losses (kW) Voltage (Bus) Bus Location
Without EVCS 5, 11, 16 511.43 0.9693(12) -
Reconguration 7, 9, 16 466.12 0.9716(12)
Case-1: 3.0 MW EVCS Placement 7, 9, 16 515.50 0.9716(12) 4
Case-2: 4.5 MW EVCS Placement 7, 9, 16 545.56 0.9716(12) 4
Case-3: 3.0 MW and 1.5 MW 7, 9, 16 538.78 0.9716(12) 4 and 13
placements
TABLE 10.10
Optimal Placement of EVS Along Reconguration
Cases
Open
Switches
Real Power
Losses (kW)
Minimum
Voltage (Bus)
Optimal EVCS
Bus Location
Without EVCS 5, 11, 16 511.43 0.9693(12) -
Case 1: 3.0 MW EVCS and
reconguration
7, 9, 16 515.50 0.9716(12) 4
Case 2: 4.5 MW EVCS and
reconguration
7, 9, 16 545.56 0.9716(12) 4
Case 3: 3.0 MW and 1.5 MW EVCS
and reconguration
7, 9, 16 538.78 0.9716(12) and 13
174 Green Engineering and Technology
FIGURE 10.15 Bus voltages for scenario 4.
FIGURE 10.16 Branch currents at every case for scenario 4.
175Electric Vehicle Charging Stations Impact
10.7 CONCLUSION
The proposed method is validated with standard three feeder IEEE 16 bus balanced
RDS considering four scenarios. Each scenario has been studied with possible three
cases optimally locating the EVCS. The objective of each scenario is not only to
reduce the losses but also to improve the voltage prole and thermal stability. The
optimal placement of EVCS onto the existing BRDS has increased the losses. With
the increase in capacity of the charging infrastructure, the losses are increasing.
However, by distributing the infrastructure, the losses decrease with optimal place-
ment of EVCS to two different feeders with deterioration in the voltage prole along
with higher branch currents. Then, the two-step optimization method is proposed to
optimize the location of EVCS before and after the optimized reconguration. Here
also, distributed EVCS has proven reduced losses at the cost of computational com-
plexity. Finally, the simultaneous two-step process of optimally placing EVCS along
with reconguration shows minimum losses with less computation time, but higher
than an existing system with stable current and voltage limits. Thus, the proposed
scheme gives the optimal solution for improving the electric transportation infra-
structure without using external compensating devices.
Furthermore, the algorithm can also be extended to the dynamic load of the system.
As the loads are not static, the EVCS can be scheduled as per the load curve. If the
EVCS works during the light load conditions of the load curve, then the load factor of
the system will increase, which, in turn, will decrease the generation cost at generating
stations. Similarly, when EVCS is operated during heavy load conditions of the load
curve, DGs need to be implemented to atten the load curve with respect to the distribu-
tion grid. Furthermore, all these schemes can be extended to practical unbalanced RDS.
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177
11 Parameter Estimation
of a Single Diode PV
Cell Using an Intelligent
Computing Technique
Shilpy Goyal, Parag Nijhawan,
and Souvik Ganguli
Thapar Institute of Engineering & Technology
11.1 INTRODUCTION
Due to many factors, such as the price of fossil fuel and its possibility of depletion
and social and environmental issues, renewable energy systems, also coined as green
energy systems, have gained much attention universally. General observation reveals
that energy harvesting from solar energy is increased to meet the demand for elec-
tricity in developing countries, carbon dioxide release obligations, and price reduc-
tion of photovoltaic (PV) modules [1]. Solar systems are thus commonly used in the
form of major PV modules in the production of electricity [2]. The simulation of solar
PV models usually consists of two stages that are the formulation of the mathemati-
cal model and the selection of parameters.
Moreover, the real presence of PV modules is generally inuenced by the unspeci-
ed parameters that can be error-prone and unbalanced when regularly encountering
aging, degradation, and unpredictable operating conditions of the system. Precise
CONTENTS
11.1 Introduction 177..................................................................................................
11.2 Problem Formulation 179 ....................................................................................
11.3 Proposed Optimization Technique 180...............................................................
11.3.1 Various Phases of HHO 181....................................................................
11.3.1.1 Diversication Phase 181..........................................................
11.3.1.2 Turning from Diversication to Intensication 182.................
11.3.1.3 Intensication Phase 182..........................................................
11.4 Results and Discussions 182 ................................................................................
11.5 Conclusions 205...................................................................................................
References 205 ..............................................................................................................
178 Green Engineering and Technology
recognition of PV module parameters is, therefore, a crucial step in advance to fur-
ther simulate and congure PV systems [3].
Presented methods are generally categorized into three types, namely, analytical
methods, numerical methods, and metaheuristic methods. In the datasheet, the volt-
age at the maximum power point (Vmp), current at the maximum power point (Imp),
short-circuit current (ISC), and open-circuit voltage (VOC) are provided [4]. The cor-
rectness of parameter estimation by analytical methods is mainly dependent on the
accurate location of these established parameters on the characteristics of solar PV
production [3].
In estimating specic electrical parameters of solar PV, the Newton-Raphson
(NR) method [5], Lambert W function [6], and Gauss-Seidel (GS) methods have
often been considered among the numerous existing numerical methods. Although
these statistical methods are more accurate than the analytical methods, these meth-
ods also bear long calculation and convergence time. They converge to local maxima
rather than global in case of incorrect selection of the rst value, particularly in NR
and GS methods.
Also, in the past year, more attention has been paid to metaheuristic algorithms
due to their theoretical and mathematical simplicity, resulting in some complex prob-
lems used effectively and exibly. Another factor is that if an appropriate balance
between fundamental modes can be obtained, then they can conduct a very efcient
response with a relatively quick and efcient search [7].
Metaheuristic techniques of various models, therefore, used to evaluate param-
eters of the PV system, such as the articial bee colony (ABC), were used to dene
parameters of solar cell models [8, 9]. Furthermore, for this problem, a modied ABC
was introduced, and relevant results were achieved. An enhanced opposition-based
whale optimization algorithm (WOA) was used for the parameter estimation of the
solar module and the tests and comparisons have veried the implementation of the
planned method [10]. Moth-ame optimization (MFO) was used for the extraction of
solar cell parameters [11]. A new hybrid Bee pollinator Flower Pollination Algorithm
(BPFPA) was used for the parameter estimation of both single diode model (SDM)
and double diode model of PV cells [12]. Convergence Particle Swarm Optimization
(GCPSO) was used to determine the PV parameters of both the single-diode and the
double-diode models [13]. The hybrid Jaya-NM algorithm was used for identifying
parameters of the single diode PV model [14]. An Ant Lion Optimizer (ALO) with
Lambert W function had been used for parameter estimation of a SDM of PV cells
[15]. A Multiple Learning Backtracking Search Algorithm (MLBSA) was used to
improve the parameter identification of a different single diode, double diode, and
PV module of PV cells [16]. An Improved Shufed Complex Evolution (ISCE) algo-
rithm was used for parameter extraction of a SDM and double diode model of PV
cells [17]. An integrated Firey and Pattern Search (HFAPS) algorithm was devel-
oped, in which pattern search served as a local optimization method with the Firefly
algorithm as a global optimizer to improve this algorithm [18]. An improved version
of the WOA that used opposition-based learning to enhance the exploration of the
search space was used [10]. The Salp Swarm Algorithm (SSA) was used for extract-
ing the parameters of the electrical equivalent circuit of a double-diode model of
the PV cell [19]. The Performance-guided JAYA (PGJAYA) algorithm was used for
179Parameter Estimation of PV Cells
extracting parameters of a single diode, double diode, and PV module of PV models
[20]. The least root means square error was obtained using MFO’s implementation.
As these algorithms could produce relatively better results compared to analyti-
cal and numerical methods, certain drawbacks, for example, requiring much time
and premature convergence, were observed. Nevertheless, further advances are still
possible for most of those heuristic-based approaches.
HHO is a newly proposed metaheuristic method. HHO’s main inspiration is the
cooperative actions, and Harris hawks’ chasing style in nature is called surprise
pounce. This work mathematically imitates these intricate patterns and behaviors to
create an optimization algorithm [21]. The paper article is presented in the following
way: Section 11.2 explains the problem formulation, Section 11.3 explains the pro-
posed optimization technique, Section 11.4 describes the results and discussion, and
Section 11.5 gives the conclusion and future scope.
11.2 PROBLEM FORMULATION
Figure 11.1 shows the PV cell’s single diode equivalent model. I−V characteristics of
a single diode PV cell model is given by Eq. (11.1) [22]:
qV
()
+IRSVI+R
I=−II
PC Sexp1
−−series (11.1)
α
KT Rparallel
where IPC is the photocurrent, IS is the reverse saturation current, Rparallel and Rseries
are the equivalent parallel and series resistances, α is the ideality factor, q is the
absolute value of charge on an electron, T is the temperature in Kelvin, and K is the
Boltzmann constant.
At open circuit, I 0 and V VOC; then Eq. (11.1) becomes= =
qV V
0e=−II
PC SxpOC −1−OC
(11.2)
α
KT
Rparallel
Therefore,
qVOC V
IPC =ISexp1
−+OC
(11.3)
α
KT Rparallel
FIGURE 11.1 Representation of a single diode model.
180 Green Engineering and Technology
At the short circuit, V = 0 and I = ISC; then Eq. (11.1) becomes
qI R
ISC =−II SC series IR
SC series
PC Sexp1
−−
(11.4)
α
KT
Rparallel
Therefore,
qI R IR
I=+IIexp1
SC series −+SC series
PC SC S (11.5)
α
KT Rparallel
At a maximum power point, V = Vmp and I = Imp; then Eq. (11.1) gives
qV
()
mp +IR
mp series VI+R
Imp =−II
PC Sexp1
−−mp mp series (11.6)
α
KT R
parallel
The goal is to estimate accurate parameters of PV cells for all three main conditions
(open circuit, short circuit, and maximum power point). Therefore, to minimize the
errors, an optimization algorithm is needed at these three points:
Equation (11.3) calculates error at the open circuit:
qV V
erre
OC =Ixp OC
S −
1+−
OC
I (11.7)
α
PC
KT Rparallel
Equation (11.5) calculates error at the short circuit point:
qI
=+ SCR
erre
series IR
IIxp −1+−
SC series
SC SC S( 8
I 11. )
α
PV
KT Rparallel
Equation (11.6) calculates error at the maximum power point:
qV
()
+IR VI+R
erre=− series
II
mp mp
mp PC Sxp −1−mp mp series −I
α
mp (11.9)
KT
R
parallel
The optimization objective is known to be the sum of the squared errors, and the
algorithm should obtain the lowest error or preferably zero error as well as the objec-
tive function given in Eq. (11.10):
erre=+rr2erre
2+rr2
OC SC mp (11.10)
11.3 PROPOSED OPTIMIZATION TECHNIQUE
The Harris hawks optimization (HHO) algorithm was encouraged by the collective
discipline, together with the Harris hawks’ chasing manner [21]. For various scientic
applications, this algorithm has been used successfully. Hawks, to surprise their prey,
181Parameter Estimation of PV Cells
swooped on them cooperatively from different paths. Furthermore, Harris hawks
could select the type of chase based on distinct prey ight patterns. HHO has three
base stages, including excellent pounce, prey tracking, and other attacking tactics of
different kinds [23]. Various phases of HHO are shown in Figure 11.2. The rst stage
is called “Diversication” in a glance and is modeled on waiting, searching, and dis-
covering the desired hunt mathematically. This algorithm’s second stage is turning
exploration to exploitation based on a rabbit’s external energy. Finally, in the third
phase called “Intensication” considering the prey’s residual heat, hawks usually take
on a soft and sometimes difcult surrounding to hunt the rabbit from new directions.
11.3.1 variouS pHaSeS of HHo
11.3.1.1 Diversication Phase
In this phase, the desired hunt is calculated to wait, search, and discover mathemati-
cally. The iter + 1 (Harris hawks’ position) is mathematically described as follows [24]:
Xr
ra (iter) −−
(
1r
Xr
an 2(
Xq≥0.5
nd d2
iter) iter) if
X(iter) 1
+=
(11.11)
()
−−iter)
()
+−
XX
rabbit3
(iter) (
mrLBr(
4UB LB)ifq<0.5
where Xrabbit refers to the rabbit position, iter refers to the current iteration, Xrand
refers to the randomly selected hawk in the given population, ri,i = 1, 2, 3, 4, q are
FIGURE 11.2 Different phases of Harris hawks optimization (HHO) [21].
182 Green Engineering and Technology
random numbers in the range [0, 1]. Xm represents the average hawk position and is
determined as follows:
1
X=∑
N
mi
(iter) X(iter)
Ni=1
(11.12)
where Xi shows the hawk’s position and N is the hawk’s length.
11.3.1.2 Turning from Diversication to Intensication
Taking T as the maximum repetition size and E0∈−(1,1) as the initial energy in each
step, HHO measures the rabbit eeing energy (E) by Eq. (11.13). Diversication and
intensication can be modied for this quality.
iter
E=−21E0
T (11.13)
In this sense, if |E| ≥ 1, the diversication phase begins; otherwise, the neighborhood
of the solutions will be exploited [24].
11.3.1.3 Intensication Phase
The hawks may nd a soft or hard siege to capture it from different directions,
depending on the prey’s residual strength. A so-called parameter “r” is used to calcu-
late the escaping chance of the prey. According to that, r < 0.5 represents a successful
escape. Moreover, when |E| ≥ 0.5, HHO takes soft surroundings and hard surround-
ings are applied when |E| < 0.5 is used. It is worth noting that even if the prey can
escape (i.e.,|E| ≥ 0.5), it also depends on r for its success. The attack technique is
conditioned by the prey and hawks’ escape and chase strategy, respectively [24].
11.4 RESULTS AND DISCUSSIONS
The HHO algorithm is used to solve the ve parameters of SDM and then com-
pare it with other well-known algorithms such as the SSA, Grey Wolf Optimization
(GWO), Sine Cosine Algorithm (SCA), and Dragony Algorithm (DA). Different
datasets were used and are reported in Table 11.1 with the manufacturer name and
model numbers, where Vm is the maximum power voltage, Im is the maximum power
current, VOC is the open-circuit voltage, ISC is the short circuit current, Ns is the num-
ber of cells, and T is the temperature. The number of search agents used in this work
is 50, and the number of iterations is 1000.
Different algorithms are used to solve ve parameters (IPV, α1, RS, Rp, I01) of SDM.
The boundaries of parameters are listed in Table 11.2 where IPV is the PV current,
α1 is the ideality factor of the rst diode, RS is the series resistance, Rp is the parallel
resistance, and I01 is the reverse saturation current of the rst diode.
Table 11.3 gives the estimated parameters and error of SDM with different algo-
rithms for the model named KC200GT. The parameters are calculated with 30 runs
so that we perform the statistical analysis of the error function.
183Parameter Estimation of PV Cells
TABLE 11.1
Datasheet of Three Marketable PV Modules at STC [4,25]
Company Kyocera Canadian Solar Schutten Solar Solar World
Model KC200GT CS6K-280M STM6 40-36 Pro. SW255
Cell type Multi-crystal Mono-crystalline Mono-crystalline Poly-crystalline
Vm [V] 26.3 31.5 16.98 30.90
Im [A] 7.61 8.89 1.50 8.32
VOC [V] 32.9 38.5 21.02 38.00
ISC [A] 8.21 9.43 1.663 8.88
Ns [cells] 54 60 36 60
T [°C]25 25 51 25
TABLE 11. 2
Ranges of the Decision Variables
Parameters Lower Bound Upper Bound
IPV(A) (photovoltaic current) 0.001 15
α1 (ideality factor) 0.5 7
RS(Ω) (series resistance) 0.001 0.5
Rp(Ω) (parallel resistance) 0.01 500
IS(µA) (reverse saturation current) 0 1
TABLE 11. 3
Optimal Parameters of SDM Using Different Algorithms for KC200GT
Run Algorithm IPV α1RSRpI01 Error
1 HHO 8.2171 1.4032 0.1759 404.55 3.69e-07 4.59e-05
SSA 9.8292 4.5570 0.1869 4.5703 2.15e-08 23.3709
GWO 9.2196 5.0000 0.0081 5.0096 5.95e-08 21.3711
SCA 9.3365 5.0000 0.0187 5.0321 0.00000 21.5546
DA 8.4887 1.4919 0.2571 82.173 1.00e-06 0.16687
2 HHO 8.2815 1.3089 0.2618 290.00 1.09e-07 0.00916
SSA 9.6927 4.7621 0.2166 4.7719 1.84e-07 23.3093
GWO 9.2194 5.0000 0.0071 5.0079 8.25e-08 21.3630
SCA 9.4067 5.0000 0.0018 4.9945 0.00000 21.4392
DA 8.4052 1.3219 0.3221 191.18 1.31e-07 0.07718
3 HHO 8.1918 1.4822 0.1045 345.81 9.05e-07 0.00094
SSA 9.4346 5.0000 0.2007 5.0083 5.36e-07 22.9400
GWO 9.2129 5.0000 0.0026 5.0084 4.93e-07 21.3275
SCA 9.2797 5.0000 0.0010 4.9860 1.51e-08 21.3731
DA 8.4341 1.4547 0.3044 396.72 6.87e-07 0.10780
(Continued )
184 Green Engineering and Technology
TABLE 11. 3 (Continued )
Optimal Parameters of SDM Using Different Algorithms for KC200GT
Run Algorithm IPV α1RSRpI01 Error
4 HHO 8.4378 1.3481 0.2489 68.736 1.80e-07 0.08940
SSA 10.108 4.1296 0.0109 4.1511 4.33e-07 23.1954
GWO 9.2115 5.0000 0.0011 5.0088 5.05e-08 21.3160
SCA 9.2871 5.0000 0.0027 5.0687 0.00000 21.7352
DA 8.9533 1.4314 0.5000 250.94 5.46e-07 1.35290
5 HHO 8.2125 1.4887 0.0696 150.13 9.58e-07 3.67e-06
SSA 9.7614 4.9362 0.4407 4.9448 5.30e-07 25.1216
GWO 9.2139 5.0000 0.0010 5.0083 3.05e-07 21.3150
SCA 9.3441 5.0000 0.0054 5.0178 0.00000 21.4109
DA 8.9140 1.4492 0.5000 500.00 6.71e-07 1.34930
6 HHO 8.1730 1.3866 0.1381 406.22 3.00e-07 0.00351
SSA 9.3095 4.9954 0.0863 5.0039 3.90e-08 21.9964
GWO 9.2228 5.0000 0.0085 5.0081 8.21e-07 21.3738
SCA 9.3018 5.0000 0.0016 5.0225 8.11e-07 21.3682
DA 8.4080 1.4161 0.3051 322.91 4.38e-07 0.08888
7 HHO 8.3164 1.4802 0.2125 275.37 8.95e-07 0.02395
SSA 9.4307 5.0000 0.2011 5.0083 4.30e-07 22.9431
GWO 9.2181 5.0000 0.0104 5.0082 1.68e-10 21.3892
SCA 9.3202 5.0000 0.0010 5.0041 0.00000 21.3486
DA 8.3511 1.4502 0.2043 133.47 6.36e-07 0.03258
8 HHO 8.2144 1.4281 0.2037 408.14 4.95e-07 0.00976
SSA 10.684 3.9520 0.2147 3.9737 8.03e-07 25.5834
GWO 9.2268 5.0000 0.0112 5.0089 2.56e-07 21.3954
SCA 9.1830 5.0000 0.0025 5.0314 7.36e-07 21.3798
DA 8.4082 1.4249 0.3139 439.51 4.87e-07 0.10177
9 HHO 8.1975 1.4911 0.1136 418.05 9.98e-07 0.00046
SSA 10.338 4.3783 0.4446 4.3937 1.96e-07 26.0418
GWO 9.2151 5.0000 0.0032 5.0083 2.83e-08 21.3330
SCA 9.1498 5.0000 0.0062 4.9807 0.00000 21.4603
DA 8.1950 1.4889 0.1078 370.86 9.74e-07 0.00064
10 HHO 8.1353 1.3756 0.0586 207.62 2.58e-07 0.01226
SSA 9.2131 5.0000 0.0010 5.0086 7.36e-07 21.3149
GWO 9.2158 5.0000 0.0018 5.0081 6.61e-07 21.3219
SCA 9.2053 5.0000 0.0030 5.0151 1.21e-07 21.3354
DA 9.0462 1.4658 0.5000 138.50 8.02e-07 1.60650
11 HHO 8.3452 1.4573 0.2535 163.08 6.90e-07 0.07451
SSA 9.7316 4.6761 0.2100 4.6879 8.24e-07 23.3644
GWO 9.2220 5.0000 0.0078 5.0084 7.40e-09 21.3688
SCA 9.3069 5.0000 0.0048 4.9907 0.00000 21.3944
DA 8.2720 1.3627 0.2182 222.03 2.23e-07 0.00638
(Continued )
185Parameter Estimation of PV Cells
TABLE 11. 3 (Continued )
Optimal Parameters of SDM Using Different Algorithms for KC200GT
Run Algorithm IPV α1RSRpI01 Error
12 HHO 8.4061 1.4662 0.2737 207.21 7.71e-07 0.08894
SSA 10.060 4.6030 0.4136 4.6149 1.71e-07 25.2904
GWO 9.2172 5.0000 0.0037 5.0097 8.43e-07 21.3365
SCA 9.3872 5.0000 0.0033 4.9676 0.00000 21.5601
DA 8.2724 1.4341 0.0016 59.706 5.04e-07 0.00740
13 HHO 8.3829 1.4687 0.2539 206.77 7.91e-07 0.06414
SSA 9.6485 4.8190 0.2189 4.828 3.77e-08 23.2626
GWO 9.2260 5.0000 0.0093 5.0085 3.89e-07 21.3803
SCA 9.1270 5.0000 0.0010 5.0291 0.00000 21.3728
DA 8.3048 1.4404 0.1972 396.85 5.78e-07 0.00851
14 HHO 8.1327 1.2591 0.1313 241.84 5.24e-08 0.01413
SSA 9.9299 4.6585 0.3639 4.6702 7.80e-08 24.7416
GWO 9.2226 5.0000 0.0076 5.0082 6.27e-07 21.3668
SCA 9.3916 5.0000 0.0034 5.0027 0.00000 21.4247
DA 8.4103 1.4227 0.2930 237.20 4.72e-07 0.08528
15 HHO 8.2034 1.3889 0.1717 455.03 3.10e-07 0.00023
SSA 9.3307 5.0000 0.1099 5.0085 6.20e-08 22.1848
GWO 9.2158 5.0000 0.0035 5.0093 4.56e-07 21.3347
SCA 9.3282 5.0000 0.0010 4.9943 0.00000 21.3672
DA 8.4262 1.3545 0.3042 126.00 2.01e-07 0.09804
16 HHO 8.3244 1.4669 0.2110 208.13 7.71e-07 0.02579
SSA 9.8638 4.7477 0.3746 4.7581 6.46e-07 24.7148
GWO 9.2151 5.0000 0.0019 5.0090 2.61e-07 21.3225
SCA 9.2798 5.0000 0.0089 5.0091 0.00000 21.3872
DA 8.1681 1.2720 0.1936 478.44 6.42e-08 0.00531
17 HHO 8.2205 1.3801 0.1935 452.87 2.78e-07 0.00014
SSA 9.8754 4.7245 0.3769 4.7355 5.57e-07 24.7642
GWO 9.2238 5.0000 0.0110 5.0080 2.10e-08 21.3937
SCA 8.9550 5.0000 0.0048 5.0514 0.00000 21.6887
DA 8.2416 1.4912 0.1666 284.13 1.00e-06 0.00744
18 HHO 8.1770 1.4771 0.0977 418.28 8.57e-07 0.00266
SSA 9.5578 4.8996 0.2312 4.9086 7.79e-07 23.2800
GWO 9.2182 5.0000 0.0021 5.0084 1.07e-07 21.3243
SCA 9.2957 5.0000 0.0017 4.9967 0.00000 21.3504
DA 8.4917 1.4715 0.2615 92.040 8.06e-07 0.14166
19 HHO 8.3417 1.4179 0.2772 456.66 4.46e-07 0.04581
SSA 10.163 4.5062 0.4152 4.5194 3.93e-08 25.4834
GWO 9.2194 5.0000 0.0051 5.0089 1.40e-07 21.3476
SCA 9.2548 4.9018 0.0085 4.9258 0.00000 21.4789
DA 8.3559 1.4926 0.1286 92.390 1.00e-06 0.02427
(Continued )
186 Green Engineering and Technology
TABLE 11. 3 (Continued )
Optimal Parameters of SDM Using Different Algorithms for KC200GT
Run Algorithm IPV α1RSRpI01 Error
20 HHO 8.4861 1.4538 0.2259 130.52 6.74e-07 0.07367
SSA 9.8583 4.8354 0.4473 4.845 9.16e-07 25.2803
GWO 9.2140 5.0000 0.0045 5.0089 6.79e-07 21.343
SCA 9.2091 5.0000 0.0033 5.0208 0.00000 21.3484
DA 8.3382 1.3861 0.2331 500.00 3.05e-07 0.01570
21 HHO 8.1897 1.3579 0.1610 315.84 2.08e-07 0.00131
SSA 10.086 4.3722 0.2096 4.3873 2.82e-07 23.9775
GWO 9.2160 5.0000 0.0011 5.0086 1.88e-07 21.3165
SCA 9.3834 4.8989 0.0027 4.9201 0.00000 21.4539
DA 8.4433 1.4349 0.3082 333.17 5.49e-07 0.10616
22 HHO 8.2664 1.4909 0.1687 294.77 9.99e-07 0.00630
SSA 9.7644 4.9569 0.4618 4.9653 3.67e-07 25.3001
GWO 9.2161 5.0000 0.0012 5.0085 2.28e-09 21.3167
SCA 9.4574 5.0000 0.0726 5.0075 3.28e-08 21.9663
DA 8.4477 1.4904 0.2659 142.56 1.00e-06 0.11557
23 HHO 8.2751 1.3823 0.2377 454.37 2.88e-07 0.00863
SSA 9.5416 4.7046 0.0305 4.7153 2.96e-07 21.8689
GWO 9.2173 5.0000 0.0017 5.0087 8.25e-08 21.3206
SCA 9.0715 5.0000 0.0014 5.0291 0.00000 21.4095
DA 8.5222 1.4138 0.3947 500.00 4.32e-07 0.33451
24 HHO 8.2440 1.4431 0.1794 369.17 5.90e-07 0.00227
SSA 9.7224 5.0000 0.4824 5.0088 3.63e-07 25.4573
GWO 9.2152 5.0000 0.0041 5.0089 3.77e-08 21.3400
SCA 9.3240 5.0000 0.0010 5.0349 0.00000 21.4359
DA 8.3160 1.4899 0.2389 500.00 1.00e-06 0.03757
25 HHO 8.3655 1.3685 0.3243 499.13 2.44e-07 0.08086
SSA 9.7216 4.9012 0.3829 4.9102 4.06e-07 24.6197
GWO 9.2142 5.0000 0.0010 5.0088 7.19e-07 21.3149
SCA 8.9913 5.0000 0.0067 5.0141 0.00000 21.5144
DA 8.4439 1.0923 0.4029 85.788 2.96e-09 0.12163
26 HHO 8.4441 1.4391 0.1126 44.089 5.32e-07 0.09151
SSA 9.5734 4.7732 0.1369 4.7835 1.20e-07 22.6242
GWO 9.2220 5.0000 0.0075 5.0081 7.31e-07 21.3658
SCA 9.2295 5.0000 0.0012 5.0162 0.00000 21.3240
DA 8.2824 1.3194 0.2314 167.88 1.25e-07 0.00848
27 HHO 8.3171 1.4830 0.2261 430.19 9.28e-07 0.02647
SSA 9.7512 4.9633 0.4707 4.9721 9.44e-07 25.3766
GWO 9.2206 5.0000 0.0043 5.0082 7.32e-07 21.3412
SCA 9.2973 4.9883 0.0018 4.9683 0.00000 21.4211
DA 8.3360 1.4026 0.2481 219.26 3.68e-07 0.03006
(Continued )
187Parameter Estimation of PV Cells
It is very much clear from Table 11.3 that HHO gives the least error out of the other
algorithms, whereas DA also provides the error with near to HHO, but the d ifferent
three algorithms SSA, GWO, and SCA give the very high error value. Theconver-
gence graph is shown in Figure 11.3.
It is very much clear from the convergence graph that HHO outperforms all the
other algorithms; also, the HHO converges to the least error value. DA also exhibits
good convergence behavior. SSA, GWO, and SCA curves are somehow managing
to perform well. Table 11.4 gives the estimated parameters and error of SDM with
FIGURE 11.3 Convergence curve for SDM (Kyocera KC200GT).
TABLE 11. 3 (Continued )
Optimal Parameters of SDM Using Different Algorithms for KC200GT
Run Algorithm IPV α1RSRpI01 Error
28 HHO 8.2296 1.4766 0.1438 316.89 8.54e-07 0.00061
SSA 9.4408 5.0000 0.2094 5.0083 9.65e-07 23.0134
GWO 9.2190 5.0000 0.0074 5.0079 3.15e-07 21.3655
SCA 9.1117 5.0000 0.0144 5.0088 0.00000 21.4608
DA 8.3197 1.4679 0.1263 95.223 7.62e-07 0.01556
29 HHO 8.3263 1.2787 0.3183 444.54 7.21e-08 0.02918
SSA 10.011 4.6978 0.4567 4.7086 1.65e-07 25.5435
GWO 9.2159 5.0000 0.0019 5.0082 2.21e-07 21.3226
SCA 9.0436 5.0000 0.0010 5.0160 0.00000 21.4022
DA 8.2342 1.3816 0.2239 500.00 2.84e-07 0.00475
30 HHO 8.2044 1.3448 0.0158 76.210 1.69e-07 0.00010
SSA 9.2138 5.0000 0.0010 5.0086 5.91e-07 21.3149
GWO 9.2182 5.0000 0.0022 5.0084 2.13e-07 21.3247
SCA 8.9968 5.0000 0.0095 5.0104 0.00000 21.5338
DA 8.6188 1.4897 0.3503 107.49 1.00e-06 0.38944
188 Green Engineering and Technology
TABLE 11. 4
Optimal Parameters of SDM Using Different Algorithms for CS6K-280M
Run Algorithm IPV α1RSRpI01 Error
1 HHO 9.4715 1.5461 0.0254 400.84 8.98e-07 0.0035
SSA 11.383 4.4894 0.0208 4.5105 6.62e-07 32.317
GWO 10.763 5.0000 0.0012 5.0128 8.26e-07 30.754
SCA 9.8939 6.1470 0.0058 6.1193 0.00000 30.539
DA 9.5900 1.5142 0.1214 264.65 6.42e-07 0.0529
2 HHO 9.5273 1.3821 0.1453 281.33 1.32e-07 0.0236
SSA 10.913 5.0000 0.1234 5.0130 5.78e-08 32.089
GWO 10.770 5.0000 0.0045 5.0124 3.77e-08 30.790
SCA 9.6139 6.7527 0.0010 6.7593 0.00000 30.889
DA 9.7010 1.4827 0.2730 500.00 4.58e-07 0.3759
3 HHO 9.5383 1.3952 0.1455 328.75 1.57e-07 0.0238
SSA 10.744 5.0000 0.0010 5.0135 1.56e-07 30.753
GWO 10.207 5.6945 0.0069 5.7000 0.00000 30.316
SCA 10.320 5.7007 0.0016 5.7254 0.00000 30.352
DA 10.088 1.4931 0.3892 360.02 5.27e-07 1.4582
4 HHO 9.6287 1.5510 0.0742 481.34 9.63e-07 0.0390
SSA 11.439 4.9215 0.4363 4.9342 1.86e-07 35.964
GWO 10.199 5.7024 0.0010 5.7075 1.34e-08 30.254
SCA 10.150 5.8517 0.0017 5.8297 0.00000 30.354
DA 9.4995 1.3893 0.0871 188.50 1.43e-07 0.0090
5 HHO 9.5731 1.4851 0.0593 112.20 4.54e-07 0.0408
SSA 11.022 5.0000 0.2140 5.0131 5.51e-07 33.121
GWO 10.203 5.6995 0.0041 5.7060 5.04e-07 30.286
SCA 10.915 5.2059 0.0021 5.2094 0.00000 30.821
DA 9.7904 1.5536 0.2217 414.68 1e-06 0.2591
6 HHO 9.4530 1.5532 0.0012 379.40 9.63e-07 0.0011
SSA 11.160 5.0000 0.3076 5.0125 8.41e-07 34.228
GWO 10.194 5.7052 0.0018 5.7107 6.70e-07 30.261
SCA 9.8715 6.0868 0.0043 6.0789 3.87e-12 30.423
DA 9.3887 1.3790 0.0017 284.59 1.25e-07 0.0034
7 HHO 9.5365 1.4866 0.1170 253.88 4.69e-07 0.0417
SSA 11.016 5.0000 0.1932 5.0124 6.09e-07 32.882
GWO 10.187 5.713 0.0010 5.7196 4.36e-07 30.253
SCA 10.220 5.7490 0.0028 5.7819 0.00000 30.367
DA 9.5714 1.5566 0.0488 185.88 1e-06 0.0285
8 HHO 9.5722 1.4685 0.1512 478.98 3.86e-07 0.0467
SSA 11.281 5.0000 0.4398 5.0140 9.18e-07 35.854
GWO 10.204 5.7093 0.0138 5.7150 6.45e-07 30.389
SCA 10.348 5.3126 0.0033 5.2889 0.00000 30.613
DA 9.8393 1.3839 0.2782 114.41 1.36e-07 0.3812
(Continued )
189Parameter Estimation of PV Cells
TABLE 11. 4 (Continued )
Optimal Parameters of SDM Using Different Algorithms for CS6K-280M
Run Algorithm IPV α1RSRpI01 Error
9 HHO 9.4891 1.5430 0.0097 207.25 8.62e-07 0.0071
SSA 11.823 4.6158 0.4636 4.6333 9.01e-07 37.074
GWO 10.201 5.6924 0.0014 5.6980 7.53e-07 30.257
SCA 10.481 5.3740 0.0018 5.3513 0.00000 30.454
DA 9.4451 1.5039 0.0092 278.29 5.66e-07 0.0010
10 HHO 9.5346 1.5050 0.1045 476.65 5.81e-07 0.0232
SSA 10.913 5.0000 0.1257 5.0130 6.23e-07 32.115
GWO 10.195 5.7017 0.0018 5.7073 3.71e-07 30.262
SCA 10.137 5.8337 0.0139 5.8179 0.00000 30.447
DA 9.8902 1.4755 0.2957 415.01 4.30e-07 0.4651
11 HHO 9.5484 1.5557 0.0925 429.88 1.00e-06 0.0318
SSA 11.944 4.5162 0.4722 4.5363 2.46e-07 37.510
GWO 10.212 5.6928 0.0108 5.6979 4.14e-07 30.357
SCA 9.5896 6.2737 0.0678 6.2840 0.00000 31.437
DA 9.7013 1.4683 0.2013 180.99 3.84e-07 0.1701
12 HHO 9.5565 1.2700 0.1875 110.31 2.62e-08 0.0684
SSA 11.354 4.5833 0.0511 4.6009 2.51e-07 32.325
GWO 10.196 5.7051 0.0014 5.7110 5.75e-07 30.257
SCA 10.428 5.6306 0.0010 5.6902 5.53e-07 30.688
DA 9.6272 1.4346 0.1107 500.00 2.61e-07 0.0407
13 HHO 9.5077 1.4446 0.0710 194.83 2.86e-07 0.0118
SSA 10.994 5.0000 0.2009 5.0134 4.92e-07 32.970
GWO 10.192 5.7062 0.0042 5.7115 1.90e-07 30.287
SCA 10.307 5.6771 0.0021 5.7016 0.00000 30.335
DA 9.6247 1.5293 0.1424 289.90 7.60e-07 0.0804
14 HHO 9.5807 1.4435 0.0791 107.59 2.79e-07 0.0438
SSA 10.959 5.0000 0.1560 5.0128 9.84e-07 32.456
GWO 10.202 5.6976 0.0042 5.7031 6.19e-07 30.287
SCA 9.9382 6.0050 0.0017 5.9788 2.29e-08 30.433
DA 9.5053 1.4772 0.1000 500.00 4.24e-07 0.0139
15 HHO 9.4693 1.5550 0.0208 421.09 9.85e-07 0.0032
SSA 10.989 4.9998 0.1790 5.0125 3.32e-07 32.719
GWO 10.201 5.7053 0.0087 5.7103 1.80e-07 30.335
SCA 10.158 5.7483 0.0012 5.7695 0.00000 30.282
DA 9.5803 1.4881 0.1292 284.03 4.81e-07 0.0469
16 HHO 9.4783 1.4391 0.0697 287.25 2.69e-07 0.0045
SSA 10.887 5.0000 0.0958 5.0128 8.66e-07 31.782
GWO 10.199 5.6965 0.0018 5.7016 7.00e-07 30.262
SCA 9.6578 6.6495 0.0069 6.6179 0.00000 30.951
DA 9.5853 1.4467 0.0529 89.64 2.88e-07 0.0450
(Continued )
190 Green Engineering and Technology
TABLE 11. 4 (Continued )
Optimal Parameters of SDM Using Different Algorithms for CS6K-280M
Run Algorithm IPV α1RSRpI01 Error
17 HHO 9.6168 1.5387 0.1173 195.33 8.33e-07 0.0739
SSA 10.768 5.0000 0.0010 5.0129 8.48e-07 30.752
GWO 10.200 5.6992 0.0010 5.7050 7.87e-07 30.252
SCA 10.105 5.6862 0.0032 5.6698 3.64e-07 30.369
DA 9.9281 1.5523 0.2590 382.99 1.00e-06 0.4227
18 HHO 9.4112 1.3743 0.0442 355.26 1.18e-07 0.0008
SSA 10.763 5.0000 0.0010 5.0131 9.01e-08 30.752
GWO 10.209 5.7031 0.0156 5.7089 1.24e-07 30.408
SCA 10.139 5.7360 0.0014 5.7151 2.90e-07 30.336
DA 9.4548 1.4853 0.0974 500.00 4.62e-07 0.0209
19 HHO 9.6413 1.5360 0.1612 400.55 8.20e-07 0.0998
SSA 10.917 5.0000 0.1166 5.0127 4.61e-07 32.014
GWO 10.206 5.6905 0.0057 5.6967 1.33e-07 30.304
SCA 10.079 5.9179 0.0023 5.9139 0.00000 30.311
DA 9.7131 1.5558 0.2495 202.40 1.00e-06 0.4746
20 HHO 9.5651 1.5290 0.0956 195.70 7.47e-07 0.0488
SSA 11.109 5.0000 0.2671 5.0124 1.28e-07 33.745
GWO 10.188 5.7149 0.0035 5.7209 2.17e-08 30.280
SCA 10.346 5.6545 0.0183 5.6461 0.00000 30.483
DA 9.5461 1.5520 0.0834 500 9.64e-07 0.0230
21 HHO 9.5709 1.5579 0.0297 117.94 9.99e-07 0.0408
SSA 11.099 4.7590 0.0152 4.7738 8.79e-07 31.419
GWO 10.197 5.7032 0.0032 5.7090 6.40e-07 30.276
SCA 10.566 5.8864 0.0017 5.9014 0.00000 31.061
DA 9.5686 1.5349 0.1178 500.00 8.06e-07 0.0417
22 HHO 9.5084 1.2873 0.1454 189.91 3.45e-08 0.0111
SSA 10.771 5.0000 0.0368 5.0139 8.34e-07 31.140
GWO 10.199 5.6929 0.0012 5.6986 4.20e-09 30.256
SCA 10.190 5.5192 0.0107 5.5222 0.00000 30.456
DA 9.5475 1.5555 0.0470 456.25 1.00e-06 0.0140
23 HHO 9.6057 1.4001 0.1912 203.57 1.66e-07 0.0922
SSA 10.781 5.0000 0.0010 5.0125 4.48e-09 30.753
GWO 10.204 5.6947 0.0051 5.7005 4.03e-08 30.297
SCA 9.9923 5.7696 0.0023 5.7188 0.00000 30.703
DA 10.051 1.5302 0.3505 500.00 7.97e-07 1.0410
24 HHO 9.5953 1.5429 0.1142 183.08 8.67e-07 0.0776
SSA 10.765 5.0000 0.0010 5.0130 2.12e-07 30.752
GWO 10.193 5.7111 0.0074 5.7176 1.49e-07 30.321
SCA 9.5719 6.4141 0.0010 6.3939 9.83e-07 30.725
DA 9.4771 1.5573 0.0010 218.71 1.00e-06 0.0060
(Continued )
191Parameter Estimation of PV Cells
different algorithms for CS6K-280M. The parameters are estimated with 30 runs so
that we do the statistical analysis of the error function.
It is very much clear from Table 11.4 that HHO gives the least error out of the
other algorithms, whereas DA also tries to give the error near to HHO but the other
three algorithms SSA, GWO, and SCA give inferior results. The convergence curve
of the PV model is provided in Figure 11.4.
It is clear from Figure 11.4 that HHO converges faster in comparison to the
other algorithms. However, DA also tries to reach the least tness function value.
TABLE 11. 4 (Continued )
Optimal Parameters of SDM Using Different Algorithms for CS6K-280M
Run Algorithm IPV α1RSRpI01 Error
25 HHO 9.4712 1.5072 0.0304 269.92 5.88e-07 0.0041
SSA 11.110 5.0000 0.3167 5.0142 4.52e-07 34.340
GWO 10.198 5.7021 0.0010 5.7077 5.54e-08 30.253
SCA 10.045 5.8901 0.0057 5.8788 0.00000 30.358
DA 9.5366 1.4867 0.1139 453.02 4.74e-07 0.0246
26 HHO 9.4783 1.4071 0.0021 116.17 1.77e-07 0.0046
SSA 11.152 4.9259 0.2226 4.9387 5.12e-07 33.357
GWO 10.202 5.6925 0.0010 5.6989 4.58e-11 30.253
SCA 9.8958 5.9688 0.0342 5.926 1.17e-08 30.978
DA 9.9049 1.5197 0.2642 402.87 7.06e-07 0.3891
27 HHO 9.5321 1.5534 0.0632 288.08 9.70e-07 0.0214
SSA 10.960 5.0000 0.1762 5.0135 9.05e-07 32.686
GWO 10.193 5.7033 0.0013 5.7091 4.78e-09 30.256
SCA 10.211 5.6364 0.0017 5.6111 0.00000 30.373
DA 10.191 1.5509 0.3629 331.45 1.00e-06 1.3091
28 HHO 9.5912 1.3906 0.0153 63.858 1.41e-07 0.0506
SSA 11.066 5.0000 0.2491 5.0131 6.62e-07 33.531
GWO 10.196 5.7048 0.0033 5.7103 4.52e-07 30.278
SCA 9.8345 5.8001 0.0040 5.7861 0.00000 30.616
DA 9.4107 1.5112 0.0010 500.00 6.15e-07 0.0004
29 HHO 9.5477 1.5442 0.0889 360.61 8.85e-07 0.0291
SSA 10.872 5.0000 0.0898 5.0130 4.72e-08 31.716
GWO 10.201 5.7011 0.0063 5.7067 6.70e-07 30.309
SCA 9.9263 5.9358 0.0590 5.9525 0.00000 31.003
DA 9.8660 1.5048 0.2605 158.59 5.88e-07 0.4360
30 HHO 9.6238 1.3725 0.2235 474.91 1.18e-07 0.0876
SSA 11.145 5.0000 0.2916 5.0123 6.60e-07 34.037
GWO 10.204 5.7005 0.0050 5.7063 3.97e-09 30.296
SCA 10.053 5.6321 0.0100 5.5966 0.00000 30.691
DA 10.053 1.5559 0.2221 65.107 1.00e-06 0.6103
192 Green Engineering and Technology
GWO, SCA, and SSA do not perform well for this model. Table 11.5 gives the esti-
mated parameters and error of SDM with different algorithms for STM6 40-36. The
parameters are estimated with 30 runs so that we perform the statistical analysis of
the error function.
It is very much evident from Table 11.5 that HHO gives the least error out of the
other algorithms, whereas DA also tries to give the error near to HHO but the other
three algorithms SSA, GWO, and SCA give very poor results. The tness value ver-
sus the number of iterations is plotted in Figure 11.5.
TABLE 11. 5
Optimal Parameters of SDM Using Different Algorithms for STM6 40-36
Run Algorithm IPV α1RSRpI01 Error
1 HHO 1.6892 1.4288 0.1993 174.27 6.89e-07 0.0012
SSA 3.9565 4.5202 0.4380 4.5430 1.26e-07 7.0660
GWO 2.8704 7.0000 0.0603 7.0112 1.51e-07 2.5743
SCA 2.9655 7.0000 0.0012 6.9952 0.00000 2.6371
DA 1.6562 1.2496 0.4669 500.00 8.71e-08 0.0001
2 HHO 1.7297 1.4190 0.3264 101.08 6.03e-07 0.0085
SSA 3.8032 4.6538 0.0774 4.6756 4.65e-08 6.8034
GWO 2.9324 7.0000 0.4996 7.0106 5.11e-07 2.5326
SCA 2.6589 7.0000 0.0099 7.0227 0.00000 2.7137
DA 1.7125 1.4664 0.3211 142.49 1.00e-06 0.0045
3 HHO 1.7514 1.3629 0.2133 73.756 3.17e-07 0.0133
SSA 3.5797 5.0000 0.0088 5.0189 2.28e-07 5.7581
GWO 2.8727 7.0000 0.0740 7.0114 7.13e-08 2.5728
SCA 2.8257 7.0000 0.0069 6.9890 1.19e-07 2.6351
DA 1.7040 1.4662 0.2350 152.15 1.00e-06 0.0029
(Continued )
FIGURE 11.4 Convergence curve for SDM (Canadian Solar CS6K-280M).
193Parameter Estimation of PV Cells
TABLE 11. 5 (Continued )
Optimal Parameters of SDM Using Different Algorithms for STM6 40-36
Run Algorithm IPV α1RSRpI01 Error
4 HHO 1.8328 1.4064 0.1155 43.754 4.68e-07 0.0488
SSA 3.6805 5.0000 0.4982 5.0182 3.58e-07 5.5353
GWO 2.9354 7.0000 0.5000 7.0117 3.59e-08 2.5325
SCA 3.0665 7.0000 0.0022 7.0110 0.00000 2.7071
DA 1.6731 1.4462 0.0798 222.12 8.28e-07 0.0001
5 HHO 1.8058 1.2959 0.1063 47.614 1.33e-07 0.0345
SSA 3.1917 6.1215 0.4043 6.1342 4.11e-07 3.4546
GWO 2.8708 7.0000 0.0520 7.0112 3.06e-07 2.5753
SCA 2.8460 7.0000 0.1654 7.0142 0.00000 2.5686
DA 1.6844 1.4205 0.4327 328.93 6.54e-07 0.0007
6 HHO 1.6832 1.4387 0.4516 394.53 7.92e-07 0.0007
SSA 2.9403 6.8909 0.3350 6.9022 9.52e-07 2.6385
GWO 2.9368 7.0000 0.5000 7.0112 2.95e-07 2.5325
SCA 2.7798 7.0000 0.1171 7.0000 0.00000 2.6119
DA 1.6499 1.4634 0.0417 500.00 1.00e-06 0.0002
7 HHO 1.6693 1.4375 0.2182 332.06 7.72e-07 6.06e-05
SSA 3.7687 4.9146 0.4783 4.9328 7.88e-07 5.7809
GWO 2.8649 7.0000 0.0185 7.0113 7.05e-07 2.5790
SCA 3.0672 7.0000 0.0108 7.0213 0.00000 2.7186
DA 1.6984 1.3691 0.1930 130.94 3.57e-07 0.0022
8 HHO 1.7284 1.4029 0.0165 82.948 4.94e-07 0.0080
SSA 3.1546 6.0333 0.0496 6.0467 9.67e-07 3.6524
GWO 2.8708 7.0000 0.0725 7.0113 1.31e-08 2.5730
SCA 2.9888 7.0000 0.0258 7.0094 0.00000 2.6243
DA 1.6895 1.4177 0.5000 376.97 6.40e-07 0.0009
9 HHO 1.7013 1.4420 0.2674 147.61 7.82e-07 0.0030
SSA 3.1847 5.9938 0.0387 6.0072 9.11e-07 3.7135
GWO 2.8632 7.0000 0.0034 7.011 1.00e-06 2.5808
SCA 2.9122 7.0000 0.0017 7.0402 0.00000 2.6750
DA 1.7685 7.0000 0.0618 17.570 6.77e-07 0.8317
10 HHO 1.6764 1.3499 0.0707 164.55 2.89e-07 0.0003
SSA 3.0344 6.5085 0.2055 6.5204 9.87e-07 3.0262
GWO 2.9007 7.0000 0.2501 7.0113 1.02e-07 2.5547
SCA 2.8388 7.0000 0.5000 7.0248 0.00000 2.5777
DA 1.6404 1.1890 0.0190 273.04 3.59e-08 0.0010
11 HHO 1.6699 1.4591 0.1913 330.96 9.57e-07 7.83e-05
SSA 3.0318 6.4308 0.0478 6.4429 1.58e-07 3.1392
GWO 2.8765 7.0000 0.0978 7.0112 7.33e-08 2.5703
SCA 2.7408 7.0000 0.0032 7.0120 0.00000 2.6243
DA 1.6772 1.4423 0.3435 357.37 8.16e-07 0.0003
(Continued )
194 Green Engineering and Technology
TABLE 11. 5 (Continued )
Optimal Parameters of SDM Using Different Algorithms for STM6 40-36
Run Algorithm IPV α1RSRpI01 Error
12 HHO 1.6809 1.3832 0.0189 150.96 4.18e-07 0.0006
SSA 3.0924 6.3335 0.0744 6.3456 7.87e-07 3.2519
GWO 2.9182 7.0000 0.3713 7.0109 1.61e-08 2.5434
SCA 2.8987 7.0000 0.0010 7.0023 0.00000 2.5928
DA 1.6538 1.4179 0.0963 368.15 6.26e-07 0.0001
13 HHO 1.6779 1.4636 0.0912 209.79 9.82e-07 0.0004
SSA 3.2348 5.9678 0.2025 5.9809 7.07e-07 3.7142
GWO 2.8935 7.0000 0.2161 7.0111 2.23e-07 2.558
SCA 2.9464 7.0000 0.0034 7.0271 0.00000 2.6295
DA 1.6499 1.4635 0.0537 500.00 1.00e-06 0.0002
14 HHO 1.7174 1.3426 0.1680 97.200 2.57e-07 0.0052
SSA 3.9724 4.6797 0.3659 4.6986 3.63e-07 6.6066
GWO 2.9359 7.0000 0.4954 7.0113 2.30e-08 2.5329
SCA 2.8915 7.0000 0.0285 7.0163 0.00000 2.5827
DA 1.6717 1.4053 0.2085 297.48 5.52e-07 6.61e-05
15 HHO 1.6541 1.2527 0.0569 220.39 8.76e-08 0.0001
SSA 3.5776 5.3492 0.3312 5.3639 9.51e-07 4.8021
GWO 2.9017 7.0000 0.2841 7.0114 9.36e-09 2.5514
SCA 2.8338 7.0000 0.0021 7.0176 0.00000 2.5868
DA 1.7993 7.0000 0.0010 17.013 1.11e-07 0.8247
16 HHO 1.6752 1.4455 0.1475 237.54 8.26e-07 0.0002
SSA 3.2921 5.6144 0.0026 5.6295 7.87e-07 4.3559
GWO 2.9048 7.0000 0.2802 7.0110 1.27e-07 2.5518
SCA 2.8875 7.0000 0.0032 7.0459 0.00000 2.7042
DA 1.6750 1.3974 0.0694 186.35 4.94e-07 0.0002
17 HHO 1.6556 1.3512 0.0223 274.72 2.99e-07 0.0001
SSA 3.0691 6.4120 0.2002 6.4241 2.20e-07 3.1347
GWO 2.8618 7.0000 0.0081 7.0112 3.48e-08 2.5802
SCA 2.7953 7.0000 0.0010 7.0208 0.00000 2.6022
DA 1.6720 1.4634 0.1333 326.06 1.00e-06 7.11e-05
18 HHO 1.6598 1.4132 0.2264 479.33 6.04e-07 3.38e-05
SSA 2.8996 6.9635 0.1264 6.9747 7.87e-07 2.5984
GWO 2.9351 7.0000 0.5000 7.0112 2.07e-09 2.5325
SCA 3.0015 6.9881 0.0010 6.9974 0.00000 2.6477
DA 1.7997 3.3538 0.0448 17.054 8.31e-08 0.8290
19 HHO 1.6928 1.3332 0.1187 126.00 2.34e-07 0.0016
SSA 3.1988 6.0811 0.1953 6.0937 2.40e-11 3.5548
GWO 2.9363 7.0000 0.4998 7.0111 4.41e-09 2.5325
SCA 3.0135 7.0000 0.2672 7.0330 0.00000 2.6425
DA 1.7540 1.4480 0.0756 68.248 7.69e-07 0.0167
(Continued )
195Parameter Estimation of PV Cells
TABLE 11. 5 (Continued )
Optimal Parameters of SDM Using Different Algorithms for STM6 40-36
Run Algorithm IPV α1RSRpI01 Error
20 HHO 1.6736 1.2615 0.1732 164.61 9.76e-08 0.0001
SSA 3.0904 6.4086 0.1791 6.4204 8.77e-07 3.1453
GWO 2.9375 7.0000 0.5000 7.0108 6.26e-08 2.5325
SCA 2.7661 7.0000 0.1540 7.0088 0.00000 2.6071
DA 1.6782 1.4458 0.1824 232.68 8.29e-07 0.0004
21 HHO 1.8232 1.2189 0.0367 41.710 4.65e-08 0.0437
SSA 2.8893 6.9498 0.0524 6.9610 8.99e-07 2.6185
GWO 2.9377 7.0000 0.5000 7.0109 1.22e-07 2.5325
SCA 2.8394 7.0000 0.0037 6.9917 0.00000 2.6211
DA 1.6879 1.4536 0.1860 185.04 8.88e-07 0.0011
22 HHO 1.7130 1.3537 0.1128 97.153 2.92e-07 0.0048
SSA 2.9049 6.8903 0.1926 6.9017 2.63e-07 2.6550
GWO 2.9365 7.0000 0.5000 7.0107 3.95e-07 2.5325
SCA 2.9424 7.0000 0.3345 6.9957 2.85e-07 2.5726
DA 1.6772 1.4642 0.2272 260.61 1.00e-06 0.0005
23 HHO 1.6623 1.3724 0.1732 297.82 3.83e-07 5.58e-06
SSA 3.776 4.9293 0.3965 4.9469 5.24e-07 5.7890
GWO 2.9001 7.0000 0.2611 7.0109 0.00000 2.5536
SCA 2.8406 7.0000 0.0013 6.989 0.00000 2.6327
DA 1.6797 1.3451 0.2643 195.37 2.78e-07 0.0004
24 HHO 1.6710 1.4637 0.1303 276.58 9.94e-07 0.0001
SSA 2.9817 6.6319 0.1577 6.6436 3.22e-07 2.9038
GWO 2.8803 7.0000 0.1183 7.0108 8.67e-07 2.5681
SCA 2.9265 7.0000 0.4225 7.0328 0.00000 2.5863
DA 1.6593 1.4631 0.0813 443.01 1.00e-06 8.71e-05
25 HHO 1.6602 1.4237 0.2174 485.03 6.75e-07 2.73e-05
SSA 2.9356 6.9044 0.2967 6.9157 9.46e-08 2.6308
GWO 2.9288 7.0000 0.4478 7.0113 1.25e-07 2.5368
SCA 2.9059 7.0000 0.0046 7.0275 0.00000 2.6139
DA 1.6476 1.2005 0.4142 500.00 4.37e-08 0.0006
26 HHO 1.7533 1.4400 0.1798 75.599 7.25e-07 0.0144
SSA 3.0545 6.4830 0.0895 6.4948 2.20e-07 3.0766
GWO 2.9103 7.0000 0.3438 7.0108 3.95e-07 2.5459
SCA 2.7988 7.0000 0.0036 7.0174 0.00000 2.5957
DA 1.6636 1.3198 0.2644 500.00 2.13e-07 0.0006
27 HHO 1.7987 1.4122 0.4042 59.603 5.32e-07 0.0310
SSA 2.9371 6.8988 0.2029 6.9100 6.41e-07 2.6467
GWO 2.9365 7.0000 0.5000 7.0111 7.71e-08 2.5325
SCA 2.9762 7.0000 0.0011 7.0248 0.00000 2.6414
DA 1.7897 5.7989 0.0089 17.215 2.53e-09 0.8256
(Continued )
196 Green Engineering and Technology
In the graph, HHO converges well and also at the least error value. However, DA
nears the HHO convergence curve. SSA, GWO, and SCA have the worst convergence
curve. Table 11.6 gives the estimated parameters and error of SDM with different
algorithms for Pro. SW255. The parameters are estimated with 30 runs so that we do
the statistical analysis of the error function.
It is quite clear from Table 11.6 that HHO gives the least error out of the other
algorithms, whereas DA also tries to give the error near to HHO but the other three
algorithms SSA, GWO, and SCA give very poor results. The convergence character-
istics are thus shown in Figure 11.6.
TABLE 11. 5 (Continued )
Optimal Parameters of SDM Using Different Algorithms for STM6 40-36
Run Algorithm IPV α1RSRpI01 Error
28 HHO 1.6807 1.4221 0.3537 269.34 6.57e-07 0.0007
SSA 3.3502 5.8369 0.2450 5.8497 8.56e-07 3.9244
GWO 2.9082 7.0000 0.3195 7.0108 3.63e-07 2.5481
SCA 2.9164 7.0000 0.5000 7.0046 0.00000 2.5380
DA 1.6541 1.3795 0.1694 500.00 4.20e-07 0.0003
29 HHO 1.6981 1.1725 0.1516 108.31 2.70e-08 0.0014
SSA 3.2473 5.9757 0.2550 5.9887 5.40e-07 3.6914
GWO 2.8871 7.0000 0.1750 7.0117 3.12e-08 2.5622
SCA 2.8768 7.0000 0.0972 7.0109 0.00000 2.5703
DA 1.6346 1.2697 0.0209 469.65 1.11e-07 0.0016
30 HHO 1.6990 1.1620 0.2054 101.96 2.27e-08 0.0020
SSA 2.9839 6.7252 0.1763 6.7365 4.91e-07 2.8101
GWO 2.8644 7.0000 0.0225 7.0112 5.35e-08 2.5786
SCA 2.8554 7.0000 0.0328 7.0108 0.00000 2.5778
DA 1.7484 1.4694 0.5000 94.994 1.00e-06 0.0163
FIGURE 11.5 Convergence curve for SDM (Schutten Solar STM6 40-36).
197Parameter Estimation of PV Cells
TABLE 11.6
Optimal Parameters of SDM Using Different Algorithms for Pro. SW255
Test Run Algorithm IPV α1RSRpI01 Error
1 HHO 8.8861 1.5099 0.0010 192.83 7.00e-07 7.76e-05
SSA 9.9565 5.6226 0.1338 5.6303 9.06e-07 27.434
GWO 9.6311 5.9067 0.0118 5.9122 4.49e-07 26.262
SCA 9.3624 6.3181 0.0011 6.3229 4.15e-07 26.263
DA 9.1838 1.5253 0.2419 331.48 8.59e-07 0.1641
2 HHO 8.9565 1.5269 0.0611 168.81 8.43e-07 0.0116
SSA 10.122 5.6668 0.3280 5.6744 8.64e-07 29.274
GWO 9.6135 5.9123 0.0011 5.9187 2.09e-07 26.167
SCA 9.7342 6.0241 0.0223 6.0135 7.24e-19 26.466
DA 8.7735 1.3686 0.0020 500.00 1.29e-07 0.0193
3 HHO 9.0630 1.5045 0.2070 331.95 6.78e-07 0.0861
SSA 9.1946 6.7866 0.1107 6.7899 7.58e-07 27.506
GWO 9.6148 5.9173 0.0012 5.9231 1.11e-07 26.168
SCA 9.5424 6.0477 0.0068 6.0190 0.00000 26.345
DA 9.2269 1.4968 0.2217 92.325 6.13e-07 0.2277
4 HHO 9.0774 1.5448 0.0780 78.751 9.99e-07 0.0815
SSA 11.199 4.7518 0.4728 4.7678 6.68e-07 32.105
GWO 9.6320 5.9045 0.0150 5.9099 9.56e-07 26.290
SCA 9.6278 5.9234 0.0015 5.9057 0.00000 26.227
DA 8.9491 1.3832 0.0709 109.80 1.55e-07 0.0080
5 HHO 8.9801 1.5347 0.0833 174.78 9.19e-07 0.0199
SSA 10.121 5.5248 0.2124 5.5333 2.77e-07 28.228
GWO 9.6220 5.9248 0.0172 5.9304 4.96e-07 26.310
SCA 9.2150 5.8842 0.0014 5.9108 0.00000 26.705
DA 9.0886 1.5070 0.2210 278.81 6.97e-07 0.1169
6 HHO 8.9531 1.4444 0.0964 166.22 3.35e-07 0.0098
SSA 9.9244 5.8069 0.2362 5.8136 8.79e-07 28.339
GWO 9.6087 5.9208 0.0010 5.9272 2.08e-07 26.167
SCA 9.6639 5.7948 0.0084 5.8099 0.00000 26.252
DA 9.3485 1.5412 0.2212 91.722 1.00e-06 0.2911
7 HHO 8.9754 1.3550 0.0534 78.191 1.05e-07 0.0171
SSA 10.101 5.3524 0.0596 5.3622 5.22e-07 26.945
GWO 9.6238 5.9071 0.0049 5.9123 8.76e-07 26.201
SCA 9.8469 5.7053 0.0077 5.6613 0.00000 26.527
DA 8.8707 1.5195 0.0023 264.27 7.79e-07 0.0001
8 HHO 8.9289 1.3843 0.1840 296.27 1.60e-07 0.0234
SSA 9.4935 6.6113 0.3410 6.6155 4.42e-09 29.498
GWO 9.6160 5.9253 0.0077 5.9316 2.83e-07 26.226
SCA 9.6197 5.9098 0.0139 5.9011 0.00000 26.303
DA 9.0744 1.4961 0.2211 422.05 6.21e-07 0.0931
(Continued )
198 Green Engineering and Technology
TABLE 11.6 (Continued )
Optimal Parameters of SDM Using Different Algorithms for Pro. SW255
Test Run Algorithm IPV α1RSRpI01 Error
9 HHO 8.8869 1.4665 0.0399 229.20 4.33e-07 6.15e-05
SSA 9.5184 6.0616 0.0038 6.0669 8.78e-07 26.205
GWO 9.6170 5.9061 0.0025 5.9116 1.92e-08 26.180
SCA 9.1089 6.3617 0.0010 6.4014 0.00000 26.546
DA 9.6171 7.0000 0.0010 5.9162 5.20e-07 26.166
10 HHO 8.9080 1.4535 0.0898 311.08 3.75e-07 0.0014
SSA 10.368 5.3910 0.3432 5.4007 5.44e-07 29.615
GWO 9.6380 5.9132 0.0201 5.9188 5.14e-07 26.336
SCA 9.7058 6.0789 0.0082 6.0702 0.00000 26.365
DA 9.1136 1.5158 0.2163 298.46 7.69e-07 0.1109
11 HHO 8.9461 1.3737 0.1707 345.73 1.40e-07 0.0088
SSA 10.019 5.5972 0.1583 5.6047 1.36e-07 27.676
GWO 9.6159 5.9129 0.0011 5.9187 0.00000 26.167
SCA 9.7920 5.5671 0.0017 5.6318 0.00000 26.583
DA 9.0369 1.5414 0.1251 262.68 1.00e-06 0.0329
12 HHO 9.0398 1.4665 0.1807 204.31 4.39e-07 0.0562
SSA 9.4757 6.8299 0.4788 6.8344 3.58e-07 30.923
GWO 9.6151 5.9140 0.0041 5.9192 9.31e-07 26.194
SCA 9.6952 5.9431 0.0069 5.9546 0.00000 26.251
DA 8.8711 1.4245 0.0776 500 2.66e-07 0.0006
13 HHO 8.9111 1.4436 0.0716 190.08 3.31e-07 0.0025
SSA 9.9059 5.8465 0.2518 5.8531 1.43e-07 28.480
GWO 9.6089 5.9176 0.0016 5.9236 4.31e-07 26.172
SCA 9.0272 6.3793 0.0057 6.3753 0.00000 26.633
DA 9.3165 1.5406 0.2993 133.26 1.00e-06 0.4693
14 HHO 8.9265 1.4760 0.0217 128.62 4.77e-07 0.0042
SSA 10.946 4.9142 0.3938 4.9272 6.37e-07 30.881
GWO 9.6235 5.9172 0.0165 5.9229 9.08e-07 26.303
SCA 9.1026 6.3331 0.0018 6.4033 0.00000 26.858
DA 8.9198 1.4508 0.1279 500.00 3.66e-07 0.0048
15 HHO 8.9615 1.5387 0.1104 435.83 9.69e-07 0.0136
SSA 9.6368 5.8640 0.0010 5.8704 9.50e-07 26.168
GWO 9.6305 5.9108 0.0128 5.9167 2.77e-07 26.271
SCA 9.5610 5.6863 0.0020 5.7039 0.00000 26.343
DA 9.1985 1.5066 0.4732 500.00 6.73e-07 2.2924
16 HHO 8.9837 1.4232 0.1528 207.04 2.62e-07 0.0211
SSA 10.074 5.4691 0.1147 5.4776 5.49e-07 27.351
GWO 9.6041 5.9276 0.0024 5.9336 4.63e-07 26.179
SCA 10.031 5.6496 0.0017 5.6298 1.63e-07 26.440
DA 8.8619 1.5175 0.0031 312.67 7.64e-07 0.0006
(Continued )
199Parameter Estimation of PV Cells
TABLE 11.6 (Continued )
Optimal Parameters of SDM Using Different Algorithms for Pro. SW255
Test Run Algorithm IPV α1RSRpI01 Error
17 HHO 8.9067 1.5421 0.0649 485.03 9.99e-07 0.0019
SSA 9.7100 6.3416 0.3963 6.3465 6.38e-08 29.920
GWO 9.6217 5.9073 0.0047 5.9136 3.51e-08 26.199
SCA 9.4306 6.2764 0.0012 6.2876 5.32e-09 26.256
DA 8.9308 1.2725 0.2125 415.60 3.38e-08 0.0046
18 HHO 8.9719 1.4928 0.1391 388.65 5.93e-07 0.0179
SSA 10.623 5.1297 0.3205 5.1408 7.85e-07 29.721
GWO 9.6122 5.9186 0.0044 5.9248 1.66e-08 26.196
SCA 9.0348 6.7598 0.0131 6.7430 0.00000 26.714
DA 9.7403 1.4681 0.5000 346.33 4.71e-07 2.3072
19 HHO 9.0108 1.4360 0.1857 286.19 3.08e-07 0.0361
SSA 9.9606 5.9474 0.3628 5.9534 7.66e-07 29.548
GWO 9.6347 5.9094 0.0211 5.9158 9.06e-07 26.345
SCA 10.062 5.3791 0.0028 5.3749 0.00000 26.439
DA 8.8669 1.5167 0.0060 269.88 7.56e-07 0.0002
20 HHO 8.8932 1.4394 0.0935 411.29 3.18e-07 0.0002
SSA 10.720 5.1163 0.4152 5.1282 1.58e-07 30.700
GWO 9.6159 5.9208 0.0083 5.9263 2.85e-08 26.231
SCA 9.0498 6.7433 0.0014 6.7640 0.00000 26.572
DA 9.0933 1.5406 0.2022 341.81 1.00e-06 0.0996
21 HHO 8.8684 1.4918 0.0075 241.70 5.75e-07 0.0002
SSA 9.2586 6.5868 0.0608 6.5906 8.59e-07 26.932
GWO 9.6192 5.9191 0.0066 5.9242 2.60e-07 26.216
SCA 9.4172 6.2250 0.0010 6.222 0.00000 26.231
DA 9.1670 1.3078 0.3617 277.26 5.79e-08 0.2853
22 HHO 8.9857 1.4667 0.1560 323.70 4.41e-07 0.0233
SSA 9.7705 5.8402 0.1125 5.8467 7.00e-07 27.174
GWO 9.6210 5.9106 0.0051 5.917 4.79e-07 26.202
SCA 9.5526 5.9752 0.0010 5.9305 0.00000 26.431
DA 9.5067 1.5190 0.3863 84.507 7.91e-07 1.1948
23 HHO 8.9750 1.4641 0.0615 110.04 4.16e-07 0.0172
SSA 9.9485 5.6655 0.1473 5.6727 7.51e-07 27.542
GWO 9.6243 5.9094 0.0078 5.9153 2.79e-07 26.226
SCA 9.4794 6.2387 0.0027 6.2739 4.90e-07 26.360
DA 8.8547 1.4779 0.0010 253.32 4.92e-07 0.0013
24 HHO 8.9649 1.4938 0.0637 83.122 5.73e-07 0.0558
SSA 9.6854 6.2221 0.2909 6.2273 6.34e-07 28.874
GWO 9.6106 5.9202 0.0046 5.9263 0.00000 26.198
SCA 9.4056 6.1562 0.0017 6.2079 1.55e-08 26.432
DA 9.2166 1.5393 0.2787 425.47 1.00e-06 0.2648
(Continued )
200 Green Engineering and Technology
HHO converges faster in comparison to the other algorithms. However, DA almost
reaches the least tness function value. The SSA, GWO, and SCA do not perform
well. Table 11.7 gives a statistical analysis of the error function of SDM with differ-
ent algorithms. The table gives the best, worst, mean, and standard deviation of error
values of Tables 11.3–11.6.
From Table 11.7, we have observed that HHO obtained the best results among
all algorithms in terms of all measurements of the sum of square error values,
showing that HHO has this capacity to show stable and reliable efciency com-
pared to the other algorithms. Furthermore, it is not adequate to carry out only the
statistical measures like best, worst, mean, and standard deviations as reected in
Table 11.7. Some non-parametric assessments also need to be performed to validate
TABLE 11.6 (Continued )
Optimal Parameters of SDM Using Different Algorithms for Pro. SW255
Test Run Algorithm IPV α1RSRpI01 Error
25 HHO 8.9410 1.5420 0.0835 351.76 9.99e-07 0.0076
SSA 10.430 5.1178 0.1689 5.1300 9.09e-07 28.257
GWO 9.6154 5.9144 0.0034 5.9206 3.29e-08 26.188
SCA 9.3620 6.1901 0.0032 6.2180 6.26e-08 26.300
DA 8.9829 1.3687 0.2037 391.79 1.32e-07 0.0206
26 HHO 8.9598 1.3849 0.1585 167.45 1.60e-07 0.0230
SSA 9.7609 6.0461 0.2406 6.0516 1.84e-07 28.368
GWO 9.6206 5.9224 0.0125 5.9279 8.70e-07 26.268
SCA 9.3531 6.2088 0.0037 6.2019 0.00000 26.279
DA 9.0007 1.3079 0.0608 67.808 5.45e-08 0.0232
27 HHO 8.8386 1.4703 0.0143 380.46 4.53e-07 0.0030
SSA 9.7284 5.7544 0.0010 5.7612 5.24e-07 26.184
GWO 9.6290 5.9147 0.0134 5.9207 4.06e-10 26.276
SCA 9.4693 6.4629 0.0016 6.4427 0.00000 26.484
DA 9.0655 1.5185 0.1939 377.89 7.91e-07 0.0723
28 HHO 8.9427 1.5356 0.0096 127.80 9.15e-07 0.0080
SSA 10.181 5.5480 0.2787 5.5561 1.52e-07 28.855
GWO 9.6122 5.9231 0.0029 5.9291 7.28e-07 26.183
SCA 9.6726 5.7603 0.0012 5.7740 0.00000 26.194
DA 8.9436 1.5429 0.0320 191.48 1.00e-06 0.0049
29 HHO 9.0626 1.4635 0.1963 203.90 4.25e-07 0.0711
SSA 9.5522 6.2461 0.1647 6.2509 8.08e-07 27.701
GWO 9.6263 5.9096 0.0118 5.9152 8.47e-07 26.262
SCA 10.038 5.5139 0.0013 5.5002 0.00000 26.390
DA 9.1090 1.4996 0.1788 132.20 6.34e-07 0.1012
30 HHO 9.0739 1.4383 0.2244 210.80 3.17e-07 0.0912
SSA 9.5809 6.2589 0.2054 6.2638 3.49e-09 28.081
GWO 9.6177 5.9124 0.0057 5.9185 2.73e-07 26.208
SCA 9.2447 6.2499 0.0063 6.2904 2.29e-07 26.475
DA 9.1204 1.5074 0.2473 500.00 7.06e-07 0.1430
201Parameter Estimation of PV Cells
TABLE 11. 7
Statistical Analysis of Error Function for SDM
Case Algorithm Best Worst Mean Std. Dev.
KC200GT HHO 3.67e-06 0.0915 0.0262 0.0326
SSA 21.314 26.041 23.909 1.4102
GWO 21.314 21.395 21.345 0.0261
SCA 21.324 21.966 21.456 0.1368
DA 0.0006 1.6065 0.2149 0.4256
CS6K-280M HHO 0.0008 0.0998 0.0352 0.0294
SSA 30.752 37.510 32.905 1.8317
GWO 30.252 30.790 30.321 0.1289
SCA 30.282 31.437 30.595 0.2829
DA 0.0004 1.4582 0.2748 0.3892
STM6 40-36 HHO 5.58e-06 0.0488 0.0075 0.0135
SSA 2.5984 7.0660 3.9492 1.3882
GWO 2.5325 2.5808 2.5527 0.0184
SCA 2.5380 2.7186 2.6212 0.0461
DA 6.62e-05 0.8290 0.1121 0.2855
Pro. SW255 HHO 6.16e-05 0.0912 0.0238 0.0276
SSA 26.168 30.923 28.483 1.5065
GWO 26.167 26.345 26.226 0.0530
SCA 26.194 26.858 26.422 0.1652
DA 0.0006 26.166 1.1507 4.7618
FIGURE 11.6 Convergence curve for SDM (Solarworld Pro. SW255).
202 Green Engineering and Technology
the results obtained in Table 11.7. The Kruskal–Wallis test [26] is thus carried out
on the sampled values. For the comparison of more than two different samples, the
Kruskal–Wallis test is a popular technique in some instances. Our test cases are no
exception to this situation. In our investigation, four different PV models have been
tested with the proposed method, namely the HHO. It is being compared with some
standard heuristic algorithms like SSA, GWO, SCA, and DA, respectively. Hence,
the Kruskal–Wallis test is a perfect t. Usually, the signicant p-values would be less
than 0.05 for a 95% condence interval considered in this work.
The graphical representation of the mean ranks for the KC200GT model is shown
in Figure 11.7. In the gure, the HHO group of results is represented by using blue
color while the other groups are marked with red lines. Similar results are also
presented for the other models, viz. CS6K-280M, STM6 40-36, and Pro. SW255 in
Figures 11.8–11.10, re spect ively.
It is clear from Figures 11.7–11.10 that the mean rank of group 1 (namely the
proposed technique) is found to be distinct as compared to the mean ranks of
groups 2–4, viz. the results of SSA, GWO, and SCA but the mean rank of group
5, viz. the results of DA, overlaps the mean rank of group 1 so the result for DA
is non-signicant. Moreover, the Wilcoxon rank-sum test [27] is also performed
on the available data test for the proposed methodology in comparison to the
other heuristic techniques. All the PV-models are once again considered for this
investigation as well. The p-values for this non-parametric test are shown in Table
11.8. For this study, 95% condence interval is taken up, meaning that values
greater than 0.05 will be considered to be insignicant. The insignicant values
are underlined in the table.
The results obtained in Table 11.8 are thus found to be highly signicant as all the
p-values reported in the table are quite less than 0.05 except for the p-value of DA
in the case of STM6 40-36. Therefore, the results for DA in the case of STM6 40-36
FIGURE 11.7 Kruskal–Wallis test performance for SDM (KC200GT).
203Parameter Estimation of PV Cells
are non-signicant. To further conrm the validity of the results, Holm–Bonferroni
[28] suggested a correction for the p-values following the Wilcoxon test. The results
are thus provided in Table 11.9. Once again, the insignicant results are underlined.
From Table 11.9, it is quite clear that all the corrected p-values reported are having
values of quite less than 0.05 except for the p-value of DA in the case of STM6 40-36.
Thus, the results found by the proposed technique are all signicant when compared
to the metaheuristic algorithms, namely, SSA, GWO, SCA, and DA, but the results
for DA in the case of STM6 40-36 are non-signicant.
FIGURE 11.9 Kruskal–Wallis test outcomes for SDM (STM6 40-36).
FIGURE 11.8 Kruskal–Wallis test diagram for SDM (CS6K-280M).
204 Green Engineering and Technology
TABLE 11. 8
Wilcoxon Rank-Sum Test Results for SDM for Different PV Models
PV Models Proposed Method SSA GWO SCA DA
KC200GT HHO 3.0180e-11 3.0199e-11 3.0199e-11 4.7138e-04
CS6K-280M HHO 3.0180e-11 3.0180e-11 3.0199e-11 0.0199
STM6 40-36 HHO 3.0199e-11 2.8646e-11 3.0180e-11 0.9705
Pro. SW255 HHO 3.0199e-11 3.0199e-11 3.0199e-11 0.0049
The underlined value is insignicant.
FIGURE 11.10 Kruskal–Wallis test results for SDM (Pro. SW255).
TABLE 11.9
Corrected p-Values for SDM for the Wilcoxon Test Adding Holm–Bonferroni
Corrections
PV Models Proposed Method Corrected p-Values
KC200GT HHO 10−3 × [0.000000120720.000000120720.000000090590.47138]
CS6K-280M HHO [0.00000000012070.00000000012070.00000000009050.0199]
STM6 40-36 HHO [0.00000000009050.00000000011450.00000000011450.9705]
Pro. SW255 HHO [0.00000000012070.00000000012070.00000000009050.0049]
The underlined value is insignicant.
205Parameter Estimation of PV Cells
11.5 CONCLUSIONS
This chapter presents a new HHO algorithm to adequately estimate the parameters
of the system of solar cells and PV modules. Several conclusions can be drawn based
on competitive and statistical experimental results. HHO is advantageous when com-
pared to the other SSA, GWO, SCA, and DA algorithms, as it has fewer errors accord-
ing to the experimental results. HHO is capable of producing better convergence than
the SSA, GWO, SCA, and DA algorithms. This chapter is unique in the sense it carries
an optimized approach for the parameter assessment of some popular solar cell mod-
els. However, the single diode is not able to dene the different current components of
the solar cell; therefore, the future demands double and three diode models. The single
diode had less convergence time and fewer parameters to be identied in comparison
to the double and three diode models. Thus, in the future, parameter estimation of
higher diode models can be carried out. Improved versions of the HHO technique
can also be employed to obtain even better results. New hybrid algorithms with HHO
can also be proposed for the parameter identication problem of solar cell modeling.
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207
12 On the Dynamics of
Cellular Automata-based
Green Modeling toward
Job Processing with
Group-based Industrial
Wireless Sensor
Networks in Industry 4.0
Arnab Mitra
Siksha ‘O’ Anusandhan (Deemed to be University)
Avishek Banerjee
Asansol Engineering College
12.1 INTRODUCTION
Current developments in the manufacturing industry have initiated a direction toward
a systematic deployment of CPS (Cyber-Physical Systems), where information from
all associated perceptions is considered toward an efcient synchronization between
the manufacturing line and the cutting-edge computational techniques. In addition,
with the use of several cutting-edge information technologies (ITs), such networked
machines in a manufacturing line are found to be able to perform effective and in
resilient collaboration. Thus, earlier days manufacturing has evolved into the next
CONTENTS
12.1 Introduction ..................................................................................................207
12.2 Related Works ............................................................................................... 210
12.3 Proposed Work ............................................................................................. 214
12.4 Results and Discussions ................................................................................ 219
12.5 Conclusions ................................................................................................... 221
Acknowledgment ................................................................................................... 221
References .............................................................................................................. 221
208 Green Engineering and Technology
generation production technology, popularly known as Industry 4.0 (Lee, Bagheri,
and Kao 2015). With efcient incorporation of the CPS with the existing manufac-
turing line, essential services, and logistics, today’s manufacturing houses may have
a true potential to be transformed into Industry 4.0. Since CPS is at the preliminary
stage toward its effective development, several guidelines have been presented by
researchers toward its successful implementation. Among several others, a 5C (i.e.,
connection, conversion, cyber, cognition, and conguration)-based CPS architecture
was presented in the study of Lee, Bagheri, and Kao (2015). A typical 5C-based
architecture is shown in Figure 12.1.
A sequential workow may be observed in Figure 12.1 that describes the con-
struction of CPS from data acquisition to nal value creation through traversal of
levels (Lee, Bagheri, and Kao 2015). The associated attributes for each level are
placed adjacent to that level (refer to Figure 12.1).
It was presented in the study of Lee, Davari, and Singh et al. (2018) that the ef-
ciency of CPS may be further enriched with the proper incorporation of industrial
articial intelligence (AI). For this reason, an incorporation of AI technologies at
each dened level of 5C-based CPS architecture (Figure 12.1) was presented in
their study (Lee, Davari, and Singh et al. 2018). In a different work, an enhance-
ment toward the existing 5C-based CPS architecture of Lee, Bagheri, and Kao
(2015) was further presented by Jiang (2018). An 8C-based architecture was intro-
duced over the existing 5C-based CPS architecture. We nd that three new Cs
(i.e., customer, coalition, and content) were considered additionally along with
Level 5.
(Configuration)
Level 4. (Cognition)
Level 3. (Cyber)
Level 2. (Conversion of data to information)
Level 1. Connection(smart)
Attributes: self-configuration for
resilience, self-adjustment for variation,
self-optimization for disturba nce
Attributes: integrated simulation
and synthesis, remote
visualization, collaborative
diagnosis, and decisions
Attributes: twin model for
components mach ines,
variation identification an d
memory; similarity-based
cluster formation
Attributes: smart
analytics for machine
health,
multidimensional-
data correction,
performance and
degradation
Attributes: plug-and-
play, communication,
sensor networks
FIGURE 12.1 A typical 5C-based CPS architecture with important attributes. (Lee, Bagheri
and Kao 2015.)
209Dynamics of Green Modeling
the existing 5Cs (i.e., connection, conversion, cyber, cognition, and conguration)
(Lee, Bagheri, and Kao 2015). We nd that most CPS-based applications may be
supported with the 5C-based architecture of Lee, Bagheri, and Kao (2015). For this
reason, we prefer to continue with our choice for a 5C-based CPS architecture for
our chapter. In our studies, we nd that Big Data, smart analytics, and industrial
IoT (Internet of Things) have potentials for use in CPS (Lee, Kao, and Yang 2014;
Cheng, Chen, and Tao et al. 2018) and are well suited with the 5C-based CPS archi-
tecture. In this regard, a categorical framework toward Industry 4.0 was presented
by Qin, Liu, and Grosvenor (2016).
A detailed survey of the existing technologies, applications, and open research
areas was presented by Lu in 2017. Besides, uses of wireless sensor networks
(WSNs) toward CPS in the Industry 4.0 scenario may be found in some studies
(Udgata, Sabat, and Mini 2009; Pyun and Cho 2009; Lee, Kwon, and Song 2009;
Shu, Wang, and Niu et al. 2015; Lin, Deng, and Chen et al. 2016). We nd that a
major emphasis of past researchers (Udgata, Sabat, and Mini 2009; Pyun and Cho
2009; Lee, Kwon, and Song 2009; Shu, Wang, and Niu et al. 2015; Lin, Deng, and
Chen et al. 2016) was on the energy-efcient deployment and/or sleep scheduling of
WSNs at a smart assembly line. Unfortunately, in our studies, we have not found a
dynamic modeling-based investigation toward said deployment and/or sleep sched-
uling of WSNs in a smart assembly line. We believe that such investigation may
further explore its true potential as a green computing model with a possibility for
cheap physical implementation. Hence, we nd that there exists a scope for further
studies toward the exploration of detailed dynamics of such an event in a view of its
true energy-efcient modeling at a low-cost physical modeling. For this reason, we
continued our investigation with cellular automata (CA)-based modeling. Reasons
for the choice of CA as an investigating tool for our presented chapter are further
described in Section 12.2.
The major contributions in this chapter are as follows:
i. It presents a theoretical background toward the deployment of a low-cost
Green model in Industry 4.0;
ii. It presents a detailed systematic investigation toward a CA-based design to
facilitate the modeling of an energy-efcient deployment and scheduling
with industrial WSNs in Industry 4.0;
iii. An efcient, though being simple and cost-effective, CA-based design with
three cells only is nally discovered, which is potentially more suitable for
low resource constraint components, e.g., WSNs, IoT, and so on, for uses in
Industry 4.0; and
iv. Attention on different xed boundary conditions-based investigation exam-
ines its true latent nature (if any) toward the cost-efciencies in view of
hardware and software implementation.
The rest of the chapter organization is as follows: Related works are surveyed in
Section 12.2; the proposed CA-based model is described in Section 12.3; results and
related discussions are presented in Section 12.4; nally, the conclusive discussion is
presented in Section 12.5.
210 Green Engineering and Technology
12.2 RELATED WORKS
It is already presented in some reports (Lee, Bagheri, and Kao 2015; Lee, Davari, and
Singh et al. 2018; Jiang 2018) that cutting-edge ITs are used in today’s CPS/Industry
4.0 to enrich the production/manufacturing lines. From our studies, we found that
sensor networks (i.e.,WSNs) are crucial for the implementation of CPS (Lee, Kao,
and Yang 2014; Lee, Bagheri, and Kao 2015; Qin, Liu, and Grosvenor 2016, Lu 2017;
Lee, Davari, and Singh et al. 2018; Jiang 2018; Cheng, Chen, and Tao et al. 2018)
and manufacturing lines (Udgata, Sabat, and Mini 2009; Pyun and Cho 2009; Lee,
Kwon, and Song 2009; Shu, Wang, and Niu et al. 2015; Lin, Deng, and Chen et
al. 2016). For this reason, several researchers have focused on the enhancements of
WSNs involving several aspects of WSNs, e.g., sensor deployment (Udgata, Sabat,
and Mini 2009), energy efciency (Pyun and Cho 2009), and so on. A brief discus-
sion on WSNs is presented next.
WSN (Castelli, Silva, and Manzoni et al. 2014) is a network constructed by many
sensor nodes generally called nodes. The nodes are congured with several units like
processing unit, storage unit, communicating unit, sensing unit, and power backup
unit. Those units are very tiny because WSN nodes are tiny devices. The storage
unit is used to store important data of WSNs. At rst, the raw data are sensed by
sensors and transferred to the processing unit. This unit is involved to process raw
data and through the communicating unit, the processed data are transferred to the
local server generally called the sink node. The WSN nodes are deployed within the
target to collect various sorts of important information and transfer that information
to the sink node.
The state-of-the-art literature review presented that enhancements in view of
Industry 4.0 rely on the inherent enhancements of CPS and cutting-edge IT. Thus,
enrichments in Big Data processing technologies, networking technologies, WSNs,
and so on play a signicant role toward the advancements in modern day’s manufac-
turing line/Industry 4.0 (Lee, Kao, and Yang 2014; Lin, Deng, and Chen et al. 2016;
Cheng, Chen, and Tao et al. 2018). A brief discussion on Industry 4.0-related issues
and scopes is already presented in Section 12.1. Among several others, we found an
interesting research in the study of Lin, Deng, and Chen et al. (2016) related to the
uses of industrial WSNs for an energy-efcient deployment along with sleep time
scheduling in Industry 4.0, which is briey presented next. A schematic ow for the
reported work of Lin, Deng, and Chen et al. (2016) with GIWSN (group-based indus-
trial wireless sensor network) is presented in Figure 12.2.
In the study of Lin, Deng, and Chen et al. (2016), GIWSN uses were presented in
the Industry 4.0 framework to enhance scalability, heterogeneity, and exibility in
the dynamic industrial environment. It was mentioned in their study that the life-
time enhancement is always a challenging task in the eld of GIWSNs. To solve this
problem, the researchers presented an enhanced lifetime for GIWSN with proper
inclusion of sleep schedules at the manufacturing lines. Energy consumption for said
structures was further taken care off with the uses of a hybrid algorithm known
as IGHSA (improved geometric selective harmony search algorithm), which is an
enhancement over existing geometric selective harmony search (GSHS) as presented
by Castelli, Silva, and Manzoni et al. (2014).
211Dynamics of Green Modeling
It was discussed in some studies (Callaway Jr 2003; Wu, Tan, and Xiong 2016;
Jaigirdar and Islam 2016) that the sink node acts as an administrator and controls
the communicative node in WSN. Depending upon the moving nature of WSN, it is
classied into two types and those are static WSN (Wang, Cao, and Ji et al. 2017) and
dynamic WSN (Ahmad, Rathore, and Paul et al. 2015). In the case of static WSN,
the whole unit is mounted and xed to a certain xed point (co-ordinate regarding
the sink node). In the case of dynamic WSN, the node is dynamic, though the sink
node is generally mounted to a xed coordinate. Now depending upon the need and
purpose, the node is selected. In our experiment, static nodes were used, where the
coordinate of the sink node and typical nodes are xed and permanent (Elhoseny,
Yuan, and El-Minir et al. 2016). In the case of a typical WSN design, the sensor
nodes are deployed to cover the target area (Alnawafa and Marghescu 2018). The
sensor nodes are deployed to sense the required data like weather information or
enemy-related information and transfer it to the sink node maybe directly or via
another sensor node.
Enhancements in WSNs always have been a focus among researchers. Among
several efforts, in the study of Han, Liu, and Jiang et al. (2015), the authors have
concentrated on the enhancement of reliability optimization. Several optimization
techniques have been explored and identied as the key factors to achieve better
reliability toward energy optimization, coverage area optimization, duty cycle opti-
mization, and so on. Furthermore, in the study of Banerjee, Garvrilas, and Ivanov
et al. (2015), advanced works were presented on energy optimization to enhance
I
n
p
u
t
O
u
t
p
u
t
Robot1Robot5
Robot4
Robot3
Robot2
Robot1’
Robot3’
(a)Initial GIWSN with
5 groups of robots
(b)Reduced GIWSN
with 3 groups of robots
Robot2’
Robot1’3’
Robot2’’
(c) Reduced GIWSN with
1.5groups of robots using
theory of symmetries
FIGURE 12.2 A typical diagram for the simplication method of GIWSN in manufacturing
lines. (Lin et al. 2016.)
212 Green Engineering and Technology
the reliability of WSNs; WSN systems were presented as complex bridge systems
and further investigations were carried out with a fuzzy-linguistic system toward
its decision in the assessment of reliability redundancy allocation. In some stud-
ies (Banerjee, Garvrilas, and Grigoras et al. 2015; Banerjee, Chattopadhyay, and
Mukhopadhyay et al. 2016), the use of a fuzzy-ACO (ant colony optimization) algo-
rithm was presented to improve reliability in WSNs. In our studies, we found that
WSNs play signicant roles toward the implementation of manufacturing lines in
Industry 4.0. Among several others, we focused on a model for “joint energy-efcient
deployment and scheduling in group-based” (Lin, Deng, and Chen et al. 2016) indus-
trial WSNs toward uses in Industry 4.0. A brief discussion on the study of Lin, Deng,
and Chen et al. (2016) is presented in Section 12.2. Though a detailed study was pre-
sented by these authors, unfortunately we have not found a cost-effective approach
to investigate the dynamics along with low-cost physical implementation capability.
For this reason, we plan to model one such WSNs-based design with CA and to
investigate further. We believe that a proper dynamic modeling at low-cost physical
modeling should be benecial in view of Industry 4.0.
On the other hand, state-of-the-art literature review explored the uses of CA in
Industry 4.0 (Bertacchini, Bilotta, and Caldarola et al. 2016; Demarco, Bertacchini,
and Scuro et al. 2019). A brief description on CA is presented next.
CA (Wolfram 1984a, b; Chaudhuri, Chowdhury, and Nandi et al. 1997) is an
effective mathematical tool toward the modeling of several complex and scientic
problems. The basic units of CA are commonly known as cells (CA cells) and are
arranged in the form of a lattice. CA progresses over discrete space and discrete
time. Evolution of CA may be realized at single or multiple dimensional CA con-
gurations. Simple CA conguration may be obtained with a three-neighborhood
(dependencies with only left neighbor, self (self-cell), and right neighbor) at periodic
boundary (PB) or null boundary (NB) condition in one-dimension. Said CA arrange-
ment is recognized as elementary CA (ECA). The next state of an ECA is computed
by using Eq.(12.1) (Chaudhuri, Chowdhury, and Nandi et al. 1997).
Sf
t+1
i=ii
()
SS
t t
−+
,,
iSt
11i (12.1)
where ‘fi’ is considered as the next state function; ‘St
i’ is considered as the present
state value of the ‘ith’ cell at time ‘t’; ‘St
i−1’ is considered as the present state value of
the ‘i−1th’ cell at time ‘t’; ‘St
i+1’ is the present state value of the ‘i+1th’ cell at time
‘t’ and ‘St+1
i’ is the future state value of the ‘ith’ cell at time ‘
(
t+1
)
’.
Important ECA terminologies with reference to our presented chapter are briey
discussed next. Reader(s) may further go through Chaudhuri, Chowdhury, and Nandi
et al. (1997) to have more reading on CAs.
ECA Rule (CA rule): By convention, ECA rules are read by Wolfram codes
(from CA rule 0 to CA rule 255). ECA rule may be described with its 8-bit binary
form. Equivalent decimal number of said 8-bit binary interpretation is known as
the CA rule.
Null boundary CA (NBCA): The leftmost edge of the left-cell and the rightmost
edge of the right-cell are grounded in such conguration.
213Dynamics of Green Modeling
Periodic boundary CA (PBCA): The rightmost-cell and leftmost-cell are linked
together in such conguration.
Uniform (homogeneous) CA: Identical CA rule is applied at each CA cell for
some CA conguration.
Hybrid (heterogeneous) CA: More than one different CA rule are applied for
some CA conguration.
Linear or non-linear CA rule: If the CA rule function incorporates only XOR/
XNOR logic, then it is considered as the linear CA rule. On the other hand, if the
CA rule function incorporates AND/OR logic, then it is considered as the non-
linear CA rule.
A typical diagram is shown in Figure 12.3 to illustrate NBCA and PBCA scenarios.
As presented in some studies (Wolfram 1984a, b; Chaudhuri, Chowdhury, and
Nandi et al. 1997), CAs are found to be effective discrete mathematical tools toward
several complex problems. CA-based modelings are popular among researchers as
inherent parallel computing capabilities and easy integrations to VLSI (Very Large-
Scale Integration) are found with CAs at the cost of D-FFs (ip-ops) (Chaudhuri,
Chowdhury, and Nandi et al. 1997). For said advantages, CA-based models were
considered for data security applications (Chaudhuri, Chowdhury, and Nandi et al.
1997), Industry 4.0 uses (Bertacchini, Bilotta, and Caldarola et al. 2016; Demarco,
Bertacchini, and Scuro et al. 2019; Xu, Xu, and Li 2018; i Casas 2019), MapReduce
design toward Big Data processing (Mitra, Kundu, and Chattopadhyay et al. 2018),
authentications in cloud environment (Mitra, Kundu, and Chattopadhyay et al. 2017),
PageRank validation (Mitra and Kundu 2015; Mitra and Kundu 2017), communica-
tions (Mitra 2016), and so on. For the capabilities toward modeling of a large number
of complex and scientic applications, several studies may be found over literature to
explore the true dynamics of CA. Among others, ECA dynamics for several homo-
geneous and heterogeneous CAs at different xed boundary scenarios were exam-
ined (Teodorescu 2015; Mitra 2016; Mitra and Teodorescu 2016). In this regard, it is
worthy to mention that NBCA may be considered as one simple case derived from
different xed boundary conditions of Mitra and Teodorescu (2016). Besides, energy
consumptions by CA-based models were investigated by Mitra and Kundu (2017). It
was concluded by Mitra and Kundu (2017) that power consumption for D-FFs (i.e.,
CA cells) varies in the range from 1.20E−05 watt to 1.17E−07 watt at different
CMOS technologies, which is very low. Detailed discussion related to the power
consumptions by CAs may be further studied from Mitra and Kundu (2017).
It is mentioned in Section 12.1 that we are interested with the research on indus-
trial WSNs for an energy-efcient deployment along with scheduling in Industry 4.0
(i-1)-th
cell
(i+1)-th
cell
(i)-th Self (i+1)-th
cell
(i)-th self
cell
(i-1)-th
cell
NBCA PBCA
FIGURE 12.3 Typical diagram for NBCA and PBCA. (Mitra and Kundu 2015.)
214 Green Engineering and Technology
(Lin, Den, and Cheng et al. 2016) and found that CA-based modeling may be pos-
sible to investigate the detailed dynamics, which may offer a cost-effective and green
(lowpower consuming) model in view of Industry 4.0. To the best of our knowl-
edge, we are rst to incorporate an ECA-based investigation toward energy-efcient
deployment and scheduling with Industrial WSNs in Industry 4.0.
Our proposed CA-based modeling is presented in Section 12.3.
12.3 PROPOSED WORK
In our proposed approach, we considered GIWSN with n-groups of robots at the
manufacturing line in an Industry 4.0 scenario. Our proposed approach for n-groups
is an enhanced model as compared to the existing model for ve groups of robots
(Lin, Den, and Cheng et al. 2016). A complete schematic ow along with brief dis-
cussions for our proposed approach is presented in Figure 12.4.
Initially for design simplicity, we restricted our CA-based modeling at NBCA con-
guration only. We observed that simplication and dynamics of GIWSN may effec-
tively be modeled with only three cells at the NBCA conguration. No CA-based
modeling compatibility was found at the PBCA scenario as rst layer and last layer
are not directly connected to each other (refer Figure 12.2). Selection of ECA rule(s)
for said NBCA conguration is presented in Table 12.1.
Let us follow the following conventions to understand the CA-based design on a
binary scale. Value ‘0’ represents no value or absence of input, and value ‘1’ represents
I
n
p
u
t
O
u
t
p
u
t
Robot1Robotn
Robotn-1
Robot3...
Robotn-2
Robot2
Left cell at NBCA Self-cell at NBCA Right cell at NBCA
Robot1’
Robot3’
(a)Initial GIWSN with
n- groups of robots
(b)Reduced GIWSN
with 3 groups of robots
(c)Mapping into NBCA for reduced
GIWSN with 3 groups of robots
Robot2’
First layerIntermediate layerLast layer
FIGURE 12.4 Proposed mapping for reduced GIWSN with three groups of robots at NBCA
conguration. (a) Initial GIWSN with n-groups of robots. (b) Reduced GIWSN with three
groups of robots. (c) Mapping into NBCA for reduced GIWSN with three groups of robots.
215Dynamics of Green Modeling
valid value or, presence of input. Inputs from ‘Input’ (refer Figure 12.4a) will be
processed into outputs at ‘Output’ (refer Figure 12.4a. Thus, following sequences
of patterns may be observed at reduced GIWSN with three groups of robots (refer
Figure 12.4b) for a continuous processing of input to output (ow is from left to right).
The different patterns achieved at reduced GIWSN with three groups of robots
are processed further with the achieved NBCA design (Figure 12.4c and Table 12.2)
to obtain the transition diagram for the proposed system. Detailed processing is pre-
sented in Table 12.3. The left-hand side of ‘left-cell’ and right-hand side of ‘right-
cell’ in Table 12.3 represent initialization at ground (NBCA conguration). For more
illustrations related to similar processing as presented in Table 12.3, readers may
refer Mitra and Kundu (2015).
Transition diagram based on the computation as discussed in Table 12.3 is pre-
sented in Figure 12.5. Let us follow the notation to understand the transition dia-
grams (directed graph) presented in Figures 12.5–12.7. Circle indicates a state,
decimal value inside the circle indicates state value (number), and arrow indicates
state transition from one state (source) to another state (destination).
TABLE 12.1
Rule Assignment
Cell CA Rule
First cell (left-cell) R0
Intermediate cell (self-cell) R1
Last cell (right-cell) R2
TABLE 12.2
Different Patterns Observed at GIWSN with
Three Groups of Robots
Robot1Robot2Robot3
0 0 0
1 0 0
1 1 0
1 1 1
0 1 1
0 0 1
0 0 0
1 0 0
0 1 0
0 0 1
0 1 0
1 0 1
1 1 0
216 Green Engineering and Technology
TABLE 12.3
Exploration of State Transition at the NBCA Conguration
Decimal Value to be
Assigned in StateLeft-Cell Self-Cell Right-Cell
0 0 0 0 0 0
0 1 0 0 0 4
0 1 1 0 0 6
0 1 1 1 0 7
0 0 1 1 0 3
0 0 0 1 0 1
0 0 0 0
0 1 0 0 0 4
0 0 1 0 0 2
0 0 1 1
0 0 1 0 0 2
0 1 0 1 0 5
1 1 0 6
046731
2
5
FIGURE 12.5 Proposed transition diagram.
3
1240
7
6
5
FIGURE 12.6 Modied transition diagram after conict removal in RMT.
217Dynamics of Green Modeling
The RMT (Rule MinTerms, the decimal values 0, 1, 2, …, 9 mentioned within the
set of parentheses in Tables 12.4 and 12.6) (Kundu, Dutta, and Mukhopadhyay 2008;
Mitra and Kundu 2015) for said system is presented in Table 12.4. Please note that
value ‘D’ in Table12.4 represents ‘Don’t care condition’.
It is observed from Table 12.4 that there are conicts at the ‘000’-th column,‘001’-th
column,‘010’-th column, and ‘011’-th column of the ‘R0’-th row. For the same rea-
son, the CA rule cannot be computed. Hence, the mentioned conicts need to be
removed. Detailed removal of conict is presented in Table 12.5 and thereafter, con-
ict removed RMT is presented in Table 12.6.
Transition diagram based on the computation as discussed in Table 12.5 is pre-
sented in Figure 12.6.
Hence, we explored that CA rule 10, CA rule 170, and CA rule 0 in hybrid CA con-
guration at null boundary CA (NBCA) conguration are capable to model the manu-
facturing lines with industrial WSNs in Industry 4.0. In addition, we found that power
consumption at the physical level by CA is very low (1.20E−05 watt to1.17E−07
watt at different CMOS technologies) (Mitra and Kundu 2017). Thus, an energy ef-
ciency is also ensured with the presented design, which is particularly important in
view of limited energy resource constraint components such as WSNs, IoTs, and so on.
In addition, we may predict the sleep schedule (which is an important criterion toward
efcient energy management). We observed a self-loop at state ‘0’ in Figure 12.6. By
convention, state ‘0’ indicates end of processing (no presence of input at all three states/
robots). Thus, a system might be scheduled with a sleep to reduce energy consumption.
Detailed properties of achieved each CA rule (refer Table 12.6) are enlisted in
Table 12.7. Presented summarized information of Table 12.7 may further be explored
(a) (b)
4
7
120
6
3
5
3
1240
7
6
5
FIGURE 12.7 Transition diagram achieved under several xed boundary conditions.
(a) At 0…0/0…1 xed boundary and (b) at 1…0/1…1 xed boundary.
TABLE 12.4
Proposed RMT Construction at NBCA Conguration
CA Rule (in Decimal) 111(7) 110(6) 101(5) 100(4) 011(3) 010(2) 001(1) 000(0)
R0D D D D 1/0 1/0 1/0 1/0
R11 1 1 1 0 0 0 0
R2D 1 D 1 D 0 D 0
218 Green Engineering and Technology
in detail from Elementary Cellular Automata in “http://atlas.wolfram.com/01/01/
rulelist.html”.
It is observed from Table 12.7 that achieved rule 0 and rule 170 were addi-
tive CA rules, and rule 10 was a non-linear CA rule (Demarco, Bertacchini, and
Bilotta et al. 2019; Additive Cellular Automaton in “https://mathworld.wolfram.com/
TABLE 12.5
Conict Removal at NBCA Conguration
Decimal Value to
be Assigned in StateLeft-Cell Self-Cell Right-Cell
0 0 0 0 0 0
0 0 0 0
0 0 0 1 0 1
0 0 1 0 0 2
0 1 0 0 0 4
0 0 0 0
0 0 1 1 0 3
0 1 1 0 0 6
1 0 0 4
0 1 0 1 0 5
0 1 0 2
0 1 1 1 0 7
1 1 0 6
TABLE 12.6
Conict Resolved RMT
CA Rule (in Decimal) 111(7) 110(6) 101(5) 100(4) 011(3) 010(2) 001(1) 000(0)
R0 (10) D D D D 1 0 1 0
R1 (170) 1 0 1 0 1 0 1 0
R2 (0) D 0 D 0 D 0 D 0
TABLE 12.7
Detailed Information for Achieved CA Rules of Table 12.6
CA Rule
Equivalent Binary
Representation
Next State Evaluating Logic Function
(Refer Figure 12.2)
Rule 0 00000000 0
Rule 10 00001010 NOT (i−1) AND (i+1)
Rule 170 10101010 (i1)+
219Dynamics of Green Modeling
AdditiveCellularAutomaton.html”). Proposed design is further investigated in detail
to explore its true dynamics and cost-effectiveness, which is presented in Section 12.4.
12.4 RESULTS AND DISCUSSIONS
ECA dynamics at several xed boundary conditions were examined (Mitra and
Teodorescu 2016) to explore its true behavior and advantage toward its implementa-
tions (both at the software and hardware level). It was discussed by some authors
(Teodorescu 2015; Mitra 2016; Mitra and Teodorescu 2016) that ECA dynamics may
vary at different xed boundary conditions. The different notations used for sev-
eral xed boundary conditions (Teodorescu 2015; Mitra 2016; Mitra and Teodorescu
2016) were
00
,
01
,
10
, and
11
. The left-most and right-most values at those
boundary conditions represent xed initialization at said boundary positions. Thus,
we nd that the NBCA scenario is a single instance from the set of all xed boundary
conditions. In the presented report, we followed the same notation of some previous
studies (Teodorescu 2015; Mitra 2016; Mitra and Teodorescu 2016) for representing
different xed boundary conditions.
We further simulated our design at different xed boundary conditions. For simu-
lation, we used ‘C’ environment at Intel® Celeron® CPU N 2480 @ 2.16 GHz and
2.16 GHz computing facility at 2.00 GB installed memory. Obtained results in the
form of transition diagrams are presented in Figure 12.7.
It is observed from Figure 12.7 that identical transitions were achieved for hybrid
CA of rule 10, rule 170, and rule 0 at
00
and
01
xed boundary; similarly,
another identical transition diagram is achieved for the same hybrid CA at
10
and
11
xed boundary. Hence, it may be concluded that the said hybrid CA dynamics
was not dependent on the left-hand boundary values; it might be xed with bound-
ary value 0 or 1 (refer to Figure 12.7a and b). Thus, it may be further concluded
that “there is no need for assigning program memory for software implementations”
(Mitra and Teodorescu 2016) for said hybrid CA, which in our belief is very impor-
tant in view of low resource constraint components such as WSNs, IoTs, and so on.
A comparative study related to the IWSNs (Industrial WSNs) was presented (Lin,
Deng, and Chen et al. 2016). In the study of Lin, Deng, and Chen et al. (2016), it was
described that in the past, an energy-efciency toward connected coverage strategies in
view of several types of time, i.e., network life, coverage, ratio of dead node(s), and aver-
age energy consumption, were examined (Han, Liu, and Jiang et al. 2015). In another
research, delay-aware and energy-efcient computing in IWSNs were presented (Suto,
Nishiyama, and Kato et al. 2015) at reduced latency. A lightweight packet error discrim-
inator concept-based design in IWSNs was presented by Barac, Caiola, and Gidlund
et al. (2014) to reduce interference and to increase information precision. Besides, to
enhance efciencies, a data fusion-based approach was presented (Hou and Bergmann
2012), tree-based data gathering algorithm was presented (Zhu, Wu, and Han et al.
2015) toward hotspot problem in local/full deployment area. Unfortunately, we have
not found any CA-based approach toward the investigation of dynamics of an energy-
efcient deployment and scheduling with IWSNs in Industry 4.0. For this reason, the
presented approach was compared with reference to the comparative studies of Lin,
Deng, and Chen et al. (2016), and key ndings are presented in Table 12.8.
220 Green Engineering and Technology
Comparative Study Toward Several IWSN-Based Works
Parallel
Computing
Compatibility
VLSI
Integration
Capability
Power
EfciencyInterference Latency Reliability Deployment Scheduling
Han, Liu and Jiang et al. (2015)
TABLE 12.8
Yes Yes Yes Not reported Not reported
Suto et al. (2015) Ye s Yes Not reported Not reported
Barac et al. (2014) Yes Yes Not reported Not reported
Hou and Bergmann (2012) Yes Yes Not reported Not reported
Zhu et al. (2015) Yes Yes Yes Not reported Not reported
Yes Ye s Ye s Not reported Not reported
Proposed CA-based modeling Yes Ye s Ye s Yes Ye s
221Dynamics of Green Modeling
It is observed from Table 12.8 that the proposed CA-based approach has advantages
of power efciency, deployment, scheduling along with parallel computing compat-
ibility, and VLSI integration capability.
12.5 CONCLUSIONS
An efcient ECA-based modeling is presented with only three CA cells at differ-
ent xed boundary scenarios toward an energy-efcient deployment and schedul-
ing with industrial WSNs in Industry 4.0. The presented hybrid CA-based design
ensures an enhancement toward simplication of n-groups of robots over existing
5-groups of robots at the low power-consuming model. It is estimated that the
proposed CA-based design requires very low power (1.20E−05 watt to 1.17E−07
watt) consumption at each physical level implementation of a CA cell (D-FFs);
additional investigations explored that it also enjoys low cost toward its software
implementation as there is no need to focus on its left boundary value at different
xed boundary conditions. Thus, it ensures a complete cost-efcient implementa-
tion both at the hardware (physical) and software level, which, in turn, is very much
advantageous toward incorporation with low resource constraint components e.g.,
WSNs, IoTs, and so on. Hence, we conclude that the presented design is an energy-
efcient and low-cost design, and it truly exhibits its true potentials toward easy
and efcient integration in Industry 4.0.
ACKNOWLEDGMENT
The authors sincerely thank the anonymous reviewers for their helpful suggestions,
which have further enhanced the quality of the chapter.
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225
13 Green Cloud Computing:
An Emerging Trend of
GIT in Cloud Computing
Satya Sobhan Panigrahi and Bibhuprasad Sahu
BPUT
Amrutanshu Panigrahi
SOA University
Sachi Nandan Mohanty
ICFAI Tech
CONTENTS
13.1 Introduction ..................................................................................................226
13.2 Basic Concepts ..............................................................................................227
13.2.1 Green Cloud Computing ................................................................... 227
13.2.2 Requirements of Green Computing
on Cloud Computing
.........................................................................228
13.2.3 Challenges of Green Cloud Computing ............................................228
13.2.4 Virtual Machine Migration .............................................................. 229
13.2.5 Genetic Algorithm ............................................................................ 231
13.3 Literature Review .........................................................................................232
13.4 Motivation and Objectives ............................................................................ 235
13.5 Problem Statement ........................................................................................ 235
13.6 Proposed Work .............................................................................................236
13.6.1 Proposed Ant Colony Optimization (ACO) Algorithm
for the Selection of the Most Appropriate VM .................................236
13.6.2 Pseudo Code of the Proposed Work .................................................237
13.6.3 Flow Chart of the Proposed Work ....................................................238
13.7 Implementation and Result ........................................................................... 238
13.8 Conclusion and Future Work ........................................................................240
References ..............................................................................................................240
226 Green Engineering and Technology
13.1 INTRODUCTION
Cloud computing is a standard in the eld of computation. The systems are large
in numbers that are connected in public and private networks. The reason behind
using cloud computing is to provide an infrastructure for applications that should be
dynamically scalable and that have been used for storing data and les. The invention
of cloud computing has reduced the cost to a much extent and along with it, it reduced
the time required for application hosting, content storage, and delivery. Applications
and services are accessed by common Internet protocols that run on a distributed net-
work using a virtualized server. It is growing fast because it is easy to use and cheap
and has attractive features such as on-demand services and the pay as use scheme.
In cloud computing, different systems are connected in public, private, and hybrid
networks. Because applications, data, and le storages are dynamically scalable by
the inventions of cloud computing, the cost of computation, application hosting, data
storage, and delivery has been reduced much. Cloud services are offered by either
the cloud service provider (CSP) or Internet service providers (ISP). In general, three
types of services are offered by cloud providers, i.e., platform as a service (PaaS),
software as a service (SaaS), and infrastructure as a Service (IaaS). Today, there are
many reasons for an organization to force cloud computing; they pay as the amount
of resource consumption and easily meet the needs of changing the markets rapidly
to ensure that they are always on the leading edge for their consumers. Several data
centers are available in cloud computing. As the interest in the data center gradually
increases, it devours a high measure of energy and it prompts more power utilization
and carbon emission [1].
Services delivered from cloud computing will be deployed in any of the cloud
deployment models: public cloud, private cloud, community cloud, and hybrid cloud.
The physical machine (PM) in the data center will be virtualized in any type of
deployment model. Virtualization is the heart of cloud computing multiple appli-
cations and operating systems are run on the same servers at the same time [19].
Different researchers are achieving green cloud computing to limit the utilization
of energy and effective processing and exploiting the computing infrastructure.
Presently, green cloud computing is the developing innovation in the cloud that con-
centrates on how people can run with environments eco-friendly and deals as we
are capable to think about the climate from harm and to reduce loads between data
centers by using virtual machine migration (VMM) techniques [1]. Using VMM
techniques, this article focuses on load balancing, fault tolerance, and reduces energy
consumption between different physical machines.
A genetic algorithm is a guideline of delicate processing, which depends on the
idea of common hereditary qualities and advancement. It deals with the standards
of biological evolution, which are easy to build, and its usage does not need a lot of
capacity, subsiding on them an adequate decision for optimization issues [10].
The remainder of this chapter is presented as follows. Section 13.2 deliberates
the basic concepts required for understanding the green cloud computing and the
work of the chapter. In Section 13.3, the authors present the Literature Review. In
this section, the previous studies related to VM migration in green cloud computing
are discussed. The authors present their novel methods, which help in eliminating
227Green Cloud Computing
this problem. In Section 13.4, motivation and objectives are highlighted, which are
achieved in this research. Section 13.5 discusses problem statements. In this sec-
tion, the problem statements are described, and problems that existed in the pre-
vious system were discussed so that a new approach could be developed for the
virtual machine. Section 13.6 is on Proposed Work. In this chapter, the proposed
work is discussed, which can make the system more efcient by using the proposed
scheme. In Section 13.7, the authors describe the implementation and results. Finally,
Section 13.8 gives the conclusion and future direction.
13.2 BASIC CONCEPTS
13.2.1 green Cloud Computing
Cloud computing is a profoundly adaptable and low-cost basis for running several
applications, for example, high-performance computing (HPC) undertaking and web
applications. Notwithstanding, there is one major basic issue in cloud computing,
which has been developing because of its growing curiosity, which has expanded
the utilization of energy in the data center. The issue of high utilization not just
expands the operation cost, which decreases the benet of cloud providers, but also
inuences nature as the high utilization of vitality prompts high discharge of carbon.
Subsequently, vitality effective arrangements are needed to limit the effect of cloud
guring on the earth [1]. The goal of making a cloud, which is environmentally
friendly, can be accomplished by the utilization of green cloud computing [1].
The environmentally friendly structure, which is procient regarding giving energy
solutions and ends up being nancially savvy, is the other need of a cloud provider in
the wake of being an improvement in the current innovation [2]. The overall adminis-
trations and information have been given to the client through green cloud computing,
which set various data centers in various areas. To limit the utilization of energy-ef-
cient, accomplishing procient preparing and using the computing framework, green
cloud computing has been utilized by various experts [5]. The use of energy will incre-
ment to an enormous sum if the current cloud computing would not have the option to
satisfy the requirements of increment in front-end customer gadgets that are collaborat-
ing with the data center accessible in the back end. The eco- accommodating utilization
of PC and related assets is known as green computing. To accomplish this objective,
energy-efcient central processing units have been applied as well the servers, periph-
erals, and a decrease in resource consumption are essential along with appropriate
dumping of electronic waste [3]. The computer, servers and other subsystems that are
associated with it are disposed of, designed, and manufactured by green computing.
All the requirements have been fullled using it without putting any impact on the
environment in terms of getting effective and efcient results [8].
There are numerous methods to green computing, i.e.:
• Prociency of the algorithm,
• Allocation of the resource,
• Server virtualization, and
• Management of power.
228 Green Engineering and Technology
13.2.2 requirementS of green Computing on Cloud Computing
Current data centers, working under the cloud computing model, are introducing a
variety of applications [9]. The requirement to deal with many applications in a data
center makes the contest of on-demand resource provisioning and allocation in reply
to time-varying assignments. The average data center consumes as much energy as
25,000 households [11]. Data centers are costly to keep up with, yet also antagonistic
to nature [21].
Cloud computing stretches with high adaptability and performance. The data cen-
ter is kept from other clusters of physical machines. The physical machine will be
virtualized to make virtual machines [1]. Virtual machines will be assigned to the
user. The services of cloud computing are rendered by the cloud service provider
(CSP), due to heavy demand of data centers leading to huge energy consumption and
CO2, this leads to huge power charge and power emissions. Hence, green computing
is desired to cut power charge and CO2 emissions and raise prots. To report this
issue, data center resources should be accomplished by using less energy to initiate
green cloud computing (Figure 13.1).
13.2.3 C HallengeS of green Cloud Computing
• The necessity of a novel optimization procedure: There is a necessity to
make an adjustment between temperature and energy that helps in accom-
plishing the elite target [1].
• Minimize architecture complexity: To minimize the power required to
start a system, the dependency of different components between each other
needs to be reduced.
• The necessity of competent data centers: In request to spare a lot of
vitality, they have to utilize extremely restricted IT apparatus that further
diminish the need for enormous data centers [1].
• Cooling of the data center: A major role has been played by sensor
networks in handling data centers’ power consumption.
• Green IT: A green software movement has become a key area of research
by the advancement in the growth of IT industries, but still there is a need
to take some more initiatives to fulll the current needs.
• Performance deprivation: The power consumption and throughput of
energy have been increased by degrading the performance of servers.
• Platform management: To deploy applications in a scalable environment
and to maintain it, there is a need to manage server density. The cost of
operation is higher related to energy that needs a large amount of server
density that supports running applications of the cloud.
• Round the clock cloud services: The data centers want constant power
because of the accessibility of clients around the globe.
• High reliability: A reliable power supply is required.
• Cost-efciency: To build less costly computation techniques.
229Green Cloud Computing
• Virtualization of servers: It has been used to reduce energy consumption
and to improve efciency effectively.
• Energy efciency: The overall energy consumption rate has been mini-
mized using it.
• CO2 emissions: Reduce the CO2 emission rate from cloud resources.
13.2.4 virtual maCHine migration
In cloud computing, virtual machine migration (VMM) is a technique for migrat-
ing operating systems and various jobs between physical systems and is also used
to reduce the load, fault management, and reduce the amount of energy consumed
across multiple physical machines. Different problems are there in green cloud com-
puting. By increasing the cloud resource utilization level with the use of virtualiza-
tion, technology cloud operation costs get reduced to consierably [7]. However, if the
use of virtualization is not done properly in cloud data centers, then the performance
of the cloud can degrade too. VMM is a technique that supports cloud service pro-
viders to prociently accomplish loud resources while abolishing the necessity of
human regulation [6,19] (Figure 13.2).
A. Needs of Virtual Machine Migration
VMM is required for providing a hassle-free virtual environment to the
client-side so that the clients can execute their tasks without any failure.
Thebasic requirement of the VMM is depicted as follows [21]:
• Load balancing: There are several numbers of DCs that are deployed in
a cloud environment to successfully execute the user request. The main
objective of cloud computing is to provide an environment where the
FIGURE 13.1 Requirements of green cloud computing.
230 Green Engineering and Technology
users can execute their tasks regardless of the computational require-
ments. Sometimes, the case may arise where one DC will have multiple
user requests simultaneously, which will make the DC more prone to
failure. So, the cloud service provider checks the load on each deployed
DC and tries to equalize the load among all active DCs by transferring
some load from a heavy-loaded DC to the lightly loaded.
• Maintenance and servicing: VMs are being migrated from one host
to another by the cloud service provider with an objective to reboot the
original host for better performance and a longer lifeline.
• Improve resource utilization: VMM is performed to utilize the avail-
able resources effectively. The cloud computing environment allows
resource sharing among different DCs and VMs. Therefore, the service
provider manages the task from one VM to another depending upon the
resource utilization to reduce the communication cost for sharing the
resource.
• Power management: Once the request is being submitted to the VM, it
will rst check for resource availability. If the resource is not available
locally, then it will go for remote access of the resource from another
VM and the requesting VM has to be active along with the server condi-
tion until the resource is being granted. Therefore, the VMM can help
in managing the power by transferring requests to the VM, which has
the requested resource.
FIGURE 13.2 Virtual machine migration.
231Green Cloud Computing
B. Types of Virtual Machine Migration
VMM is performed based on the threshold value depending upon the
resource utilization, which is being decided by the service provider or the
client in service legal agreement (SLA) [13]. In general, the high and low
degree of resource utilization is set as the high threshold and lowest thresh-
old. VMM can be done in two ways based on over and under resource utili-
zation. VMM can be categorized in two ways as follows:
• Static migration: This type of migration process pauses the current
execution and searches for the less loaded DC to which the VM can be
transferred. The benet is an easy way and the drawback is long down-
time [7,15].
• Dynamic migration: Dynamic migration allows the service provider
to transfer the ongoing task in a VM without any halt from one DC to
another and the primary objective of this process is to hide the process
from the client-side. The benet of dynamic migration is downtime,
which is not visible to the user with a speedy network [4,7,15].
13.2.5 genetiC algoritHm
A genetic algorithm (GA) is a random search algorithm that is not just a one-point
search, yet, in addition, consolidates a multipoint search highlight. It is tough to
discover a t receiver that is set up to get a supplementary assignment when a system
becomes overloaded. Therefore, the GA is applied to decide the destination processor
that can receive a task. Various viewpoints are desired in load balancing with a GA
such as load measure, tness function, coding strategy, and an algorithm [2].
• Load measure: There are three-level measure schemes that are used to
denote the load state of the processor: lightly loaded processor, normally
loaded processor, and heavily loaded processor.
• Coding methods: It is projected that a likely response to an issue might be
indicated as a set of parameters. These parameters (recognized as genes) are
united to form a string of values (often referenced as a chromosome).
• Fitness function: In dynamic load balancing, the tness function consists
of a tter node. Moreover, a task can be transformed in such a manner that
its time of completion and the cost of communication are less and system
throughput and the use of processor are high.
• Selection: During the successful completion of each generation, some
part of the existing population takes part to breed new peers. This is
based on the survival of the ttest mechanism. Individual solutions are
chosen, where tter solutions are bound to be chosen.
• Crossover: Crossover opts for genes from parent chromosomes and cre-
ates a new offspring. Here, two individuals are selected and crossover
sites are picked arbitrarily.
• Mutation: Mutation is cast-off to alteration of the genes randomly in a
chromosome. Mutation of a bit involves reversing it changing 0 to 1 and
vice versa with a small probability.
232 Green Engineering and Technology
There are three different segments of GA, which are:
1. Initial population: It is the execution time and the failure rate of each
virtual machine.
2. Cross over values: It is the probability of virtual machine selection.
3. Selection of the best virtual machine: It deals with which virtual machines
have the least probability of failure.
GA is selected, in this chapter, to measure the load between virtual machines because
the GA is not only a one-point search but also combines a multipoint search between
different virtual machines and selects the more loaded one.
13.3 LITERATURE REVIEW
Literature review includes previous studies related to green cloud computing and
the major problems arising within them due to the occurrence of VMM. Various
researchers have proposed numerous solutions to this problem, which are studied in
this section.
More et al. [11] have investigated dealing with reviewing many procedures,
and algorithms, for competent green cloud computing by using virtualization pro-
cedures. Numerous procedures are related to power saving, which can also help in
enhancing the efciency of the systems based on the server and network involved.
All such strategies are to be studied here to present a study on the existing meth-
ods [11]. The utilization of the same procedures will however not be possible
once there is a huge increase in bandwidth and network connectivity in the data
centers in the future. There is a need to understand the complete process of power
mechanisms occurring within the data centers in order to control the above-
mentioned concern. The network devices such as servers, CPUs, and switches are
the ones that consume the highest power. To design modern algorithms, research
is still being carried out. New techniques with enhanced energy efciency are
being evolved, which also include the QoS (quality of service), SLA, and VM
consolidation in these systems. They did not work on the ratio of computation and
power, which helps in utilizing the resources in a better way along with minimum
consumption of energy.
Ehsan Arianyan [12] has proposed consolidation as a new practice for energy
redeemable in cloud data centers. The main disadvantage of recent research on con-
solidation solutions is that they emphasize only one criterion and disregard the rest
of things. According to the modied analytic hierarchy process (AHP) method, this
study proposed a new multi-objective consolidation result. Three objectives have
been considered in this, namely, energy consumption, SLA violation, and the num-
ber of migrations in the decision process. The comparisons are made among various
approaches, and their results are evaluated in terms of simulation parameters. There
is minimization in the energy consumption within the results achieved through the
proposed method. By executing the proposed method in real cloud infrastructure
management products, the experiments were carried out in this study [12].
233Green Cloud Computing
Larumbe et al. [13] have presented in this article that the response time of
the systems is less for the users that are near to the VMs. This results in enhanc-
ing the QoS for the users due to the distribution of VMs near to those users. The
impact of the maximization of energy consumption of the cloud is very negative.
It might also affect the global warming of the planet. The solution to this issue is
provided by placing the VMs within the data centers, which utilize the sources
of green energy in the systems. A comprehensive optimization modeling system
is provided for managing the applications, which include such dynamic demand.
An efcient search heuristic is developed here to resolve the issues. As per the
results achieved by implementing the proposed technique, there is a reduction in
the communication delay, the power consumption is saved, and there is a mini-
mization of the CO2 emissions as well. The meta-scheduler execution time is
maintained here in the proposed approach, which helps in providing an efcient
execution time.
Chonglin et al. [14] have recommended that for research utilization, virtual
machine consolidation is the best solution found. Once the power consumption for
each VM is known, more power can be saved here. There are numerous modeling
methods proposed here to estimate the power consumption as it is not easily calculated
directly. When the multi-VMs compete with the resources on the same server, the per-
formance of current models is not very accurate. There is a correlation between the
resource features to provide modeling. A tree regression-based method is proposed in
this paper, which helps in computing the power being consumed by the VMs on simi-
lar hosts of the systems. The dataset will be partitioned as per the advantagesof this
method. Here, each dataset is an easy-modeling subset for the other. In various appli-
cations that run on VMs, the accuracy achieved by applying this proposed method
is around 98% as per the experimental results. The accuracy of individual VMs was
however not computed in this study.
HAN et al. [15] studied the cooperative behavior of multiple cloud servers and
proposed the hierarchical cooperative game model for improving energy efciency
in green clouds. An innovative technique is anticipated in this work, which provides
a change in the multiplexed methods that are needed for initial optimal solutions for
various users. This results in reducing the loss of efciency in these systems. Both
optimization and fairness have been considered by this algorithm. In a public cloud
environment to improve efciency, the game theory is applied to virtual machine
deployment. The drawback of this scheme is that when the resource allocation game
has feasible solutions, only then Nash equilibrium exists.
Marotta et al. [16] demonstrated that big data centers need to minimize con-
sumption along with the utilization of virtualization technology. This is mainly due
to the environmental pollution caused by them as well as the other economy-related
issues arising within these systems. The virtual machines consolidation method is
one of the many methods, which help in reducing the energy consumption within
these systems. In this article the major objective is to maximize the cost-efciency
along with the minimization of the number of active nodes that are being utilized
currently in a system. A novel technique is proposed here to consolidate the problem.
The attractiveness of the possible VM migrations can be evaluated based on the
234 Green Engineering and Technology
simulated annealing-based algorithm. The other issues related to the topology and
trafc of the VM are not highlighted in this article.
Wadhwa et al. [17] have proposed a novel approach that will help in minimiz-
ing the carbon being emitted and the energy being consumed within the distributed
cloud data centers. Within the distributed architecture of the cloud, the proposed
architecture utilized the data centers' footprint rate of the carbon. The authors have
also added the method of virtual machine allocation and migration to reduce the
emission from carbon and consumption of energy in the federal cloud systems.
The simulation results of the proposed work show that this novel technique helps
in minimizing the emission of harmful rays of CO2 and also the energy being
consumed in the cloud data centers. The results are also compared here with the
earlier proposed scheduling method, which includes round-robin VM scheduling
within their cloud data centers. The drawback of this article is that using the pro-
posed technique works well for a small number of VM requests, but consumption
of energy increases for a large number of VM requests.
Huang et al. [18] have proposed two new dynamic VM migration algorithms.
The potentially over-utilized servers have been predicted by applying a method
of local regression and then from migration best t VMs combination has been
found using 0–1 knapsack dynamic programming. The results of this algorithm
have been analyzed in terms of complexity of time and have seen that they are
highly scalable as compared to existing algorithms in terms of different perfor-
mance parameters. The energy consumption and the number of VM migrations
need to reboot servers that have been reduced by much extent using two new heu-
ristic schemes. Therefore, from all the results, it has been concluded that by the
use of the proposed two heuristic algorithms, the green cloud computing can be
achieved. In this article the problem of SLA violation caused by overloaded serv-
ers is not resolved by the proposed algorithm.
Kinger et al. [19] have concluded that by continuous consolidation of VMs, the
objective of saving energy can be achieved. The cloud computing thermal state cur-
rent utilization has been used for moving toward the consolidation of green comput-
ing. Both consolidation and resource management have some relation. The energy
has been saved to a much extent in the cloud using VMM. In this study, workload
management has been used for migration that helps in keeping the temperature power
consumption within the limit. Finding the target and source machine is the main
challenge faced in migration.
Rasouli et al. [20] have recommended that the use of large data centers results
in problems such as emissions of greenhouse gases and an increase in the cost of
energy. So, it has become a key topic for researchers to provide an efcient method
that reduces the data centers' energy consumption. The main aim of this approach is
to force idle nodes to go in sleep mode while there is live migration. The real-world
workload situation has been considered to assess the performance of the anticipated
method. The energy consumption has been reduced by much extent using the pro-
posed method but still, it is not proved to be efcient in improving the whole ef-
ciency of the system.
235Green Cloud Computing
13.4 MOTIVATION AND OBJECTIVES
Cloud computing is the architecture that stores data on the virtual servers, which has
the least consumption of network resources. The cloud provides exibility to users
to store and process that data. Due to the decentralized architecture of the network,
various issues get raised, which are energy consumption, fault occurrence, security,
and so on. Green cloud computing is the advanced version of cloud computing that
uses the least number of network resources for data storage and processing. The
fault occurrence is the main problem in green cloud computing, which may arise
due to the wrong assignment of the cloudlets to virtual machines. The fault-tolerant
approach is required for green cloud computing, which recovers occurred fault at the
least amount of time and consumes the least amount of network energy.
Following are the various objectives of this chapter:
1. To study and analyze various fault-tolerant techniques of green cloud
computing.
2. To propose improvement in the ACO algorithm for fault tolerance in
the network.
3. The proposed enhancement will be based on the genetic algorithm to prog-
ress the fault tolerance of the network.
4. Implement the proposed algorithm and compare it with the existing meta-
heuristic algorithm in terms of various parameters.
13.5 PROBLEM STATEMENT
Green cloud computing is an efcient approach that reduces energy consumption
for data storage on clouds. In the network architecture, virtual servers, brokers,
and cloud service providers are involved in the data communication. The brokers
are the third party that assigns cloudlets to the most capable virtual machines. In
this chapter, the technique of virtual machine migration will be proposed, which
reduces the chances of fault occurrence and also reduces resource consumption in
the network. Green cloud computing is the improved version of traditional cloud
architecture, which increases the efciency of the network in terms of their energy
consumption. Overloading is the main issue of green cloud computing and tech-
niques, which are proposed to handle overloading that can increase the number
of migrations. An efcient approach is required, which can reduce the number of
migrations in the network.
This chapter takes the research challenges with VMM techniques on green cloud
computing based on a GA. Particuarly, the subsequent research problems are explored:
• How to make cloud computing environmentally friendly?
CC gives a protable setup with high scalability and performance [1].
However, the growing demands of cloud infrastructures user lead to the
high energy consumption of data center, which results in carbon emission to
the environment and it is not environmentally friendly. Therefore, working
236 Green Engineering and Technology
on the cloud to save energy and reduce carbon emissions leads to go green
while saving time and money.
• How to design and build green data centers in a cloud?
Cloud computing stores and runs different infrastructures on data cen-
ters. To satisfy the user's needs, different physical machines on the data cen-
ter will be virtualized. There are two ways to build green data centers [21]:
(i) use green elements in the design and construction process of data centers
and (ii) greenify the course of running and functioning a data center on a
daily basis.
• When, which, and why VMM is required?
There are two-migration processes.
First, migrating virtual machines from an overloaded server to avoid per-
formance degradation. Second, migrating virtual machines from underloaded
servers to advance resource consumption and minimizing energy utilization.
A vital verdict that must be made in both situations is to determine the
best time to migrate virtual machines to minimize energy consumption,
which satises reduction of CO2 emission. Before migration, identifying
which physical machine is overloaded and preparing the destination physi-
cal machine where the migrated job can be assigned should be performed.
• Where to migrate the VMs nominated for migration?
Determining the nest settlement of new VMs or the selected VMs nom-
inated for migration to other servers is an added vital characteristic that
inuences the excellence of the system.
• When and which physical server to switch ON/OFF?
To optimize energy consumption by the system and avoid carbon foot-
print, it is required to tactfully decide the place, identication of physical
server for deactivation. By using the proper order of deactivation, it saves
energy or restarts to handle an increase in the demand of resources.
The basic problems that this chapter solves are the following:
• More power costs and high CO2 emissions occur due to the high amount of
power needed in the data center.
• Properly UN assigned jobs on virtual machines make overloads/under loads.
• Due to the decentralized architecture of the network, various fault occur-
rence issues are raised on the network.
13.6 PROPOSED WORK
13.6.1 propoSed ant Colony optimization (aCo) algoritHm
for tHe SeleCtion of tHe moSt appropriate vm
Green cloud computing is a less energy consumption tactic used to supply and
execute the data from the userbase. Fault tolerance is a key subject of green cloud
computing. In this chapter, the authors use the ACO algorithm for two purposes such
as execution and assignment of the task to various DCs for processing. Three phases
of the ACO algorithm are as follows:-
237Green Cloud Computing
1. Initial population: In the starting stage of the algorithm, the DC execu-
tion time and fault occurrence have been provided as the initial population,
which will be used by the algorithm for selecting the most dependable and
efcient DC for submitting the user request.
2. Update pheromone: In this step, the failure rate for each active machine
is calculated based on the data provided in the initial population. Eq.(13.1)
shows the method for calculating the probability of the fault for every par-
ticipating DC.
()
τη
αβ
ρ
ij
()
,
ij
,=∑,ij
()
τη
αβ
ij
,
()
ij
,
(13.1)
3. Select the best pheromone: Based on the output of the second phase, the
DC having the least fault probability is being selected for. Eqs. (13.2) and
(13.3) are applied to select the best machine.
τ
ij,,
=−(1
ρτ
)ij+∆
τ
ij, (13.2)
1iLki
kfant
∆=
τ
k
ij
0
(13.3)
13.6.2 pSeudo Code of tHe propoSed Work
1. For each Vm € host Vmlist do //Vm:Virtual Machine,Vmlist
2. Vmis migrated ← False
3. Vm.utilHistory ← get Vmutilization
4. T← built Transition matrix
5. while Machine Migrated() do
Initiate pheromone Tij
Repeat for all ants i; Construct Transition Matrix(i)
For all ant i: global pheromone updates (i)
T(i−+j) 1(
=−(1 p)Tij−)
While not yet a Transition matrix ()
T
∑(pkj−)
pi −⋅jx
pi−j
End while
End for
6. is migrated ← host.is Migrated (w,i)/// Return the machine on which task
needs to be migrated to the assigned machine
7. return isMigrated // Return the complete list of the machine
238 Green Engineering and Technology
13.6.3 floW CHart of tHe propoSed Work (figure 13.3)
13.7 IMPLEMENTATION AND RESULT
The proposed work has been simulated in a cloud sim and cloud analyst environment.
Initially, we have taken ten user bases or UBs, which are being situated in six differ-
ent regions. Each UB is being equipped with a different number of users. The popula-
tion has been decided randomly and the users of UB are responsible for generating
the requests. To handle the requests, some data centers have been implemented with
different computational capacities. For experimental purposes, we have implemented
60 numbers of VMs. We have taken the various numbers of requests such as 100,
200, 300, 400, and so on. The closest data center has been taken as the service broker
policy, which is wholly responsible for handling the requests coming from the UB
FIGURE 13.3 Flow chart of the proposed work.
239Green Cloud Computing
toward the DC. For performance measurement, the average energy consumption and
response time have been considered with respect to the number of requests. It has
been noticed that according to the trafc, the response time and the energy consump-
tion for the DCs are also increasing (Figures 13.4 and 13.5).
FIGURE 13.4 Average response time for the proposed work.
FIGURE 13.5 DC energy consumption for the proposed work.
240 Green Engineering and Technology
13.8 CONCLUSION AND FUTURE WORK
This chapter proposes a combined approach to tackle the power utilization issue.
A task-oriented resource allotment strategy (ACO) is proposed; it can decrease the
energy consumption of the data center focus adequately on the reason for execu-
tion ensure. To approve the adequacy of the proposed strategy, the simulations have
been worked on cloud sim and cloud analyst. This can be implemented using various
meta-heuristic algorithms [22–31].
In future work, the authors will attempt to consider another sort of model, which
can be incredibly powerful for breaking down specic issues in the cloud data center
including heterogeneous tasks scheduling and aw diagnosing, and authors may take
more factors into thought; for instance, not just time furthermore, power utilization
yet additionally, the condition of the hosts can impact the energy of the data center.
Another promising future work heading is to attempt to utilize other biocomputing
strategies to tackle a few issues in green cloud computing.
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243
14 Internet of Things for
Green Technology
Saurabh Bhattacharya and Manju Pandey
NIT
14.1 INTRODUCTION
Climate scientists presented the most alarming recent research on the greenhouse
effect. The accumulation of greenhouse gases (GHG) in the atmosphere is increasing
at a faster speed as predicted. “Green Technology” or “Going Green” is the most dis-
cussed topic and trend in the business world, as the thrust for adopting eco-friendly
practices gain momentum for every industry. The pioneer of the IT (information
technology) industry is searching for a path to green technology and more environ-
mentally friendly inclusion. How they can improve awareness and minimize costs by
being environmentally friendly?
It covers most of the carbon emission industries, including road transport, airlines,
and generating electricity to IT industry. The main focus of implementing is to pro-
vide eco-friendly, correct implementation to make operations more cost-effective and
CONTENTS
14.1 Introduction .................................................................................................. 243
14.2 Carbon Emission...........................................................................................245
14.3 Green IoT ...................................................................................................... 247
14.4 Steps to Achieve Green Technology for the Internet
of Things (GIoT) ........................................................................................... 249
14.4.1 Design and Develop Energy-Efcient Hardware..............................249
14.4.2 Usage of Power Management Technology ........................................249
14.4.3 More Preference for Virtualization Technology ............................... 249
14.4.4 More Dependency on the Cloud Computing Technology ................249
14.4.5 Optimizing Data Center for Energy Efciency ................................ 250
14.4.6 More Usage of Efcient Displays .....................................................250
14.4.7 Managing e-Wasteby Recycling the Systems ................................... 251
14.4.8 Encourage Work From Home ........................................................... 251
14.5 Issues and Challenges in Implementing GIoT .............................................. 253
14.5.1 Interoperability ................................................................................. 253
14.5.2 Evolution of 5G .................................................................................254
14.5.3 Issues with WSN ............................................................................... 255
14.6 Conclusion ....................................................................................................256
References .............................................................................................................. 257
244 Green Engineering and Technology
efcient, and to reduce carbon emission. An optimized way of using energy resource
is encouraging the usage of the biodegradable product or recyclable product and mini-
mizing the involvement of hazardous materials without affecting the production.
The fast-growing IT industry or information and communication technology (ICT)
[R-3,4] recently with the advancement of wireless technologies, sensors, actuators,
and remote monitoring contribute a drastic increase in carbon emission, which is as
similar as the aviation industry. Due to phenomenal demand for computing devices,
software and services, the IT industry or ICT (Figure 14.1) shows rapid growth of
carbon emissions each year.
The ICT industry plays a crucial role in designing and deploying solutions to
provide a low carbon emission system with other sectors. According to climate
group - SMART2020
ICT sector’s own emissions are expected to increase, in a business as usual (BAU) sce-
nario, from 0.53 billion tonnes (Gt) carbon dioxide equivalent (CO2e) in 2002 to 1.43
GtCO2e in 2020. But specic ICT opportunities identied in this report can lead to
emission reductions ve times the size of the sector’s own footprint, up to 7.8 GtCO2e,
or 15% of total BAU emissions by 2020.
The Internet of Things (IoT) has been designed to demonstrate numerous devel-
opments, and analysis imparts global accessibility over various physical devices.
Empowering advances like radio-frequency identication (RFID), sensor organiza-
tions, biometrics, and nanotechnologies are presently getting normal, bringing the IoT
into actual executions tending to differing applications, including smart cities, smart
health, smart logistic, smart trafc, or transportation. They mention an energizing
future that intently interconnects our physical world through green organizations [10].
Green organizations in IoT will add to decreasing outows and contaminations and
limiting operational expenses and power consumption.
The green Internet of Things (G-IoT) [2,3,12,13] is anticipated to present critical
changes in our day-by-day life and would help us understand the vision of "green
FIGURE 14.1 Information and Communication Technology (ICT).
245Internet of Things for Green Technology
encompassing knowledge”. These new brilliant items will likewise be setting mindful
and ready to play out specic capacities like self-ruling, calling for new types of green
correspondence among ‘individuals and things’, and between ‘things themselves’,
where power utilization is improved and data transmission usage is boosted. This
advancement would be pertinent not exclusively to analysts yet in addition to enter-
prises and people the same. The point of this particular problem of reducing GHG was
to minimize in on both hypothetical and usage approaches in green cutting-edge orga-
nizations that can use IoT to provide green frameworks to empower innovations. IoT
has already revolutionized the digital world and is reshaping the traditional business
concept. The primary reason for adopting IoT in various sectors such as smart health,
smart city, smart logistic, smart agriculture, smart sensing, and smart technology is
that it reduces energy consumption and carbon dioxide emission in multiple scenarios
[7,14,17,18]. IoT helps in realizing the real vision of green technology.
14.2 CARBON EMISSION
According to NASA,
carbon dioxide (CO2) comprises 411 ppm of the earth’s atmosphere as of July 2019.
Because CO2 is a greenhouse gas that traps heat, there is a strong correlation between
the amount of CO2 contained in the earth’s atmosphere and climate; the more CO2 there
is in earth’s atmosphere, the hotter it gets.
Carbon is the most common element for life on earth, from the air we breathe in
to the growing of crops. Gases that trap the heat in the atmosphere to provide suitable
climate on earth are known as GHG. About 81% of GHG consist of carbon dioxide and
remaining are methane, nitrous oxide, and uorinated gases. Whenever we discuss car-
bon emission, we are primarily focusing on carbon dioxide. Carbon dioxide exchanges
between oceans and atmosphere are the largest source of natural carbon emission [11].
Plants and animals emit CO2 through respiration process. The carbon cycle supports
life on earth through maintaining continuous process cycle over air, water, and soil.
Nature does not receive similar treatment from humans. When fossil fuel is used
as a signicant source of energy, it releases a tremendous amount of carbon dioxide
and other GHG into the atmosphere—year by year global carbon dioxide emission
increases. By the year 2020, it raised nearly to 34,000 million metric tons, and by
2040 it is estimated that it will cross 40,000 million metric tons.
In recent years, human actions led to vast deforestation and excess usage of fossil
fuels to compete with the essential requirement for living caused an increase in the
amount of carbon dioxide in the earth’s atmosphere. Climate change due to carbon
emission is a crucial concern for every country. As shown in Figure 14.2, the adverse
effect of carbon emission in our lives and well-beings is directly and adversely
affected by it because of rapid growth in the emission of CO2. By 2040, it is expected
to cross 40,000 million metric tons (MMTs) of carbon dioxide emission.
As shown in Figure 14.3, in 2018, China (10.06GT), United States (5.14GT), India
(2.65GT), Russian Federation (1.71GT), and Japan (1.16GT) are top ve countries in
CO2 em ission [11].
246 Green Engineering and Technology
Crucial carbon emission saving apprehended in the developing world. Developing
and deploying low-cost and low-power devices make precision to various industries
leading to modernizing countries much quicker. This also helps in conserving water,
soil, air, and fossil fuels. Countries are very much seeking a resolution for the has-
tening and the continuing threat of changing climate, which causes global warming.
However, technologies like IoT already help in reducing carbon emission signicantly
FIGURE 14.2 Global carbon dioxide emission, 1850–2040. (Carbon di oxide Information
Analysis center (Oak Ridge National Laboratory, 2017), World Energy Outlook (International
Energy Agency, 2019).)
FIGURE 14.3 Greenhouse gas emission for Major Economics, 1990–2030. (World Energy
Outlook (International Energy Agency, 2019), CO2 Highlights (International Energy Agency,
2019), International Non-CO2 Projections (U.S. Environmental Protection Agency, 2012).)
247Internet of Things for Green Technology
and boosting energy efciency across various industries. IoT impacts signicantly on
dipping carbon footprint.
Dependency on ICT rapidly increases in a short duration of time. When dealing
with carbon emission, the websites used daily produce an ample amount of carbon
emission. A regular website releases about 4.61 g of CO2 every single visit: popular
websites having tremendous views per month release 533 kg of CO2. There are vari-
ous tools available to calculate the carbon emission from websites, mobiles, laptops,
data centers, and sensors (www.websitecarbon.com for calculating website carbon
emission, www.openei.org for calculating carbon emission of mobile phones). Every
visit to google produces 0.30 g of carbon though it is cleaner than 80% as compared
to other websites. Considering the calculation of website CO2 emission, it depends
on ve primary factors:
1. Data transmitted over the wire.
2. Source of energy used by the data center.
3. Website trafc.
4. Carbon emission of electricity.
5. Energy consumed by the interconnected network, data center handling the
request and response, consumption of electricity at the user’s end while
using a computer/mobile.
14.3 GREEN IoT
G-IoT is a procedure adopted for energy efciency, reducing the greenhouse effect,
reducing energy consumption, and CO2 emission [4,6]. GIoT provides sustainability
to make the world smarter and safer. Figure 14.4 shows the combination of G-IoT,
which comprises all components based on green technology. The main aim of GIoT
is to provide the energy-efcient technology to smart home, smart grid, smart health,
smart logistics, smart agriculture, smart trafc, and reducing GHG emission.
According to EPA.gov transportation (29%), electrical production (28%) and
industry (22%) are the signicant contributors as the largest emitters of carbon diox-
ide. Turning every stone to reduce the carbon emission requires tremendous effort
from every single person, business organization, and government to curb emission
by exploring efcient and optimal usage of energy without disturbing the production.
Articial intelligence (AI) and IoT provide tremendous opportunities to lower the
carbon emission level by proper utilization of energy storage and optimization of
energy efciency as most of the IoT devices depend on electrical support to perform.
In recent trends, solar energy, tidal energy, and wind energy are used to harvest their
own energy for processing, leading to a reduction in the usage of battery.
The number of interconnected devices increases rapidly from 50 billion by 2020 [8]
to expected 100 billion by 2030[R-3], which generates a tremendous amount of data.
Constantly generating massive data from various devices and actuators consumes
electricity and produces heat, which lead to an increase in carbon emissions in the envi-
ronment. Due to the tremendous amount of CO2 emissions causing health and envi-
ronmental concerns, green technology or renewable technology is considered as an
attractive research area in the progression of technology. According to the world
248 Green Engineering and Technology
economic forum, using IoT and AI could cut carbon by 15%. IoT and AI utilized to
reduce the carbon emission improve resource efciency, provide optimized produc-
tion and stimulate innovation and thinking.
The signicant aspects of designing GIoT [5,15]:
1. To design and develop green computing devices, green processing, green
interconnected network, and green communication protocols to achieve opti-
mized power consumption along with maximizing bandwidth utilization.
2. To dispose of green computing devices efciently to reduce carbon emis-
sion and pollution.
3. To enhance the working and energy efciency of devices without affecting
productivity.
People have become more aware of the adverse effects of environmental degradation
and more concerned over GHG emissions causing global warming. IoT provided the
services using things like sensors, RFIDs, and actuators using data centers where
data are transmitted over the network. Recent advancement in the eld of carbon
footprinting paves the path of new upgradation from IoT to GIoT. GIoT ensures the
efcient use of things to maintain carbon and GHG emission in the environment. It
contributes to reducing emissions, pollution, operational cost, and power consump-
tion without affecting the services.
FIGURE 14.4 Green Internet of Things.
249Internet of Things for Green Technology
Energy efciency is the critical factor for designing and developing GIoT. GIoT
helps to reduce GHG emission in the existed system by minimal changes or to create
a completely new green system by focusing on green design, green production, green
implementation, green utilization, and green recycling/ disposal with a minimal
impact on the atmosphere. Designing and developing green systems help to optimize
the IoT greenhouse footprint, providing green smart cities, green smart health ser-
vices, green smart trafc, green smart education, green smart logistic, green smart
grid, and green smart home.
14.4 STEPS TO ACHIEVE GREEN TECHNOLOGY
FOR THE INTERNET OF THINGS (GIoT)
14.4.1 deSign and develop energy-effiCient HardWare
The critical aspect for attaining GIoT is to develop and design hardware, which is more
energy-efcient as compared to existing hardware. Major IT industry vendors including
laptops/desktops, servers, and workstations are evolving to meet the standard of EPA’s
energy star guidelines. These guidelines use the standard set by the IEEE to measure
environmental performance, developing multicore processors to increase in the output
without affecting energy usage, high-efciency power supplies, l ow-voltage processor,
and efcient cooling techniques for data centers. Efcient energy criteria “Energy Star
4.0” set by Electronic Product Environmental Assessment Tool (EPEAT – www.epeat.
com) is a worldwide ecolabel for the IT industry.
14.4.2 uSage of poWer management teCHnology
Advanced conguration and power interface (ACPI) is used by the latest operating
system to incorporate efcient power management. It allows us to manage and moni-
tor the power of the system over a while. Various hardware vendors provide their own
power management software to provide optimal usage of energy.
14.4.3 more preferenCe for virtualization teCHnology
Virtualization technology helps to reduce the number of physical servers and hence
provides efcient energy consumption. As virtualization supports to run multiple
servers on a single server, it virtually reduces energy consumption and helps to main-
tain carbon emission. The virtualization facility provided by VMW claims that it
decreases the energy cost by almost 80%. It helps in reducing carbon emission by
achieving high virtualization.
14.4.4 more dependenCy on tHe Cloud Computing teCHnology
As per the Pike Research, a clean technology market intelligence rm claims that
cloud computing could emerge as a signicant role changer in reducing energy
consumption. Cloud computing provided on-demand service facility to the users,
250 Green Engineering and Technology
making it more energy-convenient. Shifted to cloud computing from tradition
mode helps to reduce the energy usage in 2020 by 38% in the world’s data center.
Furthermore, these reductions of energy usage will result in a drop of 28% in
GHG emission.
14.4.5 optimizing data Center for energy effiCienCy
According to the International Energy Agency (IEA) during COVID-19 lockdown
and containment measures, the demand for video streaming, video conferenc-
ing, online movies/ games, and social networking increased tremendously. During
COVID-19, Internet trafc surge worldwide increased by 40%. Data centers man-
age most of the Internet protocol in the world, higher trafc demand higher energy
consumption. Worldwide Internet trafc surge is increased rapidly and expected to
double by the year 2022 to 4.2ZB per year. The number of mobile Internet users and
IoT connections are rapidly increasing putting pressure on data centers. In 2019, data
center’s energy consumption demand is around 200 TWh worldwide.
In the current scenario, where “Green” is the main objective, it is necessary to
redesign the infrastructure of the data center to cope up with increasing data traf-
c efciently with minimizing the carbon emission. The data center must look for
an alternate mode of power supply like solar, wind, water, and tidal energy instead
of the traditional power generation method where fossil fuels are used. The more
the demand, the more the energy required to serve. Massive consumption of energy
needed high cooling facilities in the data center. Redesigning of the data center should
consider more energy convenient infrastructure including building design, geographi-
cal area, alternative cooling techniques like liquid cooling and evaporative cooling, an
alternative source of energy, low powered servers, efcient and uninterrupted power
supplies to minimize the GHG.
14.4.6 more uSage of effiCient diSplayS
One of the signicant parts of the computer that consumes lots of energy for work-
ing is the monitor. Selecting proper monitors can save up to 60%–70% of energy
cost. Old cathode ray tube (CRT) monitors consume more power as compared to
liquid crystal display (LCD) and light-emitting diode (LED) monitors. High-efcient
monitors are used to reduce the GHG emission. High-efcient LCDs are avail-
able from several vendors. Figure 14.5 shows the comparison between CRT, LCD,
and LED, and it clearly shows that energy consumption of CRT is comparatively
very high as compared to LCD and LED monitors. Many vendors are developing
energy-efcient monitors to ensure minimum carbon emission.
Nowadays, monitor manufacturing companies are using product carbon foot-
printing to monitor and reduce the impact of carbon emission at different stages of
developmental phases. A product carbon footprint is restricted as the aggregated
amount of GHG are emitted directly and indirectly by a product over its lifetime.
It incorporates outows from materials extraction, production, appropriation, use,
and end-of-life.
251Internet of Things for Green Technology
14.4.7 managing e-WaSteBy reCyCling tHe SyStemS
Each year, obsolete, unwanted, and unused electronics items such as computers,
printers, mobiles, computer peripherals, networking devices, and IT products sum
up of 20–25 tons of e-waste. Now disposing of e-waste turns to be a global prob-
lem. No standard protocols or facility is available to destroy the e-waste. Burning/
incineration of e-waste is used as a standard method. When electronic devices are
burnt, they release highly toxic chemicals like polybrominated biphenyl and poly-
brominated biphenyl ethers. Released toxins get accumulated in the atmosphere and
harm the environment and our health. Burning/incineration causes the release of
methane, which is 25 times more potent to trap heat in the atmosphere.
Going green must redesign the electronics for a longer life span to sustain.
Reusing the electronics parts, repurposing, and recycling the old systems save the
environment from hazardous chemicals. Companies such as Dell, HP, Panasonic,
and Asus take back computers and electronic peripherals for proper recycling and
reusing. Recycling and reusing not only save money but also help to maintain a
green environment.
14.4.8 enCourage Work from Home
ICT during COVID-19 provides “work from home” as a new dimension of reduc-
ing GHG emission. Many tech giants encourage their employees to work remotely.
Reducing public and personal transport and reducing energy consumption in ofces
show a notable decline in emission, and it puts the world toward attaining long sus-
tainable energy goals and reducing GHG emission. Due to lockdown, major cities
showed the foremost downfall in road trafc from 50% to 75% around the world
(Figur e 14.6).
FIGURE 14.5 Energy efciency comparison between CRT, LCD, and LED [16].
252 Green Engineering and Technology
Work from home helps in reducing total energy demand through transport, power
consumption to maintain ofce infrastructure, and fuel consumption. Figure 14.7
shows a dramatic downfall in annual CO2 emission by 24Mt and helps to sustain
green technology effectively.
FIGURE 14.6 Average rush-hour trafc congestion in selected cities in 2019 and during
lockdowns. (International Energy Agency.)
FIGURE 14.7 Change in global CO2 emissions and nal energy consumption by fuel in the
“home-working” scenario. (International Energy Agency.)
253Internet of Things for Green Technology
14.5 ISSUES AND CHALLENGES IN IMPLEMENTING GIoT
With the continuous development of IoT, the number of sensors, actuators, RFID, and
devices is also increased, which consume more energy unprecedently. IoT devices
are deployed under a continuous working environment that makes them to be con-
sidered as a high-energy guzzler in ICT. One of the primary reasons for more energy
consumption in IoT devices is using heterogenous devices with non-standard proto-
cols. While focusing on successful implementation of IoT for green technology, focus
should be on low power consumption to maintain GHG emission. Interoperability,
the evolution of 5G, and developing green wireless sensor networks are the signi-
cant challenges in the successful implementation of IoT in green technology [1].
14.5.1 interoperaBility
Interoperability facilitates real-time communication with a similar type of device.
In the case of IoT, where the number of heterogeneous devices is high, compiling
with different standards and protocols makes interoperability a big issue. To resolve
interoperability issues, additional hardware or software are needed to be installed,
which will increase GHG emission. Signicant interoperability issues to be deter-
mined to attain efcient and effective GIoT are as follows:
a. Device interoperability: IoT comprises various devices from high-end
devices like smartphones, raspberry pi to low-level devices such as sensors,
RFID, Arduino, and actuators. The processing speed, RAM, microcontroller
architecture, communication technologies, and battery capacities vary from
accessories to devices. For ex IoT in healthcare, devices use ANT+ standard,
whereas most wearable devices support Bluetooth and NFC. Availability of
various standard communication protocols like ZigBee, Wireless Hart Z_
wave, and non-standard protocols like LoRa and SIGFOX creates complexity
while exchanging data.
b. Network interoperability: IoT devices are generally heterogeneous and
multi-service in nature; they rely on short-range wireless communication,
which makes them unreliable and intermittent. Network interoperability is
able to handle various issues such as data routing, addressing, quality of ser-
vice, and security due to heterogeneous and dynamic network environment.
c. Platform interoperability: Presently, many operating systems are devel-
oped especially for IoT devices such as Ubuntu core, Fushsia, RIOT,
Contiki, and TinyOS each having their versions and services for users. Even
every IoT platform provider uses separate data structures and programming
languages like Google brillo uses Weave, Amazon AWS uses SKD, and
Apple uses swift for their Apple HomeKit. This non-uniformity creates a
hindrance to developing a successful and energy efcient cross-platform
IoT application.
d. Semantics interoperability: The W3C denes semantic interoperability as
enabling different agents, services, and applications to exchange informa-
tion, data, and knowledge in a meaningful way, on and off the Web. IoT
254 Green Engineering and Technology
devices use various API, use a different mode of communication, generate
data in different forms like XML, CSV, or JSON, and use different schemas
and data model. This semantic irreconcilability makes it difcult to create
an interoperable environment for smooth data exchange.
e. Syntactic interoperability: Exchange of data and format between heteroge-
neous IoT systems. The standard syntactic rule must be dened for sending
and receiving data. The syntactic rule must be dened in the same grammar
for sender and receiver so that the sender’s encoding and receiver’s decod-
ing rules will remain the same or else it creates mismatch parse trees.
14.5.2 evolution of 5g
The International Telecommunication Union – responsible for radio communication
(ITU-R) – has dened
three main application areas for the enhanced capabilities of 5G. They are Enhanced
Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC),
and Massive Machine Type Communications (mMTC). Only eMBB deployed in 2020;
URLLC and mMTC taken quite a while to use in many areas.
Figure 14.8 shows the various usage scenario of 5G in various dimensions.
Enhanced Mobile Broadband (eMBB) is an advance version of 4G LTE mobile
broadband services, which provides a faster connection with more capacity for data
handling. Ultra-Reliable Low-Latency Communications (URLLC) is one of the use
cases used by 5G new radio standard. URLLC cater multiple advanced services that
require reliable, robust, and uninterrupted data exchange. The main aim to develop
5G is to support a wide range of devices, services, and applications by extending its
FIGURE 14.8 5G Usage Scenario. (www.edn.com.)
255Internet of Things for Green Technology
network capabilities to extreme performance. Machine-type communication (MTC)
is a communication paradigm to connect numerous devices, which are connected to
the network and communicate with each other with very little or no human interven-
tion. In the case of 5G, a massive number of devices are connected to serve a huge
number of things hence called massive machine type communication (mMTC). Still,
developing countries like India have to wait for 5G technologies, which include few
issues and challenges for successful implementation:
The major issues with 5G:
1. The required frequency bandwidth of 5G is up to 300 GHz as it is 50 times
greater than the current scenario of 4G, which is 6 GHz. Hence, this requires
more energy consumption.
2. 5G is based on beamforming for coverage and it covers over a shorter dis-
tance. Hence, it requires a more signicant number of antennas to cover a
large area – more numbers of antenna, more power consumption, and more
GHG emission.
3. The additional infrastructure requires base stations, antennas, repeaters,
and data centers causing the further release of GHG emission.
4. Most of the present smartphones are not compatible with 5G technology,
which increases a huge number of obsolete devices and requires quality
recycling and redesigning techniques.
14.5.3 iSSueS WitH WSn
There are several issues and challenges in the successful implementation of the
green wireless sensor networks (green-WSN/ GWSN) [9], which act as the back-
bone of implementing GIoT. Sensor nodes require a constant supply of non-renew-
able energy for working. Implementing GWSN, alternative source of energy, must
be considered to reduce GHG emission. Efcient protocols must be designed from
the beginning to provide efcient energy and management to devices. Letus dis-
cuss the major individual design issues in GWSN – fault tolerance, scalability,
production cost, hardware constraint, sensor network topology, transmission
medium, and power consumption.
a. Fault tolerance: Sensor nodes deployed in a hazardous environment make
them more vulnerable to the harsh environment. Sensor nodes can fail due
to hardware issues, software issues, or draining of power supply. Timely
detection of a fault in the network increases the efciency. Redesigning and
redeveloping of WSN protocols are needed to ensure availability of an alter-
nate path for packets. These redesigned and redeveloped energy-efcient
protocols must be adequately robust to handle a large number of node fail-
ures in the network infrastructure without much affecting the functionality.
b. Scalability: Successful implementation of WSN in any environment depends
upon the network’s scalability, which manages from a few several nodes to
a hundred thousand of the nodes without affecting its transmission range
256 Green Engineering and Technology
and power consumption. It needs to look for an alternative source of energy,
which supplies uninterruptedly and without emitting GHG. Maintaining
the performance of the network entirely relies on the high-resolution data
transmission within a huge infrastructure, potentially without affecting
productivity.
c. Production costs: Using disposable devices as sensor nodes increases the
production cost. This led to one more issue of adequately disposing devices
as many hazardous elements and metals such as iron, alumina, and zirconia
are used in various physical, chemical and biological sensors release, which
increase the GHG emission in the atmosphere. Redesigning and redevelop-
ing devices under reuse policy make production cost high.
d. Hardware constraints: Every sensor node in a network requires a sensing
device/unit, a processing unit, a power supply, and a transmission unit. These
require a constant power supply. Any additional functionality increases the
production cost and power consumption. Thus, any additional functionality
must be within cost and efcient power consumption.
e. Sensor network topology: Maintaining and redesigning topology is one of
the most important factors to reduce energy consumption in GWSN. Any
deployed topology requires several protocols, techniques, and algorithms to
efciently balance the constant supply of energy, proper usage of memory,
data transmission, and communication capabilities with various devices.
f. Transmission media: Most of the devices interconnected with each other in
a network are connected by radio communication. However, many network
structures use infrared communication and optical bers as 11a primary
communication mode. They must provide an interference-free environment
to transmit data over the network.
g. Power consumption: One of the most challenging issues in implementing
green technology in any area is power consumption. Either it is a smart
city, smart home, smart trafc, smart logistics or a smart grid, efcient
and effective implementation of power consumption is always remaining a
cumbersome task. Proper implementation to release minimum GHG gases
hardware and software used must be redesigned to ensure effective execu-
tion of energy policy to deliver efcient energy usage in the network.
14.6 CONCLUSION
Technology is tremendously changing at a fast pace. ICT is evolving as a backbone
for every industry. Phenomenal technology increases sophistication as well as chal-
lenges of GHG. To make a more sustainable earth, green technology must be adapted
with IoT. With the expansion of technology, IoT is turning to be the Internet of Every
Things (IoET), providing the facilities of anyone communicating at anytime from
anywhere with anything. IoT indisputably reformed technological advancement
toward energy-efcient technology to accomplish green technology. Green tech-
nology initiatives are essential to reduce GHG. Following should be performed to
achieve the vision of GIoT.
257Internet of Things for Green Technology
1. Designing and developing eco-friendly GIoT components that are easily
decomposable without affecting the environment and leaving a carbon
footprint.
2. Using more green renewable sources such as wind energy, solar energy,
geothermal energy, and tidal energy.
3. Sending of data should be restricted to on-demand requirements/when needed.
4. Reducing operational costs, energy usage, and optimizing output through
energy-efcient GIOT components.
5. Implementing sleep scheduling algorithms for timely or on-demand trans-
mission of data.
6. Implementing advanced communication techniques such as W-Fi direct,
multiple-input-multiple-output (MIMO), and cognitive radio utilization.
7. Implementing proper security in the overall architecture without increasing
the power consumption and memory usage.
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259
15 Green Health: Making
Green Healthcare
Using Reinforcement
Learning in Fog-assisted
Cloud Environment
Saeed A. L. Amodi and Sudhansu Shekhar Patra
KIIT Deemed to be University
Om Prakash Jena
Ravenshaw University
Suman Bhattacharya
KIIT Deemed to be University
Nitin S. Goje
Tishk International University
Rabindra Kumar Barik
KIIT Deemed to be University
CONTENTS
15.1 Introduction ..................................................................................................
.........................................................................................
..............................................................................
...................................................................................................
...............................................................................................
..........
.............................................................................................
........................................................................................
...................................................................................................
..............................................................................................................
260
15.2 Literature Review 262
15.3 Reinforcement Learning . 262
15.4 Q-Learning 264
15.5 System Model 265
15.6 Dynamic Consolidation of VMs Based on Reinforcement Learning 266
15.7 Learning Agent 267
15.8 Simulation Results 268
15.9 Conclusion . 270
References 270
260 Green Engineering and Technology
15.1 INTRODUCTION
Healthcare facilities involve doctors’ ofces, outpatient centers, medical labs, tele-
medicine centers, clinical healthcare facilities, surgical facilities, community health-
care centers, and so on.
In the US, approximately 5% of the total area is occupied by healthcare com-
mercial buildings and consumes around 10.5% of the energy consumed. The energy
consumption in the healthcare sector is due to various parameters including cooling,
space heating, lighting, medical equipment usage, ventilation, and so on. Finally, the
information and communications technology (ICT) services for the healthcare sector
is consuming the maximum [1]. In the healthcare eld, a large volume of data are
collected every day from various sources that may be from patient history or record
keeping. Many of the data are structured, unstructured, and semi-structured [2,3].
In today’s digital world, each patient's data including their disease history, medical
records, and medical reports should be digitized [4]. For better prediction, better treat-
ment of the patient, and also for the future research eld, the data should be properly
preserved and analyzed effectively with minimum cost. Therefore, there is a need for
technology, which should help us in this area by taking care of the large dataset. Big
data analytics help in giving the valuable inside from the data patterns by using data
mining [5], data science, and machine learning algorithms [6,7]. After collecting the
data, it has to be processed, and then only the important information is mined from
the data. Big data bring a lot of revolution in the healthcare industry. The data can only
be processed if it is stored securely. For storing the data, the industry is taking the help
of the cloud providers [8].
Making the healthcare industry energy-efcient [9, 10], and green is a brilliant
idea for solving climate change issues. There is an urgent need for green comput-
ing by raising awareness among the users through less electricity consumption.
Globally, this sector has growing importance, has a crucial role in the current sce-
nario, and affects the world’s economy. Nowadays, the healthcare sector is using all
the technological advancements in its uses such as advanced surgical equipment,
remote healthcare monitoring system, modern digital equipment, digital record
keeping, and so on and for all this, the sector is using advanced ICT. Figure 15.1
shows the use of a fog-assisted cloud computing environment with data mining in
healthcare.
The major reason for energy inefciency in the data centers carrying healthcare
data is the servers running at low load. Many times, with low utilization of CPU
such as with around 10% CPU usage, the energy consumption is 50% of the peak
power. The VM consolidation is one of the efcient techniques for green health in
which turning off the servers will have low CPU utilization. Through VM consoli-
dation, the QoS (quality of service) is achieved, which was mentioned in the SLA.
Virtualization is another power management technique in data centers through
which multiple VMs can run on a single physical server where each VM is running
multiple tasks.
A big challenge in the eld is how to design the best tools and techniques for
optimizing the energy consumption, availability, reliability, and sustainability of
261Green Health: Making Green Healthcare
the fog-assisted cloud system [11,12]. In the last few years, the researchers are try-
ing to optimize the energy consumption done by the data centers along with fog
centers, which helps in building efcient and cost-effective green IT solutions to the
hospitals. Considering the complexities of the healthcare industries, there is a need
for extensive study of how big data, data science, and machine learning techniques
should be used in the improvement of green healthcare. Hospitals and healthcare
sectors should take extensive measures using machine learning to improve the green
healthcare.
RL is a part of ML in which the software agents take action in the environment
to maximize the cumulative reward. After implementing the decision, the feed-
back was received, which indicates the quality of the action taken. The agent’s
aim is to learn the policy for the selection of the best alternative action out of all
possible actions.
This chapter helps in improving the energy efciency in their ICT in hospitals and
healthcare sectors by using big data, data science, data analytics, and fog technolo-
gies. This chapter uses the RL [11–13] model and optimizes the number of active
hosts by predicting the current resource requirement, which can save energy of the
fog healthcare system and optimizes the cost of data processing used by the health-
care sectors done in the fog-assisted cloud environment [14]. The system can take an
intelligent decision whether to be the physical machine is in active or sleep mode.
The proposed method is utilizing a learning agent. Using Q-learning, the agent
learns the power model from the environment, and the knowledge the agent gained
is utilized to get the efcient procedure for the assigned task.
This chapter is organized as follows. Section 15.1 gives the introduction to the
chapter, Section 15.2 depicts the literature survey work, reinforcement learning and
Q-learning technique are depicted in Sections 15.3 and 15.4, respectively, and the
proposed model and the VM consolidation technique are shown in Sections 15.5
and 15.6 respectively. Section 15.7 describes the learning agent and its work in the
dynamic consolidation of VMs based on reinforcement learning, Simulation results
are shown in Section 15.8 and nally, Section 15.9 concludes the chapter.
FIGURE 15.1 Use of fog-assisted cloud computing with data mining techniques in a
healthcare system.
262 Green Engineering and Technology
15.2 LITERATURE REVIEW
In the recent past, many research works have been carried out for minimizing the energy
consumption in the cloud and fog centers. Wu et al. [15] suggested an energy-efcient
technique for saving the energy consumption in IoT and fog computing. Meta-heuristic
algorithms play an important role in solving the task and scheduling problems in fog
servers [16–18]. Goswami et al. [19] studied the performance of the system, which
depends on the queue length to scale the VMs along with improving the QoS parameters
of the system. Patra et al. [20] designed algorithms for prot maximization by saving
energy and spotting allocation quality guaranteed services in a cloud environment.
ML approaches have been studied by many researchers for power management
in data centers, cloud centers, and fog centers. The task consolidation technique [7]
using machine learning executes the assigned tasks with a minimum number of active
VMs and, in turn, reduces energy consumption. In some studies [21, 22], an online
ML technique is suggested that selects dynamically various experts to take the man-
agement decisions and minimizes the power consumption. In some studies [23, 24],
an RL technique is used for resource allocation in the data centers. Considering the
existing ML techniques in power management and RL techniques in resource man-
agement, this work can explore RL-based learning in power management.
15.3 REINFORCEMENT LEARNING
RL is one of the types of ML, which is growing in the past few years. RL is learn-
ing through interaction. Learning is done by doing and from scratch. RL's goal is to
control large-scale stochastic environments with partial knowledge.
RL systems have two major constituents: agent and environment. The latter one
is dynamic/stochastic, non-stationary where, the agent acts on, and the agent is the
RL. The RL process starts as the environment senses the state of the agent. Then, the
agent takes some action depending on observations. In turn, the environment sends
the next state with respective reward back to the agent. The agent updates its knowl-
edge with the reward returned by the environment and its uses are to evaluate its
previous action. This loop continues till it reaches the terminal state, this means the
agent completes its tasks and gets the reward. An RL agent inuences the state dis-
tribution. Hence, it makes decisions/takes actions to maximize reward or minimize
punishment. In general, the framework for RL consists of the following:
• State space S: the number of states an agent can take perception out of the
environment.
• Action space A: the different actions the agent performs.
• Reinforcement signal r: The environment sends the reinforcement signal to
the agent. The signal may be a reward or punishment. The signal received by
the agent reects the next action to be taken by the agent, considering the suc-
cess or failure of the system. The agent tries to minimize the penalties in the
learning process. Figure 15.2 shows the process of reinforcement learning. RL
is all about controlling the environment means to nd the tradeoff between
exploration (look for knowledge) and exploitation (use your knowledge).
263Green Health: Making Green Healthcare
RL agnate inuences the state distribution. Hence, it makes decisions/takes actions
to maximize a reward or minimize a punishment.
S ∈ S: states
a ∈ A: activities
R: reward function
δ: transition probability dened by
δ
()
ss→
a
12
This is an MDP (Markov decision process).
Taking an action ‘a’ in state ‘S’ causes the transition δ(s,a,s′) with probability
p 0.8,δ(s, a, s) with probability p 0.2.= ″ =
We can write it with conditional probability P(S′ | S, a) = 0.8 and P(S″ | S, a)= 0.2.
The reward function for the action ‘a’ taken in state S = R(S, a) (indirect guidance)
R(S, a)= +10 with probability p = 0.1
R(S, a) 5 with probability p 0.3=+ =
R(S, a) 5 with probability p 0.6=− =
Hence, we have to follow trajectories.
RL goal: Find π*:Sa→
Using R(S, a) and a suitable return horizon.
But do we know
δ
(,Sa,)S′?
Two potential approaches
1. Find the value of state (difcult!)
2. Find the value of the action (easier)
Two RL schemes:
1. Policy Iteration (Monte Carlo, Temporal Differencing TD (λ))
(Evaluate the policy to Improve it)
2. Value Iteration (Q-Learning)
(Maximize accumulate reward for (S,a))
FIGURE 15.2 The process of reinforcement learning.
264 Green Engineering and Technology
15.4 Q-LEARNING
It is a popular RL method that is used in many researches. In the Q-learning algo-
rithm, the system uses Q-values (known as action values), which iteratively improves
the learning agent’s behavior.
1. Q-values or action values: Q values are a set for both states and actions.
Q(S,a) is an estimation to be evaluated how good of applying an action ‘a’
at the state S. The estimation of Q(S,a) is computed through an iterative
process using the TD-update rule.
2. Rewards: An agent in its course of a lifetime from a start state to reach its
next state from the current state makes several transactions based on the
environment in which the agent is interacting and the choice of action the
agent is taking. At each step of the transition, the agent observes the reward
from the environment due to the state of action and then takes the next step.
If at any instance of time, the agent reaches the terminating state, then there
is no further transition. The episode will be completed there.
3. TD update: The TD update formula is depicted as:
Q(,Sa)(=+QS,)aR
αγ
()
+minaA€QS
()
′′
,(aQ−Sa,) (15.1)
Here
• S: Agent’s current state
• a: The current action based on certain policy
• S′: Next state in which the agent ends
• a′: Using the estimated current Q-value, the next best action to be taken
• R: The current reward predicted looking the environment w.r.t the cur-
rent action
• γ: Since future rewards have less value than the current rewards they
have to be discounted. 0 < γ < = 1 is the discounting factor for the cur-
rent reward.
• α is the step length taken for updating the estimation of Q(S,a).
The policy π that selects the best action in state s is given by:
Π()sQ=minaA€(,Sa) (15.2)
The learning agent’s aim is to search for the optimal policy.
4. €-greedy policy for the action
By using the Q-value estimation, this policy is a simple one to choose the
action. It is taken as follows:
• having probability (1 − €), the action has to be taken, which has a maxi-
mum Q-value.
• having probability (€), an action at random can be chosen.
265Green Health: Making Green Healthcare
Algorithm 1: Q-Learning
1. Randomly initialize Q(S,a).
2. for every episode
3. do Initialize state ‘S’
4. for every step
5. do Select ‘a’ from ‘S’ by the action procedure
6. take an action ‘a’
7. Observe r, S′
8. update Q(S,a) by Eq. (15.1)
9. update S = S′
10. until S terminal
15.5 SYSTEM MODEL
Let us consider a fog center with m heterogeneous fog nodes. The processor of each
node is of multi-core and the processing capacity is dened in MIPS. Along with
this, each node has a network bandwidth, processing capacity, and memory. Them
fog nodes have n number of VMs. Users send their requests for provisioning the
VMs. Each user requirement is characterized by CPU performance, RAM, stor-
age, and network bandwidth. Since the VMs handle dynamic workloads, the CPU
utilization of a VM varies over time. The length of each task is denoted by MI. To
optimize the energy consumption and SLA violations, the VMs are consolidated
on a minimum number of hosts. When the resource utilization on a particular host
is low, the VMs are migrated to other hosts and the host may be switched to idle
mode. Similarly, when a host gets overloaded, certain VMs are migrated to other
hosts to reduce the SLA. The VM algorithm efciency can be improved by using
the learning agent. The efciency of the resource allocation algorithm with the
energy consumption measures the learning agent using Q-learning. Figure 15.3
shows the use of reinforcement learning in fog centers for VM consolidation to
handle green healthcare.
The proposed dynamic consolidation steps are carried out as follows:
1. The learning agent collects the total utilization of the VM managers
(VMMs). After receiving the utilization, the agent decides the power mode
using Q-Learning.
2. Depending on the learning agent instruction, the VM consolidation
is done. The VM consolidation optimizes the VM placement based on
power mode. When a host becomes overloaded, the VMs get migrated to
other hosts. Similarly, when the host goes to idle mode, all the VMs are
migrated to other hosts. The allocation map decides the allocation of VMs
to the hosts.
3. The VMMs send migration commands to VMs to migrate to other hosts by
the allocation map received through the VM consolidation step.
266 Green Engineering and Technology
15.6 D YNAMIC CONSOLIDATION OF VMs BASED
ON REINFORCEMENT LEARNING
This chapter proposes a dynamic consolidation VM allocation method to minimize
the energy of data centers as well as SLA violation using reinforcement learning.
The algorithm adapts the necessary number of hosts depending on the workload
to the datacenter. The algorithm decides whether there is a need for more hosts in
the system, some hosts may be put into the sleep mode and save energy and veri-
es whether or not the number of allocated hosts are sufcient. For this purpose, a
learning agent is necessary to take the decisions of the system. The proposed algo-
rithm is represented in algorithm 2 as the DC-VM-RL algorithm and is as follows:
Algorithm 2: DC-VM-RL
1. Read the power mode of each host.
2. If the power mode of a host =sleep but current mode ≠sleep then migrate
the VMs to other selected host and the host will be switched to sleep mode.
3. If power mode of a host = active and current mode ≠ active, the host will be
switched to sleep mode.
4. A function predicts the short-term usage of the hosts by taking the historical
data of utilization.
5. If the use exceeds compared to the predicted data of the available host
usage, the host will get overloaded. Then some VMs from the host migrated
FIGURE 15.3 Use of reinforcement learning in VM consolidation for green healthcare.
267Green Health: Making Green Healthcare
to different hosts to avoid SLA violation. The VM selection algorithm iden-
ties which VM should be migrated to different hosts.
When the DC-VM-RL algorithm selects a host for allocating a VM, the VM alloca-
tion procedure has been used. This procedure helps to locate those hosts that are not
overloaded at the current as well as after the allocation of the VM. This means the
hosts have idle resources that may be shared among VMs. Thereafter, a host has been
chosen from the hosts, which is not overloaded, and hence the power increasing is
minimized after the VM allocation. The VM allocation procedure is depicted in the
following algorithm.
Algorithm 3: VM Allocation Procedure
1. Calculate the PredictedUtilization of each host in the system.
2. If AvailableUtilization>PredictedUtilization + RequestedUtil izationbyVM
3. Put the host into the NotOverLoadedList.
4. Select the host from the NotOverLoadedList which will consume minimal
power by the allocation of the VM to the host.
5. Return selectedHost
15.7 LEARNING AGENT
The number of active hosts can be reduced depending on the current work-
load. This needs intelligent decisions as to when to put the hosts in the sleep or
active power mode. For this, a learning agent is required and in the algorithm
DC-VM-RL, the learning agent is a crucial part. Based on the agent decision, the
algorithm switches each host to the specied power mode and calculates the pen-
alty as the reinforcement signal. The main objective of the DC-VM-RL algorithm
is to minimize the energy consumption including the SLA violation, the penalty,
and time t can be calculated as
PP
tt
=+(SLA)(P
tPower) (15.3)
The SLA violation penalty is dened as the requested MIPS (Ur) − actually allocated
MIPS (Ua) over a time slot.
n
SLAU
tr
=−
∑
()
ia
Ui (15.4)
i=1
Here, n is the number of VMs. The penalty for the SLA violation is calculated
as the ratio between the SLA before performing the action and the SLA after the
action.
P
tt
(SLA)S=
()
LA +1SLAt (15.5)
268 Green Engineering and Technology
The penalty for the power consumption is measured as the ratio between the consumed
power at the current time slot and the consumed power of the previous time slot.
m
P=∑Powert+
t(Power)1 (15.6)
Powert
i=1
Here, the summation is for the total power consumption penalty for m hosts.
Then Eq. (15.1) can be rewritten as
Q
()
sa
tt
,,=+Qs
()
aP0.5
tt
+0.7min(Qs
′′
,)aQ−
()
sa,
aA∈ (15.7)
The algorithm for the learning agent is shown in algorithm 4.
Algorithm 4: Learning Agent
1. During the starting of the time slot t, predict the current state st.
2. Using static threshold or by agent knowledge shown in Eq. (15.2), select an
action either active/sleep.
3. Calculate Pt(SLA) the penalty caused due to SLA violation given in
Eq. (15.5).
4. Calculate Pt(Power) the penalty caused due to power violation given in
Eq. (15.6).
5. The total penalty is calculated using Eq. (15.3).
6. Using Eq. (15.7), update Q(St,at).
During the initialization state at the beginning of the learning stage and when the
current state has not visited earlier, the action taken is on a lower threshold. If the
utilization of the host is >0.4 of the available CPU capacity, then the sleep mode of
theagent is set to active mode by the agent. Otherwise, the host is under-loaded and
will be put in sleep mode.
15.8 SIMULATION RESULTS
To evaluate the performance of the proposed system, iFogSim [25] has been con-
sidered. It is a popular tool kit for the fog computing community because of its ex-
ibility, scalability, and efciency. In our simulation, we have taken ve scenarios
and the number of VMs for each scenario is distinct, which is shown in Table 15.1.
We compared the DC-VM-RL algorithm with the state-of-the-art algorithms [26].
They adopt utilization threshold dynamically based on IQR (Inter Quartile Range),
LR (Linear Regression), and MAD (Median Absolute Deviation) for estimating the
CPU utilization. Also, we have considered the THR (threshold method), which after
monitoring the CPU utilization, migrates a VM as soon as the current CPU utiliza-
tion is more than 80% of the capacity of the VM. There are two matrices such as
avg. SLA violation % and energy consumption that has been used for measuring the
proposed dynamic VM consolidation by adopting Q-Learning. Table 15.2 shows the
269Green Health: Making Green Healthcare
percentages of average SLA violations. The DC-VM-RL algorithm leads to less SLA
violation than the other algorithms, because the DC-VM-RL algorithm switches the
host from sleep to the active mode before any SLA violation occurs. Table 15.3 shows
the power consumption for the selected servers used during the simulation.
The total energy conguration by a physical node is denoted by:
∫t1
E=PU((td)) t (15.8)
t0
Figure 15.4 shows that the RL brings higher energy saving as compared to the other
algorithms.
In scenario III, it can be observed that for the enabling of the learning algorithm
DC-VM-RL, there is a reduction of 12.6%, 19.4%, 22.7%, and 28% by comparing
LR, MAD, THR, and IQR, respectively.
TABLE 15.1
The Number of VMs
Scenario Number of VMs
I 1465
II 1356
III 1245
IV 1056
V 1034
TABLE 15.2
Avg. SLA Violation Percentage
Scenario DC-VM-RL (%) MAD (%) THR (%) LR (%) IQR (%)
I 8.34 10.12 10.08 10.07 10.07
II 8.45 10.05 10.26 10.17 10.02
III 9.02 10.11 10.09 10.42 10.17
IV 9.64 10.06 10.22 10.46 10.18
V 9.82 10.46 10.76 11.25 10.35
TABLE 15.3
Power Consumption of Selected Servers
Server 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
HP Proliant
DL 380 G7
88 89.5 92.7 97 99.7 102.5 10.6.2 108.6 112.2 115 118
HP Proliant
DL 380p G8
93.6 98 101.5 105.6 110.5 116 121.7 127 130 124 136
270 Green Engineering and Technology
15.9 CONCLUSION
Healthcare data are voluminous, and a huge processing of data is required in the
future. This chapter suggests a dynamic consolidation method using reinforcement
learning to minimize the power consumption as well as SLA violation in the fog
center. Reinforcement learning helps the agent to learn the host power mode policy
without the knowledge of the environment. The proposed method is simulated using
iFogSim and compared with other benchmark algorithms, and it has been found that
the proposed learning-based dynamic consolidation method performs better when
compared to the existing benchmark methods.
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273
16 Smart Agricultural Robot
Vimal Kumar M. N.
R.M.D. Engineering College
Aakash Ram S.
KPIT Technologies Limited
Bennet Nifn N.
SciComm India
CONTENTS
16.1 Introduction .................................................................................................. 274
16.2 Background/Related Works .......................................................................... 276
16.2.1 Literature Review .............................................................................276
16.2.2 Method of Existing Agricultural Robot ............................................277
16.2.3 Summary of Related Works..............................................................277
16.3 Methodology ................................................................................................. 277
16.3.1 Hardware .......................................................................................... 278
16.3.1.1 NodeMCU .......................................................................... 279
16.3.1.2 DHT11 Sensor .................................................................... 280
16.3.1.3 Soil Moisture Sensor .......................................................... 280
16.3.1.4 L293D Module ................................................................... 281
16.3.1.5 Relay Module ..................................................................... 281
16.3.1.6 Servo Motor .......................................................................281
16.3.1.7 Buzzer ................................................................................ 281
16.3.1.8 Raspberry Pi ...................................................................... 281
16.3.1.9 Raspberry Pi Camera ......................................................... 281
16.3.2 Software ............................................................................................ 283
16.4 Experimental Results .................................................................................... 284
16.4.1 Mobile Application ...........................................................................284
16.4.2 Live Stream ....................................................................................... 285
16.4.3 Robot ................................................................................................. 287
16.5 Future Works ................................................................................................288
16.6 Conclusion ....................................................................................................290
Acknowledgment ...................................................................................................290
References .............................................................................................................. 290
274 Green Engineering and Technology
16.1 INTRODUCTION
Agriculture is one of the oldest and prime activities of the human race. It is an art of
cultivating plants and raising livestock. Water is a very precious resource and a driv-
ing force in irrigation. The water requirement of the crop depends on the type of soil,
crop, and environmental parameters like temperature and humidity. The activities
involved in agriculture are painstaking and demanding manpower throughout the
process. It is important to know the effects of the physical parameters in crop cultiva-
tion. It includes relief, drainage, climate, soil, and water resources. All these factors
are affecting the growth and distribution of crops in a particular area. The physical
parameters determine the crop pattern and agricultural operations to be undertaken.
Thus, physical parameters inuence the type of crops growing, the degree of threat
involved in agriculture, and its development.
The temperature and humidity of an environment are the primary factors affect-
ing the rate of development of plants. Temperature is the degree of heat present in an
environment. Warmer and extreme temperatures potentially impact plant productiv-
ity. In photosynthesis, plants use carbon dioxide to produce oxygen and in respira-
tion, plants use oxygen to produce carbon dioxide. It happens due to the effects of
heat on photosynthesis. There are limits in temperature for each plant, and they are
classied as maximum, optimum, and minimum temperature limits. These limits
are important as they directly depend on the plant growth. It should neither go below
the minimum limit nor above the maximum limit. Humidity is the amount of water
vapor present in the atmosphere. It affects the opening of stomata present underside
of the leaves in plants. It also has some limits for the healthy growth of plants. When
the humidity is high, the plants cannot draw nutrients from the soil. If it sustains
for a prolonged time, then the plant gets rotten. The optimum level of humidity for
the production of crops is 50%–70% RH. Table 16.1 provides the temperature and
humidity level of wheat and rice with the minimum, optimum, and maximum limits.
Soil moisture is the most important parameter in agriculture. It is the level of
water stored in the soil. It is inuenced by factors such as temperature, characteristics
of soil, precipitation, and so on. By using these factors, the type of biome present in
the soil and the suitable growing crops for the land can be determined. Crops’ health
depends on soil nutrients and an adequate supply of moisture. A decrease in the
availability of moisture in soil results in disruption in functionality and growth of a
plant. Also, crop yield will be reduced. Moisture availability will become more vari-
able during the climatic changes. Irrigation is carried out in agriculture to cultivate
the crops and to maintain moisture in the soil. Soil conditions of the eld vary and
will not remain constant. Water-holding capacity varies for different types of soil
andterrains. Over-irrigation blocks the air supply to the roots increasing the salinity
and under irrigation disrupts the crop growth. Table 16.2 shows the amount of mini-
mal soil moisture and the level for irrigation practices of clay, loamy, and sandy soil.
Water acts as a critical input in agriculture to irrigate and cultivate the plants. The
potentiality of seeds and fertilizers fail to achieve whether plants are not optimally
watered. In plants, water is used for growth, and the usage of water by crops is known
as evapotranspiration. It is the sum of evaporation and transpiration from the land
and ocean surface of the atmosphere. Transpiration is the process of exhalation of
275Smart Agricultural Robot
water vapor through stomata and evaporation is the movement of water to the air from
the soil and plant surface. Considerable evaporation is possible only when the plant
canopy or top layer of the soil is wet. Evaporation decreases sharply when the soil
surface is dried out. The process of considerable evaporation occurs only after rain or
irrigation. The root system is responsible for the extraction of water from the soil and
stored water in the soil is extracted by plants for tissue building and soil evaporation.
There are a variety of crops available for cultivation, but water requirement and the
growth duration of each crop vary. Table 16.3 shows the duration of growth and total
water requirement for rice, sugarcane, groundnut, sorghum, and maize.
Thus, the agricultural practices are tedious and painstaking due to the unpredict-
able weather conditions and constant monitoring of the eld is required. Conventional
agricultural practices involve continuous monitoring of the water level, temperature,
and humidity. The crops are at a risk of damage due to the entry of wild animals.
TABLE 16.1
Temperature and Humidity Limits
Crop Type Humidity (RH) Temperature (°C) Limits
Wheat 50%–60% 3–4 Minimum
25
30–32
Optimum
Maximum
Rice 60%–80% 10–12 Minimum
30–32
36–38
Optimum
Maximum
TABLE 16.2
Irrigation Practice Level
No Irrigation
Needed
Irrigation
to Be Applied
Low Soil
MoistureSoil Type
Fine (Clay) 80–100 60–80 Below 60
Medium (Loamy) 88–100 70–88 Below 70
Coarse (Sandy) 90–100 80–90 Below 80
TABLE 16.3
Duration and Water Requirement of Crops
Crop Duration (Days) Total Water Requirement (MM)
Rice 110 1250
Sugarcane
Groundnut
360
105
2200
510
Sorghum
Maize
105
100
500
500
276 Green Engineering and Technology
The monitoring water level of the soil improves the existing irrigation practices.
Invasion of livestock into the eld destroys the crops. Therefore, we call for a system
to resolve the challenges in the agricultural sector by monitoring and controlling the
physical status of the landforms and reducing the manpower involved in the process.
Thus, we propose an IoT (Internet of Things)-based semiautomated mobile agricul-
tural robot equipped with sensors and actuators interfaced with NodeMCU, which
delivers the data derived from sensors to the mobile application through the Internet.
16.2 BACKGROUND/RELATED WORKS
In this section, the existing work related to an agricultural robot is discussed. It con-
tains the literature review, the method of an existing agricultural robot, and the sum-
mary of related works.
16.2.1 literature revieW
An autonomous robot with ultrasonic sensors has been used in agricultural land
to reduce manpower and to increase productivity. The design includes ploughing
the land, sowing the seed, spraying the fertilizer, and navigation. Obstacles are
detected by an ultrasonic sensor, thereby controlling the movement of the robot
(Usha, Maheswari, and Nandagopal 2015). There is a need to nd new ways for the
developed agriculture to improve efciency. A new range of agricultural equipment
can be developed by making small machines to be smart (Blackmore, Stout, Wang,
and Runov 2005). Farming and robotic system are the two important elds that are
being researched by the scientists. The advancement and combination of these elds
can efciently solve many problems. Agriculture with a robotic system can be used
in more complex and dynamic systems (Dattatraya, Mhatardev, Shrihari, and Joshi
2014). It provides optimum solutions and a wide range of production with their own
merits and demerits (Nithin and Shivaprakash 2016). An Agribot is designed to
reduce the manpower but to increase the speed and accuracy of the work (Celen,
Onler, and Kilic 2015). It performs agricultural activities such as spraying, seeding
to improve application, and ensures safety (Ankit, Abhishek, Akash, and Sumeet
2015). A walking robot with hexapod body is used to walk in any direction by using
an ultrasonic proximity sensor. It can dig a hole to plant a seed and spray fertilizers
(Danfeng, Yan, Xiurong, and Huaimin 2010). It can communicate with nearby robots
through Wi-Fi (Amer, Mudassir, and Malik 2015). Due to labor scarcity and expen-
sive manpower, the output in agriculture is gradually declining. The cost of seeds and
fertilizers is increasing exponentially. It demands the use of modern technology for
the efcient use of fertilizers in a less cost (Agarwal and Thakur 2016). An automated
robot has been designed to perform the farming activities for the rhizome plants
(Sampoornam, Dinesh, and Poornimasre 2017). The robot is designed with the aim
of increasing productivity and reducing labor by executing the basic farming func-
tions (Chalwa and Gundagi 2014) (Abba, Lee and Liz Crespo 2019). It includes plan-
tation, sowing seeds, watering, and spraying of fertilizers (Suraj, Anilkumar, Pooja,
Usha, and Sheetal 2017). Human efforts have been reduced using tools and livestock
in the agricultural process. A multitasking robot in agriculture focuses on plantation
277Smart Agricultural Robot
and fertilization without any human intervention. It overcomes the drawbacks of the
traditional method (Karan, Agrawal, Dubey, and Chandra 2012). The overall ef-
ciency of the agricultural process can be increased by the application of automation
and robotics in the eld of agriculture (Shivaprasad, Ravishankara, and Shoba 2014).
An IoT-controlled multipurpose autonomous agricultural robot has been designed for
seeding and spraying of pesticides (Swarna, Jerusha, Tanwar, and Singhal 2020). It
reduces the human intervention in the cultivable land. Resources have been utilized
efciently and ensured a high yield. This is a propel technique to sow, water, and
harvest with the least labor by using a multipurpose robotic vehicle. Voice commands
are used to control the vehicle through a smartphone through Bluetooth. The vehi-
cle is capable of cultivating, sowing, watering, and harvesting. Anyone can operate
this vehicle without any technical knowledge (Ravi, Praveen, Rajesh, Kuruva, and
Thippa 2019). A robot for greenhouse monitoring has been designed, constructed,
and validated. Its functionality is to measure CO2 concentration, air, temperature, and
humidity in a greenhouse environment using the appropriate sensors.
16.2.2 metHod of exiSting agriCultural roBot
In the existing method, the robot works on an ATmega16 microcontroller contain-
ing components such as RF transmitter, RF receiver, dc motor, remote, battery, and
solar panel. The remote provides an operating command to an RF transmitter that
transmits the signal to the robot. This signal will be received by the RF receiver in
the robot and performs the desired action. The DC motor will actuate and the motor
gets revolution as per receiving commands such as reverse, forward, left, and right
with respective buttons present on the remote. When the power supply is turned ON,
a command signal is sent to the receiver using the transmitter. As soon as it receives
the signal, the robot performs the required operation as per the given commands.
Teeth are up and down during ploughing operation and during seeding operation, the
hopper is closed and opened. Thus, the existing system is based on the RF technology,
and it uses the data collected from different sensors and parameters to take accurate
actions and to better predict the crop productivity and quality.
16.2.3 S ummary of related WorkS
Thus, the Agribots are either completely manual or automated controlled. Though
the existing system has certain features, it is still insufcient to perform other agri-
cultural activities. These systems are heavy and consume more space, which makes
it difcult to make through the narrow crop lines. All features are made in manual
control mode, which demands the farmer’s attention on the mobile operation.
16.3 METHODOLOGY
The proposed system is capable of driving over any terrain for surveillance, moni-
toring the eld, and controlling farm equipment. It measures the moisture content
of the soil using a soil moisture sensor, which is attached to a robotic arm. The
robot also measures the parameters, temperature, and humidity level of the farm.
278 Green Engineering and Technology
The obtained values of moisture, temperature, and humidity are sent to a centralized
microcontroller. This helps the end-users to achieve proper irrigation by pumping
the contained water using a motor, thus averting the adverse effects of over-irriga-
tion and under irrigation. A single soil moisture sensor is sufcient to perform the
entire process. The microcontroller delivers these observations to the end-user via
a mobile application. This data is then sent from the microcontroller to the mobile
application via a cloud server. A camera is attached to a Raspberry Pi for live stream-
ing. An audio device is equipped to drive away the invading animals from the eld.
Figure 16.1 shows the block diagram of the proposed system. The proposed system is
broadly classied into hardware and software.
16.3.1 HardWare
Hardware consists of an embedded system that uses a NodeMCU as a microcon-
troller and delivers sensor input data to the microcontroller. Controller logic is used
to perform the processing and notify the user about the parameter values received
from the sensors. The sensors that are incorporated with the system are to detect the
events or changes in the environment and send the information in analog or digital
format to NodeMCU.
Some of the sensors in the proposed system are as follows:
• DHT11 sensor: To detect the temperature and humidity level in the
atmosphere.
• Soil moisture sensor: To measure the soil moisture content by detecting the
dielectric permeability of water in the soil.
• Raspberry Pi camera: To record live video of the eld.
Some of the actuators in the proposed system are as follows:
• L293D module: To control the speed and motion of the DC-geared motors.
• Servo motor: To dip the soil moisture sensor into the soil.
• Relay module: For ON/OFF control on water pump.
• Buzzer: To amplify the alert sound to drive away the invading wild animals.
• Water pump: To pump out water for irrigation.
The project is based on monitoring and collecting the data regarding the physi-
cal parameters of the eld and delivering it to the mobile application through the
Internet. Figure 16.2 shows the different hardware components used in a smart
agricultural robot. NodeMCU acts as a microcontroller unit. DHT11 sensor is used
to detect the temperature and humidity of the atmosphere. The soil moisture sen-
sor is equipped along with the servo motor to dip and measure the soil moisture
content of the soil. The DC-geared motors are used along with each wheel to enable
bot movement. The Raspberry Pi camera is interfaced to gain visual aid of the
eld. The relay module is set up to control the power supply to the water pump for
irrigation purposes.
279Smart Agricultural Robot
16.3.1.1 NodeMCU
It is an open-source rmware and a low-cost device mainly used in the IoT platform
for designing and developing the prototypes. Lua scripting language is used for this
rmware. The surface-mounted board contains MCU and antenna, which functions
as a dual in-line package. This device is also known as ESP8266. A Wi-Fi transceiver
FIGURE 16.1 Block diagram of a smart agricultural robot.
280 Green Engineering and Technology
is integrated with the device by which it can connect to the available network nor
create its own network to allow other devices to directly connect to it. The operating
voltage of the device is 3– 3.6 V. An LDO voltage regulator is mounted on the board
to keep a steady voltage of 3.3 V. It pulls 80 mA during RF transmission where the
supply up to 600 mA is reliable. A regulated 5V power supply to ESP8266 is supplied
through a MicroB USB connector or VIN pin. For external components, a power
source up to 3.3 V can be powered from NodeMCU.
16.3.1.2 DHT11 Sensor
It is a low-cost digital sensor used to measure temperature and humidity of the envi-
ronment. The output of the sensor is sent to the data pin where the temperature is cal-
culated using a negative temperature coefcient thermistor, which causes a decrease
in its resistance value with an increase in temperature and humidity with capaci-
tive electrodes where a change in capacitance is detected with two electrodes of a
moisture-holding substrate. The change is measured and processed by IC to generate
a digital output.
16.3.1.3 Soil Moisture Sensor
The moisture content of the soil is measured using a soil moisture sensor. It is
achieved by measuring the volumetric water content within the soil, where dielec-
tric permittivity is a function of water. This sensor uses capacitance to measure the
dielectric permittivity of the surrounding medium in the soil.
FIGURE 16.2 Hardware components.
281Smart Agricultural Robot
16.3.1.4 L293D Module
L293D is commonly known as a motor driver, which is used to control the speed and
direction of a motor. It is a 16 pin IC, which can control two motors simultaneously
and provides a wide supply range from 5 to 36 V.
16.3.1.5 Relay Module
Relay is an electrical device that incorporates an electromagnet for switching the
devices or circuits. It controls the circuit electromechanically when the relay gets
energized and consists of two terminals, namely normally closed (NC) and normally
open (NO). Relay is energized when an input electrical signal is applied. When the
relay is in its energized state, the circuit connected to NO will be closed and NC will
be open. When the relay is in its original state, the circuit connected to NC will be
closed and NO will be open.
16.3.1.6 Servo Motor
A Servo motor is an actuator device that is used to rotate or push an object at a
specic angle or distance. It works on the principle of pulse width modulation and
contains some gears to provide high torque with great precision. A potentiometer
in the motor is responsible to calculate the angle and stop the shaft position on the
required angle.
16.3.1.7 Buzzer
It is a low-cost electronic device used to produce sound with a simple construction. A
piezo buzzer can be split into piezo and electromechanical devices. When the voltage
is applied, the piezoelectric vibration plate in the buzzer starts to vibrate and sound
is emitted from the buzzer through piezo-piezo sounders.
16.3.1.8 Raspberry Pi
It is a credit card sized, small single-board computer, mainly used for research pur-
poses. It uses Broadcom BCM2835 SoC with ARM as a central processing unit. The
board contains GPIO pins, RCA, AUX, HDMI, Ethernet, USB, and SD card slot. It
is open source and runs in any environment. The OS should be loaded in an SD card
and inserted in the Raspberry Pi for booting the device. The operating voltage of
Raspberry Pi is 5V.
16.3.1.9 Raspberry Pi Camera
The Raspberry Pi Camera Module is interfaced with Raspberry Pi through a ex
cable connector. The ex cable connector is a at exible cable that contains 15 pins.
The camera can take Full HD (1080p) photos and videos. It can be controlled from
Raspberry Pi through a terminal window or programs.
The circuit diagram of a smart agricultural robot is shown in three phases.
Figure 16.3 illustrates the circuit diagram of the robot, Figure 16.4 illustrates the
circuit diagram of animal alert and irrigation system, and Figure 16.5 illustrates the
circuit diagram of a live stream monitoring system.
282 Green Engineering and Technology
FIGURE 16.3 Robot.
FIGURE 16.4 Animal alert and irrigation.
283Smart Agricultural Robot
16.3.2 SoftWare
The software system is a blueprint for developing the project and executing the neces-
sary tasks. The robot is designed to be remotely controlled from any location through
the Internet. Therefore, it is developed with the IoT architecture involving NodeMCU
as the microcontroller of the robot and Firebase as the cloud server. The software
architecture of IoT is shown in Figure 16.6. Mobile application is developed with user
interface for controlling the robot from remote location. It is named as multi-altran.
NodeMCU and mobile application can communicate with each other through the
cloud server. Edge computing techniques are used to implement the proper commu-
nication protocols. Parameters are created in Firebase realtime database. The mobile
application is connected to Firebase with each parameter through the Internet and
FIGURE 16.5 Monitoring system.
FIGURE 16.6 Software architecture of IoT.
284 Green Engineering and Technology
NodeMCU is connected with each parameter of Firebase through the Internet. The
mobile application fetches the values of sensor parameters from Firebase and dis-
plays the value in the user interface. Actuators of the robot are controlled from a
mobile application by sending the values to Firebase. NodeMCU fetches the values
of actuator parameters from Firebase and undergoes the required action. Sensors in
the robot sense the value and the data are sent to the Firebase through NodeMCU.
The connectivity process in Figure 16.7 demonstrates how data are collected and
reported by the sensors and mobile application to the Firebase service in the cloud.
A Raspberry Pi camera is used for live monitoring of the eld. Raspberry Pi is pro-
grammed to host the live video in a webpage through the Internet for remotely moni-
toring and controlling the robot.
Initially, mobile application (Multi-Altran) and NodeMCU, and Raspberry Pi in
the robot need to connect to the Internet through Wi-Fi access point or network data.
Once the system is online, NodeMCU and mobile application (Multi-Altran) establish
the connection with Firebase. Raspberry Pi starts to stream the live video of the eld in
the webpage. NodeMCU is congured using Arduino IDE. Mobile application is devel-
oped with Android Studio. Raspbian OS is loaded in SD card and inserted in Raspberry
Pi for booting the device. Python program is used in Raspberry Pi for streaming the
live video of the eld in webpage. By implementing IoT technology, all the monitoring
and control activities are undertaken from the mobile application through the Internet.
The owchart illustrated in Figure 16.8 describes the working of the robot and
Figure 16.9 describes the working of an animal alarm and irrigation system.
16.4 EXPERIMENTAL RESULTS
The proposed system for remotely monitoring and controlling the agricultural parame-
ters through the Internet via a mobile application has been implemented. The developed
prototype and the outcome of the smart agricultural robot are shown in this section.
16.4.1 moBile appliCation
A mobile application with user interface has been developed for the user to comfort-
ably monitor the eld as shown in Figure 16.10. It includes the navigation control of
the robot and triggering of water pump and animal alarm.
FIGURE 16.7 Connectivity process.
285Smart Agricultural Robot
16.4.2 live Stream
The webpage has been hosted from the Raspberry Pi that can be viewed from any
browser as shown in Figure 16.11. It contains the live stream video from the robot.
The movement of the robot can be controlled by watching the live stream of the eld.
FIGURE 16.8 Flowchart of the robot.
286 Green Engineering and Technology
FIGURE 16.9 Flowchart of animal alarm and irrigation system.
FIGURE 16.10 Mobile application.
287Smart Agricultural Robot
16.4.3 roBot
The robot is capable to drive over any terrain for surveillance, monitoring, and
control. It measures the moisture content of the soil, atmospheric temperature, and
humidity. A smart agricultural robot is shown in Figure 16.12. The front view, back
portion, and front portion of the robot are shown in Figures 16.13–16.15, respec-
tively. The results of the robot along with the expected and actual outcomes are
provided in Table 16.4.
FIGURE 16.11 Live stream.
FIGURE 16.12 Smart agricultural robot.
288 Green Engineering and Technology
16.5 FUTURE WORKS
By introducing articial intelligence, the bots can detect weeds based on position
and edge feature technique. The solar panels can be used to power the robot. GPS
modules can be equipped to track the location and control from any remote loca-
tion. A pH sensor can be added to detect the salinity level of the soil. An automatic
seed sowing technique can be implemented to contain the seeds and bury them
into the soil.
FIGURE 16.13 Front view of the robot.
FIGURE 16.14 Back portion of the robot.
289Smart Agricultural Robot
FIGURE 16.15 Front portion of the robot.
TABLE 16.4
Expected and Actual Outcomes of the Robot
S. No. Features Expected Outcome Actual Outcome
1. Temperature DHT11 sensor detects the atmospheric
temperature and delivers the value to the
mobile display.
Same as the expected
outcome
2. Humidity DHT11 sensor detects the atmospheric
humidity and delivers the value to the
mobile display.
Same as the expected
outcome
3. Soil Moisture A soil moisture sensor is attached to the
servo motor. For “Downward”, the motor
control horn is tilted down for the sensor to
make ground contact, and for “Upward”,
the control horn shifts back to the actual
position. The sensed moisture value is
delivered to a mobile application.
Same as the expected
outcome
4. Control L293D module is interfaced with four
wheels in pairs and the wheel speed and
direction are controlled. The options drive
the robot in the selected direction based on
the precoded speed and direction.
Same as the expected
outcome
5. Water Pump Relay module ON/OFF the water pump
according to the option selected on the
mobile display.
Same as the expected
outcome
6. Animal Alarm Buzzer is ON/OFF according to the option
selected on the mobile display.
Same as the expected
outcome
290 Green Engineering and Technology
16.6 CONCLUSION
Agriculture in collaboration with robotics and IoT technology makes the farmer get
better yield and production. The purpose of agricultural robots is to reduce the prob-
lems faced in the agricultural sector. Thus, a smart agricultural robot is designed,
developed, and tested for temperature, humidity and soil moisture sensing, live
streaming, mobile monitoring, water irrigation, and animal alert system in real-time
on actual eld. The outcome has promising values with acceptable delay. Thus, this
robot functions on all terrains to monitor, maintain, and cultivate the land.
ACKNOWLEDGMENT
The authors would like to thank Dr. K. Helenprabha, Professor and Head, Department
of Electronics and Communication Engineering, R.M.D. Engineering College, for
her support that made this research work possible.
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I. H. Celen, E. Onler and E. Kilic. “A Design of an Autonomous Agricultural Robot to
Navigate between Rows.” 2015 International Conference on Electrical, Automation
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V. N. Chalwa and S. S. Gundagi. “Mechatronics Based Remote Controlled Agricultural
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Innovation in Engineering & Management (IJAIEM) 3 no. 6 (2014): 251–255.
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Design and Applications (ISDA) (2012): 369–374. Kochi, India.
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System Using IoT.” International Journal of Recent Technology and Engineering
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293
17 A Survey of Lightweight
Cryptography for
Power-Constrained
IoT Devices: Security
Challenges and Issues
Sunil Kumar and Dilip Kumar
National Institute of Technology Jamshedpur
CONTENTS
17.1 Introduction ..................................................................................................
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294
17.1.1 Criteria of the IoT Design 295
17.1.1.1 Energy Monitoring 295
17.1.1.2 Resource Management 295
17.1.1.3 Interoperability 296
17.1.1.4 Interference Management 296
17.1.1.5 Safety Currently 296
17.1.2 Contribution 296
17.1.3 Organization of the Chapter 296
17.2 Fundamentals of Lightweight Cryptography Techniques 297
17.2.1 Techniques of Lightweight Block Ciphers 297
17.2.2 Lightweight Hash Functions 299
17.2.3 Lightweight Stream Cipher Algorithms 299
17.2.4 High-Performance Systems 299
17.2.5 Assessment of Low-Constrained Devices 300
17.2.6 Present Research Work 300
17.3 IoT Security Issues and Strategies of Prevention 301
17.3.1 Issues and Research Challenges 301
17.3.1.1 Issues 301
17.3.1.2 Challenges 301
17.3.2 Strategies and Preventive Measures for IoT 302
17.3.2.1 Asymmetric LWC Method for IoT 302
17.3.2.2 Symmetric LWC Method for IoT 302
294 Green Engineering and Technology
17.1 INTRODUCTION
IoT is a device whose aim is to communicate with everyday things and to share knowl-
edge to achieve a specic objective. This simple concept allows for a wide range of
applications, including smart towns, smart farms, industrial automation, defense,
medical services, entertainment, and so on. In the eld of sophisticated interactive
media connectivity, the IoT is a new world view. IoT is a worldwide initiative that com-
bines more interesting and valuable citizens, information, processes, and things than
ever before. This involves interconnected machines such as radio-frequency identi-
cation (RFID) tags, sensors, actuators, microcontrollers, mobile phones, and wireless
computer transceivers that can communicate and recognize through the Internet to
achieve their objectives and individuals can transfer data across a network without
contact with other humans [1, 2].
Gartner research [3] estimated that by 2020, IoT will deliver revenue of over $300
billion, excluding laptops, tablets, and smartphones. Furthermore, by 2020, smart-
phones and tablets are projected to exceed 7.3 billion units. Such systems can create
an immense and complicated network, with signicant data transmission through the
communication channel. When IoT grows quickly, it poses challenges and threats
including handling massive data volumes, handling information processing with elec-
tricity, addressing safety issues, and encoding/decoding big data. Because multiple
smart nodes are linked from IoT devices, in embedded systems, the demand for the
required cryptographic approach is increased. Nevertheless, intelligent machines typ-
ically have limited resources or may be considered low-resource equipment in terms
of their low processing capacity, existence of batteries, small sizes, small memory,
and reduced energy supply. Standard primitives could also not be ideal for smart low-
resource systems. For example, in RFID tags [4], the 1204-bit RSA algorithm cannot
be applied. Moreover, the strict constraints involved in the mass production and pro-
duction of smart devices prevent the need for a new lightweight cryptography algo-
rithm, which provides necessary protection, cryptography, low power interfaces, and
other features for the new technology allowed by IoT.
Cloud platforms are used to provide relevant data to users and receive requests
to convert them; Internet accessibility makes them available almost all the time and
every moment. However, this openness often makes them vulnerable because they
17.3.3 Pseudocode for a Lightweight Encryption System ...........................
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303
17.3.4 Pseudocode for the Decryption System 304
17.4 Suggested Lightweight Cryptographic System for IoT 305
17.4.1 Framework of LWC 305
17.5 Problems and Discussion 307
17.5.1 The Algorithms of Cryptography and Cipher Design 307
17.5.2 The Issues Associated with Block Size and Key Size 307
17.5.3 Modern Threats
17.5.4 Security and Privacy 308
17.6 Conclusion 308
References 309
295A Survey of Lightweight Cryptography
can be accessed and attacked from anywhere in the world. If successful, then any
attacker can access personal, medical, nancial, or location information and use the
actuator to damage the device as well as the health of the consumer [5]. Vulnerability
analysis and future attacks are discussed in some studies [6, 7]. Another problem is
that they are widely seen as less ingenious devices with limited resources and have
difculties in implementing security services. Traditional security systems are not
viable due to limited IT and energy resources [8, 9]. With the massive growth in IoT
(trillions expected), data were exchanged on related items in the near future [10].
There is also an incredible increase among IoT devices. IoT device designers face
many challenges and complications, except for the capacity for energy [11] and pres-
ervation of data [12] and networked security in the application layer protocal [13],
certain threats and in general, whenever resources are limited, challenges are more
severe critical data exchange devices. In addition, a power attack might potentially
drain the battery of an IoT system and trigger shutdown of the computer [12].
The NIST notes that lightweight cryptography is a subset that offers solutions for
fast-growing applications that use clever low-energy devices [14]. It targets many
applications and can be used in software and hardware. A typical cryptographic algo-
rithm works well on computers, servers, sensor networks, embedded systems, and
digital devices. The bottom ends of the spectrum are appliances such as RFID tags,
sensor systems, and implanted devices. The applications and communication channels
need lightweight cryptographic techniques. Implementation of lightweight cryptogra-
phy algorithms includes the wireless sensor networks, RFID, WBAN, IoT network,
smart cards, and so on [15,16]. IoT also uses a cloud computing model with many
security concerns [17–20]. Besides these problems, resource-constrained devices with
less processing capacity, reduced battery life, limited storage, and limited network
require efcient protection keys that will not crunch IoT resources.
17.1.1 C riteria of tHe iot deSign
Every year, IoT is rising exponentially and a critical environment requires devices
activated by IoT and ve conditions exist for potential technologies.
17.1.1.1 Energy Monitoring
The IoT-enabled intelligent devices are constantly capable of sensing, receiving, run-
ning, and processing information to make smart decision making possible [21]. The
collection and distribution of vast quantities of data limits on energy supply play
a crucial role in IoT infrastructure. Therefore, power use is one of the prominent
requirements for improving network systems and life.
17.1.1.2 Resource Management
Modern IoT network systems can be remotely accessed and congured to achieve
sustainable communications to connected resources, should therefore share and mod-
ify the real-time system beyond [22]. The task handled by this method should also
adequately balance to ensure accurate communication between the user and devices
powered by IoT.
296 Green Engineering and Technology
17.1.1.3 Interoperability
Interoperability is one of the fundamental requirements of the implementation of IoT,
which can interact between systems or devices without taking into account the tech-
nological or product requirements. Bringing the latest IoT-enabled systems should
enable adapting and communicating with different Wi-Fi techniques to enhance the
versatility of the IoT system [23].
17.1.1.4 Interference Management
Wireless technologies are linked across the Internet with a growing number of con-
nected devices in IoT architecture day after day. Therefore, disruption can vary
between two radio transmissions among multiple nodes and must be remedied in the
forthcoming IoT node [24].
17.1.1.5 Safety Currently
The crucial and challenging security problems in the IoT contexts include privacy,
secure management and storage, authentication and communication, and user authen-
tication. The size of IoT devices and utilities contributes to many bugs and node
attacks. Because of the limited processing power, conventional security approaches
suffer from multiple pitfalls and frequently do not identify the physical network
threats [21,25]. Consequently, IoT safety must be improved through ensured connec-
tivity, only allow the approved client to access the information and regularly modify
for a smart device.
17.1.2 C ontriBution
This article aims to address a thorough analysis of lightweight cryptography tech-
nologies for IoT devices with low power, IoT design security issues, research crite-
ria, and challenges to protected wireless communication in power-constrained IoT
devices like sensors, actuators, RFID tags, and so on. This article contributes to
another era of secure wireless communication with power-constrained IoT devices.
A detailed review to defend the data from attacks and power efciency in the grid,
region, condentiality, authentication, and block-chain wireless communication
schemes shall be reviewed in a secured communication channel. In addition, dif-
ferent approaches focused on many types of lightweight cryptography approaches
to transferring information have been extensively tested for communication net-
works. Modern approaches have been explored for secured communication in IoT
and will support researchers for transmission of information through a variety of
power-constrained IoT devices. Ultimately, we identify the obstacles and open
issues to secure IoT sensor devices through lightweight cryptography algorithms in
communication networks.
17.1.3 organization of tHe CHapter
The is chapter is organized as follows: Section17.1 addresses briey the introduction
on the power-constrained IoT devices and the paragraph also contains the article’s
297A Survey of Lightweight Cryptography
inspiration and contribution. Section 17.2 addresses the fundamentals of lightweight
cryptography techniques in IoT transmission, offering performance matrix low cost,
throughput, latency, and also describes existing research work. Section 17.3 discusses
the security challenges and prevention methods for IoT that include safety standards
and open issues that should be considered in future s tudies. Section 17.4 suggests
a lightweight cryptographic system for IoT. Section 17.5 describes and discusses
problems and nally, Section 17.6 provides the conclusion.
17.2 F UNDAMENTALS OF LIGHTWEIGHT
CRYPTOGRAPHY TECHNIQUES
This chapter deals with the different elements of the lightweight algorithms as dened
in Figure. 17.1 and we summarize some lightweight algorithms in Table 17.1, accord-
ing to its frame size, key size, design, and number of rounds.
17.2.1 teCHniqueS of ligHtWeigHt BloCk CipHerS
Lightweight WG-8 cipher is an encryption method optimized for small-constrained
systems of the Welch–Gong family. A variety of block ciphers have been suggested
to give good results for things like RC-5[26], AES-1 [27,28], TEA [29], and XTEA
[30]. In general, some have been modied and intended to improve efciency by sim-
plifying traditional ciphers. For example, DESL [31,32] is often referred to as light-
weight DES. The round function in DESL uses one S box rather than eight rounds,
so that the initial and end permutation is permitted to raise the execution of the
hardware SIMON and SPECK [33] that come in different widths and key sizes with
FIGURE 17.1 Classication of various LWC algorithms.
298 Green Engineering and Technology
block ciphers. Both platforms are exible and perform well in a variety of lightweight
frameworks [34].
Certain lightweight block ciphers are described below.
Smaller block sizes: Block size should be minimal, so that the efciency of the
lightweight block ciphers is achieved and money is saved. It should be less
than 64-bit instead of 128-bit. As the size of the block decreases, the plain
text size is decreased.
Smaller key size: A lightweight block cipher must be small in order to obtain
minimum battery life and power usage. PRESENT [35], for example, is
80-bit and Twine [36] is 80/128-bit.
Simpler rounds: Lightweight cipher blocks targeting low-resource restricted
devices inherently involve simple computation compared to traditional
block cipher techniques. Lightweight methods can reduce the number of
rounds. For example, 4-bit S-Boxes were used for lightweight rather than
8-bit boxes for traditional cryptography for a single S-Box. Some easier
lightweight algorithms for cryptography are as follows: PRESENT uses,
4-bit S-boxes with just four rounds of Hummingbird2 [37], and Iceberg [38].
Simpler key schedules: A program for a given key that measures round sub-
keys. Complex keys use additional storage and resources to execute them.
Lightweight block ciphers use easier key schedules, so that subkeys can be
produced. For example, the TEA block cipher splits the 128-bit into four bit
blocks of 32-bits.
TA BLE 17.1
Current Lightweight Ciphers
Ciphers Design Key Size Block Size No. of Rounds
RECTANGLE
ITUBee
SPN
Feistel
80/128
80
64
80
25
20
AES
HEIGHT
SPN
GFS
128/192/256
128
128
32
10/12/14
32
PRESENT
mCrypton
TEA
SPN
SPN
Feistel
80/128
64/96/128
64
64
64
64
31
12
64
SEA Feistel 96 96 93
LEA
DES
Feistel
Feistel
128,192,256
54
128
64
24/28/32
16
SEED Feistel 128 128 16
TWINE
DESL
Feistel
Feistel
80/128
54
64
64
32
16
3DES
TDEA
Feistel
Feistel
56/112/168
64
64
64
48
48
Hummingbird2 SPN + Feistel 128 64 4
Camelia SPN + Feistel 128/192/196 128 18/24/24
KHAZAD SPN 128 64 3
299A Survey of Lightweight Cryptography
17.2.2 ligHtWeigHt HaSH funCtionS
A traditional hash function seems to have a broad internal factor and high-power
consumption, which cannot be preferred without a lightweight hash function in the
RFID protocol [39]. A lightweight feature based on lightweight block ciphers is,
therefore, presented [40]. Some simple hash functions are PHOTON [41], Quark [42],
SPONGENT [43], and Lesamnta-LW [44]; some are easily available. Smaller output
sizes are very high for applications needing hash collision resistance. Interior and
balance sizes can be used where no collision resistance is required. This hazardous
role should have the same protection against pre-image, second image, and impact
attacks where collision safety hash skill is required, and this can decrease the inter-
nal state spectrum. Typical hash capacity for smaller message sizes will be used to
boost the large 264-bit contribution. For most detached guidelines about the abil-
ity of the lightweight hash, the standard size of the information is much smaller
(like256 bits at most). Therefore, hash functions improved for short messages may
be more suitable for lightweight implementation.
17.2.3 ligHtWeigHt Stream CipHer algoritHmS
The European Network of Excellence for Cryptology (eSTREAM) was designed
to identify modern stream statistics that would be ideal for unconditional adoption
[45]. Rivalry nalizers with three grain ciphers have been announced in 2008 [46],
MICKEY [47] and Trivium [48].
17.2.4 HigH-performanCe SyStemS
The elite framework uses unique crypto-engines to satisfy three critical needs: perfor-
mance, exibility, and safety. Some weaknesses such as area and power are regarded
to a limited extent [49]. Some of the high-performance requirement systems are
being addressed below.
Modied cryptographic CPU and crypto ALU processors use a CPU to execute
cryptography algorithms that have been designed for use. An Instruction
Set Architecture typically incorporates instructions that are cryptographi-
cally oriented. Due to the different cryptography algorithms, selecting these
types of instructions is difcult. The machine software must be changed to
something like a compiler to use innovative instruction [50].
Cryptographic co-processor improves the cipher speed and is done through
the system unit; it is dedicated to the cryptographic processor, a cryptogra-
phy company, and this is managed by the host processor. Which control of
downtimes and the Co-processor decides the general execution of data [51].
Cryptographic arrays by using parallel methods to further enhance efciency,
an encryption array of processing unit and a multicore encryption processor
was created and it is similar to computational tasks and includes the mapping
topology for transmitting information among entities and storage. It pro-
vides an extremely encrypted data rate or multiple ciphers simultaneously.
300 Green Engineering and Technology
Cryptographic multicore in comparison, the cryptographic multicore pro-
cessor does not rely on a method. The authors [52] have revealed a multi-
channel cryptographic and multi-standard processor (MCCP) for 8-core
systems. The key FPGA system for AES cryptography was introduced. The
AES core can be easily changed by other block ciphers by rearranging the
FPGA hardware.
17.2.5 aSSeSSment of loW-ConStrained deviCeS
The lightweight cryptographic algorithm offers an interplay between power and
resource to achieve the same degree of security when considering efciency metrics
of low constrained devices. The output may also be communicated along with energy
use, duration or delay, and ow capacity.
The following two types of cipher implementation are available in limited-
constrained devices.
Software implementations: The lightweight cipher algorithm can be imple-
mented using a computer machine that can be a low-cost 8-bit and 16-bit
microcontroller on the CPU. The program code is written may be machine-
dependent or independently such as C/C++ and Java. When encryption algo-
rithms implement on a low constrained device, which is based on strength,
speed, and storage, the calculation of software-specic concentrates on the
required number of gate registers for RAM and ROM.
Hardware implementation: Hardware design and deployment tools are typi-
cally represented in terms of the gate area and the complete use of custom-
izable ASICs or FPGAs. The architecture in FPGA offers advantages such
as reducing costs for production and increasing versatility. There are search
tables, ip-ops, and multiplexers [53]. On the other hand, ASIC’s custom
design relies on an automatic design process to minimize the design time.
17.2.6 preSent reSearCH Work
The current methods, for instance, use AES as a lightweight block cipher. The objec-
tive is to build AES into some kind of lightweight block cipher by considering dura-
tion and intensity values into account. The technique they proposed applied to RFID
tags and sensor nodes [54]. The QTL structure is fundamentally changed in the Feistel
design to increase the gradual diffusion of the current Feistel design. In QTL, cryp-
tography and decryption approaches are similar. QTL also inhabits fewer regions in
limited application areas and reduces the cost of energy consumption when using
hardware [55]. A key Feistel modication scheme protects the cipher from relevant
key attacks. Since a Feistel cipher is vulnerable to such a form of attack without a key
schedule, the cipher focuses on the main attacks. They have shown that it is simple
to create safe ciphers against related key attacks by using the AKF scheme [56]. A
recent lightweight-embedded security cryptography algorithm design was proposed
[57]. Their work offered an overview of the process of a bit substitution system based
on another lightweight and compact cipher framework. We create confusion using
301A Survey of Lightweight Cryptography
PRESENT S-boxes as no such confusion occurs with bits and permutation commands
in all existing algorithms.
The current S-boxes for compact algorithms and a new hybrid method, which
offers more compact results, are proposed in this article for both memory spaces.
Many proposed data safety and security mechanisms for sensor networks such as
AES, LED, KATAN, and TWINE. These protection measures, however, have disad-
vantages, security aws, and machine complications. These problems were tackled
and lightweight chips were suggested with messy maps and genetic interventions.
Their proposed scheme uses elliptical curve points to classify interacting nodes [58].
The proposed system provides security and protection of privacy along with
multi-level certainty managing. The whole system will quickly improve processing
strength and capacity so that large quantities of PHI (Individual Health Data) are
reported, while at the same time restricting disclosure security in medical services
[59]. A lightweight cryptography tool has been built in the android platform [60].
They built the user-friendly NCRYPT tool for such a system. NCRYPT provides
the option to encode certain data or picked sensitive les. Lightweight 8-round
iterative UAN communication block cipher algorithms were based on the principle
of chaos rather than the S-box. This scheme will guard against brutal attacks and
opponents [61].
An ultra-lightweight cryptography concept was suggested for general comput-
ing [62]. ANU, is available in 25 rounds and supports 80/128-bit planning. ANU
ciphers such as MITM, Zeroday, and Biclique are resistant to simple and advanced
attacks [63]. This article deals with how secure data can be held and distributed
through constrained devices to protect data from threats, along with data changes
by unauthorized users.
17. 3 IoT SECURITY ISSUES AND STRATEGIES OF PREVENTION
17.3.1 iSSueS and reSearCH CHallengeS
17.3.1.1 Issues
IoT emergence in public facilities, business organizations, workplaces, and so on is
facing safety and privacy issues, which are a major inconvenience when designing
the IoT platform. Conventional cryptography algorithms are not ideally suitable in
the IoT scenario due to many resource constraints and conditions such as power, low
battery, real-time execution, and so on. Lightweight cryptography is also more con-
sistent with IoT. There are several small cryptographic algorithms in symmetry and
asymmetry categories. However, these lightweight algorithms may not ensure real-
time security, runtime, energy consumption, and memory requirements. Symmetric
algorithms are not authenticated, although the asymmetric ones are less important,
consuming more memory. This affects the collection and processing of information
in real-time and wastes IoT resources.
17.3.1.2 Challenges
In IoT applications, the key challenge is to ensure privacy, safety, and data integ-
rity. The main problem is, however, related to the authorization, authentication,
302 Green Engineering and Technology
and management mechanism. IoT relies therefore on the ability and efciency of
communications on future IPv6 conventions, which fulll the inclination and ver-
satility requirements.
More challenges are related to the IoT framework:
Less human interference can lead to physical and logical assaults.
Several research studies on the security faults in wireless IoT networks have
already resulted in many attacks including DoS/DDoS, response attacks,
and more.
Another challenge is to restrict resources for energy usage, less battery life,
bandwidth, diverse system, and complex safety strategies that can delay sys-
tem performance.
17.3.2 StrategieS and preventive meaSureS for iot
17.3.2.1 Asymmetric LWC Method for IoT
In general, RSA is not part of the LWC method due to its maximum key size. Due to
the use of two wide modulo operations and prime numbers, RSA ensures better secu-
rity and user privacy. Compared with the RSA algorithm, the ECC needs a lower key
size, speed, and storage space. Then it is implemented in the small hardware area,
leading to smoother real-time calculations [64]. The 6LoWPAN nodes use the ECC
algorithm that can be used for restricted systems. The lightweight symmetrical and
asymmetrical IoT environment algorithms are calculated by the size of the code, the
block size, number of rounds, the key sizes, internal structures, and potential attacks
in Tables 17.2 and 17.3.
17.3.2.2 Symmetric LWC Method for IoT
NIST says that AES includes three variants: AES-128, AES-192, and AES-256.
It is used with a CoAP solution in the application layer. The cryptography feature
includes a four 128-bit block matrix. The internal status is arranged by sub bytes,
move rows, mix columns, and add round keys.
TWINE: The Feistel structure is used to call eight times a round and to apply
the 4×4 S-box XOR on the subkey. TWINE is a complex permutation and
mixture for optimizing propagation comparison to CLEFIA and HIGHT. In
TWINE, just half of the cycle is changed for a single sub-block separation
and it involves a permutation to disperse all sub-blocks.
TA BLE 17. 2
Asymmetric LWC Methods
Asymmetric Method Code Size Block Size Potential Threats
RSA 900 1024 Modules attack
ECC 8838 160 Timing attack
303A Survey of Lightweight Cryptography
HIGHT: The high protection and lightweight height (HIGHT) operation of the
Feistel network uses very simple and basic operation. During cryptography
and decryption, this key is created. The authors suggested a parallel imple-
mentation that would need fewer resources and a small number of codes,
and that the RFID system would be improved [65]. HIGHT has susceptibil-
ity to degradation threats.
PRESENT: This depends on the substitution and permutation network, which
consists of 31 rounds. PRESENT is used as a lightweight cryptography
algorithm. It is 64-bit long with two 80-bit and 128-bit keys. It is applied to
the embedded system on the substitution layer using 4-bit input and output
of the S-box.
17.3.3 pSeudoCode for a ligHtWeigHt enCryption SyStem
The cryptography algorithm takes a plain text input as a 64-bit xed-size block and
then divides it into two halves of 32-bit fragments. The Feistel function F () oper-
ates in each round of the cryptography scheme along with a secret key size ranging
from 64-bit to 128-bit. The incorporation of H function H (), which is an invertible
function, operates at electronic speed to generate a 32-bit cipher at each round of the
Feistel function. The resultant two halves of 32-bit are then swapped and merged
to get the desired 64-bit ciphertext straight after the end of 14 rounds of the Feistel
function of the cryptography algorithm. The following is the description of the cryp-
tography algorithm shown in algorithm 1.
Algorithm 1.0: Pseudocode for Encryption System
Plaintext input if 64-bit (PT)
Splits PT into two 4 bytes: PTL, PTR
For i = 1 to 14:
PTL = PTL XOR P (i)
PTR = PTR XOR (P (i) XOR F (PTL))
XL = XL XOR H (XR)
Switch PTL and PTR
End For
TA BLE 17. 3
Symmetric LWC Method
Symmetric
Algorithm Technique
Code
Length
No. of
Rounds
Key
Size
Key
Length Potential Attacks
HEIGHT GFS 5672 32 128 64 Saturation attack
TEA Feistel 1140 32 128 64 Related key attack
PRESENT SPN 936 32 80 64 Differential attack
RC5 ARX Not xed 20 16 32 Differential attack
304 Green Engineering and Technology
Switch PTL and PTR (Undo the last swap.)
PTL = PTL XOR P15
PTR = PTR XOR P16
Switch PTL and PTR
PTL = PTL XOR P17
PTR = PTR XOR P18
Re-combine PTL and PTR
64-bit ciphertext is generated.
Function Box - F ():
F(PT): ((S1(a, b) + S2(a, b)) XOR (S3(a, b) + S 4(a , b)))
H-Function - H ():
H(PT): ~ (F(PT) XOR PT(L/R))
}
17.3.4 pSeudoCode for tHe deCryption SyStem
The decryption algorithm is a reverse engineering process of the cryptography
system in which the plain text is generated using the same shared secret key and pro-
cess for 14 rounds in the Feistel function. Therefore at the end of all rounds, the two
halves of 32-bits are joined to produce original plaintext data of 64-bit. The following
is the description of the decryption algorithm shown in algorithm 2.
Algorithm 2: Pseudocode for the Decryption System
Ciphertext input if 64-bit (PT)
Splits PT into two 4 bytes: PTL, PTR
PTL = PTL XOR P18
PTR = PTR XOR P17
Switch PTL and PTR
PTR = PTR XOR P16
PTL = PTL XOR P15
For i = 14 to 1:
PTR = PTR XOR H (PTL)
PTL = PTL XOR (P (i) XOR F (PTR))
PTR = PTR XOR P (i)
Switch PTL and PTR (Undo the last swap.)
End For
Re-combine PTL and PTR
64-bit Original Plaintext is generated.
End Decryption Algorithm
Function Box - F ():
F(PT): ((S1(a, b) + S2(a, b)) XOR (S3(a, b) + S 4(a , b)))
H-Function - H ():
(PT): ~ (F(PT) XOR PT(L/R))
305A Survey of Lightweight Cryptography
17.4 SUGGESTED LIGHTWEIGHT
CRYPTOGRAPHIC SYSTEM FOR IoT
17.4.1 frameWork of lWC
The existing LWC framework for IoT combines asymmetric and symmetric cryptog-
raphy methods. The lightweight asymmetric method has a greater level of protection
than symmetric algorithms, while they are machine complex and have larger key
sizes in a constrained IoT sense [65]. Thus, a resource-based IoT environment is
developed to deliver asymmetric and symmetric lightweight methods, with tiny key
size, fewer calculation time, consumption of low power, minimum storage size, and
equal protection. In addition, intelligent space involves several devices with limited
power and memory, but several devices have ample amounts of battery power, com-
putation processing, and storage space. The suggested technique, therefore, incorpo-
rates all aspects of cryptography, considering all computer parameters applicable to
an IoT paradigm mentioned in Figure 17.2.
Figure 17.2 shows a ow diagram that accepts IoT system variables to be an input,
as well as its result, and proposes versatile data cryptography for this intelligent
device. LWC comprises four research steps using four input parameters: storage space
(SS), information space (IS), battery power (BP), and processing power (PP) [66].
FIGURE 17.2 Framework of LWC.
306 Green Engineering and Technology
The proposed LWC system incorporates the principle of hierarchy. Every smart
home device combines, data collection processors, provide sufcient guidance, and
articulate the system goal information that proves a hierarchical organized model
[67]. The LWC framework offers two output cryptography algorithms (slight sym-
metric and asymmetrical cryptography), depending on the examination of device
parameters. The lower number, key size, key length, and code size indicate the light-
weight release of conventional algorithms.
This shows that the proposed scheme is acceptable and suited for IoT devices
such as RFID, the WSN, and so many more. The desired efciency, in this case, is
lightweight symmetric cryptography as per current research [68,69]. Otherwise, the
next processing step continues, which is the variable check of the device battery of
IoT. Formulas (17.1) and (17.2), will impact the computing capacity, the storage size,
and the capacity of batteries of the IoT device [70], respectively. The notation of those
equations is given in Table 17.4.
If the estimated battery power value is less than the threshold and is based on
research [71], lightweight symmetric cryptography is suggested and advised by the
proposed method. Even if the IoT device has enough battery power, it goes through
the process of analyzing the memory space during the next step. For evaluation of
memory capacity, the metric component depends on the platform, like code length,
and is connected to the chosen processor with its instructions. The amount of itera-
tion or/and XOR inclusion may also affect storage space. The cryptography method
does not need more storage if substitution boxes are not used. Equation(17.1) shows
the design performance:
NNF×
Throughtout==
B B (17.1)
TBCB
The process of design frequency is described by throughput. Current number
(cycles) associated with the command set stored in the memory of the processor.
Each processor, therefore, has a different frequency and number of cycles. a capac-
ity booking for information stored on IoT systems is different from restricted capac-
ity and adequate memory. The LWC framework veries whether the computer has
TA BLE 17.4
Symbols and Metric
Symbols Metric
KS Key size
GE Gate Equivalent
TB Time for encoding 1 block
A Design area
CB Cycles Nb. for encoding 1 block
F Frequency
Th Throughput
307A Survey of Lightweight Cryptography
a limited memory for analytical calculation capacity; otherwise, it is lightweight
asymmetric cryptography [72,73].
The measurement systems power is considered as the effectiveness metric deter-
mined in the report of the ow rate determined with a dened clock frequency for
the area during the last stage of LWC studies [74]. This variable also calculates the
area costs and specications for handling a single cipher text bit. The efciency is
demonstrated in the following formula.
Th NNF×
Efficiency == B=B (17.2)
ATA
B×CA
B×
An LWC relates the threshold value to the computer power of a device by taking
certain efcacy levels of the device into account. According to the current quest in
some of the studies [68,75], if the calculation power value reaches the threshold level,
the suggested technique for this system is lightweight asymmetric cryptography, and
then the framework shows lightweight symmetric cryptography.
17.5 P ROBLEMS AND DISCUSSION
The importance of a number of contributions to the cryptographic system and its
application areas will have to be examined in future studies. In the previous chapters,
we have surveyed and summarized many current lightweight algorithms for low-
resource IoT devices. This chapter focuses on research subjects related to lightweight
and traditional encryption. In this chapter, we further dene the problems mentioned
as follows.
17.5.1 tHe algoritHmS of CryptograpHy and CipHer deSign
The design of cryptography examined in this work allows the global output of vari-
ous cryptographic designs to be exposed. However, a reliance on technology and
resources deforms the ndings and ends with big discrepancies between research,
and so, it is better to understand different ways by proposing a new cryptography
comparable with the current traditional cryptography. The new model improves the
accuracy of lightweight encryption. To enhance cryptographic energy, authors have
suggested a hardware design power, which has achieved optimal power in 32 rounds
from Katan cipher [71]. In addition, they proposed compacting their model by consid-
ering the physical, architectural, and algorithmic problems. The framework includes
a traditional Feistel structure with slow cipher diffusion. Adjustment between both
the complexities of a round also raises the number of rounds, pipelines, and roll-ups.
In addition, a middleware system name PalCom to exchange lightweight data for the
IoT environment was suggested [76].
17.5.2 tHe iSSueS aSSoCiated WitH BloCk Size and key Size
The development of a lightweight, resource constrained technique emphasizes on the
considerable size and characteristics of the block. Even as the key length is increased,
308 Green Engineering and Technology
the size of ciphertext immediately increases, and as a result, the computing power
becomes more essential. It also applies for block size. Intruders may use a certain
key to kill the algorithm with a multi-key attack. The privacy property is breached,
if the hackers obtain the key.
17.5.3 modern tHreatS
Most methods are proposed to prevent and recognize threats that affect system secu-
rity and break the models implemented [76]. Therefore, the cipher capacity must be
modernized to be vulnerable to such attacks. The major problem with the energy-
restrained method is Hardware Trojan. A substitute unresolved issue necessitates
creating a universal pattern that consolidates the design of HT and hardware to deter-
mine the complexities and tradeoffs of protection.
17.5.4 S eCurity and privaCy
The security and privacy for constrained devices and their resource structure can
be adapted, which enhances the attention and interest in safety metrics. In general,
no safety metric can reliably estimate the security and privacy for the cryptographic
eld. Encryption is a subject for decryption, which is to break the encryption process
by using a list. The security level may be rated as less safe, secure, or moderate,
relying on the effective list of attacks. Nevertheless, common security systems still
require upgrades and more clear-cut safety requirements for cryptography algorithms
for resource-constrained devices in IoT systems still require updates and anticipate
criteria for key cryptographic security.
17.6 CONCLUSION
We have worked in-depth on the latest branch of traditional cryptography called
lightweight encryption techniques; however, many low constrained devices perform
computational methods and are constrained in terms of personal-sufciency, energy
usage, and memory. We often face the additional issue of security and safety and
consider the way IoT employers maintain safety. In addition, we explored different
types of lightweight cryptographic algorithms that can also be applied with soft-
ware or hardware implementation. Different types of algorithms lead to the creation
of safe, powerful, lightweight, key-sized encryption algorithms, reducing the calcu-
lation strength and rapid process. In this study, we have introduced a new method
that can be used in a smart city. We have also addressed the opening points on
block size, key size, cipher structure, deployment, security measurements, and new
attacks. Our expectation will research the cost of these solutions and the probabil-
ity of integration into restricted IoT systems. Furthermore, we plan to develop the
algorithm used to calculate the threshold value for an individual system parameter
previously provided in this work.
309A Survey of Lightweight Cryptography
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315
18 Nanogenerator-based
Sensors for Human
Pulse Measurement
Ammu Anna Mathew, S. Vivekanandan,
and Arunkumar Chandrasekhar
Vellore Institute of Technology
18.1 INTRODUCTION
Self-powered systems that can be operated without any external power source are a
topic of great interest in the last few decades [1]. The technology of generating elec-
tricity by converting the mechanical/thermal energy is called a nanogenerator (NG).
The mechanical/thermal energy used for conversion may be due to some small-scale
deformations. Displacement current is the driving force for conversion, irrespective
of the material being used. As the NGs have the capability to operate without exter-
nal power, they can extend their application to different elds, mainly contributing to
the biomedical and healthcare devices [2].
NGs are classied into four types: piezoelectric nanogenerator (PENG), triboelec-
tric nanogenerator (TENG), pyroelectric nanogenerator (PYNG), and thermoelectric
generator (TEG). PENG and TENG make mechanical to electrical energy conversion
whereas the latter converts thermal energy to electrical energy. NGs harvest energy
from small-scale deformations and large-scale deformations in different elds, as
shown in Figure 18.1. Nanotechnology incorporated with energy harvesters produces
CONTENTS
18.1 Introduction ..................................................................................................
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315
18.2 Working and Fundamentals Mechanism of NGs 316
18.2.1 Piezoelectric Nanogenerator 316
18.2.2 Triboelectric Nanogenerator 317
18.2.3 Pyroelectric Nanogenerator 318
18.3 Choice of Materials 318
18.4 Applications of NGs in Pulse Measurement . 319
18.4.1 PENG-Based Sensors 319
18.4.2 TENG-Based Sensors 322
18.5 Conclusion 324
Conict of Interest 325
References 325
316 Green Engineering and Technology
more sensitive and accurate NGs. The most commonly used NGs are PENG, TENG,
and PYNG, which are explained below in detail.
The physiological signals are to be monitored, evaluated, and analyzed in
appropriate time to remain healthy. The advancements in the eld of biomedical
and healthcare, considering factors such as power consumption, biocompatibil-
ity, and nano-materials, have provided a healthy, risk-free life to many individuals
[3]. Figure 18.2 shows a graphical representation of various NG classications and
their application in various elds. NGs nd their application in numerous elds but
this chapter concentrates on pulse monitoring in the biomedical eld. This chapter
provides a brief description of the working mechanism of NGs followed by param-
eters to be considered while designing an NG. An overview of PENG and TENG-
based pulse sensors for health monitoring has also been discussed.
18.2 WORKING AND FUNDAMENTALS MECHANISM OF NGS
Maxwell’s displacement current theory is the basis of NGs. The output characteris-
tics from which the output current and output voltage equations are derived are based
on Maxwell’s equation. The three main NG classications are explained below.
18.2.1 piezoeleCtriC nanogenerator
The type of energy harvesters that converts the external kinetic energy to electrical
energy by means of nanostructured piezoelectric materials is called a PENG. PENG is
fabricated using materials having the piezoelectric effect. The phenomenon of induc-
ing electric potential as a result of generated electric dipole movement due to the stress
is called the piezoelectric effect. In addition to the material, stretchable substrates and
FIGURE 18.1 Nanogenerator technology.
317Sensors for Human Pulse Measurement
connecting electrodes are also used. With respect to biomedical applications, the choice
of biocompatible piezoelectric materials, device design, and encapsulation are of pri-
mary concern. The PENG has three different geometrical congurations: (i) vertical
nanowire integrated nanogenerators (VING), (ii) lateral nanowire integrated nanogen-
erators (LING), and (iii) nanocomposite electrical generators (NEG).
18.2.2 triBoeleCtriC nanogenerator
The type of energy harvesters that converts the external mechanical energy into
electrical energy by combining contact electrication with electrostatic induc-
tion is called a TENG. The charge transfer occurs between the two thin organic/
inorganic lms with opposite tribo-polarity in the inside circuit and electron ow
between the electrodes attached to the lms to balance the potential that occurs
outside the circuit. The triboelectric effect can be observed in day-to-day life for
mechanical energy collection. The simple and miniature design, exibility and
portability, cost-effectiveness, higher sensitivity, accuracy, and material availabil-
ity are the promising points of TENG-based designs. There are four different
modes of operation for TENG: (i) vertical contact – separation (CS) mode, (ii)
lateral sliding (LS) mode, (iii) single electrode (SE) mode, and (iv) freestanding
triboelectric – layer (FT) mode.
FIGURE 18.2 Classication and major applications of nanogenerators.
318 Green Engineering and Technology
18.2.3 pyroeleCtriC nanogenerator
The type of energy harvesters that converts the external thermal energy to electrical
energy by means of nanostructured pyroelectric materials is called a PYNG. The
heat energy wasted is harvested by this technique for conversion to electric energy.
The working principle includes primary pyroelectric effect (charge developed under
strain-free conditions) and secondary pyroelectric effect (charge developed under
strain conditions). This type of NGs nd their application in elds where time-depen-
dent temperature variation occurs such as in active sensors. The output voltage of the
PYNG will be high but with a very less output current.
18.3 CHOICE OF MATERIALS
Materials used for constructing NGs are the primary concern in the design. The
materials should be chosen such that they should have the ability to produce the NG
effect. The dynamic charge transfer ability based on capacitance characteristics,
NG efciency in terms of surface charge density, work function of energy required
for electron movement, and so on are also to be considered while nalizing the
materials [4].
While choosing materials for PENGs, usually materials with wurtzite struc-
tures and perovskite structures are considered with the advantage of simplicity
and cost-effectiveness in fabrication. ZnO, CdS, and GaN are examples of wurtz-
ite structure-based materials. Several experiments with different techniques were
carried out to improve the piezoelectric effect on nanowires, thus developing
new wurtzite structure-based piezoelectric materials like p-type ZnO nanowires.
The p-type structures developed more output signal than that of n-type wurtzite
structures. The perovskite structures displayed more piezoelectric effect com-
pared to wurtzite structures. Barium titanate (BaTiO3) nanowire is an example of
a perovskite structure, which has 16 times more output signal than a ZnO nanow-
ire. The other materials include PVDF, ultra-long potassium niobate (KNbO3)
nanobers, and lead magnesium niobate-lead titanate (PMN-PT). PMN-PT was
further improved to a single-crystal PMN-PT nanobelt to obtain a higher piezo-
electric constant [5].
Almost all materials such as metal, polymer, wood, and silk can be used to
demonstrate the triboelectric effect. TENG has a wide material choice compared to
PENG. Electron gaining or losing ability determines the material charge, thereby
determining the polarity. Material that gives up electrons is assigned positive
polarity and that which gains electrons is given negative polarity. Based on this
principle, a list of materials called triboelectric series are made assigning positive
and negative charges with neutral materials in the middle. The position of materi-
als in the series may vary slightly depending on the environmental and surface
conditions. To get a better triboelectric effect, the materials will be chosen such
that more positively charged materials will be combined with the most negative
material. Surface morphology and functionalization are also considered to improve
the triboelectric effect [4].
319Sensors for Human Pulse Measurement
18.4 APPLICATIONS OF NGS IN PULSE MEASUREMENT
Mutual and immediate communication of physiological signals has been established
through the development of real-time biomedical monitoring systems. This develop-
ment has brought incredible advancement in the medical eld, which has aided auto-
matic medical analysis. Wearable self-powered devices based on the NG approach
are capable of detecting body signals with greater sensitivity and accuracy. Pulse
sensors are one such devices capable of examining and monitoring the circulatory
system activities as the pulse is very closely related to heart. The essential signals of
the body such as respiration rate, heartbeat, and blood pressure are to be monitored
from time to time, and the absence or variation from normal signals may lead to a
life-threatening situation or mortality [6]. Many sensors with the NG approach have
been introduced to monitor the cardiovascular conditions based on the human pulses.
Here we have classied the sensors based on two types of NGs: PENG and TENG.
18.4.1 peng-BaSed SenSorS
Self-powered sensors have become a promising category to resolve the issue of power
consumption in wearable healthcare devices. The vulnerability problem and abra-
sion resistance are overcome in piezoelectric sensors. The conversion of mechani-
cal deformations to electrical energy in exible energy harvesters to improve the
efciency of output current is an important property of the piezoelectric effect.
The piezoelectric capability mainly depends on the piezoelectric charge coefcient
of the material utilized in fabrication, and it should be high for the desirable effect.
This section deals with some of the existing piezoelectric-based pulse sensors used
for health monitoring [7].
J. McLaughlin et al. in 2003 introduced a reliable non-invasive technique for mea-
surement of pulse wave velocity (PWV) from the human peripheral artery (arterial
pulse wave velocity (APWV)) using a piezoelectric pressure sensor by an ultrasound
Doppler. The reproducible results used an analysis program where measured data are
ltered for calculating the APWV. Different techniques were used for analyzing the
wave velocity, which includes peak-to-peak detection (PPAPWV), foot-to-foot detec-
tion (FFAPWV), cross-correlation PWV, and apparent arterial pulse wave velocity
(AAPWV). The mean value of all these was considered as the true value of PWV. The
pulses in two different locations are measured simultaneously in PWV measurement
using two piezoelectric sensors, which produced an output voltage when mechanical
deformation occurs at the output contacts. The stress/strain (piezoelectric strain con-
stant = 23 × 10−12mV−1) is converted to proportional electrical energy (piezoelectric
voltage constant = 216 × 10 −3mN−1) using piezoelectric materials, which has a wide
range of frequency ranging from 0.001 to 109 Hz. In addition, such materials have low
acoustical impedance and less moisture resistance. They are exible, lightweight,
and thin but mechanically strong. The experimentation was validated by human tri-
als with different age groups. Here PVDF is made electrically conductive by deposit-
ing a nickel-copper alloy on either sides of PVDF. The proposed instrument nds its
clinical application when it converts the PWV (6–15 m s−1) to the value of peripheral
320 Green Engineering and Technology
artery wall elasticity. This technique was found to be benecial for peripheral arte-
rial disease patients and pre- and post-surgery/ treatment patients [8].
GT. Hwang and co-workers in 2014 introduced a self-powered, exible, and highly
efcient articial pacemaker made of single-crystalline rhombohedral piezoelectric
1.7 cm × 1.7 cm thin lm of 0.72 PMN-0.28 PT onto an ultraviolet light-cured polyure-
thane-coated PET substrate (110 μm). The Cr/Au lm deposited on the PMN-PT plate
acts as a bottom electrode and is attached to the Si wafer. Cr/Au (10 nm/100 nm) was
deposited as the top electrode. Deposition was done using a DC sputtering technique
on an 8.4 μm PMN-PT plate as shown in Figure 18.3. The SU-8 (Microchem) layer acts
as a protective layer. This thin lm converts the biomechanical motion to electrical
energy with maximum values of current and voltage to be 0.223 mA and 8.2 V respec-
tively. A stress-controlled exfoliating method is performed to transfer the PMN-PT
thin lm onto a exible substrate. This method utilizes the inherent stress available
in a 20 μm nickel lm. The real-time simulation was done on the cardiac muscles of a
live rat. The proposed device can be used as an energy source for recharging batteries.
The work was extended by performing 3D stacking of piezoelectric lms onto a single
PET substrate and the porcine organ movement is harvested [9].
In 2015, A. Bongrain and others reported two different versions of an ultrathin
AlN piezoelectric sensor, which is capable of measuring the micro-deformations.
This sensor nds its application in the measurement of ECG-correlated cardiac
pulse wave signal, thus predicting cardiovascular diseases from the wave shape. The
CMOS companionable hygienic processes involved (i) piezoelectric AlN deposition
FIGURE 18.3 Piezoelectric exible PMN-PT sensor. (a) Graphical representation of the
fabrication process and its biomedical application. (b) Digital images of a power genera-
tion sensor in its original, bending, and release positions. (Reproduced with permission [9].
Copyright John Wiley & Sons.)
321Sensors for Human Pulse Measurement
on the rst electrode (200 nm) as an aluminum electrode by DC magnetron sputter-
ing (rst version) and (ii) piezoelectric AlN deposition on the rst
electrode as platinum by lift-off (second version). The second electrode (200 nm)
in both versions is aluminum. The second version was found to be more effective in
exhibiting piezoelectric properties, though the production yield was higher in the rst
version. A consistent biocompatible parylene layer (<10 μm) encloses the sensitive
part thereby increasing the sensor exibility. The piezoelectric AlN layer (1000 μm)
is sandwiched better two transducer electrode layers and is deposited on silicon oxide.
The silicon oxide is on a silicon wafer (500 nm). The deposition is done by selective
backside etching using deep reactive ion etching. A mechanically set up transducer
induces membrane deformation due to the uidic pressure variation with high sen-
sitivity in pC/mmHg. The annular structures showed better sensitivity. The selected
materials aided the CMOS-based manufacturing process, hence facilitating industri-
alization and single-chip integration. The AlN dielectric permittivity is found to be
9.1 with a dielectric value of 8.5. The deformation area and magnitude are smaller on
the wrist and clavicular (2 mm diameter) compared to carotid (5–10 mm diameter). A
delay (160 ms) is present between the ECG signal and piezo signal as a result of the
time taken for the blood to travel from the heart to the artery and this delay is used
as a reference in PWV calculation [10].
In 2017, D. A. Park et al. demonstrated a piezoelectric arterial pulse measure-
ment device. An inorganic-based laser lift-off process was used to fabricate the self-
powered epidermal inorganic pulse sensor on an ultrathin substrate (4.8 μm). Using
the sol–gel method, a PZT thin lm is obtained and is transferred to a very thin PET
substrate. The PZT thin lm consists of gold interdigitated electrodes (200 μm) with
an inter-electrode gap (100 μm). The normal pressure can generate electrical energy
when the sensor is attached to rugged skin using a biocompatible liquid bandage
(Nexcare, 3M), which responds to pulses, and the same was analyzed through nite
element analysis. A sensitivity of 0.018 kPa−1 for 60 ms response time under 5000
pushing cycles was delivered by the exible sensor thereby giving good mechani-
cal strength. The sensor responded to both lower (0.2–5.0 Hz) and higher frequency
sound waves (240 Hz). In this proposed system, the output voltage (0.3–1.85 V) corre-
sponding to the arterial pressure was capable of operating the LED and speaker mod-
ule of the signal processing unit. By using MCU and Bluetooth transmitter, the pulse
signal is transmitted to the smartphone wirelessly. The sensor was attached on the
epidermis of a human being for real-time measurement of the trachea movements,
radial/carotid artery pulse movements, and respiratory activities thereby providing a
personal health status. An average Vpp of 65 and 81.5 mV is generated in by the radial
artery pulse before (BPM ≈ 73) and after (BPM ≈ 100) exercise respectively with a
measured average value of AIr of 0.54 and that of ΔTDVP of 0.23 s [7].
T. Katsuura et al. in 2017 reported a exible piezoelectric lm array for the mea-
surement of PWV thereby helping the early detection/treatment of hypertension and
arteriosclerosis. The piezoelectric lm produces an electric polarization on defor-
mation proportional to the applied pressure. The piezoelectric lm is made of 1
cm × 3 cm PVDF. A silver coating (28 µm) is printed on either side using serigraphy.
The PVDF lm is enclosed on both sides with a conductive layer. Silicone (1 mm
thin) is placed at the bottom of a PVDF lm for better contact with the skin. In
322 Green Engineering and Technology
addition to the PWV estimation, the pulse transit time (PTT) measurement is also
performed using the proposed single device integrated with a piezoelectric lm array
where signal processing is performed using cross-correlation to reduce the signal–
noise ratio (SNR). Measurement using the proposed algorithm resulted in a positive
correlation with a coefcient of 0.94 at low noise conditions. The position of the sen-
sor should be accurate for measuring the propagated vibration from the artery. The
initial test was performed on four individuals and an average correlation coefcient
of r = 0.6 or greater was obtained between the prototype and reference sensor [11].
18.4.2 teng-BaSed SenSorS
TENG is the most recent promising technology for energy conversion, especially in
the eld of personal and healthcare electronics [12]. The physiological and biomedi-
cal data available from the TENG output enable the self-powered active sensors to
detect the biomechanical motion [13]. The biomechanical signals are converted to
electric charge thereby dealing with a self-powered device. This section focuses on
triboelectric pulse sensors for health diagnosis.
H. Ouyang et al. in 2017 reported an ultrasensitive pulse sensor to detect cardio-
vascular diseases based on the TENG principle. This system gives a durable per-
formance of 107 cycles and converts the biomechanical pulse signal to an electrical
signal. The device is capable of direct voltage signal acquisition where the signal
is dependent on the second derivative of regular pulse signal directly. The device
consists of (i) two frictional layers acting as triboelectric layers: (a) 20 mm × 10
mm × 0.1 mm nanostructured Kapton lm and (b) nanostructured copper lm, (ii) two
electrode layers: (a) 50 nm Cu layer deposited on the reverse side of the Kapton lm
and (b) nanostructured copper lm, (iii) encapsulation layer made of PDMS (0.3 mm),
and (iv) a spacer. The device helps in the symptom diagnosis and anti-diastole of
cardiovascular diseases like coronary heart disease, atrial septal defect, arterioscle-
rosis, and arrhythmia. The sensor was used to build a wireless heart health monitor-
ing system for long-term real-time health monitoring by Bluetooth chip integration.
Using two self-powered ultrasensitive pulse sensors (SUPSs), the PWV measurement
is made possible thereby indicating the degree of arteriosclerosis. The proposed SUPS
design is cost-effective and gives better output performance (1.52 V and 5.4 nA) with
a high PSNR value (45dB) and extends its application in wearable healthcare applica-
tion using mobile phones [14].
Z. Lin and others in 2017 reported a cost-effective heart rate monitoring sensor
based on TENG. The proposed body sensor network (BSN) operating at lower fre-
quencies is a wire-less system for real-time heart rate monitoring using a non-invasive
technique by incorporating a heart-rate sensor and a power management circuit with
a signal conditioning unit. Wireless data transmission is done by Bluetooth module.
The TENG present here is in the form of a downy-multilayered structure consist-
ing of two parts: (i) two freestanding triboelectric layers made of 65 mm × 20 mm
Cu-coated PTFE and thin Cu lm adhered at both sides on an acrylic sheet and
(ii) two stationary pairs of downy structure created by an alternate arrangement of
65 mm × 30 mm PTFE lms and thin Cu lm. The device integration was completed
by an elastic rubber where the triboelectric layers were sandwiched by two downy
323Sensors for Human Pulse Measurement
structure pairs. The triboelectrication enhancement is done on PTFE thin lm
surface modication by creating a nanowire array. Copper lm plays the role of
both electrode and triboelectric layer. The inertial energy generated while walking
is harvested by the D-TENG to deliver a maximum output power of 2.28 mW for
an effective conversion efciency of 57.9% at 10 Hz frequency. This invention has a
breakthrough in wearable medical devices and personal healthcare devices, mainly
for cardiopathy patients [15].
In 2018, X. Cui et al. demonstrated a exible single electrode pulse sensor based on
the triboelectric principle. The proposed design was simple and cost-effective with a
TENG size of 40 mm × 20 mm × 0.55 mm. This sensor was proposed to measure the
pulse signal from the radial artery thereby extending its relevance in wearable electron-
ics. The sensor design includes a triboelectric layer with a trench structure made of
PDMS and this layer is stacked on the electrode layer made of ITO coated with PET.
The two reliable parameters to analyze the arterial stiffness were found to be (i) radial
artery augmentation index (AIr): ∼0.51 and (ii) peak-to-peak time difference from the
output signal (ΔTDVP): ∼0.27 s. The device output is optimized based on the trench
width. The sensor gave a current pulse of 86 min−1 pulse rate for a time period of 0.7s.
The device stability was further veried by 2500 press and release [16].
X. Chen and co-workers in 2018 demonstrated a self-powered, durable, and
stretchable sensor based on the triboelectric effect for pulse measurement by attach-
ing the device to the nger or wrist of the human body. The proposed waterproof
TENG structure with an air bag structure includes a exible electrode made of a
polyurethane (PU) nanober, a frictional layer (micro-patterned), and an encapsu-
lation layer made of PDMS. The PU nanober sensitivity is improved by adding
silver nanowires. The soft materials used in fabrication enables the device to detect
the weakest movements/vibrations in the body parts. The tensile strain due to the
mechanical deformations occurring in the body generates an electric charge. This
charge formation can even occur without any material-to-material contact due to
the air bag structure present in the device. The applied mechanical movement deter-
mines the output current. When the device is pressed, the peak-to-peak output cur-
rent (Ipp) is 257.5 nA. Similarly for stretching and bending the Ipp is 50.2 and 33.5 nA
respectively. The real-time radial artery pulse detection sensor is independent of
environmental humidity conditions. The soft, thin, and exible encapsulation layer
present is capable of detecting the bending degree. The proposed system extends its
application in sensing technology and wearable healthcare devices due to its ability
to detect even the weak vibrations [17].
Z. Liu et al. in 2019 presented a dual purpose triboelectric-based sensor for respi-
ratory and pulse monitoring as shown in Figure 18.4. The proposed microsphere-
based cost-effective ultrahigh sensitive pressure sensor is exible exhibiting 150
mVPa−1 sensitivity for a weight percentage mixing ratio of 1%. An expandable micro-
spheres and PDMS mixture was used for sensor fabrication. Thermally expandable
microspheres were spin coated on a at PDMS substrate, which upon heating forms a
microstructure on PDMS. This structure was spin coated on uorinated ethylene pro-
pylene (FEP), which acts as a triboelectric layer. Copper lm (100 nm) sputtered on
the bottom of 50-μm FEP acts as an electrode. The sensitivity of the sensor showed
a linear relationship with the microsphere mixing ratio adjustment and thereby
324 Green Engineering and Technology
enabling the sensor to respond to very weak human body movements. This nds its
application in health status monitoring while attached to human body parts such as
chest or wrist, thereby monitoring respiration and pulse, respectively. A 8 × 8 mm2
sensor when operated as a pulse sensor measured a pulse rate of 100 beats per min-
ute. The sensor was placed on the radial artery for measurement [18].
18.5 CONCLUSION
In summary, the use of biomechanical energy available in human bodies can help in
the fabrication of a self-powered healthcare device, thereby promoting the introduc-
tion of a long-term wearable medical device. Different methods and devices are used
to harvest the biomechanical energy. Various NG-based approaches have been uti-
lized in biomedical elds [19]. Here, this chapter focuses on the existing pulse moni-
toring sensors and devices based on the NG approach. Some of the piezoelectric and
triboelectric sensors for human pulse measurement have been discussed concentrat-
ing on the fabrication process and its corresponding output. A wearable NG-based
pulse sensor provides a good electrical stimulation output and hence deals better with
the individual health status.
The simplicity of design and miniaturization, cost-effectiveness, low power con-
sumption, exibility, and durability are the highlights of the NG-based sensors. The
choice of the encapsulation layer should be such that the device should be protected
and corrosion-resistant. In addition to the device integration, output performance opti-
mization and power management are important factors to be considered during the
design. This trending technology can bring out a variety of commercialized products
by replacing battery-based products. On future expansion, NG-based sensors/devices
FIGURE 18.4 Triboelectric pressure sensor. (a) Graphical representation of the fabrication
process and structure. (b) Microsphere-based sensor output voltage having a weight percent-
age mixing ratio for pressure ranging from 5 Pa to 1 kPa. (c) Output voltage of the pulse rate
monitoring test. (Reproduced with permission [18]. Copyright Elsevier.)
325Sensors for Human Pulse Measurement
can be the ultimate solution for real-time health monitoring devices, thereby exerting
a major impact on the healthcare eld. NG approach utilization is in its budding stage
and more in-depth studies in this eld can lead to more signicant inventions.
CONFLICT OF INTEREST
The authors declare no conict of interest.
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327
19 Future Challenges
and Applications
in Green Technology
Kali CharanRath
GIET University
Ravindra N. Bulakhe
Korea National University of Transportation
Anuradha B. Bhalerao
K.K. Wagh Institute of Engineering Education & Research
CONTENTS
19.1 Introduction ..................................................................................................
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328
19.2 Aim, Objectives, and Goal of GTs 329
19.2.1 Few Important Objectives of GT 329
19.2.2 Aside from the above Other Extra Destinations of Green
Innovations are 329
19.2.3 Pillars of GT and Sustainability . 331
19.2.4 Sustainability 331
19.2.5 National Benets for Energy Generation 331
19.3 Purpose for GT Sustainability . 331
19.4 Innovative Applications 332
19.5 GT Advantages and Disadvantages ........ 333
19.5.1 GT Advantages 333
19.5.2 GT Disadvantages 333
19.6 Types of GT 333
19.7 Tools of GT . 334
19.8 GT Opportunities in India . 334
19.9 Technological Applications Involving Green Innovation 334
19.9.1 GT for Photovoltaic Energy Conversion Applications 334
19.9.2 Photovoltaic Effect 335
19.9.3 Working of a Photovoltaic Cell ... 335
19.9.3.1 Theory 335
19.9.3.2 Characteristics of a Solar Cell 337
328 Green Engineering and Technology
19.1 INTRODUCTION
Green creations are earth agreeable developments that regularly include energy
productivity, reusing, security and well-being concerns, and sustainable assets,
and the sky is the limit from there. The world has a xed measure of normal
assets, some of which are as of now exhausted or destroyed.1,2 Green designing is
the plan, commercialization, and utilization of cycles and items that limit contam-
ination, advance supportability, and ensure human well-being without relinquish-
ing nancial feasibility and productivity. Innovation has inuenced the general
public and its environmental factors from multiple points of view and assisted
with growing further developed economies including the present worldwide econ-
omy.3,4 Innovation is a logical information of creation, application, and utilization
of specied methods through interrelation with human life, people, and the envi-
ronment, drawing upon such subjects as designing, applied science, unadulterated
science, and modern expressions. Two unmistakable classes of green innovation
are (i) monetarily feasible green technology (GT) (hydrolytic energy, geothermal
energy, thermal energy storage, solar energy, biomass, cogeneration, clean coal
technology, energy efciency, and so forth) and (ii) non-commercially viable GT
(under the additional turn of events and exploration).
Green innovation items will be things that consider ecological mindfulness, their
plan, and use. Green innovation items are expected to lessen squander, cut contam-
ination, and even reduce non-renewable energy source use. A portion of the sig-
nicant sorts of green innovation items incorporates energy creation items, green
synthetics, maintainable or recyclable items, and innovation that suddenly spike in
demand for elective energy. Items that help make elective energy, for example, sun-
oriented boards and warm warming circles, are probably the most signicant green
innovation items utilized in regular daily existence.
In this paper, we propose how the advancements can be caused reasonably by
including a green segment so that they too can maintain a strategic distance from
ecological corruption and change over into green innovations to give a spotless
climate to people in the future. This article likewise examines the chances and
difculties for green innovation for farming, green innovation for consumable
water, green innovation for sustainable power, green innovation for structures,
green innovation for airplane and space investigation, green innovation for train-
ing, green innovation for food and preparing, and green innovation for well-being
and medication in the 21st century.
19.9.3.3 Applications of Photovoltaic Cells .....................................
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337
19.9.3.4 Advantages of PV Electricity 338
19.9.3.5 Applications of a Solar Cell 338
19.10 Conclusions 339
Acknowledgment 340
References 340
329Green Technology: Future Perspective
19.2 AIM, OBJECTIVES, AND GOAL OF GTs
Innovation and technology are characterized as a lot of cycles for making, adjust-
ing, utilizing, and knowing about apparatuses, machines, strategies, specialties,
frameworks, and techniques for sorting out them so as to take care of an issue,
improving a prior answer for an issue, accomplishing an objective, taking care of
an applied information/yield connection, or play out a particular capacity.5–7. GT
is an ecological mending innovation that decreases natural harms made by the
items and advances for people groups’ accommodations. Green innovations do
not uphold any sort of ecological debasement. Green innovations at times addi-
tionally allude to cleaner advances or natural advances. The ve main qualities of
green innovations are (i) high prociency energy and asset use, (ii) requirement of
minimal effort, (iii) not creating auxiliary poisons, (iv) utilization of sustainable
power as well as materials, and (v) being advantageous to human well-being and
biological systems.
19.2.1 feW important oBjeCtiveS of gt
Becoming environmentally viable can just assist us with emerging from the current
predicament. Green innovation, an earth cordial innovation, is created and utilized
in a manner that ensures the climate and preserves characteristic assets.8,9 Some sig-
nicant goals are as follows:
a. To lessen the pace of improvement of energy utilization along with the
improvement of the economy.
b. To give condence for the development of the GT industry and promoting
its commitment to the public economy.
c. To increase the application with respect to advancement in green innovation.
d. To assure practical turn of events and save the climate for people in the
future.
e. To increase public mindfulness and training on green innovation and ener-
gize its far and wide utilization.
19.2.2 aSide from tHe aBove otHer extra deStinationS
of green innovationS are
a. Pollution anticipation
b. Waste decrease
c. Materials management
d. To produce enhancements
e. Cost successful execution
f. Ergonomic afrmation
g. To diminish well-being dangers
330 Green Engineering and Technology
The objective of green innovation is to secure the climate and at times, to try and
x past harm done to the climate. Instances of green innovation include the inno-
vation foundation used to reuse squander, purge water, create clean energy, and
moderate common assets.
Green technology is the term of eco-friendly umbrella that portrays the utilization
of innovation and science to make items and administrations that are earth agreeable.
GT is identied with cleantech, which explicitly alludes to items or administrations
that improve operational execution while likewise lessening costs, energy utilization,
waste, or negative consequences for the climate.
TABLE 19.1
The Goals of Green Innovations in Certain Fields of Society10 –12
Sr. No. Area Objectives of Green Technologies
1. Consumer products To create an assortment of new-age purchaser items
without results and without corrupting climate in
any creation, bundling, and in real use by buyers.
2. Health Utilization of green innovation and green cycles in
all well-beings and clinical administrations.
3. Construction To construct natural well disposed, energy-effective,
brilliant structures.
4. Potable water To huge scope channel utilized water and ocean
water through green cycles without natural
corruption.
5. Agriculture To evade natural corruption in farming cycles.
6. Food processing To wipe out harmful substances from food and to
keep away from the green gas outow and
ecological debasement in all food bundling
measures.
7. Sustainable energy To create advances for reaping potential
characteristic fuel sources to produce expected
energy to human progress without corrupting
climate.
8. Automobiles To deliver energy effective, zero discharge
vehicles utilizing environmentally friendly
power measures.
9. Industrial To create modern cycles, which are natural
automation benevolent, no green gas outow, recyclable
side-effects utilizing efcient power energy.
10. Computer and To create and use ecological amicable, recyclable
Information electronic and PC parts that utilize environmentally
Communication friendly power and procient execution.
11. Aircraft and Space Utilization of efcient power energy and green
travel materials and ecological inviting cycles in air and
space travel.
12. Education/ Utilization of green innovation in all training
academics administrations.
331Green Technology: Future Perspective
19.2.3 pillarS of gt and SuStainaBility
The term ‘innovation’ incorporates specialized strategies, abilities, measures, meth-
ods, instruments, crude materials, and so on and can be inserted in machines, PCs,
gadgets, and manufacturing plants that can be worked by people. To be straightfor-
ward, one can say that the innovation is utilized to allude to the assortment of proce-
dures, aptitudes, strategies, and cycles utilized in the creation of good or benets or
accomplishing the logical examination.13
19.2.4 SuStainaBility
The green innovation strategy to give guidance and inspiration to ceaselessly appre-
ciate great quality and a solid climate ought to be found on four columns: energy,
environment, economy, and social.
Energy: Seek to accomplish energy freedom and advance productive usage.
Climate: Conserve and limit the effect on the climate (utilization of assets, mate-
rials, and squanders reusing the board, hazards, pace of progress, and normal and
socia l scene).
Economy: Enhance the public monetary improvement using innovation (nan-
cial development, prociency, seriousness, adaptability, security, creation/utilization,
work, and worldwide exchange).
Social: Improve the government assistance, equivalent chance, social union,
global solidarity, and upkeep of human resources.
19.2.5 national BenefitS for energy generation
The green innovation strategy gives guidance and inspiration to ceaselessly appreci-
ate great quality and a solid climate ought to be found on four columns:
a. Energy: Seek to achieve energy autonomy and advance effective usage.
b. Environment: Preserve and limit the effect on the climate.
c. Economy: Improve the public nancial advancement using innovation.
d. Social: Improve personal satisfaction for all.
19.3 P URPOSE FOR GT SUSTAINABILITY
The limit with respect to the information age is unmistakably prevalent in cutting-
edge industrialized nations with fantastic private and public examination colleges,
research facilities, and organizations that draw in the best homegrown and unfamiliar
ability. Even though the revelation of new innovations is critical for information-based
seriousness, to date, green advancements have not released a self-supporting and cost-
lessening specialized advancement.
GT is a climate amicable innovation and is improved to use the climate in a mod-
erate way to ensure characteristic assets.14 The noteworthiness of green innovation
cannot be disregarded. Some green innovation thoughts have been actualized and
some others are in measure for usage (Table 19.2).
332 Green Engineering and Technology
19.4 INNOVATIVE APPLICATIONS
Green advancements or eco-developments are less naturally unsafe than accessible
choices. Progressed industrialized nations have produced and received green devel-
opments widely. Progress economies slack here because of various obstructions to
the assimilation and dispersion of such developments.15 Such obstructions can be
defeated with the guide of lucid approaches that help in eco-advancement. Some
innovative applications of GT are as follows:
a. Bioreactors
b. Anaerobic digestion
c. Wind turbine
d. Alternative fuel vehicles
e. Hybrid electric vehicles
f. Unleaded gasoline
g. Solar energy
h. Small-scale rural electrical energy system
i. Biodegradable materials
j. Green insulation
k. Low-energy house and zero-energy building design
l. Wastewater treatment
m. Generation of energy from waves
n. Vertical gardens and farms
TABLE 19.2
List of Green Innovation Applications in Different Fields
Notwithstanding
Setbacks, Pushing
Ahead in Clean
Innovation
Molten Salt
Storage
Lithium-Ion
Batteries Green Concrete Biochar
Recycling e-waste Solar tower Fuel cells
Rooftop
wind power
Green construction
materials
EcoATM
Algae biofuel Custom
biofuels
Tidal power Modular nuclear
power
Volvo warning
helmet
Algae food Electric cars Tidal power Articial
photosynthesis
Biolamps
Thin-lm solar Smart
meters
Green IT Waste to energy Solar roadway
Radical eco-activism Rehome a
mutt
Hypermilling Air purifying
roof tiles
Ocean thermal
energy
conversion
333Green Technology: Future Perspective
19.5 GT ADVANTAGES AND DISADVANTAGES
Energy prociency and request side innovations are regularly seen as attractive
because of their capability to diminish emanations while likewise sparing expenses.
Request side innovations, nonetheless, have seldom been sent at their maximum
capacity in any event, when cost reserve funds are normal.
19.5.1 gt advantageS
a. Does not transmit any matter that is unsafe into the air.
b. Will bring monetary advantages.
c. Requires less support.
d. Renewable, implying that it will never run out.
e. Can moderate the impacts of an unnatural weather change by decreasing
CO2 emissions.
19.5.2 gt diSadvantageS
a. High executing cost.
b. Lack of data.
c. No known elective compound or crude material information sources.
d. No known elective cycle innovation.
e. Improbability about implementation impacts.
f. Lack of person’s skills.
19.6 typeS of gt
Green tech alludes to a kind of innovation that is considered ecologically neighborly
dependent on its creation cycle or its exible chain.16 Green tech – which is a trunca-
tion of “green innovation,” – can likewise suggest for cleaning energy creation and
is the utilization of elective lls and advances that are less unsafe to the climate than
petroleum derivatives. Various types of innovative GT are as follows:
a. Energy conservation
b. Solar power
c. Wind turbine
d. Geothermal power
e. Smart power bars
f. Water purication
g. Air purication
h. Sewage treatment
i. Solid waste management
j. Recycling
k. Environmental remediation
l. Environmental forecasting
334 Green Engineering and Technology
19.7 TOOLS OF GT
The following criteria get satised with green innovation products, equipment, or
systems.
a. It minimizes the humiliation of the atmosphere;
b. Zero or low greenhouse gas (GHG) emission is secure for application and
can improve the environment for all forms of life;
c. It saves the utilization of energy and characteristic assets; and
d. It advances the utilization of inexhaustible assets.
National GT policy tools:
a. Conduct GT development programs for environment control.
b. Support and enhance GT R&D work.
c. Increase the qualied and skilled resources for the development of GT
industries.
d. Promote public awareness programs toward GT development.
e. Strengthen the institutional framework for better innovation in the GT area.
19.8 GT OPPORTUNITIES IN INDIA
GT produces the best opportunities in India growing faster and powerful to overcome
the traditional energy sources. It reduced the pollution and increased the growth rate.
Currently, India’s rank in the utilization of clean and green technology is fth in the
world. The GT industry in India may increase the market from 25 billion dollars to
40% more in 2030 as given in an economist report. Rooftop solar in India is one
of the leading ways to complete the demand for energy and increase the market for
green energy. It will increase the GDP of the country and reduce the much pollution
in the environment. One of the ways to apply GT is modernizing the electricity grid.
Increasing the production of solar panels in India will boost the market and employ-
ment too. GT creates eco-friendly products and services with social entrepreneurs.
Also, it increases the cash ow in the market and helps to reduce poverty alleviation.
It also boosts the regional and international cooperation.
19.9 TECHNOLOGICAL APPLICATIONS INVOLVING
GREEN INNOVATION
19.9.1 gt for pHotovoltaiC energy ConverSion appliCationS
We go over different types of energy in our everyday schedule. It is in this manner a
signicant affecting component in our life. Different types of energy, for example,
heat, sound, light, power, and so forth, assume a signicant part in the advance-
ment of the individual. In the prior ages, energy needs were met by utilizing regular
assets like wood, daylight, re, and so on. In any case, lately, these requirements
are developing at a disturbing rate because of expansion in the populace, improved
335Green Technology: Future Perspective
ways of life, and advancements in a mechanical, logical, and innovative eld.
Thus, the fuel sources like oil, coal, gas, power, atomic energy, and so forth are
in blast to satisfy the interest of energy. Alongside energy interest, energy issues
like exhausting oil assets and the risk of boundless utilization of ordinary energies
drove energy as a difcult issue. The economic growth and prosperity of developed
nations are entirely based on the availability of cheap and pollution-free energy
sources. In light of the changing worldwide scene, signicant world forces and
established researchers are focusing on the improvement and renement of more
effective sustainable power sources.17
Among different sustainable power sources, the Sun-based force is the most huge,
limitless, contamination free, and perpetual fuel source. Direct change of sun-based
energy into electrical energy utilizing photovoltaic rule is the need of the present
world. The fundamental necessity for the manufacture of a Sun-powered cell is that
the material ought to have a band hole coordinating with the sunlight-based range
and high portability of charge carriers.18
19.9.2 pHotovoltaiC effeCt
The photovoltaic impact was found by a French researcher, Edmund Becquerel, in
1839. Becquerel noticed the creation of electromotive power when an electrolytic
uid arrangement was illuminated with light. He recognized a photovoltage across
AgCl and platinum metal anode somewhere down in an electrolyte.19,20 A time span
of a century has been crossed to raise PV impact as an innovation.
When a p–n junction cell absorbs solar radiation, it develops an electromotive
force (e.m.f.), which is called as photo e.m.f. and the effect is called as the photo-
voltaic (PV) effect.21,22 The following basic steps must be followed together concur-
rently in a PV cell to achieve the PV effect. Therefore, PV cells have to perform
these elementary functions23:
1. Absorption of photons utilizing a reasonable material.
2. Creation of electron-opening sets (e-h sets) by breaking connections
between the particles.
3. Separat ion of oppositely charged free transpor ters before their recombination.
4. Collection of photogenerated charge transporters through electrical con-
tacts and their entry through an outer circuit to make valuable electric ow.
19.9.3 Working of a pHotovoltaiC Cell
19.9.3.1 Theory
A PV effect is experienced in the semiconducting device called a photovoltaic cell
(solar cell). The structure of a solar cell is shown in Figure 19.1. It is made up of
semiconductor materials, e.g. Si, which has a band gap of 1.1eV with a p–n junc-
tion embedded in clear plastic. When sunlight is incident on the solar cell with a
photon energy larger than the energy gap of solar cell material, valence band elec-
trons jump into the conduction band to create an electron–hole pair. Electrons in an
336 Green Engineering and Technology
electron–hole pair are drifted toward the N-side of the cell, while holes are drifted
toward the P layer. This develops a potential across the junction. However, due to the
recombination effect, this process is very weak. To improve this effect, a diode-like
structure is led at the junction such that it supports the minority charge carriers.24,25
For solar cell application, a junction is a shallow region with immobile ions to
maintain a space-charge electric eld. When this junction is illuminated by light with
a photon of sufcient energy, it creates an electron–hole pair. At the junction region,
a space charge eld separates the electron–hole pair before recombination. The hole
diffuses into the P region and the electron diffuses into the N region. By connecting
two regions by a wire, current ows through the external circuit from the P region to
the N region. This is called the photovoltaic effect.21 Figure 19.2 shows the working
of solar cells based on the energy band structure.
FIGURE 19.1 Structure of a p–n junction solar cell.
FIGURE 19.2 Working of a solar cell based on the energy band structure.
337Green Technology: Future Perspective
When the approaching light is caught up in the valence band of the semiconductor,
it energizes valence electrons in the conduction band followed by an electrochemical
interfacial response causing the current to pass through the cell.26
19.9.3.2 C haracteristics of a Solar Cell
Short-circuit current (Isc): A denite non-zero current is obtained for a zero applied
voltage, which is proportional to the light intensity.
Open-circuit voltage (Voc): Under open-circuit conditions, no current ows and
the e.m.f is across the open terminals of the junction.
Fill factor: The ratio of maximum useful power to the ideal power is called the
ll factor. This factor shows squared nature of the I–V characteristics. A higher factor
indicates a large energy conversion tendency of the solar cell.27
VI
FF⋅=mm
⋅
VI
oc ⋅
sc
19.9.3.3 Applications of Photovoltaic Cells
The Sun discharges colossal measure of brilliant energy into the nearby plan-
etary group. Estimation of sunlight-based consistent is 1353 Wm−2. At the point
when sun-based radiation goes through the earth, air gets constricted. The level
of weakening relies upon different boundaries, among which separation went by
the daylight through the environment is generally signicant. The proportion of
a genuine way length of the daylight to its negligible separation (when sun is at
peak) is known as the optical air mass (AM).28 AM1.5 is alluded as the standard
phantom circulation. It compares to a point of 48.20 between the Sun’s position
and the peak. At the irradiance of AM1.5, the power thickness is 827 Wm−2, com-
municated as pinnacle watts. Mankind has not yet successfully used pinnacle
watts.29 To successfully use these pinnacle watts, two direct courses are acces-
sible as follows:
(i) One strategy is to straightforwardly utilize retained warmth energy by a pho-
tothermal impact. (ii) The subsequent technique is to change over photon energy into
electrical energy by PV impact (PV electricity).30
FIGURE 19.3 Circuit diagram for the study of solar cell characteristics.
338 Green Engineering and Technology
19.9.3.4 A dvantages of PV Electricity
1. Solar energy is abundantly available in equatorial countries of South Africa
and Asia with no fuel cost or fuel supply problems.
2. Solar energy conversion equipment needs least maintenance and least oper-
ation cost.
3. Solar cell reliably converts the energy of solar radiation into electrical energy.31
19.9.3.5 Applications of a Solar Cell
19.9.3.5.1 Rural Electrication
In remote areas, where no transportation is available, PV power is the only electricity
source available in visibility. Worldwide 80% people yet are not able to connect with
grid electricity. As per estimation of the UN, about two million villages from the
equator region have no source of electricity or fossil fuel. In these areas, maintenance
of an electricity transmission system is expensive and difcult due to long distances
along with comparatively small load. Fuel supply for diesel or petrol is also difcult
due to unavailability of transportation facilities in such regions.32
Lack of provision of electricity to these rural or remote places affects worldwide
social and economical developments. By providing electricity to remote area vil-
lages, it will be easy to provide health care facilities, agricultural facilities with better
irrigation, and water supply systems. In fact, it is estimated that a large number of
people are living in a climate suitable for utilization of PV electricity.
19.9.3.5.2 Water Pumping
In various regions due to load shading of grid electricity, farmers are in big trouble to
provide water to the farms in proper timing. PV-powered pumps for water supply and
irrigation are effectively operating throughout the world. They provide steady elec-
tricity and water in daytime. Low solar insolation period is utilized for water storage
purposes. In the case of unavailability of water storage tank, crops with v ariable
water requirements are yielded so as to supply water directly.
19.9.3.5.3 Domestic Supply
Independent PV homegrown exible frameworks are regularly experienced in
non-industrial nations and distant areas in industrialized nations. The size range
uctuates from 50 Wp to 5 kWp depending on the current way of life. Ordinarily
bigger frameworks are utilized in far off areas or island networks of created
nations where family machines incorporate various electricity gadgets like refrig-
erators, water pumps, TVs, and home lightening systems. Hence, for domestic
electricity supply in developing regions, systems with 5 kWp are used for village
electricity supply and systems with 200 Wp are developed for individual house
electricity supply.33
19.9.3.5.4 Health Care
The world is presently suffering with the pandemic of COVID-19 virus. When vac-
cines will start working on this virus, then to provide immunization in rural areas,
339Green Technology: Future Perspective
vaccines should be kept in a specic range of temperature. For this purpose, provi-
sion of refrigeration facility is called as vaccine cold chain. Vaccine cold chain is also
required for a wide range of vaccination programs against common diseases. These
programs will only be successful only if remote or rural areas are provided with PV
electricity for refrigeration.
19.9.3.5.5 Ocean Navigation Aids
Deep ocean regions cannot be supplied with grid electricity. Hence, to obtain power
for navigation aids, PV systems are required. Numerous beacons and most oats are
presently controlled by sun-oriented cells.
19.9.3.5.6 Telecommunication Systems
Telecommunication systems are based on communication satellites. All satellites in
space have solar energy as the only available source of energy. Hence, their life
depends on the life of PV electricity systems. In space satellite, radio wave transmit-
ter and receiver and telephone boxes are powered by PV systems.
19.9.3.5.7 Electric Power Generation in Space
Satellite Vanguard-I initially opted power generation techniques using solar cell in
1958. Since then, with tremendous evolutions in power generation from few Watts
to hundreds of kilowatts, PV-based power has become the prime choice for satel-
lite power. With various evolutions, the solar arrays are nowadays becoming more
reliable due to their sustainability under critical space conditions by maintaining
increasing trend of the power-to-weight ratio.27
19.9.3.5.8 Lighting
One of the prime applications of solar cells is the PV-powered lightening system with
thousands of installation units present worldwide. These units provide power to home
as well as community lightening systems. Community systems include school lighting,
street lighting, signal lighting, security lighting, tunnel lighting, and health lighting.34
19.10 CONCLUSIONS
Consumer demand for GT products is on the rise. GT now assumes an even greater
signicance. The GT products are being installed in the R&D phase. GT gives the
boost to maintain the environmental cleanliness level. It enhances the GDP of the
nation and decreases the rate of unemployment by giving products in various elds
of energy conversion and storage processes. Every technology has its own advan-
tages and disadvantages. GT is the only path for sustainable development as its
advantages overcome its disadvantages through an eco-friendly approach. Finally,
we can conclude that GT will produce green energy with enhancing a green employ-
ment to boost the national and international cooperation for the society. The use of
photovoltaic energy for satellite communication is the advanced application of GT,
which increases the life of satellites and hence the quality of human life.
340 Green Engineering and Technology
ACKNOWLEDGMENT
This research was supported by the Basic Science Research Program through the
National Research Foundation of Korea (NRF) funded by the Ministry of Education
[grant number 2018R1A6A1A03023788].
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343
20 Implementation and Use
of Green Computing
in Polish Companies
versus Implementation
of Features Characteristic
of Teal Organizations
Agnieszka Rzepka
Lublin University of Technology
Maria Kocot
University of Economics in Katowice
Elżbieta Jędrych
Academy of Finance and Business Vistula
CONTENTS
20.1 Introduction ..................................................................................................
..................................................
........................................................................
.....................................................................................
......................................................
.............................................................
.................................
.........................................................................
.............................................................................................................
344
20.2 Determinants of Sustainable Development 344
20.3 Green IT As a Tool for Implementing the Idea of
Sustainable Development 345
20.4 Role of Teal Management Model in Effective Implementation
and Use of Green IT 347
20.5 Green IT in Nadolny MM – Case Study 349
20.6 Relationship between the Introduction of Green IT in Nadolny MM
and the Level of Company’s Agility 352
20.7 Use of Green IT in Teal Organizations – Conclusions 353
20.8 Practical Recommendations 354
References . 355
344 Green Engineering and Technology
20.1 INTRODUCTION
Today’s companies face the challenge of operating in a turbulent and unpredictable
environment. The advent of Industry 4.0 promoted the use of digital technologies,
now considered a key feature that denes the business environment of an organiza-
tion. However, market conditions are forcing companies to look for new ways to
protect the environment and contribute to sustainable development. These goals
can only be achieved by an organization that has developed qualities found in Teal
organizations.
This chapter deals with the issue of Green Information Technology (Green IT) in
companies. It explains this phenomenon in detail and emphasizes the fact that Green
IT is synonymous with sustainable development in the production and use process.
At the same time, it is important to note that these solutions are most often intro-
duced by companies that have developed qualities of a Teal organization.
This chapter aims to analyze the phenomenon of Green IT applications in Polish
companies that have developed agility, which is a characteristic feature of Teal orga-
nizations. To illustrate the phenomenon, we used the example of Nadolny MM as
a case study. Efforts have also been made to show that today's companies manifest
different characteristics of Teal organizations (in which Green IT has become part of
their business strategy) in their activities and management.
Efforts have been made to demonstrate a statistical correlation between the pres-
ence of Green IT and the company’s level of agility (the example of Nadolny MM).
This chapter uses the research results from a project led by Prof. Dr hab. Agnieszka
Rzepka. The aim was to examine the degree to which contemporary organizations
manifest the qualities of Teal organizations in the Industry 4.0 era.
20.2 DETERMINANTS OF SUSTAINABLE DEVELOPMENT
Sustainable development is always linked to the need to implement economic,
social, and environmental standards of the highest quality, which must reect the
capacity of ecosystems (in accordance with the principle of intra- and intergen-
erational equity) (Rogall 2010). The concept of “sustainable development” was
introduced by Hans Carl von Carlowitz. Originally, the term referred to a form of
forest management in which each felled tree could be replaced by a new seedling
(Mazur-Wierzbicka 2005).
Today, the main idea of sustainable development is to maximize the net bene-
ts of economic development, while at the same time protecting and restoring the
usability and quality of natural resources over the long term (Zailani, Jeyaraman,
Vengadasan, and Premkumar 2012). In the literature, sustainability is often identi-
ed with ecological development, with the remark that it assumes that all economic
activities should be conducted in harmony with nature, and in such a way that they
do not cause irreversible changes. The conclusion is that all agriculture should be
ecologically acceptable, economically justied, and socially desirable. It is about
reconciling economic activities with respect to the natural environment (Doś 2011).
The sustainability of a company, therefore, means sustainable development in har-
mony with the requirements of environmental protection. In the course of developing
345Green Computing in Polish Companies
this activity, the needs of the present generation should be met without reducing the
chances of satisfying the needs of future generations (Doś 2011).
Ecological development can therefore be understood in the broadest sense as the
greening of all operational processes, while at the same time attempting to meet
the expectations of the individual stakeholders, i.e., the nancial expectations of the
owners and shareholders, the need for stability for employees and the need for secu-
rity (Grudzewski, Hejduk, Sankowska, and Wańtuchowicz 2010).
Turbulent market conditions place new demands on the modern company. One
of these demands is undoubtedly the implementation of the principle of sustain-
able development. A sustainable company is an economic entity characterized by
exibility, the ability to adapt to change and the ability to operate under crisis condi-
tions. These characteristics are closely related to the need for agility, which, in turn,
remains an integral part of Teal organizations (discussed later).
It should also be pointed out that the main objective of a sustainable organiza-
tion is socioeconomic development, always with respect to the environment. The
process of quantitative and qualitative change, which aims to implement economic,
social, and environmental assumptions simultaneously, can therefore be equated
with the sustainable development of a company (Rzepka 2019b). The factors that
largely determine the sustainability of a company include social responsibility, eco-
innovation, organizational culture, eco-efciency, leadership, and trust, but also the
continuity of activities and effective risk management (Brzozowski 2012).
It is important to formulate the expectations of stakeholders, owners, shareholders,
local communities, and employees. These measures should go hand in hand with the
achievement of the economic and environmental goals set (Gorczyńska 2010), which
requires a holistic view of the company and its processes and the creation of suitable
management models that can adapt exibly to the changes taking place. The sustainable
development of an organization can be seen as a specic philosophy of quantitative and
qualitative changes that are dealt with over a long period of time. It should be borne in
mind that in the short term, company managers are prepared to sacrice part of the
nancial surplus to use it to fulll current social and environmental tasks (Sudoł 2006).
The implementation of the idea of sustainable business development requires
companies to formulate suitable goals and measures, as shown in Figure 20.1.
20.3 GREEN IT AS A TOOL FOR IMPLEMENTING THE
IDEA OF SUSTAINABLE DEVELOPMENT
Turbulent market conditions make it necessary to increase the importance of respon-
sible management. This manifests itself in various aspects such as the continuous
improvement of management systems aimed at creating added value for suppliers,
customers, and shareholders, and the introduction of innovative solutions known as
Green IT or Sustainable IT (Itfocus).
IT is of great importance in reducing the negative environmental impact of busi-
ness activities. The term IT has received a large number of denitions in the litera-
ture. Most commonly, it refers to the technology used to store, transfer, and process
data, information, knowledge, and ultimately wisdom (De Sutter 2007).
346 Green Engineering and Technology
The 21st century is a time of computerization, digitization, and electronization
of ows and virtualization of management activities. The development of IT has
contributed to the fact that in the eld of management, many activities can be carried
out using electronic means of communication, in particular the Internet, without the
physical involvement of those involved in the transaction.
IT refers to technologies related to computers and software, but not to communica-
tion and network technologies. It includes IT-related devices and computers, computer
networks, and software (system and utility), and methods of handling them. In prac-
tice, the term is often abbreviated from its English name IT, therefore it is also trans-
lated into Polish as Information Technology or information techniques. (Pearce 2012).
The term also refers to the resources used by an organization to manage the
information necessary to fulll its mission. Another meaning of the term refers
FIGURE 20.1 Aims and activities of a sustainable enterprise.
347Green Computing in Polish Companies
to a collection of users, information systems, and management methods (Turban,
Leidner, McLean, and Wetherbe 2006). It can also describe an isolated part of a
computerized information system (Kisielnicki 2009). Green IT plays an impor-
tant role from the perspective of sustainable development. Green IT refers to green
information technology solutions. Broadly speaking, Green IT refers to the design,
manufacture, deployment, and use of computers, servers, subsystems, and devices
such as printers, monitors, disks, and communication and network systems in an
ecologically efcient way, while reducing or completely eliminating the impact
on the environment (http://www.raport-erp.pl/slownik-erp/364-aps-zaawansowane-
planowanie-i2 harmonogramowanie.html, 12.05.2018).
In essence, Green IT means the consideration of economic, social, and ecological
criteria in the production and use process. This can therefore be seen as an environ-
mentally friendly method of managing equipment and ICT networks. These mea-
sures aim at minimizing the energy consumption of computer equipment and using
it in an environmentally friendly way. As a result, this leads to a signicant reduction
of the carbon footprint, i.e., a decrease in greenhouse gas emissions.
IT solutions support the implementation of the concept of sustainable develop-
ment in various areas. The ecological aspect seems to be of great importance. IT
solutions facilitate the transition from an energy-intensive economy to a model
that is geared toward protecting the environment. The outcome is an increase in
energy efciency, achieved by monitoring energy consumption, through innova-
tions to reduce energy consumption in buildings, through special accounts for the
mutual settlement of energy costs between customers and suppliers, and through
an integrated approach to energy management in individual energy systems
(Raschke 2010).
As far as the social aspect is concerned, the result of the implementation of IT
solutions allows continuous communication between employees and contractors, as
well as the exchange of information and the taking of measures in real time. From
an economic point of view, it leads to an improvement in nancial and asset indica-
tors and to efcient management and analysis of data (Szkudlarek and Milczarek
2014). The introduction of environmentally friendly solutions reduces the use of haz-
ardous materials in the production of computer equipment and the introduction of
recyclable IT products, the design of energy-efcient and environmentally friendly
equipment and facilities, the improvement of energy efciency in the production life
cycle (https://www.alumniportal-deutschland.org/).
As already mentioned, the skillful use of Green IT requires the use of favorable
management methods, one of which includes the teal model.
20.4 ROLE OF TEAL MANAGEMENT MODEL IN EFFECTIVE
IMPLEMENTATION AND USE OF GREEN IT
The effective implementation and use of Green IT remains one of the key factors
for achieving a competitive advantage in the market. According to P. F. Drucker, a
company that wants to achieve this must “create a climate of innovation, introduce
systematic measurement of a company’s performance as an innovator, change its
348 Green Engineering and Technology
practices pertaining to organizational structure and introduce a system of incentives
and rewards” (Drucker 1992). Not every organization will be able to do this, as it is
determined by many factors.
The Green IT implementation process requires integrated and coordinated
actions, isolation from which is impossible to achieve market success. This is deter-
mined not only by the unique resources the company has but also by a whole range
of other factors. A company will be able to implement Green IT if it has sufcient
resources and an internal structure that can facilitate the creation of this process.
The management model of an organization also plays an important role in this
respect. Creative attitudes of employees are undoubtedly encouraged by a Teal style
of management.
In recent years, sustainable development has become an important factor in
developing competitive advantages. Its implementation is only possible, thanks to
the creativity of employees and the resulting attitude of openness to change, innova-
tion, and entrepreneurship. To achieve this, it is necessary to apply an appropriate
method of human capital management based on specic assumptions and supported
by tailor-made tools (Juchnowicz 2016). An example of such a solution is undoubt-
edly the concept of Teal organizations.
Teal organizations operate largely without organizational structures, management
hierarchy, quarterly targets, or other traditional management strategies (Rzepka
2018) and are characterized by self-management and a deeper sense of purpose.
They form independent teams by using intuition-based reasoning and decentralized
decision-making processes (Allen and Velden 2005).
As is evident from the scientic literature and practice, a Teal-like management
approach is of great benet to employees. They certainly need incentives, which are
usually offered in different forms by self-managing Teal organizations (Jeznach and
Eichelberger 2017). The adoption of Teal qualities (Rzepka 2019a) by a company
can guarantee an impressive range of benets, such as improved product and service
quality, a better organizational culture, immediate knowledge acquisition, and more
effective customer service (Herzenberg, Alic, and Wial 1998; Hopp, Tekin, and Van
Oyen 2004; Herzenberg, Alic, and Wial 1998).
The following features specied by Breu et al. indicate that employees have teal-
like workers: immediate reaction to external changes, the possibility to develop one's
own skills, rapid adaptation to new working conditions, the ability to implement
change, immediate access to information, use of mobile technologies, indifference
to where the work is done, mobile access to information, sharing of knowledge, and
technologies geared toward collaboration and work in virtual teams (Breu, Hafner,
Weber, and Novak 2002).
Changing and turbulent market conditions in the business environment and
work demands bring about the development of teal qualities among employees.
Theseinclude (Bełz and Barbasz 2014):
• the ability to quickly identify market opportunities,
• the immediate recognition of threats from the environment,
• the effective execution of tasks,
• the ongoing monitoring of the implementation of these tasks,
349Green Computing in Polish Companies
• the categorization of the situation in the context of opportunities and
threats, and
• the correct assessment of the adequacy of resources and the possibility of
obtaining them from the environment, a combination of visionary approach
and operational management, which means the dissemination of ideas and
their embedding in the activities of the company.
Y. Doz and M. Kosonen have added the following features (Doz and Kosonen 2008):
• the ability to recongure business systems,
• efcient decision-making,
• full team commitment, on high-level management teams, and
• strategic sensitivity interpreted as the acuity of perception of consciousness
and attentiveness.
When employees were faced with the need to adapt to changing organizational and
market conditions, this forced them to constantly develop their behavior. As a result,
they were qualied to use the latest IT solutions. Thanks to a teal style of manage-
ment, these employees have become a driving force for innovation by sharing their
convictions, views, and arguments to underpin their position. These employees can
work as part of a team, are multi-functional, and have expertise in negotiating and
applying advanced production strategies and technologies (Gunasekaran 1999).
Developing the capacity to respond effectively to changes in the business environ-
ment requires radical innovation-triggering measures. They lead to maximum ef-
ciency and customer satisfaction. A competent and appropriate response to external
impulses, competence, and exibility enables the efcient use of innovative practices
and tools found in companies that have developed teal qualities. The ability of a
company to create new products, services, or business processes becomes an integral
feature of Teal companies, which can make full use of the skills of their employees
(Dahmardeh and Banihashemi 2010).
20.5 GREEN IT IN NADOLNY MM – CASE STUDY
Nadolny MM is a company specializing in prepress, i.e., processes of preparing les
for a printing press, which occur before the nal printing. The processes include,
in particular, various methods of screening, color management, quality control of
projects with regard to dedicated printing technologies, electronic imposition, and
quality control of lm, printing plates, and prints. The company offers Computer-to-
Film (CTF) printing services using photographic lm and Computer-to-Plate (CTP)
using offset printing plates.
The company successfully builds inter-organizational relationships in the virtual
world. Nadolny MM cooperates with many printing houses in preparing and trans-
ferring (burning) les to lm and ctp plates. The company receives nished digital
les from partner printing houses onto an ftp server. Then Nadolny MM transfers
them to plates, which are then delivered to other offset printing houses, where the
next steps (printing and bookbinding) are performed. This cooperation with other
350 Green Engineering and Technology
printing houses in the processes connected with transferring les to plates is the
company’s leading service.
The applicant's company provides services to other printing houses, including
its business partner Agencja Poligraczna MULTIPRINT s.c., outsourcing services
for the preparation of les for printing, electronic imposition, and transfer of les to
lm in the CTF technology or offset printing plates in the CTP thermal technology.
To enable le transfer in Studio Pre-Press (exposure studio), an FTP server with
password-protected access has been set up. The Nadolny MM company acts as an
agent and uses the machinery of other printing houses by subcontracting printing
services for a fee.
The company has successfully been implementing the policy of sustainable devel-
opment. A server virtualization project launched in 2010 aimed at reducing the
maintenance costs of the server rooms and reducing primary energy consumption. It
turned out that the increase in computing power, the capacity of RAM, and the num-
ber of cores installed in the individual servers meant that the actual utilization of the
servers was no more than 15% of the hardware capacity. The gradual virtualization
allowed the development of the company’s IT systems by optimizing the resources of
the equipment available at Centrum Przetwarzania Danych (Data Processing Centre)
without the need to extend the technical infrastructure or install additional servers.
The virtualization has made it possible to limit the number of servers and guaran-
tee the possibility of a smooth increase in the allocation of resources while reducing
the costs of maintaining the infrastructure. Thanks to these measures, Nadolny MM
achieved various savings that resulted from:
• reducing the energy consumption for powering the servers and cooling and
limiting the space required for server storage,
• reducing the purchase of new IT equipment, and
• no need for a technical extension of the Data Processing Center.
The company decided to increase the temperature in the server room from 19°C to
23°C. The myth that each of the IT devices in the Data Processing Center should
operate at a temperature of 18°C–20°C was abandoned. The temperature change
resulted in signicant savings in electricity. This was made possible by increasing the
cooling power of the precision air-conditioning system installed in the room.
The company also decided to launch an integrated platform for printing, copy-
ing, scanning, and faxing. This move improved print management methods, reduced
printing costs, and increased the efciency of services. Older printers were gradu-
ally phased out, resulting in a rapid decrease in the number of device malfunctions
and crashes. This system also enables the company to efciently control the costs of
purchasing ink and toner. The company purchased high-end network multifunction
devices that offer good print quality with low toner and energy consumption. The
next step in reducing energy and material consumption was to eliminate desktop
printers, which were replaced by devices from Print System. It is possible to man-
age the printer network remotely, so that the operation of the printing devices can be
optimally adapted to the needs of users at a specic location.
351Green Computing in Polish Companies
One of the elements of the company's Green IT policy is the standard setting
of double-sided printing to optimize paper consumption. Another element is the
automatic shutdown of devices after working hours. This allowed the end-user to
receive printouts on any device located anywhere in the company. It is also worth
mentioning that the company also implements several other measures related to its
environmental policy.
The company also introduced a modern Workow software. All repeated activi-
ties performed in the company are dened in the software. The company’s IT system
allowed the automation of service provision and communication between the part-
ners. The company introduced the possibility of remotely placing new orders from
the partners and remotely controlling each stage of the order through the system
where it is possible to change order status, leave comments, and remotely conrm
orders. The company’s IT system has been adapted to the systems used by the com-
pany’s partners who have implemented a service management system themselves.
The implementation was based on a modular solution with the possibility of further
expansion with new functions in the future.
This allowed for the reduction of CO2 emissions through lower electricity con-
sumption and better use of generators and air conditioning. Administrative costs
were also reduced.
Green IT implementation has become a conscious element of the company’s busi-
ness strategy. The strategy includes an eco-oriented environmental policy, including
environmental protection in decision-making, increasing energy efciency and the
emphasis on waste reduction, recovery, and recycling. Such intentions could only be
realized by a company that has qualities found in Teal organizations, i.e., one that
follows the Teal management model.
Nadolny MM is part of a research project of Prof. PL, Ph.D. Agnieszka Rzepka
under the title “Teal organizations in the age of Industry 4.0”. The survey was con-
ducted by means of a survey consisting of eleven parts and one hundred and eight
questions distributed among managers from different countries. The survey aimed
to investigate the extent to which contemporary companies exhibit the character-
istic features of Teal organizations. It should be noted that organizational agility
remains one of these characteristic features, without which it would not be possible
to implement Green IT.
The survey has shown that the employees of Nadolny MM have the necessary space
to perform their tasks, thanks to which they can engage and inspire other employ-
ees. The company has formed an interdisciplinary team of specialists whose task is
to identify problems and improve the area of electricity consumption. The company
attaches great importance to investments in modern IT technologies. The employees
have at their disposal tools that use the latest technologies.
The survey has also shown that Nadolny MM employees can quickly adapt
to the requirements of new equipment and work on several projects at the same
time. In addition, when the situation demands it, they quickly switch from work-
ing on one project to another and easily adapt to new workows. It is also impor-
tant that the ow of information between employees remains high. There are no
hierarchical positions in the company, and the roles within the company are not
352 Green Engineering and Technology
predetermined. The company’s employees are characterized by independence,
creativity, partnership, trust, and autonomy. These characteristics can be classi-
ed as attributes of agility.
20.6 RE LATIONSHIP BETWEEN THE INTRODUCTION OF GREEN IT
IN NADOLNY MM AND THE LEVEL OF COMPANY’S AGILITY
As part of the research project of Prof. PL Ph.D. Agnieszka Rzepka, surveys were
conducted among 100 employees of the company. The survey aimed to investigate
the level of dependency between the degree of Green IT implementation and the
degree of agility in the company. It should be remembered that agility is a key char-
acteristic of Teal organizations.
To verify hypotheses on the dependency between two variables in the function,
the χ2 test for independence was used. The independence of variables was the null
hypothesis, whereas their dependence was the alternative hypothesis. In any case, the
accepted signicance level was 0.05 (i.e., p > 0.05). The following formula was used
to determine the test value:
2
k l nn−
χ
=∑∑
()
ij ij
2
n
ij
i=1j=1
in which:
k = total number of columns,
i = a single column,
l = total number of rows, and
j = a single row.
Degree of freedom = (k − 1) *(l − 1)
The χ2 test is the most important nonparametric test. Using the χ2 independence test,
one can verify the hypothesis that there is no relationship between two qualitative
or quantitative variables (Sobczyk 2006). No correlation analysis was carried out, as
the given data were not gures but merely an indication of elements having the same
non-measurable characteristics. The independence test allowed for checking whether
there was a relationship between elements with the same feature, that is, whether
there was an impact on the number of examined features.
Data
Sample – 100 employees
The presence of solutions implemented as part of Green IT
Yes – 50 employees
No (including the lack of knowledge about the existence of such a system) – 50
employees
The degree of employee agility
Very good – 21 employees, Good – 51 employees, Satisfactory – 26 employees,
and Sufcient – 2 employees.
353Green Computing in Polish Companies
The theoretical value table:
Test value: χ2 = 12. 32
At the signicance level of p < 0.05, it can be concluded that there is a relationship
between the presence of Green IT in Nadolny MM and the level of its agility.
20.7 U SE OF GREEN IT IN TEAL
ORGANIZATIONS – CONCLUSIONS
Today's businesses have come to operate in a turbulent, unpredictable market environ-
ment where sustainability policy continues to take on importance. They have been
forced to adopt strategic orientation, which would allow the company to face the
growing demands of a changing reality. Each organization must therefore adapt to
changes. These changes include the necessity to implement ‘green’ IT solutions that
aim to eliminate toxic substances from products, as well as the collection and recy-
cling of outdated or damaged equipment.
The implementation of the above is only possible if a company develops organiza-
tional agility attributes, which are an important part of the idea of Teal management.
This chapter examined the case of Nadolny MM. The company is successfully
introducing and applying the Green IT technology, such as Workow software that
enables full integration with the service management systems implemented by the
company’s partners, to integrate printing production processes performed by the
company's partners into a coherent whole.
The chapter also tries to answer the question of the reasons for the company's suc-
cess. The research conducted under the direction of Prof. Ph. D. Agnieszka Rzepka
has positively conrmed the thesis put forward in the introduction of the chapter and
proved that Nadolny MM undeniably possesses several qualities found in agile orga-
nizations that operate according to the idea of Teal management.
The research, the case study, and the presence of statistical correlations presented
above make it possible to conclude that the development of Green IT in a company is
part of the business strategy found in Teal organizations. These results are consistent
with those of other researchers.
Degree of employee agility
Presence of solutions implemented as Very good Good Satisfactory Sufcient
part of Green IT
Yes 9 21 20 0
No 11 30 6 3
Degree of employee agility
Presence of Green IT solutions Very good Good Satisfactory Sufcient
YES 10 25.5 13 1.5
NO 10 25.5 13 1.5
354 Green Engineering and Technology
It seems that Kidd (1995) was right in assuming that only those companies that
have developed agile qualities and can adapt to the conditions of the new industry 4.0
era can successfully implement innovative IT solutions (including Green IT).
Based on this knowledge, some strategies can be proposed that would lead to
worker agility. The rst proposed strategies relate to the way a company is managed
in response to the challenges of Industry 4.0. This requires the use of new Green
IT technologies. Such smart and exible technologies create technological agility.
However, a company achieves holistic agility through the skills and experience of
people who create an agile workforce. A representation of these dependencies is
shown in Figure 20.2.
A company can be considered agile on the condition that it develops a at struc-
ture, which is a core idea of the Teal model of management. For the effective imple-
mentation of exible and intelligent information technologies in the Green IT, the
ability of employees to adapt quickly to the requirements of new equipment is essen-
tial. An organization achieves agility by integrating multiple sources into a coordi-
nated, interdependent system consisting of exible organizational structures. For this
reason, developing the skills of employees and increasing their environmental aware-
ness become an essential part of running an agile company, managed according to
the methods characteristic of the Teal management model.
The considerations presented herein may provide inspiration for further research.
It would seem particularly interesting to examine the relationship between providing
employees with autonomy and their willingness and commitment to introduce Green
IT solutions.
20.8 PRACTICAL RECOMMENDATIONS
1. The research ndings and considerations presented in this chapter allow for
several recommendations for practitioners:
2. A modern company that wants to survive in a new reality of Industry 4.0
has to face numerous market challenges. It should therefore develop durable
FIGURE 20.2 Determinants of an agile organization implementing Green IT solutions.
(own elaboration.)
355Green Computing in Polish Companies
assets that determine its uniqueness. It must therefore develop qualities that
are the answer to the impulses that come from the market environment.
Organizational agility is undoubtedly one such attribute.
3. It is a good idea for a company to form an interdisciplinary team of special-
ists whose task is to identify problems related to sustainable development
and to improve the electricity consumption in the company.
4. A good business practice is to include Green IT in the company's business
st r ategy.
5. It is important to remember that the company’s management methods have
a direct impact on employee agility, which helps raise awareness and alter
employee behavior to protect the natural environment.
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357
21 Design of a Pentagon
Slot-based Multi-band
Linear Antenna Array
for Energy-efcient
Communication:
Future Challenges
and Applications
in Green Technologies
Satheesh Kumar P. and Balakumaran T.
Coimbatore Institute of Technology
21.1 INTRODUCTION
Radiation elements for an advanced wireless scenario for a smart green environ-
ment should have certain features such as high directivity, decent bandwidth (BW),
and beam steering [1] achieved by side lobe-level control (SLL) for a space popu-
lated by embedded systems, sensors, use terminals, actuators, and other wireless
communicating services. The above features cannot be accomplished by a single
CONTENTS
21.1 Introduction ..................................................................................................
.................................................................................................
.....................................................................................
..............................................................
......................................................................................
...............................................................
.............................................................................
.........................................................................................
....................................................................................................
..............................................................................................................
357
21.2 Planar Array 358
21.3 Simple Patch Design 358
21.4 Design of Pentagonal Slot Antenna 360
21.5 Dual-Band Antenna 361
21.6 1 × 2 Linear Pentagonal Slot Array 363
21.6.1 Simulated Results 363
21.7 Tri-Band Antenna 363
21.8 Conclusion 364
References 367
358 Green Engineering and Technology
antenna element, as bad SLL and BW management. The antenna arrays would be
capable of providing the ideal better BW radiation pattern, half control BW and
SLL with proper geometric and electrical array properties [2]. The impulse for the
highly probable communication systems is introduced in this chapter. The simplest
kind is a linear array, in which all elements are arranged in a direct line – straight
line. For different array geometries, many standard digital techniques are deceived.
These standard methods are time-consuming and are challenging numerical tests.
The antenna arrays are evenly classied into two groups [3]. Antenna arrays of
non-uniform antenna components consist of the rst type and linear antenna arrays
that consists of the second type often consider a group of unevenly spaced antenna
arrays with an odd or even number of antenna elements of uniformly distanced
antenna arrays to synthesize efcient arrays. Therefore, an additional scope for
planar antenna arrays to obtain different array geometries by preserving those
edges in the FALSE condition [4] and the overall SLL of the resulting arrays can
then be further reduced by the optimization of the array element and inter-element
distances. The article reports two array geometries by eliminating several elements
from a quadratic antenna array, followed by optimizing the element location to
minimize the SLL in the resulting array geometry.
21.2 PLANAR ARRAY
Figure 21.1 displays the symmetrical plane sequence of isotropic elements
(2M + 1) × (2N + 1) aligned in the XY plane. Then, the pattern of the 2D array can be
represented as follows [5,6]:
N
AF(,uv)(=Am,)nj
∑
nN=−
nM
∑
M
exp(
()
kdxmu)e
xp
()
jkdn
y()v (21.1)
=−
For symmetric array,
d−−mm
=−dm1<<M (21.2)
d−−nn
=−dn1<<N
21.3 SIMPLE PATCH DESIGN
The equations used for designing an antenna are mentioned below. The thickness of the
middle layer substrate regulates the gain and directivity of an antenna. The thickness
of the substrate depends on the resonant frequency of an antenna. The thickness of the
substrate is chosen within the range for antenna conguration [7]. The antenna gain is
determined by the substrate thickness. The substrate thickness depends on the antenna’s
resonating frequency. The substrate height is calculated based on 0.003λ < h < 0.05λ.
λ
=Cf
r, (21.3)
359Energy-efcient Communication
where
C = velocity of light and
fr = resonant frequency.
For the frequency of 2.4 GHz, the thickness of the substrate is 1.6 mm. The rela-
tive permittivity is a parameter, which varies depending on the substrate material
used. The material used depends on the application of the antenna. The εr = 4.4
for the FR4 material, and the length (Lsub) and width (Wsub) of the substrate [8]
should be
Wh
sub=+6W (21.4)
LL
eff=+2∆L (21.5)
The equivalent (effective) dielectric constant is calculated by using,
εε
−0.5
ε
=rr
+1−1h
eff+11+2
2 2 w (21.6)
FIGURE 21.1 Equally spaced planar array geometry.
360 Green Engineering and Technology
The extra length due to the fringing effect is
()
w
ε
eff++0.3 0.264
h
∆=Lh(0.412) (21.7)
()
w
ε
eff−+
0.258 0.8
h
ΔL = Length extension due to the fringing effect.
21.4 DESIGN OF PENTAGONAL SLOT ANTENNA
For the best axial ratio results on an innite ground level, the patch geometry and the
probe positions are optimized and subsequently extended to nite ground planes of
various forms to get better delity. With the largest side of λ and other sides of equal
ratio to the radiating pentagonal patch, the nite pentagonal plane was constructed.
In addition, longitudes of radius h were also planned and simulated. Here, h is the
frequency of innite ground planes with the lowest axial ratio effect. In Figure 21.2,
the dotted line on the opposite side is a 50 Ω microstrip line. The proposed antenna
is fabricated on commercially available FR4 substrates with h = 1.8 mm, εr = 4.4, and
tan ∂ = 0.02. The width of the feed lines, corresponding to the standard impedance of
50 Ω, is chosen as 3 mm to streamline design and discussion [9].
The antenna is fed through a proximity electromagnetic coupling through a
microstrip thread. For matching, a quarter-wave circuit is used. The high-frequency
structure simulator 3D electromagnetic eld tool is used for antenna design [10]. The
side length for the slot is 23.5 mm, g = 0.8 mm, and d = 2.4 mm. W hen Lf = 26 mm and
Wf = 0.5 mm, the corresponding impedance condition is optimized. In this case, the
length is adjusted, when fastening b to 0.7 mm.
FIGURE 21.2 Single-band pentagonal slot antenna.
361Energy-efcient Communication
Figure 21.3 describes assessing, return loss of simulated with measured return
loss at 4.8 GHz fabricated with optimized measurements, RT/duroid 6010/6010LM.
The 70.7% beam width for a single-band patch is noticed in the E-plane far-eld
radiation pattern at 4.8 GHz as shown in Figure 21.4.
21.5 DUAL-BAND ANTENNA
The outer and inner r ings were made to resonate at 4.2 and 5.8 GHz. The prototype model
of the slot antenna, as shown in Figure 21.5, gives geometric parameters. The antenna
has the geometric dimension L1 = 17.4 mm, L2 = 16.7 mm, a1 = 0.4 mm, a2 = 0.3mm ,
b1 = 0.1 mm, b2 = 0.1 mm, g1 = 0.1 mm, g2 = 0.1 mm, Lf = 23.5 mm, and w = 3 mm.
Figure 21.6 indicates the frequency comparison reactions for the antenna being tested
and simulated. For the two resonant frequencies, the practically attained impedance BW
(10-dB) is 14.3% and 8.1%. From simulation to measurement, the frequency transition
FIGURE 21.3 Return loss of a single-band pentagon-shaped antenna, resonating at 4.8 GHz.
FIGURE 21.4 Radiation pattern of a single-band pentagon-shaped antenna resonating at
4.8 GHz.
362 Green Engineering and Technology
is lower than 76 MHz. The results show that the dual-band CP mode of the antenna at
4.2 GHz is mainly supported by the broad pentagonal slot [11,12].
Radiation patterns at the two resonant frequencies are calculated with their
parameters, as shown in Figure 21.6. The slot antenna is a two-way radiator; it has
identical radiation patterns on either side. The structural congurations in the upper
half of space radiate a circular (LHCP) left-hand wave and in the under-half circular
(RHCP) wave. The opposite circular polarising radiation can be done when the feed-
ing line is on the right. The peak gains measured are 3.7 and 3.2 dBi radiating for 4.2
and 5.8 GHz, respectively.
FIGURE 21.5 Pentagonal slot antenna for a dual band.
FIGURE 21.6 (a) Far-eld radiation pattern radiating at 4.2 GHz. (b) Far-eld radiation pat-
tern radiating at 5.8 GHz.
363Energy-efcient Communication
21.6 1× 2 LINEAR PENTAGONAL SLOT ARRAY
The antenna element’s laminate is congured and calibrated. The array architecture
dimension constitutes h = 3.2 mm, εr = 2.4, and tan δ = 0.0012. The 1 × 2 linear array
arrangement with the same elements is shown in Figure 21.7.
21.6.1 Simulated reSultS
It is important to note that for both linear array and linear bands analyzed multi-
band impedance BW and gain values were obtained. Parametric analysis is car-
ried out to determine the impact of the element size and the inter-element distance
between the components. In terms of impedance variance, response, and BW, the
simulation observation was performed. For the linear array, this generates 47 and
80 MHz of BW for the dual bands of 4.2 and 5.8 GHz, respectively. The proposed
approach is used to work with multiple frequencies using array geometry and also
for advanced wireless technology applications. Figure 21.8 shows the comparative
analysis of reection coefcients (S11) for slotted pentagon, 2 x 1 linear array and
measured results and Table 21.1 shows the comparative returns loss results for
different congurations.
Gain vs. frequency response is shown in Figure 21.9 for a at array congura-
tion that resonates in phi = 90° in E-plane. In high and low bands, the display gives
better patterns, but inferior patterns seen in the middle band of the spectrum. This is
because of the misleading effects of inset fed and also because of the transforming
impedance at the element joints.
21.7 TRI-BAND ANTENNA
Figure 21.10 shows the prototype model of the planar slot antenna. The outer, mid-
dle, and inner rings resonate for 2.4, 3.5, and 5.8 GHz. The prototype model of the
slot antenna has its geometrical parameters weighed as L1 = 17.4 mm, L2 = 16.7 mm,
a1 = 0.4 mm, a2 = 0.3 mm, b1 = 0.1 mm, b2 = 0.1 mm, g1 = 0.1 mm, g2 = 0.1 mm,
Lf = 23.5 mm, w = 3 m m, L4 = 18.1 mm, and g3 = 0.1 mm [13,14].
FIGURE 21.7 Prototype of 1 × 2 linear pentagonal slot array.
364 Green Engineering and Technology
Figure 21.11 indicates the frequency comparison reactions for the antenna
being tested and simulated. For the three resonating frequencies, the measured
result shows impedance BWs (−10-dB) of 14.3%, 8.1%, and 12.4%. From simula-
tion to measurement, the frequency transition is lower than 76 MHz. The results
show that the antenna is clearly in the CP mode of the lower band and is mainly
from the major pentagon at 2.4 GHz. It is important to see that the pentagonal slot-
formed antenna feature inuences multi-bands, impedance, and gaining values
studied for linear and planar array models. To assess the effect of the element size
and the inter-element distance between the components, a parametric analysis is
performed. The simulation observation was carried out in terms of BW, imped-
ance variance, and response.
21.8 CONCLUSION
A multi-band pentagonal slot antenna has been experimentally studied and ana-
lyzed that the antenna su ites better for smart g reen energy-efcient communication.
The proposed antenna supports the new generation of wireless sensor networks,
either locally or in a wide region, with collaboration and communication entities
and community mobility. The pentagonal model of the proposed design is suc-
cessfully implemented and the results of single-band pentagonal slot, dual-band
pentagonal slot, 1 × 2 linear array, and tri-band pentagonal slot antenna actively
engage green environmental communication. On comparing the gain, linear 1 × 2
arrays offer better directive gains of 6 and 5.4 dBi, which are better than those
of the planar antenna, the compactness offered here in the planar slotted antenna
is better. The proposed antenna provides a fascinating solution for mobile com-
munication systems, thanks to its compactness and circular polarization. Finally,
FIGURE 21.8 Comparative analysis of reection coefcients (S11) for the slotted pentagon,
2 × 1 linear array, and measured results.
365Energy-efcient Communication
TABLE 21.1
Comparative Analysis of Linear Array and Slotted Multi-band Antenna
Frequency in GHz Return Loss (S11) in dB −3dB % Bandwidth
Approach • Size of the Antenna (mm2) Simulated Measured Simulated Measured Gain in dBi (MHz)
Single-band pentagonal slot 58.0 × 64.0 4.8 4.9 −18 −17 3 550
Dual-band pentagonal slot 78.3 × 84.0 4.2 4.3 −25 −30 3.7 120
5.8 6.3 −38 −35 3.2 90
1 × 2 linear array 130.0 × 78.3 4.2 4.3 −23 −30 6 47
5.8 6.3 −22 −35 5.4 80
Tri-band pentagonal slot 76.1 × 91.0 2.4 2.4 −32 −34 2.5 72
3.5 3.6 −25 −18 3.1 95
5.8 5.9 −15 −22 3 81
366 Green Engineering and Technology
a smart pentagon-shaped array antenna achieves peak antenna gains of 5, 6, and
9 dBi for multiple resonating frequencies of 2.4, 3.5, and 5.8 GHz respectively.
From the results of the simulation, point-to-point communication systems are an
important observation of energy efciency. Maximum energy efciency requires
more transmission power as the power consumption of the antennas increases. In
addition, the improvement in energy efciency output is neutralized by the power
consumption to equip more antennas.
FIGURE 21.9 Far-eld radiation pattern at the frequency of 5.8 GHz.
FIGURE 21.10 Prototype of the tri-band pentagon-shaped slotted antenna.
367Energy-efcient Communication
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coefcients (S11) for the tri-band planar pentagon-shaped slotted antenna array.
369
Index
access 3–6, 14, 18
accuracy 317, 319
acetylcholinesterase (AChE) 122, 123, 124, 125,
129, 133
ACO 235–236, 240
AC-DC-AC converter 105, 108, 117
action space 262
activated 73, 74
action values 264
active front-end converter 104
actuators 276, 278, 281, 284
Acyrthosiphon pisum 124, 125, 129
adaptable 227
advantages of PV electricity 338
advancement 10, 14, 16–17
AES 297, 298, 301
agile 353, 354
agile organization 354
agility 344, 352, 354
agriculture 274, 276–277, 290
AHP 232
algorithms 1, 8–9, 11–12, 22–23, 27, 30
Alternanthera sessilis 122, 133
AMQP 43
amplifying gain 140
analytical 2, 15
animal alarm 284, 289
antenna gain 93
aper iodic 367
applications 315, 317, 319, 325
applications of a solar cell 338
ARCore 69
array 357, 358, 359, 360, 361, 364, 365, 367
array geometry 358, 359, 363
articial intelligence (AI) 85, 208, 247, 288
AR, VR mechanism 60
assessment of low-constrained devices 300
asymmetric LWC method for IoT 302
automation 2, 17–18, 351
axial ratio 92, 93, 360
bandwidth 88–90, 92–94, 357, 365
battery 81, 87
beam steering 357
big data 2, 5, 9–10, 12, 14, 16–19
biocomputing 240
biodegradable 72
biomass 72
biomedical 315–317, 319–320, 322, 324–325
bio-pesticides 122, 133
bio-waste material 72
blend 75, 78, 82
block ciphers 296, 297, 298
bottlenecks 24
branch current prole 162
branch incidence matrix 153
bus voltage prole 161
business 344
buzzer 278, 281, 289
camera 278, 281, 284
capacitance 280
capacitor-excited asynchronous generators 104,
105, 107, 108, 110
carbon 72, 78, 80, 82
carbon-neutral cycle 72
carbon emission 244, 245
cellular automata (CAs) 209, 212, 213
channel fading 140, 144
characteristics of a solar cell 337
characteristics of IoT 40
chitosan 81
circular polarization 364, 367
circularly symmetric complex Gaussian
(CSCG)142
clay 274–275
clim ate 331
cloud 294
cloud computing 2, 4–6, 14, 16, 19, 249
cloud server 278, 283
cloud sim 238, 240
cloudlets 235
CoAP 43
CO2 2, 12, 228–229, 233–234, 236, 247
coding 231
cognitive radio network 137–138, 149–150
computing 177, 205–206
communication. 40
community 59
communication systems 358, 364, 367
computational tools 22–25, 28–30
conductivity 75–77, 80–82
conict 216, 217, 218
constant current constant voltage charging 163
constant current load 163
constant impedance load 163
constant power load 163
context-awareness 41
control 277–278, 281, 284, 287–289
conve rsio n 315, 318–319, 322–32 3
conversion efciency 87, 90, 95–98
370 Index
convergence 178–179, 187, 191–192, 196, 201,
205–206
converter 104, 105, 108–110, 112, 117
cooperative spectrum sensing 138, 141, 149–150
cooperative users 140, 149
cost-efciency 209
coefcients of torque and speed 106
CPS architecture 208
CPU utilization 260
crossover 231
crop yield 274
cryptographic arrays 299
cryptographic co-processor 299
cryptographic multicore 300
CSP 226, 228
cut-throat 22
cyber-physical system (CPS) 207, 208, 209
cypermethrin 122, 126, 127, 130
data analysis in the IoAT architecture 48
data applications 2, 17
data center 1, 9, 19, 226–229, 232–236, 238,
240,249
data layer 2, 12, 13 –14
data transmission 138, 140–144, 146, 148–149
database 283
data-driven 12
dataset 233
DC 229–231, 236–237, 239
DC-geared motor 278
DC-link capacitor 105, 108, 110, 115, 117
DC-link voltage 113, 116
DDS 43
decision variable 183
defective ground structures 89, 90
deformation 315, 319–321, 323
density 72, 74, 78, 80
density of water 106
deployment 220, 221
designing GIoT 248
destinations of green innovations 329
detection threshold 141, 143
devices 319, 322, 324
DFIG 104
DHT 11 278, 280, 289
diagnosis 322
diazinon 127, 130
dielectric 88, 89, 91
dielectric constant 359
dielectric permittivity 280
different types of energy 334
digital technologies 344
diode-based rectier 96
direction 276, 281, 289
directivity 357, 358
discrete event simulation (DES) 24
diseases 320, 322
dispose 248
disposing 251
diversication 177, 181–182
docking 129
dopant 76, 78
Dragony Algorithm (DA) 182
d- STATC OM 105, 110, 112 –118
dual-band 357, 361, 362
dynamic consolidation 266
dynamic model 23, 25–31
EBG 90, 91
ECA dynamics 219
ECA rule 212, 213
eco-friendly 71, 78, 80, 82, 226
eco-friendly umbrella 330
ecological development 345
economic development. 22
economic growth 22
ecosystem 22
ecosystem 344
effect 316–319, 323, 325
effective 5, 6, 8
efciency 141, 143, 318, 319, 323
efcient display 250
electric power generation in space 338
electrode 78, 79
electrolytes 74, 75, 82
electromagnetic 138, 139
elementary cellular automata (ECA) 212
EMBB 254
embedded 278
emission 22, 29
energy 315–326
energy consumption 226, 228–229, 232–235
energy detection 140
energy efciency 260, 347, 366
energy harvesting 137–139, 141, 143, 145, 147,
149–150
energy harvesting methods 85–87
energy monitoring 295
energy prociency 333
energy savings 138
energy star 4.0 249
energy system 347
energy utilization 24, 26 –27
energy-efcient 209
environment 219
environmental 22, 24, 28–29
e-plane 361, 363
ESP8266 279–280
evapotranspiration 274
e-waste 251
existing works 44
experimental model 23
fabrication 80
facilities 2, 6, 10, 16
far-eld 361, 362, 366
371Index
fault occurrence 235–237
fault tolerance 256
delity 363
llers 76–78
tness function 231
rebase 283–284
5G 85, 254
xed boundary conditions 219
ex cable 281
exible organizational structures 355
ow rate 106, 110, 112
fog-assisted cloud system 261
forward and backward sweep load ow 153
fossil fuels 104
FR4 substrate 360
framework 2, 3, 12–17, 19
framework of LWC 305, 306
free-owing water 104
frequency response 363
fringing effect 360
functional blocks of IoT 38
fu nda me nt als 315–316
fusion center 138, 141
future 72, 78, 82
fuzzy PI 105, 109, 110, 112, 117, 118
GA 231–232, 235
gain 358, 362, 363, 364, 365, 000
gea rbox 104
gelatin 81
generator 104, 105, 107–110, 112–117
generator side converter 105, 108, 109
genetic algorithm 226, 231, 235
geometric selective harmony search (GSHS) 210
global warming 246
Going Green 1, 9
green advancements or eco-developments 332
green city framework 59
green cloud computing 226–229, 231, 232,
234–236, 240
green communication 137–139
green computing 1, 9, 209, 214
green creations 328
green data center 1, 9
green developments 332
green engineering 2, 4, 6, 8, 10, 12, 14, 16, 18
greenhouse gases 243
greening 60
Green Innovation Applications 332
green innovations 329, 330
green IoT 2–3, 5–8, 9–11, 13, 15, 17–19
green IoT 244, 247
green IT 345, 353, 355
green model 209
green smart healthcare 1, 6
green smart home 1, 5
green smart transport 1, 7
green Technology (GT) 243, 329
green-energy 364
greenhouse gas (GHG) 334
Grey Wolf Optimizer (GWO) 182
ground plane 360, 367
group-based industrial wireless sensor network
(GIWSN) 210
GT advantages and disadvantages 333
GT opportunities in India 334
GT signicant goals 329
hardware constraint 256
hardware implementation 300
hardware platforms used in the IoAT
architecture48
Harris Hawks Optimization 180, 181, 206
harmonic 105, 117, 118
harmonic rejection 93
harvesters 316–319
health 316, 319, 321–322, 324 –325
healthcar e 315–316, 319, 322 –325
high-performance systems 299
HIGHT 303
humidity 274–275, 277–278, 280, 287, 289–290
hybrid (heterogeneous) CAs 213
hydro turbine 104, 106
ICT 260
IEEE 16 bus BRDS 160
IGBT 105, 108
impedance 87–90, 93–95
implementation 352
improved geometric selective harmony search
algorithm (IGHSA) 210
independent and identically distributed (IID) 142
indoor navigation 58, 62, 68
industrial automation 330
industrial IoT (IIoT) 209
industrial sector 23, 26
industry 4.0 208, 344, 351, 354
initial population 232, 237
information technologies (ITs) 207
innovation 328, 331, 345
innovative applications of GT 332
inorganic 72, 73, 75, 76
in-silico 122, 124
integrated into the information network 41
intelligent information 354
intelligent decision-making capability 41
intensication 177, 181–182
Inter Quartile Range(IQR) 268
interdisciplinary team 351
interference management 296
international energy agency 250
interior modeling 60
Internet 276, 278, 283–284
Internet of Things 276
372 Index
Internet of Things in agriculture 46
interoperable communication protocols 41
interoperability 253, 296
introduction, problem statement 58
invading animals 278
in-vitro 123
IoAT applications 53
IoAT architecture 46
IoT 85, 86, 98, 294, 295, 302
IoT applications 44
IoT enabling technologies 44
IoT protocols 42
IoT protocols stack 42
irrigation 274–276, 278, 281, 284, 290
issues and research challenges 301
IT 345
IWSNs (industrial WSNs) 219, 220
Kruskal–Wallis test 202–204
Karush–Kuhn–Tucker (KKT) 145
L293D 278, 281, 289
Lagrange multiplier 145
Lantana camara 122, 123, 129
LC lter 105, 108
lightweight cryptography 295, 297
lightweight hash functions 299
light-weight stream cipher algorithms 299
line data of 16 bus BRDS 160
linear band 363
linear regression 268
lithium 72, 76, 81, 82
live stream 278, 281, 285, 290
load 104, 105, 108, 110, 112–118
load balancer 118
load data of 16 bus BRDS 161
load ows 152
loop matrix 166–167
load side converter 105, 108, 109
loamy 274–275
low pass lters 93
low power 221
low-cost 212, 221
LWC 305, 306, 307
machine learning techniques 49
Ma mda ni r ule 111
management 40
man power 22
manufacturing layout 22, 27, 30
manufacturing line 210
market competition 22
market environment 353
Markov decision process 263
matching circuit 87, 94, 95
materials 315–319, 321, 323
maximum power point 178, 180
mean 139, 142 –144
meandered slits 89, 91, 93
meas urement 315, 319–32 4
mechanism 77–81, 316
Median Absolute Deviation (MAD) 268
melt casting method 76
metaheuristic 177–178, 203
metamaterial 89, 90–92
methamidophos 127, 130
methane 245
microcontroller 277–278, 283
microhydropower generation (MHPG) 104, 106,
110, 112 , 115
microstrip 88, 92–95, 360, 367
MIMO 257
miniaturization 88, 89, 98
mobile application 276, 278, 283–284, 289
modeling 23–24, 26–31
modeling of distribution system 156
modeling of EVCS 163
modern threats 308
modied Newton Raphson loadow 153
money 22
model 345
monitor 250, 275–278, 281, 284, 287, 290
monitoring 316, 319, 322, 325
Monte Carlo simulations 146
MOSFET-based rectier 96, 97
Moth Flame Optimization (MFO) 178, 206
MQTT 42
MTC 255
multi-Altran 283–284
mutation 231
nanogenerator 315–318, 325–326
nanotechnology 315
national benets for energy generation 331
national GT policy tools 334
natural environment. 355
navigation 61
network 253
network layer 2, 13
neural network 104
NavMesh 69
NG 315–319, 322–325
nodeMCU 276, 278–280, 283–284
noise 141, 142
non-parametric test 202
null boundary CA (NBCA) 212
objective function 165
objectives of Green Technologies 329, 330
obsolete technologies 24
open circuit 179–180
open-source 279
optical air mass 337
optimal reconguration 166
373Index
optimal placement of EVCS 167, 170 - 172
optimization 22, 26–31, 139, 140, 145, 146, 149
optimization techniques 164
organizations 344, 348
organization’s view 23
organizational agility 351
output 316, 318–319, 321–325
overloaded 231, 234, 236
painstaking 274–275
Parthenium hysterophorus 122–123
parameter estimation 177–178, 205–206
Particle Swarm Optimization 165
PENG 315–316, 318 –319
path loss exponent 142, 144
pentagonal slot 358, 360, 362, 363, 364, 365
performance 228–230, 233–236, 239
periodic boundary CA (PBCA) 213
Permanent Magnet Synchronous Generator 104
permeability 90
permittivity 90
perturbation 92
Photovoltaic Energy Conversion Applications 334
photovoltaic impact 335
physical level implementation 221
physical parameters 274, 278
phyto-constituents 122, 123, 126, 127, 129, 133
piezoelect ric 315–316
pillars of GT 331
pin diodes 92, 93
PI-Network 94
planar array 358, 359, 364
platform 253
polarization 90–92
Polish companies 344
porous 73, 79, 80
potable water 330
power consumption 256
power density 72, 77, 78, 80
power efciency 220, 221
power management 249
power management unit 86, 87
power supply 250
power transfer 139, 150
power utilization 226, 240
PRESENT 301, 303
primary user 138, 150
probability of detection 141, 143, 145
probability of false alarm 141, 143, 145
probe-feed 367
product lifecycle management (PLM) 23
production 210
production cost 256
production operations 22
productivity 22, 25–26, 31
prototype 359, 363, 364
proximity electromagnetic coupling 360
pseudocode for a lightweight encryption system
303, 304
pseudocode for the decryption system 304
PSO algorithm 164, 165
PSO update equation 165
PV cell 177–180, 206
PV effect 335
PV module 177–179, 183, 205
PWM 105, 108, 109, 112
PYNG 315–316, 318
pyro elec tric 315, 318
q-learning 264
QoS 232–233
qualities 348
q-values 264
radiation eld 367
radiation pattern 358, 361, 362, 366
random process 3
rapid growth 22
raw materials 22, 26–28, 30–31
Rayleigh 3–7, 11–13, 15
recongurability 92
reconguration of 16 bus RDS 162
rectifying circuit 87, 93, 94
redox potential 75
reection coeffecients 363, 363, 367
refractive index 90
reinforcement learning 262
relationship 353
relay module 278, 281, 289
reliability 228
reliability optimization 211
remote 277, 283–284, 288
renewable 6, 7
renewable 72, 82
renewable energy 104
reporting slot 142
resonating frequency 358
resource management 295
resource utilization 22–25, 28
REST HTTP 43
returnloss 361, 365
reverse saturation current 179, 182–183
rewards 264
RF energy 86, 87, 95, 98
RFID 244, 294, 295, 296, 303
RL lo ad 110, 112–114
Rule Min Term (RMT) 217
rule base 110
run of the river 104, 106
rural electrication 338
safety currently 296
Salp Swarm Algorithm (SSA) 178, 206
sampling frequency 141, 142
374 Index
sandy 274–275
scalable 226, 228, 234
scalability 256
scheduling 24, 220, 221
Schottky diodes 93, 96, 98
secondary receiver 140
secondary transmitter 140, 149
secondary user 138, 141
security 40
security and privacy 308
security issues 53
segmental motion 74, 78, 82
segments 15–16
selection 231–232, 236
self-adapting and dynamic 40
self-conguration 40
self-powered 315, 319–320, 322–326
semantic 253
sensing duration 140, 146, 149
sensing slot 141
sensor network topology 256
sensors 2– 6, 8, 11–13, 17–18, 244, 276, 278, 284
sensors used in the IoAT architecture 48
services 40
Servo motor 278, 281, 289
shaft torque 108
short circuit 180, 182
shorted posts 89
side lobe level 357
signals 316, 319, 322
signicant goals 329
simpler key schedules 298
simpler rounds 298
simulation 23–26, 28–31
Sine Cosine Algorithm (SCA) 182
single feed 91, 92
single-band 360, 361
single diode 177–179, 205
size reduction 89–91
SLA 231–232, 234, 265
sleep scheduling 209
slot antenna 360, 361, 362, 363, 364, 367
slots 89, 91, 93
smaller block sizes 298
smaller key size 298
smart cities 1, 2, 8–13, 18–19
social 22, 24, 26, 28
sodium salt 72, 73, 76–78
soft 73
software 351
software implementations 300
soil 274- 278, 280, 287–290
soil moisture 274–275, 277–278, 280, 289–290
solar cell application 336
solid state 72
solution 352
sources 74
specic capacitance 80, 81
spectrum 363
spectrum efciency 144
spectrum sensing 138, 141
speed 276, 278, 281, 289
split ring resonator 90
stacked patch 92
starch 81
static VAR compensator 104
state space 262
strategies 348, 349
structure 74
statistical analysis 182, 191–192, 196, 200–201
substrate thickness 358
subsystem 347
sum of square error (SSE) 200
supercapacitor 72, 78, 81
surveillance 277, 287
sustainability 22, 24–28
sustainability policy 353
sustainable 1, 2, 5, 7, 9–10, 15, 18–19, 71, 72
sustainable development 23, 344, 346
sustainable energy 330
switching combinations 162, 167
symmetric LWC method for IoT 301, 302
synchronous speed 106, 108
synchronous speed test 108
syntatic 254
swept area 106
teal 345
techniques of lightweight block ciphers 297, 298
technology 315, 322–325
TD update 264
temperature 274–275, 277–278, 280, 287, 289–290
TENG 315–319, 322–323
terrain 274, 277, 287, 290
THD 110, 114, 115, 117, 118
3D 58, 59, 60, 67
threshold method 268
throughput 137, 139, 140, 144–150
tie-lines 162
T-Network 94
tools of GT 334
total losses of the system 155
transformation process 22
transforming impedance 363
transition diagram 215, 216, 217
transmission media 256
transmission power 366
tri-band 357, 361, 363, 364, 367
tr iboele ctr ic 315, 317
triangular membership function 109
triple bottom line (TBL) 23
turbine model 106, 107
TWINE 301, 302
types of innovative GT 333
375Index
UB 238
UGU engine 67, 68
ultra-lightweight cryptography 301
uniform (homogeneous) CA 213
unity 69
unity engine 64, 65
UnityEngine.AI 64, 65
unique identity 41
URLLC 254
usable goods 22
variable MHPG 104–106, 113, 115, 117, 118
variable voltage 104
variance 141–143
various IoT-Based cloud service platforms 48
virtual manufacturing 24
velocity of water 106
virtualization 226–227, 229, 232–233
virtualization 249
VM 226, 231–234, 236
VM allocation 267
VM consolidation 260
VM manager (VMM) 226, 229–232, 234–236, 265
VM migration 265
VM selection 267
voltage source converter (VSC) 105, 109, 110, 112
voltage multiplier 87, 97
waste 72
water pumping 338
wearable electronics 85
web sockets 44
whale optimization algorithm (WOA) 178. 205
wide-band 367
Wilcoxon rank-sum test 203–204
wind power 104
wireless sensor networks (WSNs) 209, 210, 211
wireless technologies used in the IoAT
architecture 48
work from home 251
work station 26
working of photovoltaic cell 335
WSN 255
WSN node 87
XMPP 43
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