Enterprise IoT Modeling: Supervised, Unsupervised, and Reinforcement Learning

Abstract

The Internet of Things (IoT)—the internetworking of physical devices—has been a significant advancement in recent decades and has been the catalyst for several other innovations. New Industrial Internet of Things (IIoT) platforms aim to solve the most complex challenge of manufacturers: consolidating all production systems into a single data model. They are used in smart cities, security and emergencies, environmental applications, energy, healthcare, logistics, industrial control, home automation, agriculture, and animal farming. These objects/devices/appliances can generate, collect, and exchange data without human-to-human or human-to-computer interactions. The IIoT is creating an explosion in structured and unstructured data from a growing army of sensors capable of registering locations, voices, faces, audio, temperature, sentiment, health, and others. Billions of IoT devices are interconnected and a huge volume of data is generated. Every device features automation to assist people in the planning, management, and decision-making of their day-to-day activities. Machine learning (ML) techniques are applied to further enhance the intelligence and capabilities of an application. Many researchers are interested in producing advanced IoT technology, combining ML and IoT Techniques. Through ML, IIoT devices learn to perform tasks such as predication, pattern recognition, classification, and clustering. To provide for a learning process, IoT devices are trained using various algorithms in ML and statistical models to analyze sample data. The various fields of data sets (structured and unstructured data) are characterized by measuring functional parameters. Later, ML algorithms are applied to the data set to find features, provide useful output, identify patterns or make decisions based on the data set, draw inferences from real-time data streams, make their results available to analysts, and embed their results directly in business processes. In ML, the real-time problem is classified by classification, clustering, regression models, and association rules. Based on the learning style, ML algorithms can be categorized as supervised, unsupervised, semi-supervised, and reinforcement learning.

Full text

EAI/Springer Innovations in Communication and Computing
AnandakumarHaldorai
ArulmuruganRamu
SyedAbdulRehmanKhan Editors
Business
Intelligence
forEnterprise
Internet
ofThings

EAI/Springer Innovations in Communication
and Computing
Series Editor
ImrichChlamtac,European Alliance for Innovation,Ghent,Belgium

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Anandakumar Haldorai
Arulmurugan Ramu • Syed Abdul Rehman Khan
Editors
Business Intelligence
for Enterprise Internet
of Things

ISSN 2522-8595 ISSN 2522-8609 (electronic)
EAI/Springer Innovations in Communication and Computing
ISBN 978-3-030-44406-8 ISBN 978-3-030-44407-5 (eBook)
https://doi.org/10.1007/978-3-030-44407-5
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Editors
Anandakumar Haldorai
Computer Science and Engineering
Sri Eshwar College Engineering
Coimbatore, Tamil Nadu, India
Syed Abdul Rehman Khan
Tsinghua University
Beijing, China
Arulmurugan Ramu
Presidency University
Rajanakunte, Yelahanka, India

v
Preface
The Internet of Things (IoT) has become an important research domain as enterprise
applications, systems, infrastructures, and their applications have shown their
potential in recent years. IoT is an enterprise bound for signicant growth, and it
will have a major impact on the lives of consumers and professionals around the
world. It will enable enterprise industry to be a multitrillion dollar industry by 2025,
including enterprise manufacturing, enterprise transportation, enterprise smart mar-
ket, enterprise utilities, enterprise healthcare, etc. It will also change the way we
think about producer and consumer networks. The expectations of IoT and its rele-
vant products in this new era are quite high. Instead of smartness alone, consumers
of IoT products and services would like to see IoT technologies bring about more
intelligent systems and environments.
This book, Business Intelligence for Enterprise Internet of Things, presents the
most recent challenges and developments in enterprise intelligence with the objec-
tive of promoting awareness and best practices for the real world. It aims to present
new directions for further research and technology improvements in this important
area. Its chapters include IoT enterprise system architecture, IoT-enabling enter-
prise technologies, and IoT enterprise services and applications, for example, enter-
prise on demands, market impacts, and its implications on smart technologies, big
data enterprise management, and future enterprise Internet design for various IoT
use cases, such as share markets, healthcare, smart cities, smart environments, smart
communications, and smart homes.
This book also covers ideas, methods, algorithms, and tools for the in-depth
study of performance and reliability of business intelligence for enterprise Internet
of Things. The scope of business intelligence is to explore and present numerous
research contributions relating to the eld of neural network computing, business
specications, evolutionary computation, enterprise modeling and simulation, web
intelligence, healthcare informatics, social relationship, energy, and end-to-end
security in enterprise-aware management system in enterprise Internet of Things.
In this book, we present techniques and detailed perspectives of business intel-
ligence for enterprise Internet of Things that can be used in overcoming and solving

vi
complex tasks in enterprise system. This book is based on various research horizons
and contributions focusing on IoT enterprise system challenges over:
• Development of innovative enterprise architecture for the Internet of Things
• Enterprise IoT modeling: supervised, unsupervised, and reinforcement learning
• The Internet of Things evolutionary computation, enterprise modeling, and
simulation
• Development of new IoT technologies for business intelligence and large-scale
enterprise analysis
• Uncertainty modeling in big data analytics for IoT
• Providing solutions to pressing problems across areas including connected and
autonomous vehicles, automation, healthcare, and enterprise security using the
Internet of Things
• The management of enterprise in mobile transparent computing for the Internet
of Things
• Bridge developments in articial intelligence to real enterprise applications in
collaboration with IoT partners
• New generation of scientists to address the skills shortage in these areas and
increase competitiveness
• Applications and services for enterprise systems such as complex systems, multi-
agent systems, game theory, and statistics
• Advanced future perspective in enterprise for the Internet of Things
This book opens the door for authors toward current research in enterprise
Internet of Things systems for business intelligence.
We would like to thank Ms. Mary E.James, Senior Editor, Applied Sciences,
Springer, and Ms. Eliska Vlckova, Managing Editor, European Alliance for
Innovation (EAI), for their great support.
We anticipate that this book will open new entrance for further research and
technology improvements. All the chapters provide a complete overview of busi-
ness intelligence for enterprise Internet of Things. This book will be handy for aca-
demicians, research scholars, and graduate students in engineering discipline.
Coimbatore, Tamil Nadu, India AnandakumarHaldorai
Rajanakunte, Yelahanka, India ArulmuruganRamu
Beijing, China SyedAbdulRehmanKhan
Preface

vii
Contents
1 Internet of Things (IoTs) Evolutionary Computation,
Enterprise Modelling and Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . 1
A. Haldorai, A. Ramu, and M. Suriya
2 Organization Internet of Things (IoTs): Supervised,
Unsupervised, and Reinforcement Learning . . . . . . . . . . . . . . . . . . . . 27
A. Haldorai, A. Ramu, and M. Suriya
3 Enterprise IoT Modeling: Supervised, Unsupervised,
and Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
Rajesh Kumar Dhanaraj, K. Rajkumar, and U. Hariharan
4 An Overall Perspective on Establishing End-to-End Security
in Enterprise IoT (E-IoT) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
Vidya Rao, K. V. Prema, and Shreyas Suresh Rao
5 Advanced Machine Learning for Enterprise IoT Modeling . . . . . . . . 99
N. Deepa and B. Prabadevi
6 Enterprise Architecture for IoT: Challenges and Business
Trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
A. Haldorai, A. Ramu, and M. Suriya
7 Semi-Supervised Machine Learning Algorithm for Predicting
Diabetes Using Big Data Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
Senthilkumar Subramaniyan, R. Regan, Thiyagarajan Perumal,
and K. Venkatachalam
8 On-the-Go Network Establishment of IoT Devices to Meet
the Need of Processing Big Data Using Machine Learning
Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
S. Sountharrajan, E. Suganya, M. Karthiga, S. S. Nandhini,
B. Vishnupriya, and B. Sathiskumar

viii
9 Analysis of Virtual Machine Placement and Optimization Using
Swarm Intelligence Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169
R. B. Madhumala and Harshvardhan Tiwari
10 Performance Evaluation of Different Neural Network
Classifiers for Sanskrit Character Recognition . . . . . . . . . . . . . . . . . . 185
R. Dinesh Kumar, C. Sridhathan, and M. Senthil Kumar
11 GA with Repeated Crossover for Rectifying Optimization
Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195
Mayank Jha and Sunita Singhal
12 An Algorithmic Approach to System Identification in the Delta
Domain Using FAdFPA Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203
Souvik Ganguli, Gagandeep Kaur, Prasanta Sarkar,
and S. Suman Rajest
13 An IoT-Based Controller Realization forPV System
Monitoring andControl . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213
Jyoti Gupta, Manish Kumar Singla, Parag Nijhawan,
Souvik Ganguli, and S. Suman Rajest
14 Development of an Efficient, Cheap, and Flexible IoT-Based
Wind Turbine Emulator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225
Manish Kumar Singla, Jyoti Gupta, Parag Nijhawan,
Souvik Ganguli, and S. Suman Rajest
15 An Application of IoT to Develop Concept of Smart Remote
Monitoring System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233
Meera Sharma, Manish Kumar Singla, Parag Nijhawan,
Souvik Ganguli, and S. Suman Rajest
16 Heat Maps for Human Group Activity in Academic Blocks . . . . . . . . 241
Rajkumar Rajasekaran, Fiza Rasool, Sparsh Srivastava,
Jolly Masih, and S. Suman Rajest
17 Emphasizing on Space Complexity in Enterprise Social
Networks for the Investigation of Link Prediction Using
Hybrid Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253
J. Gowri Thangam and A. Sankar
18 Overview on Deep Neural Networks: Architecture,
Application and Rising Analysis Trends . . . . . . . . . . . . . . . . . . . . . . . . 271
V. Niranjani and N. Saravana Selvam
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279
Contents

ix
About the Authors
Anandakumar Haldorai is Associate Professor and
Research Head in the Department of Computer Science
and Engineering, Sri Eshwar College of Engineering,
Coimbatore, Tamil Nadu, India. He has received his
Master’s in Software Engineering from PSG College of
Technology, Coimbatore, and his PhD in Information
and Communication Engineering from PSG College of
Technology under Anna University, Chennai. His
research areas include Big Data, Cognitive Radio
Networks, Mobile Communications, and Networking
Protocols. He has authored more than 82 research
papers in reputed international journals and IEEE con-
ferences and 9 books and several book chapters in
reputed publishers such as Springer and IGI. He is
Editor in Chief of Keai-Elsevier IJIN and Inderscience
IJISC and Guest Editor of several journals with Elsevier,
Springer, Inderscience, etc. Also, he served as a
Reviewer for IEEE, IET, Springer, Inderscience, and
Elsevier journals and has been the General Chair,
Session Chair, and Panelist in several conferences. He
is Senior Member of IEEE, IET, and ACM and Fellow
Member of EAI research group.

x
Arulmurugan Ramu is a Professor at Presidency
University, Bangalore, India. His research focuses on
the automatic interpretation of images and related prob-
lems in machine learning and optimization. His main
research interest is in vision, particularly high-level
visual recognition. He has authored more than 35
papers in major computer vision and machine learning
conferences and journals. He completed his PhD in
Information and Communication Engineering and his
MTech and BTech in Information Technology all from
Anna University of Technology, Chennai. He has
guided several PhD research scholars under the area of
Image Processing using machine learning. He is an
Associate Editor of Inderscience IJISC journal. He was
awarded Best Young Faculty Award 2018 and nomi-
nated for Best Young Researcher Award (Male) by
International Academic and Research Excellence
Awards (IARE-2019).
Syed Abdul Rehman Khan is an expert in Supply
Chain and Logistics Management. He achieved his
CSCP (Certied Supply Chain Professional) Certicate
from the USA and successfully completed his PhD in
China. Since 2018, he has been afliated with Tsinghua
University as a Postdoctoral Researcher. He has more
than 12 years’ core experience of supply chain and
logistics at industry and academic levels. He has
attended several international conferences and has been
invited as Keynote Speaker in different countries. He
has published more than 155 scientic research papers
in different well-renowned international peer-reviewed
journals and conferences. He has authored four books
related to the sustainability in supply chain and busi-
ness operations. He is a Regular Contributor to confer-
ences and workshops around the world. In the last 2
years, he has won ve different national-/provincial-
level research projects. In addition, he has achieved sci-
entic innovation awards three times consecutively by
the Education Department of Shaanxi Provincial
Government, China. Also, he holds memberships in the
following well-renowned institutions and supply chain
bodies/associations: APCIS, USA; Production and
Operations Management Society, India; Council of
Supply Chain Management Professionals, USA;
Supply Chain Association of Pakistan; and Global
Supply Chain Council, China.
About the Authors

1© Springer Nature Switzerland AG 2020
A. Haldorai etal. (eds.), Business Intelligence for Enterprise Internet of Things,
EAI/Springer Innovations in Communication and Computing,
https://doi.org/10.1007/978-3-030-44407-5_1
Chapter 1
Internet ofThings (IoTs) Evolutionary
Computation, Enterprise Modelling
andSimulation
A.Haldorai , A.Ramu , andM.Suriya
1.1 Introduction
The Internet of Things (IoTs), for a few decades, has constituted a lot of vital topics
concerning the future state of industries. Information and Communication
Technology (ICT) is applicable in small telecommunication devices, which are
affordable and regarded more effectively in terms of processing to accessing the
internet. Moreover, Big Data technology was founded to allow businesses to store
massive amounts of information, and evaluate the incoming streams of data with
rened algorithms in actual time. As such, the evolution of the Internet of Things
(IoTs) has allowed companies to create useful remedies for various case scenarios
in various domains. In that regard, the IoTs can be regarded as the shelter terminol-
ogy for various disciplines, which have already been considered in organizational
automations. IoTs also enhances the integration of technological disciplines such as
the automation of sensing information with enterprise resource organization data.
Despite the fact that IoTs technologies are being promoted extensively, just a few
of the disciplines utilize cases which have to be implemented in organizational
cases. Contrary to that, there are about a hundred of IoTs technologies and plat-
forms, which have not been exploited yet. Moreover, there are various initiatives
that explain various technological standards and their respective developments from
establishments. The oversupply of the technological vendors enhances the imple-
mentation of integrated tech solutions. For instance, the research on development of
A. Haldorai (*)
Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India
A. Ramu
Presidency University, Bangalore, India
M. Suriya
KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India

2
IoTs suggest that interoperability, security and internet connectivity represent
approximately three vital concerns in implementing various Internet Technology
(IT) solutions. For the industries and the developers that have implemented the IoTs
remedies, the performances initiated by them have now been considered based on
connectivity. As such, this reects our evaluation, which means that the rate of per-
formance is considered based on architecture and developments created by users
within a network cycle.
1.2 Background Analysis ofInternet ofThings (IoTs)
forModern Manufacturing
In this part, the connection of novel manufacturing paradigms and Internet
Technology (IT) are discussed. The necessities of the organization models for effec-
tive adaption of IoTs are also considered. Many novel infrastructures for organiza-
tions are considered rst. The discussions indicate that IoTs is planned effectively
based on the architecture of the manufacturing industries. Consistently, every sys-
tem element in the ES requires a data unit, which enhances the implementation of
decisions on the elementary behaviours of the obtained dataset. A part from that, the
acquisition of information, information transfer and the process of decision-making
are fundamental functions that have to be considered in each model. In reference to
the Reliability Theory, the Internet of Things is capable of proposing novel reme-
dies that enhance scheduling, planning and the control of manufacturing organiza-
tion at any level.
1.2.1 IoTs Robustness Evaluated Based ontheReliability
Theory
Based on the aspect of scalability, the idea of reliability and robustness is consid-
ered. Thus, the IoTs shall be composed on a lot of electronic appliances whereby a
lot of them are difcult to recongure. The idea of replacement is different from
reconguration on tablet or desktop computers, which necessitate regular imple-
mentation of software updates to enhance modern upgrades. These updates require
CPU power, memory and disk space. As mentioned by the Metcalfe’s Laws, more
novel interconnectivity between systems and subsystems enhances the degree of
system probability that is concerned with the evaluation of system failures. The
capability of IoTs to function for a long time irrespective of the condition of soft-
ware and hardware is crucial to achieving user trust and acceptance. The bits errors
can possibly lead to uncontrollable issues in massive networks, which might possi-
bly characterize the IoTs. OR methods like the Reliability Theory will potentially
be used to forecast on IoTs reliability and robustness. As such, there was need to
A. Haldorai etal.

3
formulate an approach based on the Reliability theory for the present subsystem
networks, which are dependent on applications and perception layers. The total reli-
ability RT is calculated using the below formula.
RRR
k
R
Tk
=××− =−
()





15
14
2
1
π
(1.1)
In the Eq. (1.1), R1 and R5 represent the relative forms of reliabilities of application
and perception layers, whereas R2, R3 and R4 show the reliability dependent on the
internet, satellite networks and mobile networks. This framework can therefore be
updated and expanded in accommodating the remaining IoTs features and charac-
teristics of novel IoTs devices in operation. As such, the RT can potentially be
applied onto the remaining IoTs features like data reliability. This application initi-
ated the introduction of an application known as Fog Computing onto the IoTs
whereas the researcher in [1] evaluated reliability of the methodology through the
combination necessities of cloud and grid based on actuators and sensors in the
IoTs. Resultantly, it was evident that reliability subsystems can be controlled. The
IoTs software reliability in the IoTs can potentially be another important concern
because software reliability may be termed as a special concern in RT.The reliabil-
ity of hardware can be considered based on material or component failure, prevent-
ing a subsystem to perform as intended earlier. The reliability of software in the
IoTs is considerably difcult to evaluate because software can potentially provide
unanticipated ndings for a lot of reasons like unutilized information retrieved from
another networking appliance, which has not been noticed in the designing phase.
The obsolescence of the entrenched software in the IoTs subsystems might not be
maintained, altering reliability.
The researchers in [2] recommend a vital technique in which IoTs can be applied
in business to enhance productivity. Nonetheless, the application of IoTs may differ
for various data subsystems when it comes to providing high-resolution information
in actual time. As such, this diminishes the overall costs of moving information
from the actual world into the virtual world; for instance, the RFID tags can poten-
tially eliminate costly manual stocks in the process of computing information into
the computer. Based on the research, there are various means of applying IoTs in
business to boost productivity. These include proximity triggers such as self-
checking in libraries, automated sensor triggers like the networking smoke deter-
miners and automatic product securities. As such, this indicates that IoTs represents
the quantum leap-forward retrieved through the internet with the capability of act-
ing as an element that governs the entire discipline for managing complex systems
and organizations. Moreover, System Dynamics (SD) has also been implemented in
organizational investment based on new technologies, particularly the incorporation
of internet in China and India. Principally, organizational investment in the IoTs can
potentially be modelled in the same manner. As such the researchers in [3] acknowl-
edge that SD can potentially be represented to quantify the ‘soft’ variables that are
fundamental in the process of incorporation of both technical and social aspects.
1 Inter net of Things (IoTs) Evolutionary Computation, Enterprise Modelling…

4
The major segment in SD denes the results evident in the behaviour of subsystems
from interactions in feedback loops. Actually, different standardized feedback
archetypes and looks have been noticed, including the ‘fails and xes’ and the ‘bur-
dens and shifts’. In that manner, the dominant element of the internet incorporation
was, over the years, argued to be the Contagion factor. With this effect or factor,
innovators begin the process of adoption before developing the process of commu-
nication. This contagion effect can potentially spread the message to the users, mak-
ing the process of adoption to be assumed by the imitators. Eventually, there will be
no new clients left, hence tampering with the whole adoption process, which leads
to market saturation. Moreover, there will also be a negative feedback factor related
to adoption like the concerns realized from security problems, internet dynamics
and IoTs. As such, internet incorporation will be dependent on the balancing aspect
on negative feedback loop organization.
1.3 Literature Analysis ofNext-Generation ofEnterprises
1.3.1 Characteristic oftheUpcoming Generation
ofEnterprises
In this part, we evaluate the characteristics of next-generation enterprise, which are
crucial in the process of analysing the IoTs centred on ES that can mitigate prob-
lematic requirements. The characteristics include the following:
• Decentralized Decision-Making Process: The various levels and domain of orga-
nizational activities are progressively becoming more diversied compared to
how they were years back. As such, hierarchical planning is applicable in effec-
tive enterprise planning for subsystem incorporation. Nonetheless, system com-
plexities can therefore be enhanced critically based on system dynamics and
scale. The centralized subsystem can potentially lead to fundamental inexibility
and time delay, which enhance the response transition quickly. In that case,
decentralized and distributed architecture could be fundamental in dealing with
system dynamics and complexities.
• Dynamic and Flat Structures: Timely feedback to urgent issues necessitates more
decentralized and distributed organizational architecture. In that regard, the
obtained information can be utilized in the process of decision-making in a
timely manner. Based on the association between various networking compo-
nents, one can witness various problems such as delivering data to the concerned
components, particularly under central organization. The collected data is thus
gathered and transferred to a centralized database before being sent to the object
the moment the subsystems receive the requests from various objects.
Nonetheless, the centralized structure has its own problems, especially when
handling Big Data in a heterogeneity surrounding.
A. Haldorai etal.

5
Based on the perspective of Big Data, the management of massive streams of
data faces two challenges altering the incorporation of information systems to the
upcoming generation of enterprises. These issues include the costs incurred during
the process of decision-making. These costs normally increase when subsystem
complexity advances. As such, this leads to resource redundancy in maintaining
information locally, which is the second issue. The redundancy level wastes more
resources and time for information transfer when Big Data is transferred to other
decision-making structures.
Based on the heterogeneity surrounding, there is diversied and increased manu-
facturing resources, which have advanced the heterogeneous condition of manufac-
turing surroundings. These varieties exist at the facet of customized products,
location distributions, regulations, cultures, suppliers, standards and optional
organizations.
1.3.2 Characteristics onIoTs forIndustrial Applications
The features of IoTs in industrial application include the following:
• System Dynamics: The organization of IoTs is never static; hence, it permits
various system elements to be congured any moment whenever there is need to
do so. Thus, this allows the integration of data over various industrial boundaries.
The cornerstone industrial segment can potentially assimilate with the virtual
companies, which potentially establish dynamic connections with a certain proj-
ect. The structure can therefore be dismissed whenever the projects are com-
pleted, and especially when the industry should proceed to another project.
Enterprises should have the capability to control the restructuring of virtual
industrial alley.
• Assimilated WSNs and RFIDs Networks: These features represent one of the
fundamental elements on IoTs as an information transfer protocol for data shar-
ing and acquisition. An industrial system includes a lot of sensors to obtain
machine appliances, actual-time information actuators, conveyors and features.
The ancient assimilated communications are linked to peer-to-peer or point-to-
point, which makes it possible to make any changes. WSN and RFID assure
effective methods of supporting the decentralized and distribution of industrial
resources.
• Cloud Computing: Modern industrial operation engages various activities of
decision-making that necessitates intensive data and high capacities of comput-
ing. The manufacturing industries used to necessitate a lot of computing resources
acting as servers in decision-making units and databases. As such, this leads to
wasted investments, minimal productivity, information exchange and unbal-
anced utility of manufacturing elements among others. Cloud computing is
advantageous since it is recommended to remedy networking issues. All the
information can therefore be stored in both public and private cloud services,
1 Inter net of Things (IoTs) Evolutionary Computation, Enterprise Modelling…

6
which make sophisticated decision-making process to be supported by vital
cloud computing techniques.
• IoTs and Humans: Associations normally take place between human, things and
things, and things and humans. Various forms of these interactions are composed
of various mechanisms used to support various forms of interactions. Based on
the advancement of IoTs, various forms of interactions can therefore be consid-
ered under a single umbrella. In that regard, users can possibly concentrate on the
various tasks without worrying about the associations. These associations are
responsible for making operations and designs of manufacturing subsystems to
be productive. As for the machine and human association, behaviour of humans
in a virtual surrounding can be determined. Therefore, it is possible to recognize
the behaviours of humans in WSN.
The developments in the IoTs have signicantly led to transformative implica-
tion on the community and the environment via massive application segments,
including smart agriculture, smart homes, healthcare and manufacturing. In order to
completely accomplish this, a signicant portion of heterogeneous IoTs appliances
is networked to effectively support the actual actuation and monitoring over various
domains, As such, analysts in [4] propose about 50billion IoTs devices have to be
connected by the end of 2020. Earlier on, the massive portions of data that are gen-
erally referred to as the Big Data were transferred to the cloud by the IoTs devices
to enhance further analysis and processing. Nonetheless, centralized processing in
the cloud cannot be relied when it comes to the analysis of massive IoTs application
due to a number of reasons. One of the reasons is that applications necessitate close
coupling dening the feedback and requests. The second reason is that the delays
witnessed by the centralized cloud-centred deployment are not acceptable for a
number of latency-sensitive applications. Third, there are extreme chances of net-
works failing or data being lost. Lastly, this has resulted in the transition of edge-
computing remedy.
Irrespective of the fact that the present sources of literature differentiate between
fog and edge computing, this research abstracts all of these cases. Moreover, novel
computing paradigms are also considered as a vital concern of edge layering. The
initiation of edge computing presents these major concerns based on the provision
of computational capability in close proximity to information-generating devices.
The smart edge network devices like the smartphones, Pi and UDOO boards pro-
vide fundamental support to local processing and information storage extensively
but to a smaller segment. Nonetheless, constituent networking devices in edge com-
puting is termed as heterogeneous as they are composed of certain architecture fol-
lowing a given protocol in communication. Different from the cloud where locations
of data centres are xed, IoTs and edge devices are controlled by batteries, and solar
linked to an eternal power supply compared a cloud storage data centre linked to a
stabilized power supply. To completely take advantage of the goodness of edge
computing, it is vital to comprehend the capabilities and features of IoTs and edge
devices alongside the elements of IoTs data evaluation systems. The diversication
of the underlying edge and IoTs devices, formats communication mediums, formats,
A. Haldorai etal.

7
functional complexities and programming frameworks enhance the process of net-
working evaluation, which also makes it more time consuming and challenging.
Analysing the systems in an actual surrounding assures the best performance
behaviour; but it is not normally easy to evaluate different frameworks in advance.
Despite the availability of infrastructure, it is fundamental to perform various exper-
imental settings since it is necessary to apply skills based on associated edge and
IoTs devices that are not intuitive. Executing a lot of experiments meant to deter-
mine the correct framework necessities reconguration of a number of devices and
transitions to necessitated parameters, which promptly become untenable appli-
ances as a result of volume transitions required. Moreover, executing these experi-
ments in an actual environment is signicantly costly because of the maintenance
and setup. Thus, the actual surrounding is considered dynamic, which means that it
is difcult to reproduce much results representing various iterations leading to mis-
interpretation based on evaluation. These issues possibly hinder the application of
actual environments used to benchmark edge computing surroundings. In order to
effectively overcome this problem, a feasible alternative relates to the application of
simulators. These simulators provide novel chances of enhancing the evaluation of
the recommended policies and frameworks in a basic, repeatable and controlled sur-
rounding. A simulated surrounding has to mimic the major heterogeneity and com-
plexity of actual networks that support a lot of multiple cases affecting the
deployment of IoTs.
Contrary to that, the research in [5] gives a performance analysis of the various
protocols governing the application layers of IoTs structure. In that regard, these
researchers provide a comparison of the Hypertext Transfer Protocol (HTTP) and
the Constrained Application Protocol (CoAP) that enhances the process of format-
ting messages, transferring information and requesting users to evaluate various test
beds. There are various deployments of evaluating various performance benchmarks
for IoTs; nonetheless, this is done on the levels of platforms.
To effectively address the initial research concern, various levels and developer
duties in IoTs architecture have to be considered. Figure1.1 indicates the basic IoTs
architecture (Device, Gateway and Cloud Platform), which has been diminished to
three fundamental layers. In the rst layer, we have the constrained controllers and
devices that stand in for the IoTs [6]. Second, there are gateways, smart devices and
routers that enable fog computing in each edge and can also associate as a pre-
evaluated dataset from networking devices. The third layer represents the platform
store, aggregate and process data retrieved from various sources, allowing enter-
prise application to report and analyse data to the end users. Communication and
connectivity in different levels is thus not designated to a single direction. Moreover,
non-functional necessities, for example security, performance and interoperability,
are considered the key factors inuencing the levels of performance. There are key
questions that pop-up for performance engineers. These include the following:
• How can computing devices, such as disk, CPU, network and memory, be
adjusted at every level?
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8
• What are the implications of protocols, interoperability and security choices in
reference to general networking performance?
• Do the architecture scale effectively based on the increased number of gateways
and devices?
Considering the fact that IoTs stack renders more diverse, various engineers and
developers are engaged in the process of implementation. Systems and embed-
ment are concerned with the device levels, which are a bit concerned with the
gateway levels. Since gateway levels continue to expand technologically, they con-
tinue to run more advanced operating systems. Moreover, application engineers
and developers have continued to be a major segment of gateway levels. On this
platform level, a mixture of information experts, application analysers and web
developers implement the visualization and integration of information [7]. Based
on the mixed interests on every level and role, it is possible to comprehend the
inuencing conditions in a holistic viewpoint, which has to be added to our pro-
spective model of approach.
1.4 Challenges andContributions ofIoTs Technologies
As mentioned earlier, IoTs increases and assures the chances of linking various
approaches, for instance, the approaches requiring capacity architecture for gate-
ways and devices based on a formal model that is acknowledged in the domains of
enterprise data systems. Due to the fact that there are a lot of modelling approaches,
there is need to review the challenges and contributions of technologies used in the
various levels.
Fig. 1.1 IoTs Architecture Abstract
A. Haldorai etal.

9
In the device level, for example, the AutoFOCUS31 stands for the associated
model-centred tools used to develop various processes in an embedded framework.
This therefore includes those activities involved in evaluating modelling necessities,
hardware platforms, software structure and deployment, including the generation of
codes. Software architecture is created based on various software components lined
to each other to enhance the interactions broken down into a lot of hierarchical com-
ponents [8]. The architecture dening hardware includes different resources such as
memory and processors that are connected. This also involves the platform structure
applied in the runtime and execution environments like the operating architecture
and Java Virtual Machines. The combination and integration of various models
facilitate developers to effectively apply various synthesis and analysis techniques
like model-evaluation, testing, automatic scheduling and deployment. An Eclipse
Framework representing the distributed enterprise control and automation is a major
segment of the Eclipse IoTs environment that represents a case for gateway level
modelling. This therefore assures an open-source infrastructure dening the distrib-
uted industrial process evaluation and the control framework based on IEC 61499
protocols. To effectively model the software architecture, the 4diac incorporates an
application editor, which is used to represent the functional block network that con-
sists of a single functional block, including its various interactions based on its
events. In the same manner, the separator editor is added to the model’s specica-
tions of hardware through modelling resources and devices. Through various edi-
tors and their runtime ecosystems, the 4diac permits the use of enterprise IoTs
application, hence allowing interoperability, portability, scalability and congu-
rability based on the promotion of IEC 61499 [9]. In the platform level, various
associated approaches for supporting, integrating and automating engineering ele-
ments in software’s lifetime are controlled using the Performance Management
Work Tool (PMWT). This incorporates an automated generation of models for orga-
nization application with reference to the performance measures, modelling com-
plex individual application behaviours and simulating the overall status of Big Data
applications.
With reference to performance elements, the current remedies based on net-
working devices and gateway levels normally concentrate on assuring safety and
functionality. Contrary to that, there are novel performance models that are used to
analyse and predict the platform level behaviour. The present framework on the
level of architecture assures users of the benets this research seeks; nonetheless,
we have based on enterprise application involving various necessities. For exam-
ple, the workloads of enterprise applications are categorically user-initiated like
the parallel user accessibilities where IoTs factors such as velocity, volume and
incoming datasets are initiated by data. Moreover, parallel and massively distrib-
uted resource clusters include properties that are normally found in enterprise
applications. In that regard, there is need to combine the present model techniques
and the missing functionalities to enhance the performance of various models of
architecture. Therefore, solvers and simulators can be implemented in deriving
various metrics like throughput, response time and utility of resources. To effec-
tively evaluate our recommended approach, we propose to model an application
1 Inter net of Things (IoTs) Evolutionary Computation, Enterprise Modelling…

10
based on IoTs benchmarks to adapt and suit for models in different cases such as
enhancing the IoTs and enhancing resource capacities. The challenges and contri-
butions witnessed include the following.
1.4.1 Challenges
Modelling and simulating a real-life IoTs case is signicantly problematic for a
number of reasons. These are as follows:
• Various IoTs devices require combination of the cloud and edge systems to effec-
tively satisfy various application requirements.
• Second, the process of modelling network graphs in various diverse forms of
IoTs and edge devices is challenging.
• Third, control ows and modelling data dependencies over the edge and IoTs
layer to support massing information evaluation and work ow structures are
considerably non-trivial.
• Capacity evaluation over the edge computing segment depends on different con-
guration parameters such as data volume, upstream, downstream, data speed
and bandwidth.
• Information transfer between IoTs and edge networking device is varied from
cloud data centre communication that is centred on wireless or wired protocols.
Connection between edge and IoTs computing layer, as evaluated in this research,
is diverse. In that case, it is challenging to create an abstract without going
against expressiveness.
• Mobility levels remain to be a fundamental element of IoTs devices since the
sensor embedded in various physical is somewhat mobile. Due to the fact that the
edge devices are considerably limited, the mobility of sensors can possibly lead
to a handoff. Moreover, the information transferred to the edge devices for the
purpose of process cannot be in the present range of IoTs devices. In that regard,
to obtain the processed information, the edge-to-edge communication is neces-
sary. Modelling a handoff or mobility for massive IoTs devices with various
velocities is considerably problematic.
• Dynamicity of the IoTs ecosystem computes to either the removal or addition of
IoTs and edge devices in most cases. This is thus caused by various factors such
as network link failure or device failure. To model the scalability of IoTs devices
using heterogeneous characteristics at a rapid rate is signicantly problematic.
• Due to the fact that IoTs ecosystem is an upcoming development, novel applica-
tions should be created in the future. It is vital that simulators permit users to
personalize their frameworks with reference to their networking necessities.
Creating an overall simulator, which permits easy personalization, is consider-
ably problematic.
A. Haldorai etal.

11
Various simulators have also been proposed in past works. These include
GreenCloud, and Cloud that represents the cloud environment. Nonetheless, iFog-
Sim and EdgeCloudSim had been proposed for the purpose of implementation in
edge computing ecosystems. However, there exists no simulator that can potentially
mitigate the challenges outlined above.
1.4.2 Contributions
This research proposes the IoTSim-Edge simulators, which is meant to permit users
to analyse the edge computing cases more easily since this simulator is more cus-
tomizable and congurable in the ecosystem. The IoTSim-Edge is created based on
the simulators that have been proposed in the past. However, the IoTSim-Edge cap-
tures the general behaviour of IoTs and the edge computing planning deployment
and development. Mostly, this model deals out the challenges that have been dis-
cussed. Apart from that, this simulator can be used in analysing the present and
future IoTs applications. Particularly, the model can potentially model the
cases below:
• Novel IoTs application graph modelling abstract that permits practitioners to
explain the information analytic operations and mapping of different infrastruc-
ture segments such as edge and IoTs.
• An abstraction supports modelling of the heterogeneous IoTs protocol with
energy use proles. This therefore permits practitioners to explain conguration
of IoTs and edge devices alongside certain protocols that support the process of
networking.
• An abstraction permits modelling of cellular IoTs devices. It critically captures
the consequences of handoff as a result of IoTs movement network devices [10].
To effectively maintain a consistent information transfer channel, the simulator
is useful for enhancing an edge communication, permitting the transfer of data to
IoTs devices, that is, edge to edge.
1.5 Edge andIoTs Computing
This part provides the background analysis of the IoTs based on modelling prob-
lems. Moreover, this part also explains the overall architecture of the edge comput-
ing used to model based on the proposed simulator.
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12
1.5.1 The IoTs Ecosystem
The IoTs are defined as ‘the state-of-the-earth sensors’ that are embedded in
physical things and humans that are surrounding us and linked to the internet
to control and monitor connected IoTs. Various IoTs application advance our
day-to-day lifecycle in various vertical domains such smart healthcare services
and smart homes and in disaster management. The functions of IoTs are initi-
ated by six vital elements such as communication, sensing, identification, com-
putation, semantics and services [11]. The IoTs devices have the capability to
evaluate the ecosystem while separating ubiquitously controlling both the envi-
ronmental and physical surrounding information. Thus, IoTs devices are con-
sidered more identical based on various application techniques and requirements
used to implement various applications. The process of computation is distrib-
uted over various IoTs edges, devices and cloud data centre considering the
desired and functional Quality of Services (QoS) and application parameters.
To attain this, data is transferred from the IoTs devices to the network edge
using various communication protocols. The results of computation can be uti-
lized to initiate more decisive operations to attain the desired application
processes.
Based on a simple sample of the smart homes, which control a lot of devices in
urban environments, it is easier to potentially ease the lives of inhabitants. IoTs
devices include the sensors added to devices such as heaters, refrigerators, cars and
light bulbs, including mobile phones and gateways. The intelligent home systems
utilize the private cloud information centre resources. Home appliances are linked
to the gateways with light weight protocols based on the CoAP protocols enhancing
the transfer of information. Mobile phones are linked to 4G whereas gateways are
linked to Wi-Fi for information transfer to the private cloud [12]. In case the resident
individual leaves for ofce, the intelligent home systems possibly switch off the
heaters and light bulbs. Moreover, the systems have the capability to determine if
there are things such as foods, in the refrigerator, before sending message to some-
one to collect them.
Modelling actual IoTs applications necessitates the linking of different
actuators, sensors and edge devices on a massive scale with various operating
ecosystems. This is a difficult task because of the heterogeneous features of
IoTs and the edge devices requiring progressive optimization for resource allo-
cation, migration, provision and fault tolerance in processing various applica-
tions [13]. Moreover, the process of implementation is onefold, providing a
more generic framework for IoTs application in case the application necessi-
tates level abstraction. The key problems based on modelling application, IoTs
mobility, networking protocols and the consumption of energy are evalu-
ated below.
A. Haldorai etal.

13
1.6 Application Composition
Generally, the IoTs is made up of a series of tasks executed using some detected
data. This can be shown using a number of techniques, although the chapter
adheres to IoTs applications in a clear and (Dag) direct acyclic graph for the
MELs (microelements). Every MEL is regarded to be an abstract component
for each application, which stands for every assets, information and services
exclusively creating the microdata, micro-service, micro-computing and micro-
actuation. Therefore the modelling of every application so that they can be like
the DAG for the MEL can be very difficult; hence, there is need for encapsulat-
ing a number of features altogether. Additionally, the MEL sequences have a
crucial role since they are used for representing the flow of data within an
abstract phase.
1.6.1 Communication Protocols
Within the IoTs surroundings, various messages and network connection protocols
normally play crucial part during the communication process. Considering the
genetic elements within the IoTs environment, there are various complex network
linkage amidst various IoTs environment features. Centred on the range, certain
limitations, device type and other protocols to be used within the IoTs network, the
edged gadgets can be used for transferring information. There is need for mobile
devices to leverage certain protocols unlike the static ones.
Modelling protocols using the application graph can be difcult. Moreover,
some messaging processes can be accessible in order to ease the process of transfer-
ring the data from the sensors to every edged gadget and later to cloud servers.
There are certain protocols that have been set for this objective. Distributing infor-
mation with the aid such protocols may inuence the execution processes of the
systems in dissimilar ways. Therefore a single message protocol being not in a posi-
tion of satisfying various requirements of the IoTs complex can also be used. For
these reasons, it is necessary for every device to link up every protocol for every
gadget and dissimilar layers to be used [14]. The modelling of such events using a
handshaking amid these protocols can be difcult since the movement of the IoTs
devices using the IoTs devices can be embedded using cars or rather smart phones
that aid the various users in a exible manner. Knowing the fact that the edged
devices are normally transxed, every IoTs device is allowed to go from one range
edged device to a different resulting in a handoff. In some cases the handoff may be
audible as soft or hard based on the velocity of the IoTs gadgets and the signal
strength of each edge device. Therefore simulating the mobility in an adverse man-
ner, there is need for incorporation of various features such as the IoTs devices
1 Inter net of Things (IoTs) Evolutionary Computation, Enterprise Modelling…

14
velocity and acceleration, the pathway of the motion, topological maps, edge range
intersection, etc. Taking into account all these factor, the real mobility of the simula-
tion can be a hard role since there will be huge data number points that comprise of
extensively reliant data one the relationship and characteristics [15]. Additionally,
the transfer of information can be unsuccessful at any moment if the IoTs devices
may be moving from a certain location to a different one basically due to poor signal.
1.7 Battery Drainage
Much IoTs drainage is driven by the battery, which is normally a restriction and
hence should be recharged at various intervals. Hence it is crucial that the devices
should be in a position of holding off the battery for an increased time mostly when
the applications cannot be very easy recharging, for example, the sensors within
rivers or places of havoc. Holding the battery off for an increased period of time will
highly save much time, and this is crucial almost for every application. Therefore
transferring information at dissimilar intervals and utilizing various communica-
tions protocol.
1.7.1 Architectural Computing forIoTs Edged Devices
As shown in Fig.1.1, the architecture of the IoTs edge has been shown in detail.
Moreover the IoTs technology comprises of a number of components such as the
sensing nodes and also actuator nodes. The sensing nodes are responsible for the
collection of data within the sensors and transmit the data whereby it will be required
to be processed and stored. The actuators will therefore be centred on the data anal-
ysis [16]. Thus the subsequent layer will be for the edged infrastructure that com-
prises of different forms of edged devices, for example, the arduino and raspberry
Pi. Such gadgets may be easily accessed openly with the aid of various forms of
containerization and virtualization techniques. These mechanisms provide suf-
cient infrastructure that is used for the deployment of various raw data created
through various sensor nodes. For many instances, if the edge can be in a position
of creating more data, then there is no need of transmitting the information via the
cloud for processing. Lastly, its outcome is later transmitted to its actuator so that a
certain action can be executed. Therefore the services and the application layers
comprise of dissimilar services which can be accessed to various clients. Later the
applications can be acquired via a subscription channel. Some of the examples for
these services include smart city and smart home. Every MEL is normally distrib-
uted within an edge or rather the cloud information centre. Additionally, the pro-
gram helps in managing every QoS necessity within the prospective load and any
error handling. Moreover, it delivers services, for example management of resources,
device management and storage management.
A. Haldorai etal.

15
Service issued by such a layer normally ascertains that the QoS needs have been
successfully met. The present application distribution and scaling methods created
for the distributed environmental algorithms, for example, the cloud or grid, cannot
be effective when employing the new IoTs environment. The main reason is there
are extensive features and characteristics used for the smart gadgets aside the mobil-
ity elements and the latest applications technology which is made of a limited reli-
ance and hence needs process distribution. Based on the type of application, there
has been collaboration amidst the IoTs, cloud and the edge that are required for
attaining a prospective QoS desires. Therefore, creating new applications deploy-
ment alongside its scaling methods is very crucial. Moreover, it is important to
analyse and test such methods prior implementing the actual deployment.
Nevertheless, analysing these kinds of techniques require a real environment incor-
porating a set of conditions at every moment which is expensive and time consum-
ing. Additionally, based on the alignment of ownership of various gadgets,
assessment needs a number of mechanisms that may make something become much
difcult. Hence, various simulation frameworks, for example, the IoTSim simula-
tion that aids the distribution of these applications that assess the execution pro-
cesses various methods, certain events within certain conditions may be necessitated
[17]. Additionally, assessing these methods in certain circumstances may be exe-
cuted at low cost within a simulation environment.
1.7.2 The IoTs Sim Edge Architecture
Based on the architecture for the prospected simulator, it shows a series of layers. A
concise analysis of its distinctive components has been highlighted in this section.
IoTs Sim edge is structured within clouds simulation equipment. The cloudsim
delivers an underlying system that is used for assisting some basic communication
processes within various subscribed components that use the event management
mechanisms. Hence the primary components used for the cloudsim have been
extended so that they can stand in for the edge infrastructure within a line compris-
ing edged characteristics and features. The IoTs asset layers comprise dissimilar
forms of IoTs gadgets in which they have their own elements and characters along-
side execution processes used for actuation and sensing [18].
1.7.3 Implementation andDesign
When the IoTSim edge is enacted, the missing gap amidst the cloudSim has to be
extended alongside vast numbers of classes so that the model can be shown in an
edged and IoTs environment. A certain entity that increases the SimEntity class may
seamlessly transmit and acquire various events for its partners via the event man-
agement engine. Moreover, this research exhibits a basic trait for the simulators, a
1 Inter net of Things (IoTs) Evolutionary Computation, Enterprise Modelling…

16
cycle that comprises of various actions and procedures. Therefore, to model the
edge infrastructure, there was need for the designation of vast number of classes
within the network. Major classes include the edgebroker, edgedevice, edgedata-
centre, microelement, edgedatacentrecharacteristics and the edgelet. For instance,
the edgedatacenter’s role is used for creating a link between the IoTs devices and
edge centred on some provided IoTs protocols, and the process is accompanied by
asset edge provisioning scheduling rules and assessment of edge processes.
Moreover, the system is also responsible in the creation of the edgedevice, submis-
sion of the edgelet, establishing of a network amidst the IoTs infrastructure, assess-
ing network accessibility, etc. These particular characteristics for the edgedatacentre
are always fed using the edgedatacenter characteristics class [19].
For this reason, the edgebroker’s role is acting in place of the clients based on
establishing a connection from the IoTs devices and the edge devices, submission of
the edge and IoTs requests, and acquiring various outcomes. This class outlines a
model for the edged gadgets specically when acquiring and processing informa-
tion from the IoTs in which the data processing is executed following a specic
edgelet rules and policies. On the other hand, the MEL class is used for modelling
the abstract processes that later performs within the IoTs data for each edged side,
or every cloud information centre. Considering the present implementation for each
MEL, only the running of the edged devices will be prioritized. Hence the edgelet
model class is required to be performed within the MEL.In some situations the edge
device could consist a battery such as a mobile phone, hence going around, thus, the
mobility and battery class will be designated to activate the edged device so that it
can extract its constituent characteristics. Therefore, the Moving Policy class pre-
scribes the conditions and characters for each edge device [20]. At the same IoTs
infrastructure can be modelled, vast number of new classes have been designed.
Regarding speed rate such as Wi-Fi, the Network Protocol is a recognized com-
munication system where it can transfer network packets at a velocity of 200 Mbps,
and the 4G LTE will be at a speed of 150 Mbps. Hence the frequency for the edgelet
in different terms, the delay period that will be required to transmit the edgelet to the
datacentre that is normally accessed from the network policy class. On the other
hand, the class models for every feature within the IoTs protocol in relation to the
battery and QoS consumption rate has been mentioned [21]. Since each IoTs pro-
cess comprises of dissimilar processing methods, everything is structured in a man-
ner that they may have dissimilar energy consumption rate. Much more detailed
explanation based on the classes has been given in the following sections.
1. EdgeDataCenter Class
This class is responsible for controlling various core edge infrastructures. At
every stage counter-intercept various incoming calls and processes and execute dis-
similar protocols centred on the payload of a particular event, for example asset
delivering and handing over various edglet requirements to its desired MELs. When
the battery of every edged device is discharged, the edgedatacenter will detach auto-
matically and send various unserved requests to its subsequent edged devices.
A. Haldorai etal.

17
2. EdgeBroker class
In this class, there are some clients’ proxy, whereby various requests are created
focusing on the prescribed necessities. This class comprises of a range of roles for
executing, for example, the submission of edge and provisioning of the MEL
requests to every datacentre, getting the IoTs devices to create and transmit data to
its prospective edged devices and acquiring its nal processing outcome. The class
aids various power aware models for every IoTs gadgets. Since the edge broker
consistently assesses the consumption of every battery for the IoTs gadgets, imme-
diately there will be a disconnection from the nearby IoTs devices.
3. EdgeDevice class
The class is assumed to be the same as actual edge devices. Its main role is host-
ing a number of MEL and simplifying the protocols of the CPU sharing appliances
through a specied CPU sharing rule. Moreover, they are further linked to have a
certain number for each IoTs device that transmits its data for processing purposes.
Focusing on the present version of the simulator, a set of four classes has been used
to extend the IoTs devices class which include lightsensor, voicesensors, carsensors
and temperature sensors. Therefore, an improvised form of IoTs device may easily
extend the IoTs device class and hence enact a new set of features. Within an actual
IoTs environment, each IoTs gadget harbours an IoTs and network protocol; hence,
the class comprises of same features when utilizing the networkprotocol and
IoTProtocol classes as analysed in subsequent phases in this chapter.
4. MEL class
The role of this class is representing a single component using the IoTs applica-
tion graph. It basically represents the core processing necessities used by the appli-
cation components. Centred on the application requirements, setedgeoperation
techniques may be congured. Hence the reliance amidst certain components may
be shown by using an uplink and downlink that can be easily structured in order to
portray some possible difcult applications [22]. This can help in conguring which
stands in for any difcult IoTs applications centred on the clients necessities.
5. The EdgeLet class
The class models every IoTs created data and its subsequent MEL processing
information. At any moment that the IoTs gadget created a unique link using its
respective edged gadgets, there will be an immediate response that will generate
IoTs sensed data that will be required in the form of the EdgeLet which comprises
of a payload. The role of a payload is encapsulating the correct data so that it may
direct the MEL at the processing podium, for example, the size of each data set cre-
ated, the route followed by the MEL ID, and the IoTs device ID [23]. When using
the size of the EdgeLet data size, the delay of every network will be needed to trans-
mit the EdgeLet to its required MEL and hence can be calculated considering the
network transmission frequency.
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6. Mobility class
The class is made up of a model of IoTs and alongside various edge devices. Its
mobility has a crucial role within the IoTs edge system in which upholding actual
time consistency is crucial so that the performance processes can be executed. Its
prospective attributes may comprise of velocity, location, range and the time inter-
val. Every attribute taking off time comprises of some distinctive values so that they
may represent the vertical and horizontal direction for every edged IoTs devices.
Moreover, this chapter portrays some possible examples of the way various attri-
butes have been utilized using the edge datacentre so that the location can be
reviewed by the edge and IoTs devices. On the other hand, algorithm 1 portrays
pseudo-code used for simple tracking methods enacted within the EdgeDataCenter
class. Various clients may easily increase every class so that they can implement
their mobility models.
7. The Battery class
This class is used for modelling the battery features for the portable IoTs and the
edge devices. Through using the transmission rate modelling, the time used for
transmitting every data may be achieved considering the size of each EdgeLet.
8. Policies classes
The main functions of these classes are modelling the policies in three distinct
groups, which include: battery consumption, device movements and network trans-
mission. Therefore, the device movement rules may instruct edge datacenters so
that there can be tracking of the location and the movement of both the edge and
IoTs devices. The consumption of the battery rules normally calculates and tracks
the subsequent power capabilities for each edged and IoTs devices. The transmis-
sion of the network policy calculates the duration of time taken so that the data can
be distributed from one IoTs device to another edged device [24]. These kinds of
policies may be lengthened so that a new IoTs edged solution and strategy can be
derived. Both the classes may be extended so that various client policies can be
extended and implemented.
9. User Interface Class
This class normally provides the required techniques that can aid effective con-
guration for each IoTs applications not knowing other data based on the simula-
tors. Moreover, it permits the user explaining every parameter while using associative
interfaces that can be converted to a required conguration le. The content below
shows the data in the user interface.
"ioTDeviceEntities":[
{
"mobilityEntity":{
"movable":false,
"location":{
"x":0.0,
A. Haldorai etal.

19
"y":0.0,
"z":0.0
}
},
"assignmentId":1,
"ioTClassName":"org. edge. core. iot. TemperatureSensor",
"iotType":"environmental",
"name":"temperature",
"data_frequency":1.0,
"dataGenerationTime":1.0,
"complexityOfDataPackage":1,
"networkModelEntity":{
"networkType":"wi",
"communicationProtocol":"xmpp"
},
"max_battery_capacity":100.0,
"battery_drainage_rate":1.0,
"processingAbility":1.0,
"numberofEntity":5
}]
1.7.4 Computation andEvent Processing
Various simulation processes occur on time for initializing the proper IoTs edge
structure that has been extracted for a certain le conguration. Moreover this
research exhibits the conguration test for every le within the IkT gadgets. After
the creation of the MELs and the IoTs gadgets, every edge broker will request data
from its centre so that effective connections can be established amidst the IoTs
devices and hence its respective MELs.
At any moment that the edge broker gets the new established connection (ACK),
there will be an immediate notication from the IoTs devices based on the connec-
tion. Moreover, the IoTs devices can commence creating data as edgelets, distribut-
ing the edgelets to its nal MELs and reducing the battery power based on the rate
of drainage. Hence the IoTs devices can maintain the execution processes (Fig.1.2).
1.8 Case Studies: Sim Assessment fortheIoT
In order to assess the usefulness of the IoTs sim edge, three scholarly studies have
been carried out. Explanations of each study has been identied in the below case.
1 Inter net of Things (IoTs) Evolutionary Computation, Enterprise Modelling…

20
1.8.1 Case One: Healthcare Sector
Considering various works that involve activity information has been segmented
into various micro-applications, there were also some realizations about the similar
circumstances. So that the raw information utilizing the phase counts calculation,
every micro-operation is represented to be as the MEL which can be undertaken by
the unforeseen IoTs gadgets. There is a basic MEL graph portraying the one neces-
sary for deploying the edge within computing vicinity. For nding out the pareto-
customized deployment answers within a deployed IoTs infrastructure, it is required
to evaluating dissimilar deployment strategies for the matter to be effective. Due to
the fact that there has been limited reliance within the MEL, it is crucial considering
such a factor during the deployment process. In order to enact the main simulator,
two-edged gadgets can be embedded within the IoTs devices. For these reasons, the
devices create information centred on some elaborated data frequency.
Scientic evidence is evidence that serves to supportthe rate at which batteries
drain that the rate of transfer is always greater compared to its algorithm. For the
fact the edged devices powered by a battery, the process that happens within the
device edge, then the rate at which every battery drains is handled in this chapter,
although executing much more performance processes close to the IoTs gadgets can
augment the performance time that is crucial in many instances. Additionally, vari-
ous outcomes have shown and analysed overall performance duration in dissimilar
scenarios. Centred on actual scenarios, the MLs have been allocated dissimilar
MELs. Moreover, this research evaluates a quick rational about battery hours that
comprise of edged processes which demand a shrinking element. Hence various
shrinking features have occurred within the edged gadget.
Hence the resultperformance rate for every edged devices was 90% that was
embedded which results to saving much more battery power unlike conveying raw
information to the rest of the devices. Therefore the outcome for every processing
time is 90% for each execution of the device which maximizes the utilization of the
AMQP
loTProtocol
loTDevice
EdgeBroker
Battery
Mobility
MovingPolicy
MELEdgeLet
EdgeDataCenter
EdgeDevice
1
1
1
1
11
1
1
1
1
1
1
11
11
1
1
1..
1.. 1..
1..
1.. 1..
1..
1
1
1
1
1
NetworkProtocol
NetworkPolicy
VoiceSensor LightSensor TemperatureSensor CarSensor
MQTTXMPP CoAP
Fig. 1.2 Class diagrams of the IoTSim-Edge model
A. Haldorai etal.

21
battery by 266% unlike executing 10% operation. For the fact that the edge E1 com-
prises of a minimal processing and harbouring power, every process may not be
explained in this section. Dissimilar forms of evaluation may be executed by using
various scenarios. Clients may additionally suggest dissimilar computations so that
an effective deployment strategy can be found to focus on quite a number of objec-
tives found.
1.8.2 Case Two: Smart Building
Previously, smart building systems automatically caused the heating, lighting, air-
quality, air-conditioning, etc., which have gained much focus. Various forms of
sensors that transmit at certain intervals and specic sites have the capacity of
sending information to various linked devices that may process and analyse vari-
ous data sets. Therefore edged gadgets conduct common processes and transmit
information to the cloud and when this happens there is increased storage or com-
plex analysis necessitated. Various IoTs devices transmit its data to edged devices
complying with a common communication process. Various elements such as
latency imminently rely on the rate of the data and size of the packet [25].
1.8.3 Case Three: Capacity Planning Units forRoadside Self
Driving Cars
Some of the incoming advancement whereby every vehicle can be embedded sen-
sors that transmit data to be RSU controllers which consider the consistency of the
trafc and maintain the safety of the road. Moreover, the RSUs may provide an
excellent platform for the edged devices to transmit the data acquired from a spe-
cic vehicle and may create a runtime decision. The car ranges alongside the RSUs
are restricted, and hence every vehicle is directed at a certain speed to a certain point
of the road. Therefore the area covered by the RSU solely relies on the protocol
transmission. Probably, the car’s connection diminishes alongside the linked RSU at
a point of time. At some moment the handoff can be difcult following the previ-
ously mentioned RSU so that various decisions can be made, while an RSU-RSU
information delivery will also be created.
Based on the point of focus of the car, an RSU may convey a message to a differ-
ent RSU that can be later directed to the nal car. Because the Gap and the processor
capability of the RSU are always restricted, evaluating the count of vehicle data to
be processed is possible by making sure that there is no data loss. Additionally, it is
essential to assure that the requirements for the QkS such as the application time
response are available. The scenario was effectively structured using a simulator. At
rst there is an establishment of a brand new link for the RSU referred to as RSU1,
1 Inter net of Things (IoTs) Evolutionary Computation, Enterprise Modelling…

22
which commences transferring information. On the other hand, at time t3, a certain
point is reached whereby the range of both RSUs is attained. Relying on its mobil-
ity, a connection can be created by RSU2 but is still transmitted to RSU1. Regarding
its motion, the connection can be made, hence data will be sent to RSU2. Moreover,
RSU1 nds out that every car has its range and can be centred on the data being
transferred as exhibited by time t4. Such an event stipulated that these mobility
features are cooperative when accessing days pertaining every simulator.
On the other hand, precise conguration of each simulation has been considered
in this research specically. These edged devices are unique except the location
which includes E1: 0, 0, 0 and E2: 50, 0, 0. Based on this fact, the outcome high-
lighted can precisely prove the idea [26]. Because the edged devices number are
always constant and the processing manner is timely, various requests may congest
prior processing, resulting in a greater performance processing time. Furthermore,
when a certain vehicle goes further away from the RSU, therefore the RSU need to
transmit the data to the other RSU whereby the data is later transmitted to the other
cars. Because such comes among the processing time, hence performance period
will be great. Therefore the greater the number of various requests for a certain care,
then the edge can take up great energy as assessed by these simulation that portrays
the averagely energy utilization rate for the edged devices as they increase from one
number to the other within the IoTs network.
1.9 Related Sources ofLiterature
If there is much interest in the computing edge of the IoTs, vast simulation equip-
ment would have been created for previous years. Various tools can be extended
considering the present network and the loud simulator, although there exist some
gap within the simulator and actual modelling edge and the vicinity of the IoTs.
This phase analyses the present simulation equipment used within the network, IoTs
environment, cloud and the way they cannot model the present IoTs edged environ-
ment. Moreover, it is shown that the simulation structure is in a position of meeting
the expectation of the accessible limitations using holistic methods.
1.9.1 Network Simulators
Some tools used for the simulation process have been recommended and hence
preferred to be used within the computer networking system for the last years.
Considering all this, this section analyses some common recognized network simu-
lation equipment. These tools include the C++ and the OMNeT++ focusing on the
discretion of various events of the environment alongside the communication net-
work. The system aids a parallel simulation network and a real-life implementation
protocol for the simulation models. Additionally, various network processes can be
A. Haldorai etal.

23
supported although cannot harbour the edge communication protocols [29].
According to scientic analysis the OMNeT++ system was created so that there can
be consistent simulation body area network and some accompanying lower powered
gadgets. Furthermore, the simulation can be endured within the network with an
extensive number being dynamically driven. By increasing the energy support for
every model different protocols for the communication process can be used. An
example of a source-driven simulation includes the TOSSIM, which is used within
the WSN (wireless sensor network). Therefore, the energy usage may not use any
mobility modelling and its consumption. In order to aid energy consumption rate
within the WSN, a different simulator has been suggested, referred to as the pow-
erTOSSIM, which is achieved by extending the TOSSIM. Therefore, the pow-
erTOSSIM takes into account various nonlinear characters for the battery
simulation model.
Furthermore, the mobility elements have not been mentioned. Whereas the NS-3
will a different C++ model for a simulation whereby it is made up of the python
interface. Such equipment is used for the simulation that normally distributes visual
aid to the neighbouring environment. The NS-3 will not be an effective IoTs simula-
tion within an edged level as they do not provide support for the application and
scheduling of various features. For the cloud simulation, majority of them have been
recommended to be used as the best cloud computing tool which provides excellent
outcomes [27]. Amidst them, the cloud sim has been regarded to be prominent sim-
ulator so far within the research sector. Various clouds’ sim that are event driven and
that aid the system and behavioural modelling within the cloud environment have
been mentioned. Nevertheless, the IoTsim edge has been created to aid extensive
scale simulations whereby there can be support for various communication systems
and also physical modelling. Such simulators comprise of customized international
hypervisor that can aid cloud brokering rules. Generally it may comprise of the
Amazon public clouds scenarios and aid the MPI.Additionally the network cloud-
sim can be a different simulation that permits such models within the network that
comprises of cloud information centres [28].
In these cases, such simulators may not be in a position of aiding the IoTs and the
edge simulators. A different cloud simulator includes the green cloud that can be
increased using the NS2 simulation equipment. On the other hand, greencloud is
made up of packet-level equipment which is used for establishing the amount of
power usage for the components within the data centre. The main functions of the
simulator are computing the energy used in order to ascertain that there is energy
awareness placement. Moreover, there is no support of the edge and the IoTs simu-
lation. The DCSim is a different cloud simulation that entirely reects on the IaaS
cloud vicinity simulation. The main role is aiding the simulation and modelling of
various data centres, the VM and the host using restricted application number and
management of asset policies. Conversely, the IoTs simulation and the edged envi-
ronment cannot be aided using this type of simulator. Much simulation equipment
has been recommended for the simulation within an edged environment. The
SimIoT is known to be simulation equipment that effectively models the
1 Inter net of Things (IoTs) Evolutionary Computation, Enterprise Modelling…

24
communication amidst the cloud data centres and the IoTs devices, although they do
not take into account various edged device within the simulation [30].
Generally, not all simulators support the edge information transfer protocols and
the battery power. Moreover, most of these simulators are not capable of dening
the application composition in the IoTs environment. These characteristics are vital
for use in any IoTs application. The composite simulation ecosystem of the Edge
and IoTs is vital to researchers and enterprises since it helps them to gain actual
control of edge processing.
1.10 Conclusion andFuture Work
In conclusion, this chapter pursues and recommends a model-centred approach used
to evaluate and predict the performances of IoTs systems and architectures. The
IoTSim-Edge model shall potentially allow engineers and developers to evaluate
various designing choices in systems, in advance. Moreover, the model will effec-
tively locate possible bottlenecks, size the resources and predict any potential feed-
backs to visualized results. The future prospects are bases of works that integrate
and combine various model approaches used to embed systems with Big Data
frameworks and enterprise data systems. In that regard, there is need to formulate a
prototype that will be used to intergrade modelling ecosystems. The present manu-
facturing ecosystems have been evaluated to effectively analyse the vital necessities
of ESs based on modern organizations.
References
1. Anandakumar, H., & Umamaheswari, K. (March 2017). Supervised machine learning tech-
niques in cognitive radio networks during cooperative spectrum handovers. Cluster Computing,
20(2), 1505–1515.
2. Abbas, N., Nasser, Y., & Ahmad, K. (2015). Recent advances on articial intelligence and learn-
ing techniques in cognitive radio networks. EURASIP Journal on Wireless Communications
and Networking, 2015(1), Available: https://doi.org/10.1186/s13638-015-0381-7.
3. Belkin, M., & Niyogi, P. (2004). Semi-supervised learning on Riemannian manifolds. Machine
Learning, 56(1–3), 209–239. Available: https://doi.org/10.1023/b:mach.0000033120.25363
.1e.
4. Benhamou, E. (2019). Similarities between policy gradient methods (PGM) in reinforcement
learning (RL) and supervised learning (SL). SSRN Electronic Journal. Available: https://doi.
org/10.2139/ssrn.3391216.
5. Cecin, F., Barbosa J., & Geyer, C. (2006). A communication optimization for conservative
interactive simulators. IEEE Communications Letters, 10(9), 686–688, Available: https://doi.
org/10.1109/lcomm.2006.1714546.
6. Chang, X., Mi, X., & Muppala, J. (2013). Performance evaluation of articial intelligence
algorithms for virtual network embedding. Engineering Applications of Articial Intelligence,
26(10), 2540–2550, Available: https://doi.org/10.1016/j.engappai.2013.07.007.
A. Haldorai etal.

25
7. Anandakumar, H., & Umamaheswari, K. (October 2018). A bio-inspired swarm intelligence
technique for social aware cognitive radio handovers. Computers & Electrical Engineering,
71, 925–937. https://doi.org/10.1016/j.compeleceng.2017.09.016.
8. Farhat, S. (2018). Resource sharing in 5G multi-operator wireless network. International
Journal of Digital Information and Wireless Communications, 8(3), 156–161, Available:
https://doi.org/10.17781/p002431.
9. Fu, Y., Wang, S., Wang, C., Hong, X., & McLaughlin, S. (2018). Articial intelligence to man-
age network trafc of 5G wireless networks. IEEE Network, 32(6), 58–64, Available: https://
doi.org/10.1109/mnet.2018.1800115.
10. Gueguen, C., Ezzaouia, M., & Yassin, M. (2016). Inter-cellular scheduler for 5G wire-
less networks. Physical Communication, 18, 113–212. Available: https://doi.org/10.1016/j.
phycom.2015.10.005.
11. Gueguen, C., Ezzaouia, M., & Yassin, M. (2016). Inter-cellular scheduler for 5G wire-
less networks, Physical Communication, 18, 113–124, Available: https://doi.org/10.1016/j.
phycom.2015.10.005.
12. Haldorai, & Kandaswamy, U. (2019). Supervised machine learning techniques in intelligent
network handovers. EAI/Springer Innovations in Communication and Computing, 135–154.
https://doi.org/10.1007/978-3-030-15416-5_7.
13. Huang, R. P., Zhou, & Zhang, L. (2014). A LDA-based approach for semi-supervised docu-
ment clustering. International Journal of Machine Learning and Computing, 313–318.
Available: https://doi.org/10.7763/ijmlc.2014.v4.430.
14. Iosidis, A. (2015). Extreme learning machine based supervised subspace learning.
Neurocomputing, 167, 158–164. Available: https://doi.org/10.1016/j.neucom.2015.04.083.
15. Kansal, P., & Shankhwar, A. (2017). FBMC vs OFDM waveform contenders for 5G wire-
less communication system. Wireless Engineering and Technology, 08(04), 59–70. Available:
https://doi.org/10.4236/wet.2017.84005.
16. Kaur, K. S., Kumar, & Baliyan, A. (2018). 5G: A new era of wireless communication.
International Journal of Information Technology. Available: https://doi.org/10.1007/
s41870-018-0197-x.
17. Ke, C., & Shihai, W. (2011). Semi-supervised learning via regularized boosting working on
multiple semi-supervised assumptions. IEEE Transactions on Pattern Analysis and Machine
Intelligence, 33(1), 129–143, Available: https://doi.org/10.1109/tpami.2010.92.
18. Khanale, P., & Chitnis, S. (2011). Handwritten Devanagari character recognition using arti-
cial neural network. Journal of Articial Intelligence, 4(1), 55–62. Available: https://doi.
org/10.3923/jai.2011.55.62.
19. Khanale, P. (2010). Recognition of Marathi numerals using articial neural network. Journal
of Articial Intelligence, 3(3), 135–140. Available: https://doi.org/10.3923/jai.2010.135.140.
20. Ma, J. Y., Wen, & Yang, L. (2018). Lagrangian supervised and semi-supervised extreme
learning machine. Applied Intelligence, 49(2), 303–318, Available: https://doi.org/10.1007/
s10489-018-1273-4.
21. Manasa, H.R., & Pramila, S. (2015). Architecture and technology components for 5G mobile
and wireless communication. International Journal of Engineering Research and, 4(06),
Available: https://doi.org/10.17577/ijertv4is061112.
22. Mythili, K., & Anandakumar, H. (December 2013). Trust management approach for
secure and privacy data access in cloud computing. In 2013 International Conference on
Green Computing, Communication and Conservation of Energy (ICGCE), doi:https://doi.
org/10.1109/icgce.2013.6823567.
23. Nastase, V., & Strube, M. (2013). Transforming Wikipedia into a large scale multilingual
concept network. Articial Intelligence, 194, 62–85. Available: https://doi.org/10.1016/j.
artint.2012.06.008.
24. Nishii, R. (2007). Supervised image classication based on statistical machine learning. SPIE
Newsroom, Available: https://doi.org/10.1117/2.1200612.0449.
1 Inter net of Things (IoTs) Evolutionary Computation, Enterprise Modelling…

26
25. Sądel, B., & Śnieżyński, B. (2017). Online supervised learning approach for machine schedul-
ing. Schedae Informaticae, 12016, Available: https://doi.org/10.4467/20838476si.16.013.6194.
26. Shang, Z. (2005). Application of articial intelligence CFD based on neural network in vapor–
water two-phase ow. Engineering Applications of Articial Intelligence, 18(6), 663–671.
Available: https://doi.org/10.1016/j.engappai.2005.01.007.
27. Singh, P. (2016). Importance of wireless sensor network and articial intelligence for safety
prerequisite in mines. International Journal of Engineering and Computer Science. Available:
https://doi.org/10.18535/ijecs/v5i11.35.
28. Singh, P. (2016). Safety of underground coal mine using articial intelligence and wireless
sensor network. International Journal of Engineering and Computer Science. Available:
https://doi.org/10.18535/ijecs/v5i11.38.
29. Vishnoi, R., Gaur, S., & Verma, A. (2018). Comparison of various waveform contenders of
5G wireless communication based on OFDM. International Journal of Trend in Scientic
Research and Development, 2(4), 2468–2473, Available: https://doi.org/10.31142/ijtsrd15637.
30. Zhao, H. (2018). Semi-supervised extreme learning machine using L1-graph. International
Journal of Performability Engineering. Available: https://doi.org/10.23940/ijpe.18.04.
p2.603610.
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27© Springer Nature Switzerland AG 2020
A. Haldorai etal. (eds.), Business Intelligence for Enterprise Internet of Things,
EAI/Springer Innovations in Communication and Computing,
https://doi.org/10.1007/978-3-030-44407-5_2
Chapter 2
Organization Internet ofThings (IoTs):
Supervised, Unsupervised,
andReinforcement Learning
A.Haldorai , A.Ramu , andM.Suriya
2.1 Introduction
Internet of Things (IoTs) links up various data sensing appliances via the Internet,
which is meant to realize smart management and identication. Different intelligent
sensors are a vital building block used in establishing Internet of Things (IoTs)-
based business process applications. In this case, the business process management
(BPM) framework is not only relevant in enhancing the prociency of collaboration
in cross-sensing organization procedures, but it is also relevant in assisting to invoke
effective management competencies before an emergency snowballs into disaster,
i.e., trafc jams, re hazards, or networking failures. Due to these diversities in
sensing and the variations of their different functions, the direct models of the IoTs-
centered organization processes and applications are signicantly difcult.
Developers require using more coding practices, which bring out the aspect of
ignoring organizational logic orchestration. Perhaps, the fundamentals of the IoTs
applications before organizational procedure automations represent the creation of
organizational procedure models. These models are computed based on all forms of
organizational procedure obligations that signify novel functional units that have
been implemented by various services. The BPM frameworks shall benet from
representing modeling when fundamental sensor devices can assume the position of
sensor resources for personal organizational procedures.
A. Haldorai (*)
Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India
A. Ramu
Presidency University, Bangalore, India
M. Suriya
KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India

28
The Internet of Things (IoTs) is conrmed as the distributed and interlinked
network of embedded frameworks that communicate via wireless or wired com-
munication advancements [1]. As such, this has been conrmed to be the network
of physical things and objects that have been empowered with limited storage,
communication, and computation capacities, which include embedded electronics
like actuators and sensors, network connectivity, and software that are vital for
objects used to gather process and transfer information. The IoTs represent the
objects realized from various life scenarios that include intelligent devices kept in
buildings. These devices include smart adapters, smart bulbs, smart refrigerators,
smart meters, and smart detectors. Other more sophisticated devices include the
radio-frequency identication devices (RFID), accelerometers, heartbeat detec-
tors, and sensors located in parking lots, among others. There exist a lot of services
and applications provided by the Internet of Things (IoTs) that range from novel
infrastructure to military, agriculture, personal healthcare, and home appliances.
Moreover, the domains dealt by the Internet of Things (IoTs) services include med-
ical, energy, retail, building management, manufacturing, and transportation,
among others.
A massive scale of the Internet of Things (IoTs) network utilizes novel
approaches like the management of various devices such as the absolute scale of
information, communication, storage, security, privacy, and computation. Up to
this moment, a signicant research analysis has been conducted concerning the
various elements of IoTs such as communication, architecture, applications,
protocols, privacy, and security. Nonetheless, the foundation of IoTs commer-
cialization and its technological advancements are centered on privacy and secu-
rity guarantees, which include user satisfaction. The idea of IoTs using the
enhanced technologies like the software- dened networks (SDNs), fog comput-
ing (FC), and cloud computing (CC) means that landscaping the threats for
attackers has advanced. The generation of information based on IoTs devices is
signicant, and therefore, the ancient information collection processes and stor-
age techniques are not operated under this scale. Moreover, the absolute infor-
mation set can be utilized based on behavioral controls, patterns, assessments,
and prediction techniques. Moreover, the data heterogeneity produced by the
Internet of Things (IoTs) formulates more fronts proposing the present informa-
tion processing techniques [2]. As such, to effectively harness the worthiness of
information retrieved through the IoTs, there is a need for the implementation of
novel techniques. Resultantly, it is considerable to conclude that machine learn-
ing (ML) is a vital computation paradigm used to provide embedded smart
devices based on IoTs.
The idea of ML is fundamental for intelligent devices and machines to inject
vital competency from humans and device-generated data. Moreover, this idea
can be referred to as the competency of intelligent devices to automate and
adapt to various behaviors or situations based on knowledge, and this is viewed
as the fundamental element of implementing IoTs solutions. Thus, the ML
technologies have been utilized in obligations like regression, classification,
and estimation of density. Different applications like fraud detection, comput-
A. Haldorai etal.

29
erized visions, malware detection, and speech recognition make use of ML
technologies and algorithms. Similarly, these technologies are used to leverage
the idea of IoTs in availing smart services to users.
2.2 Radio-Frequency Identication Devices ofInternet
ofThings (IoTs) Features
2.2.1 Internet ofThings (IoTs) Network Features
In this section, we shall evaluate the novel features of Internet of Things (IoTs)
networks. These features include the following:
Heterogeneity: The Internet of Things (IoTs) networks include a lot of devices
that are equipped with various capacities, information transfer rules, and features of
communication that relate to one another. To put this into simple language, the
devices can utilize various forms of information transfer techniques, including the
various communication paradigms like Ethernet and cellular paradigms. Moreover,
the variable constraints concerning the hardware resources are also some of the
techniques considered.
Large-scale deployment: It is considered that thousands of devices linked to one
another via the Internet shall surpass the capacities of the present Internet condi-
tions. The large-scale deployment of Internet of Things (IoTs) also proposes signi-
cant issues. These issues include the designing of networks and their storage
architectures for intelligent devices, information transfer protocols, and effective
data sets. The effective data sets communication rules, proactive protection, and
identication of Internet of Things (IoTs) are fundamental for securing against
malicious attacks, technological standardization, and management of application
interfaces.
Inter-connection: The Internet of Things (IoTs) is purposed to be linked with
international data and communication infrastructure, which can be retrieved from
any location and at any moment. This form of connection is dependent on various
applications and services that are produced by IoTs service providers. In some
moments, the connection can be termed as local while in other cases it is considered
to be international. The local cases include the instances of interlinked motor tech-
nologies and swarm sensing [3]. On the other hand, the international cases will
include the intelligent home accessibility via the mobility infrastructure and crucial
management of infrastructure.
Information transfer in close proximities: One of the most outstanding features
of the IoTs is information transfer in close proximities that has to eliminate the
centralized standards such as base stations. The device-to-device (D2D) informa-
tion transfer inuences the features of on-point information transfer, for instance,
the dedicated short-range communication (DSRC) and many other technological
advancements that can be rated the same. The architectural segment of the ancient
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Internet is considered to be inclined more on network-centric information transfer
as compared to how communication presently has been delinked to complement the
services of the IoTs spectrum.
The ultra-reliable and low-latency communication (URLLC): The URLLC is
considered as another feature of the IoTs networks necessitated in novel actual-time
applications and services such as organizational procedural automations, smart traf-
c, remote surgery, and smart transportation frameworks. The vital performance
limitations include reliability and delay aspects.
Inexpensive and less-energy information transfer: More connectivity linking the
IoTs devices necessitates ultra-less-energy and inexpensive remedies that facilitate
smooth operation of networks in the modern age. Individual-organization and
individual- healing features are necessary for contemporary and urgent IoTs infor-
mation transfer, which include disaster and emergency conditions [4]. In these
cases, the reliance of networking infrastructure is never considered an option,
which means that individual organization of networks has to be considered. The
vibrant transitions in networking are composed of a signicant number of devices,
which required management in the most effective manner. All these devices have
the capability to act vibrantly. For example, the wakeup or sleep duration of devices
shall be dependent on various applications, including the time when these devices
utilize the Internet.
All these features have to be included in IoT networks. One of the vital features
among them considers the safety of networks. Among these features, however,
safety is a vital element that enhances the smooth operation of IoTs networks. The
safety of networks is considered to be signicant not only for the devices but also
for the consumers. This is due to the fact that IoTs devices linked to the Internet can
potentially be tampered as individual information has been shared with different
devices. Moreover, the security and privacy of these devices is a fundamental factor
that has to be considered in networking.
Smart networking is also an intriguing feature of Internet of Things (IoTs), which
enhances informed and timely decisions that have to be executed in organizations.
The information generated by IoTs devices has to be created in a manner that facili-
tates the performance of actions to effectively make decisions enabling the process-
ing of information [5].
The intelligent urban environments utilize modern data and communication
technologies to analyze and integrate the information gathered from the vital
frameworks that navigate through the urban environments. At the moment, the
intelligent cities are capable of executing smart responses to different cases such
as weather forecasting, trafc controls, and organizational and economic activities.
An intelligent urban environment with its trafc-routing system entails a massive
number of cameras that are meant to monitor the road networks and the intelligent
algorithms that navigate across the cloud networks proposing the optimum routes
used for individuals. Moreover, the intelligent motor navigation framework per-
mits individuals to change and set up destinations through the inbuilt audio appli-
ances. The two frameworks in pairs are used to provide actual-time interactive
routing systems. Nonetheless, the individuals’ voice commands can be translated
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31
into motor edges and their sides before they are transferred into cloud systems
where intelligent routing frameworks operate. The most vital route can be trans-
lated to the voices that guide individuals to their preferred destinations. The appli-
cations that have been mentioned above are utilized in different computing
resources such as edge, IoT devices, and cloud services, which include modern
language techniques that enhance developments of the various ML-centered IoTs
applications [6]. These advancements are challenging for modern language models
and IoTs frameworks. To deal with the available gap, this research has orchestrated
the enhancement lifecycle of the modern language-centered IoTs applications. In
the subsection outlined below, we have included an analysis of the enhancement
lifecycle alongside the detailed taxonomy, which surveys the techniques needed in
the enhancement of applications.
2.2.2 Privacy Issue ofInternet ofThings (IoTs) Deployment
Privacy and security issues are the vital factors that enhance commercial realiza-
tions of Internet of Things (IoTs) applications and services. The present Internet
setting is an attractive segment for privacy concerns that are relevant for a few tasks,
which includes the corporate levels and well-linked privacy breaches that have a
signicant effect to various organizations, businesses, and healthcare facilities. The
connes of the Internet of Things (IoTs) environments and devices operate in a
manner that possesses more issues in terms of security for various devices and
applications. Until now, privacy and security concerns have been analyzed exten-
sively in IoTs domains from various aspects like data security, information transfer
security, architectural security, privacy, malware analyses, and identity management.
2.2.3 Existing Gaps inPrivacy Resolutions inIoTs Networks
To effectively realize the fundamentals of IoTs, analysis of its privacy and security
concerns is relevant. Most signicantly, IoT has been retrieved from the present
technologies, which makes it possible for users to identify its challenges. As such,
it is possible to embrace new or old technologies that have been in existence over
the past few decades. Past research analyses focused on the differences and similari-
ties of the privacy concerns in IoTs, with reference to ancient IT devices. Moreover,
these past evaluations settled on the security issues. The fundamental driving factors
that provide the basis of the differences and similarities include networking, appli-
cations, hardware, and software. With reference to these applications, there are
major similarities between privacy concerns in ancient IoTs and IT domains.
Nonetheless, the major concern of IoTs remains to be the resource constraints that
limit the adaptations of the novel privacy remedies of IoTs networks. Moreover, the
remedies to the privacy and security concerns in IoTs necessitate the cross layers,
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the designs, and the optimized algorithms [7]. For example, because of the
computation constraints, the IoTs devices require unique samples of optimized
cryptographic and other algorithms to effectively cope up with the privacy and secu-
rity concerns. Apart from that, a number of IoT devices create signicant challenges
for the privacy technologies.
A lot of privacy concerns are more complex since the remedies obtained cannot
be considered discrete. For example, as for the privacy issues like intrusions and
DDoS, there is evidence of the probability of the untrue positives that will provide
remedies that are not effective on the attacks. Moreover, these positives can poten-
tially diminish the trust of consumers hence reducing the efciency of the potential
remedies. Nonetheless, the holistic privacy and security approaches over the IoTs
will render nomination from the available present security resolutions, including the
advancements of novel intelligence, evolution, and robust and scalable technologies
used to mitigate the privacy concerns in IoTs.
2.2.4 Machine Learning (ML)– TheRemedy toIoTs Privacy
Concerns
The concept of machine learning (ML) represents the smart methodologies utilized
to fully optimize the performance standards that make use of the present sample
data and those recorded through the present learning experience. More signicantly,
ML algorithms propose the model of conditions that makes use of the mathematical
methods on massive information sets. ML is vital in enhancing the performance of
smart devices, in terms of learning, without being programmed explicitly. All these
competencies are utilized to enhance the predictions useful in the future based on
novel data input. ML has been considered interdisciplinary in nature since its roots
can be retrieved from a number of disciplines such as engineering and science con-
sidering articial intelligence, data theory, optimization theory, and cognitive sci-
ence, among others. ML is also used when human competencies do not use or exist
based on navigation hostility where users do not have the competencies to utilize
their expertise, for example, speech recognition and robotics. These competencies
are also used in cases where the remedies to certain issues change anytime, such as
routing in PC networks or locating malicious codes in applications and software.
Moreover, this aspect is practically used in smart systems; for example, Google
makes use of ML in evaluating the risks against application and mobility endpoints
in operating Android systems. As such, this is vital for the identication and removal
of malware from all the infected devices.
In the same way, the Amazon Company introduced its service Macie, which
makes use of machine learning to categorize and sort out information that has been
stored in cloud storage services. Despite the fact that machine learning typically
performs well in most sectors, there are normally some real negatives and untrue
positives that have been recorded in the past. In that case, ML methods require
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33
modication and guidance to effectively model the most accurate predictions.
Contrary to that, deep learning (DL), which is considered to be a novel option of
ML, is a model used to evaluate the accuracy of all the possible predictions. Due to
the condition of self-nature of the DL model, users consider it as a more precise
framework used in prediction and categorization tasks to enhance the innovation of
IoTs application based on personalized and contextual assistance. Despite the fact
that ancient approaches have been utilized extensively in various segments of IoTs
such as protocols, services, architecture, application, resource allocation, data
aggregation, analytics, and clustering, huge-scale deployment of IoTs advocates for
smart, reliable, and robust networking techniques. Until now, DL and ML are con-
sidered to be the novel techniques for IoTs networks because of a number of rea-
sons, i.e., IoTs networks propose an absolute amount of information that are
necessitated by DL and ML methodologies to enhance smart competencies into
systems [8]. Moreover, the effectiveness of information produced by IoTs is ef-
ciently used with DL and ML methods that enhance IoTs systems to enable more
smart and informed decisions. DL and ML are massively applied in privacy, secu-
rity, and malware evaluation and during the detection of potential attacks. The DL
method can also be utilized in IoTs devices to perform all the complex recognition
and sensing tasks used to enhance the realization of novel services and applications
and to determine the actual-time interactions between humans, the physical envi-
ronment, and the intelligent device. Some of the privacy-related actual-world appli-
cations of ML are considered as follows:
• Forensics face recognition: Such as posing, occlusion, lighting, make-ups, and
hairstyling.
• Feature recognition for privacy recognition: Includes the various handwriting
competencies.
• Malicious code identication techniques: These competencies are used to iden-
tify the dangerous codes in software and applications.
• Distributed denial of service (DDoS) detection: This form of detection technique
is used in identifying DDoS attacks that inuence infrastructure via behavioral
evaluation. Based on the application of DL and ML methods of IoTs application,
users have realized other novel challenges. All these challenges are considered
multifaceted [9].
For example, it is a pending concern to effectively formulate the best model that
will be utilized in processing information from various IoTs applications. In the
same way, labeling inputs information in a precise way is considered to be one of
the most difcult obligations. The second challenge is evident in the utility of mini-
mal labeling information in the process of learning. The third challenge is realized
in the deployment of the models on resource-centered IoTs devices where there is a
need to diminish storage or processing overhead. In the same way, novel infrastruc-
ture and actual-time applications do not have the capability to withstand anomalies
formed due to the DL and ML algorithms. Thus, it is considerable to systematically
analyze the privacy remedies of IoTs, which leverage the DL and ML.
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2.3 The Present Literature Surveys
Presently, IoTs is extensively considered, based on existing literature sources,
since a lot of research has been done concerning the various aspects of IoTs
privacy. In that regard, this section provides a brief overview of the present sur-
veys before comparing these analyses with our research. According to this
research, surveys have minimally focused on ML methods utilized in IoTs.
Moreover, the present surveys either are based on applications or do not com-
pletely consider the complete spectrum of privacy and security concerns in IoTs
networks. The present literature surveys deal with the privacy concerns in IoTs
through the analysis of the available ancient remedies based on the emergent
technological initiatives. Nevertheless, surveys discussing the DL- or
ML-centered remedies are still not available. Moreover, DL and ML have been
considered in a number of research analyses, but the general data concerning the
usage of these two methods is still scarce. As for the present gap in research, we
have done an analysis of the detailed survey of the DL and ML methods, which
are utilized in IoTs privacy.
2.3.1 Survey Scope andResearch Contributions
In this chapter, we have done a detailed systematic analysis of DL- and
ML-centered privacy remedies of IoTs. Initially, we had evaluated the privacy
requirements of applications in IoTs networks against threats and attacks.
Thereafter, we focused on the obligation of DL and ML in IoTs, which includes
the analysis of the various DL and ML methods used to leverage the IoTs services
and applications. To entirely focus on the practical element of IoTs, we have done
an extensive evaluation of DL- and ML-centered privacy remedies in IoTs. This
evaluation also includes the analysis of the present challenges in literature and
future concerns that focus on the analyses of DL and ML for IoTs networks. The
main purpose for this is lling the available gaps between the necessities of IoTs
privacy and the capacities of DL and ML that will permit the process of address-
ing the privacy concerns on IoTs networks. Thus, we have practically evaluated
the IoTs from the perspective of DL and ML [10]. This evaluation focuses on
privacy and security concerns of the IoTs networks. Typically, this chapter
includes a detailed evaluation of the security problems and risk frameworks in
IoTs. The analysis encompasses the privacy requirements and threat surfaces of
IoTs, which includes the analysis of DL- and ML-centered remedies in mitigat-
ing the possible privacy attacks in IoTs networks. As such, it is considerable to
argue out that the value of the present surveys is still considered to date. Moreover,
the research analysis includes the recently done works in the elds of DL and ML
for IoTs technologies.
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35
2.4 Security Problems andRisk Frameworks inIoTs
Essentially, IoTs utilizes the transformational approach to effectively provide users
with a lot of services and applications. This form of pervasive deployment of an
extensive number of network devices fundamentally advances the degree of threat
surface. Moreover, the idea that IoTs devices are normally resource-based is unfea-
sible to utilize novel privacy techniques to mitigate infamous threats. Moreover, it is
critical to detail that the initial Internet had not been created for the IoTs. As such,
it is signicant to avail IoTs privacy to the present security techniques. Until the
present day, IoTs makes use of various communication competencies like ZigBee,
IPv6, Bluetooth, 6LoWPAN, Wi-Fi, Z-Wave, and near-eld communication (NFC)
[11]. All these communication technological advancements possess their challenges
and shortcomings that have been borrowed from IoTs domains. Moreover, other
problems include that of IP- and TCP-centered information transfer, which is prone
to issues like complexity, scalability, conguration, addressing techniques, and the
use of insufcient resources that pose limitations on how heterogeneous and diverse
networks are applied on IoTs. Until now, various alternative technological advance-
ments like data-centric networks and the software-dened networks (SDNs) are
applicable in serving the dominant information transfer infrastructure for IoTs. In
this manner, this research provides a brief evaluation of the attacks and threats that
IoTs is facing. Eliminating the losses of generality, privacy concerns of IoTs can
categorically be divided into various attacks as evaluated below. These threats
include the following:
2.4.1 Physical Threats
When evaluating the physical threats, the intruders are provided with a direct acces-
sibility to potentially manipulate various aspects of networking devices. To fully
access all the physical devices, social engineers are required to propose the most
effective methods that the attackers will utilize to access the network devices or
undertake an actual-time attack ranging from physical damages on networking
devices to side-channeling, eavesdropping, and many other potential attacks.
Irrespective of the various technologies being used at the physical segment of IoTs,
the condition of physical threats principally is similar to the requirements of social
engineering and their potential approaches. Moreover, to effectively launch physical
threat, attackers have to avail themselves in the close proximities of hardware and
other devices with various intentions such as facilitating physical damage of hard-
ware, limiting the devices’ lifetime, endangering information transfer techniques,
and inuencing the energy resources.
Physical attacks can be considered as a foundation for other potential threats
such as tampering with alarms in homes, which lead to burglary and other serious
damages in intelligent home environments. In the same way, the replacement of
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36
sensors with any vulnerable sensors can potentially result to the leakage of sensitive
information. The incorporation of vulnerable nodes into networking devices can
potentially lead to any attacks hence opening doors for other intruders to take advan-
tage of the privileges and facilitating a dangerous attack. Moreover, this form of
tampering with the security of devices can be a major facilitator of potential attacks
proposing changes in security keys and routing tables that have an effect in com-
munication to the upper degree. Other possible physical threats include jamming
radio-frequencies that deny the aspect of information transfer in IoTs environments.
Adding to the many other consequences, jamming leads to the denial of consumer
services for the IoTs hence tampering with the operation of IoTs applications. It has
therefore been considered that the intruders make use of various social engineering
techniques to pose physical accessibility to the devices and hardware due to a num-
ber of reasons, which include the potential attacks mentioned above. With social
engineering, intruders can possibly manipulate users to embrace physical control of
the networking devices.
In IoTs environments, various issues can be created using the techniques
embraced in this research. All these applications range from networking to systems,
intelligent urban environments, and intelligent grids, among others [12]. Focusing
on modeling, it is fundamental for consumers of technologies to make use of effec-
tive learning concepts during the initial stages. The most fundamental selection
method can be divided into:
• Power-use-centered selection
• Functional-centered selection
As for the functional-centered selection method, users are given the chance to
select the best concept with reference to functional variations. For instance, RL
advantages from the iterative ecological perspective include agent interactive prop-
erties that require interaction with the ecology and can be used in different applica-
tions and smart systems such as intelligent temperature control frameworks and
cold issues. The TML algorithm is effective for the purposes of modeling structural
information, based on the highest degree of semantic character, mostly when inter-
pretability is needed [13]. The DL-based models are normally utilized to frame out
the complex unstructured information such as audios, time-series data, and images.
They are the best selection criteria since big data is considered with minimal require-
ment concerning interpretability.
On the other hand, the power-use-centered selection method focuses on selecting
the best model that proposes constraints in computation latency and power. Contrary
to TML, DL and RL are typically categorized as extreme computational expenses
since they normally compose the complex networking structures; hence, the accu-
racy determinants seem to exceed the TML based on computational overhead costs.
With reference to TML at its inference stage, ideal accuracy can be attained with the
most effective characteristics such as extreme-level attributes obtained from fea-
tures of engineering.
1. Link Layer and Physical Privacy Problems
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37
IoTs includes different information transfer technologies at a minimal
dimension of IP and TCP protocol stacks and therefore gives more complex
heterogeneous network. These networking advancements include WSN,
ZigBee, Wi-Fi, MANET, NFC, and RFID.Moreover, these technologies pos-
sess their own privacy problems. This part focuses on the privacy problems in
data and physical link layers of IoTs. According to literature gaps identified in
this article, privacy problems in IoTs at various layers and their remedies have
been evaluated. As analyzed before, heterogeneity has been proposed at its
physical layers in IoTs, which further proposes various amendments in infor-
mation links. For example, key channel designs will depend on the prevalent
physical layer technologies [14]. Until now, security technologies of IoTs have
to incorporate the heterogeneity at its data and physical link layers. There are
various security concerns in physical layers of IoTs depending on the prevalent
technologies; for example, in the case of sensor nodes, the attacks on these
nodes have to be mitigated. Moreover, the identification of any malfunctions in
hardware is a fundamental element that has to be handled with care to eliminate
any form of anomaly in the upper network layer. Another privacy problem is
intrusion and this requires effective countermeasures made from prevention
and detection standpoints. It is significant to consider that there exist a lot of
attack vectors seen in intrusions at an upper segment, for example, during the
routing threats.
Based on fault detection standpoints, it is vital to identify any fault nodes in
IoTs since they critically inuence the quality of services (QoS) of the IoTs appli-
cations. The core purpose of IoTs is to present a ground for a minimal-energy-
constrained thing (device) that co-exists at the minimal layer that assures the same
information transfer for heterogeneous devices. Until now, IEEE introduced a
guideline referred to as the IEEE 802.15, which allows a constrained device to
transfer information in the best manner. In IoTs, the high layers utilize minimal
power standards such as the Constrained Application Protocol (CoAP) and
6LoWPAN, since this is the mechanism required at a minimal layer that enables
the guidelines to operate seamlessly. In this case, IEEE 802.15 gives the required
changes at a minimal layer that represent the recommended standards. It is funda-
mental to consider that IEEE 802.15.4 includes the security concerns dening the
information link layers.
Information link or MAC layers are obliged for enhancing channel accessibil-
ity for various devices in addition to access management, framework validation,
security, and time management. In this research, we have focused on privacy
problems in the high layer given by IEEE 802.15.4. These securities given by the
standards at MAC layers ensure that the levels of the nodes and information trans-
fer are kept secure. Moreover, these securities ensure that the securities in the
upper layer are maintained. In that case, symmetric cryptographic algorithms like
the AES are recommended to be implemented efciently and rapidly on chips;
hence, all these implementations in IEEE 802.15.4 hardware shall be considered
in the lower layer privacy. The IEEE 802.15.4 standard provides the AES algo-
rithms and various applications that are recommended to control the resource con-
2 Organization Internet of Things (IoTs): Supervised, Unsupervised…

38
straints of the networking devices. The guideline supports various privacy nodes
at the link layers; for example, information might not be encrypted by just execut-
ing an integrity check.
2.4.2 Networking Layer Threats
In the networking level, the threats are focused on routing, trafc evaluation, man-
in- the-middle, and spoong attacks. Apart from that, Sybil inuences are poten-
tially seen in the layers of the network where false Sybil identities are utilized in
creating illusions in networking. Intrusions via various means propose a manner in
which the intruders of the networking system can potentially launch some possible
threats. As such, ensuring the safety of networks is fundamental to deal with the
threats at their initial stages. At the networking layers, the intruders have possibly
leveraged the vulnerable nodes to perceive them as fake-forwarding nodes facilitat-
ing the formation of sink holes.
This form of threat normally is linked with the mobile ad hoc and sensor net-
works, which pose a signicant effect on IoTs environment. With all these threats,
there is a chance for launching an associated DDoS threat that will affect the entire
IoTs network. In the networking layer, the intruders can potentially affect the net-
work by bombarding it with a lot of trafc that comes from the compromised nodes
beyond what their network can possibly handle. Affecting the IoTs nodes and tam-
pering with the identities will pose a signicant effect on networks since there are
fake nodes that allow these intruders to prepare Sybil threats where the Sybil nodes
provide an illusion to the major networks as though the actual nodes were transfer-
ring the data sets [15]. To draw the general assumption of the threat vectors in the
networking layer, users should focus on the information transfer aspects of IoTs and
utilize the resource constraints, authorization frameworks, and sophisticated
authentication.
2.4.3 Transportation Layer Threats
The transportation layers are tasked with the obligation to enhance step-by-step
delivery of transportation standards that enhance the procedure followed during the
exchange of information. In this case, the ancient transportation layer privacy con-
cern typically persists. The most serious threat in this layer is the denial of service,
which affects the network applications. Moreover, it is critical to note that due to the
status of IoTs, UDP and TCP standards have no scale with the resource-constrained
device, and hence, the lightweight version of transportation guidelines had been
recommended in research. Nonetheless, the privacy problem of the standards is of a
major concern that effectively enhances the DDoS and DoS threats in IoTs.
A. Haldorai etal.

39
2.4.4 Application Layer Threats
The IoTs applications are considerably the best target for the intruders since attacks
on the application layer are comparatively easier to launch. The most known threats
include buffer-overow threats, denial of service, malware threats, phishing, cryp-
tographic threats, exploitative web app threats, man-in-the-middle, and side- channel
threats. The buffer-overow attacks are considered to be the widely utilized threat
vector in various applications [16]. The present methods used to deal with the
buffer- overow attacks include dynamic nodes and static nodes analyses, which
include other complex techniques such as symbolic debugging techniques.
Nonetheless, the methods cannot be utilized with IoTs because of resource scarcity.
The IoTs applications are also affected by malicious code incorporation due to
buffer- overow attacks, in addition to vulnerabilities like cross-site scripting, SQL
injection, and object referencing, among others.
According to the Open Web Application Security Project (OWASP), there are a
lot of susceptibilities that lead to various threats on network applications. One of the
latest susceptibility that OWASP recorded was in 2017. The susceptibility produces
a collection of a lot of threats, which can be launched by attackers at any time. For
example, attackers can choose to inject malicious nodes, access controls, perform
phishing, and tamper with privilege escalation. Moreover, using malicious code
injection, the intruders can possibly collect sensitive data, tamper with the data sets,
and do a great deal of malicious activities. Botnets pose another fundamental threat
to the IoTs application and infrastructure. Controlling the threats produced via
smart botnets poses a signicant problem for the IoTs due to the fact that these bot-
nets effectively crawl and scan the networks searching for unknown vulnerabilities
before exploiting them to enhance the launching of massive DDoS.It is signicant
to mention that because of the scarcity of resources, the modern cryptographic stan-
dards are unfeasible for IoTs devices that expose them to potential intruders who
launch the cryptographic attacks. Generally, these threats are seen on the applica-
tion layers of the IoTs infrastructure, which makes it much costly to control.
2.4.5 Multiple-Layer Threats
The multiple-layer threats add to the aforementioned threats in this chapter and are
launched to the IoTs infrastructure. These threats include side-channel, trafc eval-
uation, man-in-the-middle, relay, and standard threats. A lot of these threats have
been evaluated in the section above. The trafc evaluation threat is considered as an
attack where the intruders control the trafc and make use of it. Users nd it dif-
cult to control this threat since communication parties normally have no knowledge
that their networks are being monitored. The intruders are searching for vital data
sets in the Internet trafc; the data include personal details, company logic informa-
tion, and credentials, among others. The idea of information transfer privacy is of
2 Organization Internet of Things (IoTs): Supervised, Unsupervised…

40
great signicance to IoTs. The information that is produced in the IoTs ecology is
utilized for the purpose of decision-making. Hence, it is fundamental to ensure the
health and quality of information. Tampering with the privacy of information in
IoTs will pose a signicant challenge on the vulnerable applications. It is also fun-
damental to mention that SDN has been signicantly leveraged to attain a wide
speculation of advantages from the IoTs security and applications [17]. The novel
functionalities given by the SDN control plans enhance companies to signicantly
control a lot of things and sensors in the IoTs paradigm. Nonetheless, irrespective of
the SDN merits that have been mentioned above, the available interface in SDN
provides risk to threats on the vulnerable IoTs infrastructure, applications, and
devices. In that regard, the privacy concern of the IoTs is dependent of the privacy
of the SDNs.
2.5 Machine Learning andtheIoT Safeguarding
This section discusses about different machine algorithms alongside their useful-
ness within the IoT applications. First, the section shall commence with the study of
machine learning which can be segmented into four major sections, which include
supervised, semi-supervised, unsupervised, and reinforcement machine learning.
Supervised machine learning: This type of learning is executed if accurate tar-
geted points have been prospected to be obtained from a specic input set. Therefore,
based on this system of learning, there is labeling of the data, which comes rst, and
then the training of the marked data sets. These types of sets voluntarily rule from
the availed sets of data and furthermore give meaning to different types of classes
and in the end foresee the elements that fall under a prospective class. On the other
hand, supervised learning algorithms may be utilized if data X aligns itself with data
Y, which are provided for the training whose main objective is to adjust to a map-
ping function which is Y: ← f (X). There has been extensive application of super-
vised learning algorithms within the IoT sector; for these reasons, there should be
an introduction of various classiers as shown in this chapter. Both logistic regres-
sion and perception can be regarded to be the easiest linear classiers. Hence, for
the two frameworks, they can be understood to be the simplest linear transforma-
tions. Whereas perception may execute binary categorization centered on the signal
that the input data is being transitioned, RL can increase the rate of the transforma-
tion to a specic probable value, prior to getting a threshold function, which will be
applied when making the classication decision [18]. Moreover, the RL may also be
increased to multi-class classication events with the aid of the softmax function,
which is a scaling function that has class-related likelihood to the primary output.
The articial neural networks (ANN) can simply be as previously mentioned linear
classiers. In contrast to the perception or rather the RL that is a linear-related proj-
ect input data which is linked to the output, the ANN comprises extra “secretive
layers” that permits the ANN for modeling non-linearity.
A. Haldorai etal.

41
However, in relation to the linear classiers, it is the additional secretive layer
that leads it to become rarely known to see any linked association amid the output
and input data sets. However, in theoretical basis, having one hidden layer within
the ANN model will lead to the modeling of non-linear functions, in relation to the
restricted capacities during the encounter with unforeseen data sets. Moreover, the
ANN that has more layers will be known to be a deep neural network, which may
seem to have improved modeling capacities as mentioned in the following sections.
Unsupervised machine learning: Regarding unsupervised learning, the vicinity
normally issues the available inputs deprived of the targets desired. However, there
is no need for a labeled data set which can analyze the uniqueness amid the unla-
beled data and categorize the data into dissimilar sections. The supervised and unsu-
pervised learning tactics specically aim at the evaluation of different data set
challenges, whereas reinforcement learning can be utilized for decision-making
processes and various comparisons. Therefore the grouping and choice selection of
the ML methods relies on the manner at which the data can be accessible. If the
input data and prospected output are known, supervised learning can be employed.
During these scenarios, the entire framework comprises only trainees so that the
input can be mapped appropriately and a stipulated input can be achieved. Regression
and classication are various instances of the supervised techniques whereby regres-
sion operates consistently while classication works alongside discrete outputs.
There are different regression methods such as supported vector regression (SVR),
polynomial regression, and linear regression, which are the mostly utilized meth-
ods. Subsequently, the grouping operates with various discrete output gures.
This is mostly used in instances for grouping various algorithms such as the
k-nearest neighbor, SVM, and logistic regression. Other algorithms may be utilized
for the grouping and classication, for example, the neural networks. Outputs have
not been properly dened and clustered withvarious classier objects centered on
known criteria for example k-clustering [19]. The extent of accuracy for the fore-
seen analytics is based on how enough the ML methods have utilized recent data for
creating new models and the way they have been used for predicting various mod-
els. Various algorithms, for example, the SVR, the naive Bayes, and the neural net-
works, have been utilized for predictive frameworks.
2.5.1 The Main Objective
The main objective of the unsupervised learning algorithms is to enhance the
understanding of the web relationship amid information that comprises only X
data, which is only present when the class Y is not available. For instance, algo-
rithm clustering may be utilized in order to identify various capable series with
others that have not been named and acquired outcomes that can be utilized for
further evaluation. Therefore, k implies to be the core component analysis, which
falls among the recognized unsupervised algorithms. With k, it means that the main
objective is identifying a group series amid information through assigning various
2 Organization Internet of Things (IoTs): Supervised, Unsupervised…

42
samples of clusters centered on the range existing between the centroid and the
samples based on every cluster. On the other hand, the PCA can be dimensionally
utilized as a reduction that can relate with various raw elements prior to choosing
ones that are informative.
The semi-supervised learning: Comparing the initially mentioned groups, there
will be no tags that exist for the observation of the data sets or rather the labels,
which have presented all notable observations. Considering the vast number of
practical scenarios, the expenses used in labeling can be very high; hence, it needs
equipped and talented human resource to be employed. In reinforcement learning
(RL), having no accurate results can be explained, and every agent can understand
from the response once it has interacted carefully with the neighboring surround-
ings. For this reason, it executes various actions that lead to the implementation of
various decisions based on the reward acquired [20]. Therefore, for an agent which
has been rewarded for the execution of recommendable actions or castigation of
poor actions and utilize feedback methods so that they can increase long-term rec-
ompenses.This has been immensely inspired through the learning of various char-
acter traits of animals and people. These behaviors have established a pretty
approach within the dynamic use of bots whereby different systems may learn to
accomplish various roles by not using explicit programming. It is crucial that people
should select an excellent reward so that the triumph and catastrophe of these agents
may rely on various gathered rewards.
For this segment, there will be an introduction of various techniques that can be
utilized in formulation of the previously mentioned streaming video example that
comprises RL.Considering the previously mentioned results, RL, which is an agent
that associates itself with the neighboring environment, to get a customized control
policy, can be gained through experience. Therefore, there is a need for involvement
of three basic elements, which include action, reward, and observation. Centered on
the mentioned elements, formulation of the adjustable bitrate streaming challenges
can be easily possible. Uniquely, various observations may buffer various occupan-
cies, the network throughput, and so on. For each phase, every agent may decide on
the bitrate of the incoming chunk. Hence, a reward can be acquired once an agent
receives the response. The algorithm proposes, gathers, and then simplies the out-
comes used for executing the initial decisions; hence, it can optimize various poli-
cies based on various network coverages. The RL based algorithms may lead to
environmental based noises such as unforeseen networking situations, video assets
etc. Various DL architectural features that can be accessible in literature may include
the CNN (convolutional neural networks), BM (Boltzmann machine), DBN (deep
belief networks), RNN (recurrent neural networks), FDN (feed-forward deep net-
works), GAN (generative adversarial networks), and LSTM (long-short-term mem-
ory). For the CNN and the RNN, they are extensively utilized for deep architectural
learning [21].
Deep reinforcement learning: In this segment the DL can be a type of ML method
that is utilized for estimating the function, grouping, and foreseeing while the RL
will be a different type of ML technique which can be employed in decision-making
processes whereby every software may be educated pertaining to how various
A. Haldorai etal.

43
optimal actions can be achieved though the interaction with the nearby environment
with some states. Both the RL and the DL chip in within these circumstances and
the data is dimensionally huge while the environment can be non-stationary. Hence,
conventional RL will not be sufcient. Through the combination of both the DL and
the RL, various agents may learn on their own and draft effective policies in order
to extract maximum substantial rewards. Considering this approach, the RL receives
aid from the DL in order to allocate an excellent policy while the DL executes vari-
ous action estimations so that it can get the quality of actions within a prospective
state. Moreover, the RLL and the DL gain advantages from one another [22]. The
DL is in a position of understanding the more complicated series although it is
exposed to various inaccurate groupings. For this situation, the RL comprises a
powerful capacity for automatically understanding the environment by not con-
structing and aiding the DL during effective grouping.
2.5.2 IoT Security Machine Learning Methods
In this section, there will be a discussion of some ML algorithms that are speci-
cally aimed at underlying safety and condential challenges within the IoT net-
working system. Accurately, there will be consideration of various authentic
invasion techniques and other mitigation practices, DDoS threats, intrusion sensing,
anomaly, and some malware evaluations. The supervised learning algorithms oper-
ate using tagged information and use an IoT network within spectrum sensing,
adaptive cleaning, channel approximation, and localization challenges. Such a sec-
tion comprises two speculated forms of methods, which include regression and
grouping. Grouping within supervised machine learning can be utilized in foresee-
ing and also in modeling accessible sets of data. On the other hand, regression can
be used in predicting consistent patterns of variables. Some few widely employed
classications of algorithms include the naïve Bayes, decision tree, SVM, and ran-
dom forest [23]. Therefore, the SVM employs a framework known as kernel, which
is utilized in identifying various disparities between two common points that com-
prise different classes.
The SVMs are in a position of modeling the non-linear sections. The SVM can
geneticallyinclude the memory and hence be hard deciding the effective kernel,
there bymodeling largesets of databecomes difcult. Hence, a random forest is
usually recommended unlike the SVM.On the other hand, the naïve Bayes (NB) has
been using Dover time in helping solve global problems such as the spam sensing
and classication of text. Considering having all naïve and other input elements,
every other random forest provides an ideal environment for the modeling of actual
problems. With random forest algorithms, it will be much easier to implement and
adjust huge-sized data sets. Such algorithms may take a much longer period of prac-
ticing compared to the supervised algorithms, which include the NB and the
SVM.However, they can attain a greater accuracy and may consume less amount of
time in foreseeing various issues. Moreover, they have been centered on the
2 Organization Internet of Things (IoTs): Supervised, Unsupervised…

44
structuring of various graphs that segment into leafs and branches, hence showing
the class and decision that have been implemented.
2.6 An Analysis ofPresent Machine Learning Centered
onSolutions Within theIoT Security
This section provides a quick survey of the present ML-centered solutions that high-
light dissimilar safety problems within the IoT. Authentication and access within the
IoT system has been a primary requirement for users within the IoT.Every user
should be authenticated so that every application or service can be utilized effec-
tively. Normally, these applications can focus on the exchange of data within differ-
ent conditions. Such data can be accessed within the IoT devices and it is processed
and directed to the decision-aided system in order to acquire accurate meaning from
it. Such processes can range based on the architecture of the IoT although the ow
of information can be the same within these frameworks. Having no loss in general-
ity, if a certain application or any client requires some data within the IoT gadgets,
then the entity should be effectively authenticated [24]. If this happens, the request
of accessing the information can be neglected. Similar to other networks, the access
control can be of signicant use within the IoT networking system and similarly can
be problematic considering that it can be restricted by network volume, heterogene-
ity, asset limitation of various devices, security of various networks, vulnerable
threats, etc. Moreover, it is crucial for giving and denying various clients access to
critical information for the applications that use the IoT services. As discussed
regarding the ML-centered mechanisms within the IoT, rst it is essential to focus
on various segments that have access control.
According to scientic research, the access control can be divided into different
sections such as CWAC (context-aware access control), RBAC (role-based access
control), and PBAC (policy-based access control). Other researches have shown
that improved groupings include the ABAC (attribute-based access control), CAC
(capability-based access control), UCAC (usage control-based access control), and
OAC (organization-based access control). Some of the present works may be seen
within the mentioned sections. On the other hand, a comprehensive survey was car-
ried out and utilized the access control mechanism within the IoT network hence
pointing out various pitfalls within the IoT.Various authors may analyze the present
mechanism centered on the outstanding applications for the IoT.Such applications
are widely segmented in primary classes, the enterprise and personal. There are a lot
of applications such as healthcare, minor ofces, digital homes, sensor networking
systems, smart rms, perilous infrastructure, business applications, and many more.
The access control systems are utilized within applications and within the architec-
tural levels.
For the architectural layers, every product or service comprises a collection of
decisions which include the access control languages such as XACML (extensible
A. Haldorai etal.

45
access control mark-up language), UMA (user-managed access), and OAuth (open
authorization). Such architectural phase control contrivances can be utilized by
using the present protocols within the IoT network; for example, the OAuth can be
enacted across the present IoT protocols, for example, the CoAP (constrained appli-
cation protocol) and the MQTT2. Additionally, the various services provided by
Facebook, Google, Netix, Microsoft, and many others comprise many client
accounts, which all utilize OAuth [25]. Subsequently, a discussion pertaining to the
present DL- and ML-centered authentication and various accessible mechanisms
within the IoT networking systems is presented below.
Access control for ML-based authentication processes within the IoT:
According to extensive analysis, a physical layer of authentication mechanism has
been suggested within the IoT network. Hence, the suggested mechanism utilizes
various physical properties, which include the strength of every signal. The
essence is that some revolutionary techniques have been used for physical layer
verication processes and gaming theoretical approaches, and these techniques
are machine-based, which ensures that there is effective isolation of various spoof-
ers which emanate from the benign IoT clients. Theauthentication systems can be
created spoongmethod that can beattempt the utility while increasing the threat
frequency. On the other hand, the frequency channels get feedback, which is uti-
lized in establishing a Nash equilibrium (NE). Hence, it is important to point out
that every packet acquired via the radio channels comprises various channel states
that can leverage the test threshold focusing on the authentication of every deci-
sion, which would have been created. Due to this reason, various scholars have
attempted to use Q-learning and Dyna-Q that aid in comprehending the state of
every channel while not acquiring enough data pertaining to the channel. With the
aid of experiments, scientists have concluded that sensing precision and execution
process for the Dyna-Q model is effective compared to the Q-learning methods. A
familiar physical authentication layer project within the distributed Frank-Wolfe
(dFW) algorithm has been recommended.
The access control of the DL-centered authentication process within the IoT:
The prospected client’s authentication methods used within the IoT focusing on
the physiological events caused by human beings have been inuenced via Wi-Fi
signals. Therefore, the recommended scheme has been based on various recogni-
tion events for humans. Activities may be executed using data that are coarse-
grained and have features that are smaller. Due to this reason, the information
states for every channel within the Wi-Fi signals have been created by the IoT
gadgets, and hence these characteristics are so much dissimilar considering their
features. The DNN (deep neural network) can be used in learning more about
human physiological and various behavioral qualities, which can be useful within
the authentication process. For the three layers, there will be extraction of differ-
ent forms of events, and at the second layer, there will be learning of the activity,
while the third layer will comprise highly based elements that are centered on the
authenticity of each user.
2 Organization Internet of Things (IoTs): Supervised, Unsupervised…

46
2.6.1 Mitigation andDetection ofThreats
Following the subsequent sections, there was concise explanation of various inva-
sions that were launched within several layers of the IoT network. Due to the limited
number of resources and heterogeneity for the IoT devices, they create a sufcient
platform for threats to come in. Normally, attackers may use the identied vulner-
abilities within all the networks, and the gadgets can be executed using dissimilar
types of invasions. Therefore it is recommendable to mention that the invasions may
range from low-prole hacking to a gadget that is enormous, for example, the
WannaCry, and can be much more complicated such as that of Dyn and Mirai.
Conventional invasion sensing and various mitigation systems can be centered on
cryptography, and at times there can be inadequate precision, and this may result to
inaccurate positives. For this reason the ML-centered methods, for example, the
DL, KNN, and SVM, and the unsupervised learning methods can be used. In this
section, there will be a brief outline of the ML-based techniques that are used for
attack sensing and various mitigation processes within the IoT networking system.
Moreover, in this section there will be an analysis of present DL and ML techniques.
The ML invasion sensing and its mitigation used within the IoT network: There
has been proposition of the semi-supervised attack sensing mechanism that can be
used within the IoT.In real sense, the recommended system is centered on the use
of the ELM (extreme learning machine) algorithms alongside the FCM (fuzzy
C-means) techniques, generally referred to as the ESFCM. The ESFCM can be
enacted within the fog substructure. One of the speculated features of the ESFCM
is that it deals with tagged information hence augmenting the sensing rate within the
evenly spread attacks. Although the sensing precision within the ESFCM can be
lower compared to the initially mentioned DL mechanisms, it may outwit the con-
ventional mentioned ML algorithms used for sensing various invasions [26].
Nonetheless, some semi-supervised learning systems draw various elements from
both the unsupervised and supervised learning systems, which may make them
effective compared to other systems. As stated previously, the IoT comprises a vast
number of breeds in which some of them include personal networks and may
become more complex within the infrastructural industry, for example, the
smart grid.
The DL-centered invasion sensing and mitigation techniques within the IoT: The
sensing of the DL-based invasions within the IoT through leveraging of the fog
system has been stated. For this reason, the invasion sensing system can be enacted
within the edge of the smart infrastructure. Hence, the aligned attack sensing mech-
anism comprises various account parameters that include the learning systems and
will choose the type of output that will be allocated to certain data. Hence, the the-
ory that has been employed in utilizing the fog technology is an effective asset limi-
tation and a natural application within the IoT.In an event where there is intense
infrastructure, therefore the learning mechanism can be close to the information
nodes so that there can be timely delivery and quality decisions can be made when
there is an attack.
A. Haldorai etal.

47
2.6.2 Attacks Within theDoS andDistributed DoS (DDoS)
DoS and DDoS are the two most dangerous invasions whose attacks are always
difcult to alleviate within the IoT surroundings. Many claims have been stipulated
so that enough reasons can be provided to satisfy the question of why these attacks
are immense. Some of the reasons may include a sheer number of linked IoT gad-
gets being heterogeneous, the Internet, insufcient mechanisms considering the
limitation of resources for the IoT gadgets, communication within platforms,
extensive communication, etc. Such scenario illuminates various dangers for the
IoT gadgets in contradiction to the DDoS invasions. These types of attacks can be
evaded so that there can be assurance of sufcient operation of the IoT applications.
Based on this, the IoT can be known as a land of chances applicable to the DDoS
invaders. During 2016, there was an extraordinary increase in invasions in contra-
diction to the IoT substructure extensively. An example of such an attack was
Mirai4 that led to the decline of the Internet whereby devices, for example, printers,
babycams, etc., have been utilized like robots for the execution of the DDoS inva-
sions upon many rms [27]. Equally, some like Mirai bots were reported also. With
its inception in 2016, the malware led to substantial disruption within Internet ser-
vices based on its complicated contagious contrivance within the IoT networking
system. Due to the claim that only IoT devices can be hacked, they can be utilized
and be regarded as front tools for the execution of disreputable invasions in contrast
to various organizations, which advocate intense intellectual safety measures,
which can safeguard their equipment.
Until now, crucial research outcomes have been acquired via different methods
in order to alleviate the DDoS threats within the IoT, although dissimilar architec-
tures ensure that it is hard to create a combined system in order to curb the DDoS
threat within dissimilar IoT podiums. Conventional DDoS sensing and vindication
systems within the IoT networking system have been used on routers, gateways, or
accessible points with the aid of intrusion sensing and anticipation methods. Based
on the previously mentioned protocols, both the CoAP and the MQTT can be uti-
lized as protocols for the IoT.According to scientic research, the DDoS invasion
has been evaluated based on the attack on the generic IoT, which utilizes the
CoAP.Such works can be utilized to simulate an invasion in order to evaluate the
capacities within the IoT network. Additionally, in order to sense and mitigate the
DDoS attacks, some enabling technologies, for example, fog and cloud computing,
can be utilized for assisting the detection of DDoS systems within the IoT.For
example, fog computing can be used as a centered approach for safeguarding vari-
ous malicious threats. Amid several avors for the IoT networking systems, various
critical development-assisted networks should portray rigid resilience for curbing
the DDoS invasions.
Cloud computing and fog DDoS mitigation frameworks can also be sug-
gested. Within the DDoS levels, a conventional mechanism can be employed
within several layers within the IoT industrial networking systems. Hence, the
minimum levels that utilize the computing edge along with the SDN gateways
2 Organization Internet of Things (IoTs): Supervised, Unsupervised…

48
and the networking traffic can be accumulated within a fog computing level that
comprises the DDoS.Moreover, the honeypots can be used within this phase.
At last, the cloud computing levels can be created and evaluated within a cloud-
based platform in order to sense various vulnerable DDoS invasions. Based on
the previous studies, it is vividly clear that there will be no bullet, which is in a
position of catering for the DDoS threat within asset-restricted network cover-
age, and it is known that literature that is intellectual can be important in sens-
ing and mitigating the DDoS.Moreover, falsified values cannot be questioned
in these scenarios; hence, the request can be neglected [28]. For these reasons
the asset-limited and insufficiently effective gadgets within the IoT networking
system may bring out an alluring environment that can be good for harmful
threats. In spite of the latest developments in understanding such types of inva-
sions, still it is important to work on the intellectual contrivances that do not
only accompany the traffic amount but are also characters of the threat. In con-
sidering the subject of this issue, machine learning can be regarded as an effi-
cient candidate that can be leveraged for use in DDoS sensing within the IoT
networking systems. In order to alleviate the DDoS invasions within the IoT
networks, the research sector has been in a position of leveraging some ML and
DL experts.
1. The ML-centered methods to highlight the DDoS and the DoS invasions within
the IoT: First, there is a detailed rationale based on the present ML systems for
the DDoS sensing within the IoT networking systems. The typical elements
within the IoT systems in which involved consistent communication lacking the
back end networking servers. Moreover, there was a comparison between the
decision tree, the k-nearest, the random forest, the neural networks, and the SVM
based on whether they can sense DDoS in the IoT.On the same note, within the
SDN environment, the SVM-centered DDoS sensing systems have been used.
Apart from this, for DDoS sensing, the SVM has been proved to have high accu-
racy compared to other mechanisms. Other techniques can also be compared
with others such as the RBF (radial basis function), naïve Bayes, bagging, and
random forest.
2. The ML-based IDS within the IoT: There was a proposition of using ML-based
IDS that is lightweight, has low power, and can be used for operating the
6LoWPAN.This mechanism utilizes three methods such as the K-means, the
decision tree, and the hybrid methods which combine the previously mentioned
methods. Furthermore, the leveraged ML methods can sense the intrusions
within the IoT pathways.
3. The IDS within the IoT heterogeneous networking systems: Within a heteroge-
neous networking system for example, the RNN (recurrent neural network) has
been utilized within the LSTM infrastructure. RNN (random neural network) has
also been used that can be effective for the realization of the fast intrusion-based
sensing within minimal power of the IoT networking system.
A. Haldorai etal.

49
2.6.3 Analysis ofMalicious Programs Within theIoT
The depreciation in number alongside the heterogeneity of IoT gadgets has pro-
vided a cool and excellent platform for cybercrimes. The potential vulnerability can
be utilized within the system. A notorious invasion within the domains can be done
through vulnerable code injection which can affect the performance of the present
IoT gadgets. The threat, which can be performed by injection of different malware,
can be linked to the use of various authorizations, security protocols, and authentici-
ties. Aside from the mentioned techniques altering the IoT gadgets so that the soft-
ware can be physically modied can aid attackers inject vulnerable codes. Prior to
involving yourself into deep data based on the malware, it is crucial to comprehend
various groups of malicious programs that may put the IoT security at risk.
Generally, a malware can be a persistent threat based on the previously mentioned
vulnerabilities that can be performed via various invasions. Common forms of mal-
ware include, although not restricted to, ransomware, bot, Trojan, adware, etc.
This section will summarize the groupings of different malware, which inuence
the operation of the IoT gadgets, and further analyze the present solutions such as
ML-centered methods, which maintain the IoT gadgets’ security. According to sci-
entic study, a vast number of smart gadgets that have been linked to the Internet are
deprived of enough security, which not only is dangerous to the devices but also
may permit the invader to gather resources meant for enormous attacks, for exam-
ple, the DDoS.For example, scientists have attempted to test different music gad-
gets for various problems and used vulnerable codes to show that these devices are
highly prone to different attacks when linked to the web.
Invaders also can utilize Internet-based webcams installed in public places, res-
taurants, and residential places for threatening intentions. Furthermore different
types of malware have been created for the disintegration of normal operation of a
business and other entities; these malware programs include, although not restricted
to, the Night Dragon, NotPetya, CryptoLocker, Stuxnet, etc. There are detailed clas-
sications and generalized principles for various malware programs. Such may
include the generic attacks but there are also customized families for the malware
threats which are specically aimed at the IoT gadgets. Many malware invasions
may include CryptoLocker, WannaCry, Stuxnet, etc. Such attacks have caused sub-
stantial losses for the industry and depreciation of public image for the entire rm.
Information connected to these threats is all over the entire chapter. It is crucial that
people comprehend a generalized concept for the invaders for launching the threat
using a malware.
Precisely, the invaders may accumulate ideas pertaining to capable targets, for
example, various network sensors via taking a reconnaissance. A vast number of
methods are present which can be used for executing a reconnaissance, such as
Wireshark, Nmap, and Metasploit, alongside the use of social engineering tech-
niques. Various present tools give a chance of additional data based on the different
uses that may create a more conducive platform for threats to come in. For this
reason, every attacker will rst think about the type of threat he/she would use in
2 Organization Internet of Things (IoTs): Supervised, Unsupervised…

50
relation to the uniqueness of the device class. Until now, certain application evalua-
tion techniques, for example, the OWASP (Open Web Application Security Project),
give access to the primary sources of threats by attackers making them to select an
effective exposure [29]. Generally, the OWASP gives an opportunity to various
sources so that they can exploit the resources that can be utilized by various invad-
ers, for example, the injection of SQL, misconguration of security measures, mis-
guided authentication, and XSS (cross-site scripting).
Moreover, based on the device type and its exploitation, the attacker can transmit
a payload to a specic target via the use of various media, for example, rootkit,
phishing, and various updates. For these reasons, the malware group can range from
a simple sole malware to a multifaceted, intellectual, inactive, and versatile emanat-
ing malware. Currently, intelligent malware can be very adaptive based on the vicin-
ity of the IoT whereby it can be used for getting access to the prospected network
and adjusting the performance processes based on the network. For example, vari-
ous malware may go astray when detected and can be latent for a certain period;
hence, they do not perform malevolent coding during that period so that their main
intention would not be compromised. Based on this concept, various measures that
can be used for evading the detection method are as follows:
1. Malware elusion methods: Different techniques that are used for evading these
malware can be used. Encoded malware implies to the encrypted harmful codes
in relation to the subsequent decryption so that it may surpass the signature-
linked antivirus. Although the decryptor tends to be the same, for dissimilar ver-
sion in relation to the same malware, it still remains to be detected. In order to
overwhelm all of these, a decryptor can be transformed and hence will avoid the
detection contrivance. Such a form of malware is known as oligomorphic mal-
ware. Correspondingly polymorphic malware creates diverse decryptors that
will make it hard for sensing engines to identify the malware [30]. The metamor-
phic malware can be indeed the complicated malware among other groups since
it emanates to a new generation whereby it can be dissimilar to any other for
detection.
2. Malware ML-centered evaluation within the IoT: Moreover there has been
accumulation of various supervised learning methods that use random forest
groupings which are employed for sensing malware that is Android-based.
Therefore every classier’s sensing precision can be evaluated based on the
malware checklist with the application of Androids. There has been an investi-
gation on the techniques for sensing various malware and their propagation in
the WMS (wireless multimedia system) in IoT.Research has shown that cloud-
based techniques within the SVM have been leveraged in order to sense vari-
ous capable malware and their propagation and can be utilized in dissimilar
gaming so that they can minimize the malware threats. Just after acquiring the
Nash equilibrium, there has been an attempt of looking out for a conducive
plan for the WMS so that it can protect itself against malware. Correspondingly,
A. Haldorai etal.

51
the prospected SVM linear methods for the classication of various malware
that are Android- based are used.
The detection system is centered on ANN before being evaluated by malware
and benign application in Android devices. Essentially, MalDozer is centered on a
series like API technique calls in resource permission, Android, and fresh method
calls, among others. Moreover, the recommended methods include the automated
engineering characteristics during the process of training. Moreover, the research
proposes an image recognition-centered DDoS malware-detecting method of IoTs
network. The research recommended a solution, whereby the researchers collected
and categorized two signicant segments of malware known as Linux and Mirai.
Thereafter, research practices convert the segment binaries of the IoTs application
to the grayscale image. Afterward, CNN is conditioned to categorize the images to
malware and good-ware. Other researches considered using the deep auto-encoders
to show the botnet threats in IoTs. In the solution identication, the researchers
evaluated the behavior of networks before using the deep auto-encoders to separate
any form of anomaly in the network. Thus, the discussion concerning DL and ML
approaches for the detection of malware in IoTs networks indicated that the tech-
niques used in data training are used to identify indenite malware.
2.7 Conclusion andFuture Contributions
In conclusion, the emergent advancements recorded over the past few years
show that Internet standards and computer systems have enhanced the process
of communication between various network devices. Approximately 25billion
devices are speculated to link up with the Internet by 2020. This speculation
gives rise to the novel development idea of the IoTs, which is a combination of
the embedded advancements such as wireless or wired communication devices,
actuator and sensor devices, and physical objects linked to the Internet. The
most vital objective of computing is to enrich and simplify human experiences
and activities which is also one of the visions linked to twenty-rst-century
computing. The IoTs necessitates information to provide effective services to
individuals or improve the IoTs system evaluation to attain a smart environ-
ment. In that way, frameworks have to be able to access raw information from
various resources over the networking devices and evaluate data to extract user
competencies. Big data is explained as a high-velocity, high-volume, and high-
variety data set, which is expensive and innovative to enhance user automation,
decision, and insight. In consideration to the challenges caused by big data, the
future relies on the launching of novel concepts of intelligent data to enhance
productivity, effectiveness, and efciency.
2 Organization Internet of Things (IoTs): Supervised, Unsupervised…

52
References
1. Anandakumar, H., & Umamaheswari, K. (March 2017). Supervised machine learning tech-
niques in cognitive radio networks during cooperative spectrum handovers. Cluster Computing,
20(2), 1505–1515.
2. Anandakumar, H., & Umamaheswari, K. (October 2018). A bio-inspired swarm intelligence
technique for social aware cognitive radio handovers. Computers & Electrical Engineering,
71, 925–937. https://doi.org/10.1016/j.compeleceng.2017.09.016.
3. Adli Mehr,K., &Musevi,N.J. (2016).Security bootstrapping of mobile ad hoc networks using
identity-based cryptography. Security and Communication Networks, 9(11),1374–1383,
Available: https://doi.org/10.1002/sec.1423
4. Bredt, S. (2019). Articial Intelligence (AI) in the nancial sector—Potential and pub-
lic strategies.Frontiers in Articial Intelligence, 2, Available: https://doi.org/10.3389/
frai.2019.00016
5. Chittaro,L.,&Ranon, R. (2004). Hierarchical model-based diagnosis based on structural
abstraction. Articial Intelligence, 155(1–2), 147–182. Available: https://doi.org/10.1016/j.
artint.2003.06.003
6. Chatzigiannakis,I., &Nikoletseas, S. (2004) Design and analysis of an efcient communication
strategy for hierarchical and highly changing ad-hoc mobile networks. Mobile Networks and
Applications, 9(4), 319–332. Available: https://doi.org/10.1023/b:mone.0000031591.74793.52
7. Haldorai, & Kandaswamy, U. (2019). Supervised machine learning techniques in intelligent
network handovers. In EAI/Springer innovations in communication and computing (pp.135–
154). NewYork: Springer. https://doi.org/10.1007/978-3-030-15416-5_7.
8. Delgadillo,G. Ó. (2016).Fishes and mobile macroinvertebrates of articial habitats in Taganga
Bay, Colombian Caribbean. Bulletin of Marine and Coastal Research, 38(1), Available:https://
doi.org/10.25268/bimc.invemar.2009.38.1.338.
9. Dubois,D.,&Prade,H. (2001). Special issue of the journal Articial Intelligence on “Fuzzy set
and possibility theory-based methods in articial intelligence.”Articial Intelligence, 127(2),
269–270, Available: https://doi.org/10.1016/s0004-3702(01)00080-7.
10. Dubois,D.,&Prade, H. (2003).Fuzzy set and possibility theory-based methods in arti-
cial intelligence. Articial Intelligence, 148(1–2), 1–9, Available: https://doi.org/10.1016/
s0004-3702(03)00118-8.
11. Ebendt,R.&Drechsler, R. (2009).WeightedA∗search– Unifying view and application. Articial
Intelligence, 173(14), 1310–1342, Available: https://doi.org/10.1016/j.artint.2009.06.004.
12. Giudici, P. (2018).Fintech risk management: A research challenge for articial intelli-
gence in nance. Frontiers in Articial Intelligence, 1. Available: https://doi.org/10.3389/
frai.2018.00001.
13. Gong,T., &Bhargava, B. (2012).Immunizing mobile ad hoc networks against collaborative
attacks using cooperative immune model. Security and Communication Networks, 6(1),
58–68, Available: https://doi.org/10.1002/sec.530.
14. Gu,B., &Chen, I. (2005).Performance analysis of location-aware mobile service proxies for
reducing network cost in personal communication systems. Mobile Networks and Applications,
10(4), 453–463, Available: https://doi.org/10.1007/s11036-005-1557-x.
15. Guo, M. (2011).A simple and rapid authentication protocol in mobile networks. Security and
Communication Networks, 7(12), 2596–2601, Available: https://doi.org/10.1002/sec.386.
16. Jávor,A.,&Szűcs, G. (1998).Simulation and optimization of urban trafc using AI.
Mathematics and Computers in Simulation, 46(1), 13–21. Available: https://doi.org/10.1016/
s0378-4754(97)00154-7.
17. Kharin, Y. (1994).Articial intelligence frontiers in statistics AI and statistics III. Knowledge-
Based Systems, 7(1), 57–58, Available: https://doi.org/10.1016/0950-7051(94)90017-5.
18. Lasi, H. (2013).Industrial intelligence– A business intelligence-based approach to enhance
manufacturing engineering in industrial companies.Procedia CIRP, 12, 384–389, Available:
https://doi.org/10.1016/j.procir.2013.09.066.
A. Haldorai etal.

53
19. Leo Kumar, S. (2017).State of the art-intense review on articial intelligence systems applica-
tion in process planning and manufacturing. Engineering Applications of Articial Intelligence,
65, 294–329. Available: https://doi.org/10.1016/j.engappai.2017.08.005.
20. Marquis,P.&Schwind,N. (2014).Lost in translation: Language independence in propositional
logic– Application to belief change. Articial Intelligence, 206, 1–24, Available: https://doi.
org/10.1016/j.artint.2013.09.005.
21. Mihăiţă, A.,Dupont,L.,&Camargo,M. (2018).Multi-objective trafc signal optimization using
3D mesoscopic simulation and evolutionary algorithms. Simulation Modelling Practice and
Theory, 86, 120–138, Available: https://doi.org/10.1016/j.simpat.2018.05.005.
22. O’Halloran,S.,&Nowaczyk, N. (2019).An articial intelligence approach to regulating systemic
risk. Frontiers in Articial Intelligence, 2, Available: https://doi.org/10.3389/frai.2019.00007.
23. Panneerselvam, S. (2019).Survey of articial intelligence used in industries a review. Industrial
Engineering Journal, 12(5), Available:https://doi.org/10.26488/iej.12.5.1188.
24. Park,S.,&Moo,Y.S. (2010).Updating route information in mobile ad hoc networks. Journal of
Mobile Communication, 4(1), 27–32, Available: https://doi.org/10.3923/jmcomm.2010.27.32.
25. Suganya, M.,&Anandakumar, H. (December 2013).Handover based spectrum allocation in cog-
nitive radio networks. In 2013 International Conference on Green Computing, Communication
and Conservation of Energy (ICGCE). doi:https://doi.org/10.1109/icgce.2013.6823431.
doi:https://doi.org/10.4018/978-1-5225-5246-8.ch012
26. Rao, M. (1992).Frontiers and challenges of intelligent process control. Engineering
Applications of Articial Intelligence, 5(6), 475–481. Available: https://doi.
org/10.1016/0952-1976(92)90024-e.
27. Tünay, O. (2010).Chemical oxidation applications for industrial wastewaters. Water
Intelligence Online, 9, Available: https://doi.org/10.2166/9781780401416.
28. Varaprasad, G. (2012).Stable routing algorithm for mobile ad hoc networks using mobile
agent. International Journal of Communication Systems, 27(1), 163–170, Available: https://
doi.org/10.1002/dac.2354.
29. Vôhandu, L. (1994).Articial intelligence frontiers in statistics. Engineering Applications of
Articial Intelligence, 7(1), 87, Available: https://doi.org/10.1016/0952-1976(94)90049-3.
30. Wu, Y.,Deng,S.,&Huang, H. (2012).Information propagation through opportunistic com-
munication in mobile social networks. Mobile Networks and Applications, 17(6), 773–781,
Available: https://doi.org/10.1007/s11036-012-0401-3.
2 Organization Internet of Things (IoTs): Supervised, Unsupervised…

55© Springer Nature Switzerland AG 2020
A. Haldorai et al. (eds.), Business Intelligence for Enterprise Internet of Things,
EAI/Springer Innovations in Communication and Computing,
https://doi.org/10.1007/978-3-030-44407-5_3
Chapter 3
Enterprise IoT Modeling: Supervised,
Unsupervised, andReinforcement
Learning
RajeshKumarDhanaraj, K.Rajkumar, andU.Hariharan
3.1 Introduction
This chapter introduces the Internet of Things (IoT) and machine learning (ML),
including algorithms [1]. According to Statista[26], the worldwide articial intel-
ligence marketplace totaled $9.51billion (U.S. dollars) in 2018 and was predicted
to reach $14.69billion in 2019 (Fig.3.1). Statista also estimated that the number of
connected devices in 2019 would total 26.66billion (Fig.3.2).
Currently, IoT and ML are fast-growing digital systems. Contemporary systems
often intersect, as progress in any kind of technology is no longer achievable in
isolation. IoT and ML systems have robust intersections that allow for a variety of
strategies, as discussed in the following sections.
3.1.1 Internet ofThings
In recent years, the Internet has been extended to nearly all connected products
(called “Things”) and their virtual representations. The Internet of Things (IoT) has
provides for many possible uses, services, and products in numerous areas, includ-
ing smart homes, healthcare, and smart transportation [2, 3]. Research in this area
has received plenty of interest, and naturally plenty of funding. It is supported
through the efforts of academia and business, along with standardized systems for a
R. K. Dhanaraj (*)
Galgotias University, Greater Noida, India
K. Rajkumar · U. Hariharan
Galgotias College of Engineering and Technology, Greater Noida, Uttar Pradesh, India

56
number of towns in telecommunication, health insurers, the semantic Web, and
informatics.
For many years, typical methods were created only for specic uses with mini-
mal versatility. Therefore, when a device was working, it could not be transformed
exibly and dynamically. The rst step in introducing the IoT (or more generally,
the potential future of the Internet) calls for service platforms, products, and
applications that can record, communicate, shop, share, and gain access to informa-
Market revenue in billions U.S dollars
140
120
100
80
60
40
20
0
2018
9.52 14.69
22.6
34.89
51.27
70.94
94.41
118.6
2019 2020 2021 2022 2023 2024 2025
Fig. 3.1 Growth of the articial intelligence industry
Connected devices in Billions
80
70
60
50
40
30
20
10
0
2018
23.14
26.66
30.73
35.82
42.62
51.11
62.12
75.44
2019 2020 2021 2022 2023 2024 2025
Fig. 3.2 Estimated number of IoT-connected devices worldwide from 2015 to 2025
R. K. Dhanaraj et al.

57
tion through the actual physical planet; in fact, they are able to speak together
around the world. This will likely result in brand-new possibilities for many differ-
ent domains, including health and tness, eco-friendly power, manufacturing, smart
residences, and personalized end-user apps.
In this way, IoT plays a much more crucial role in daily life. The volume of infor-
mation on the Web and the Internet is already too much to handle and continues to
grow at an incredible pace: Each day, approximately 2.5 quintillion bytes of infor-
mation are produced. In addition, 90% of the current information was produced in
the last few years [4]. Sensory specics, the info originating from receptors, may be
analyzed by means of algorithms and switched into straight in machine info that
designs possess a denite understanding about the legitimate data. By doing this, the
device is able to act human in some way (some call this type of technique “articial
intelligence”). In addition, we are able to innovate much more useful programs,
services, and products that alter our lives dramatically and automatically. For exam-
ple, meter readings can be used to predict and balance energy usage within smart
grids; examining a combination of trafc, pollution, water, and congestion sensory
details can provide much better information to site visitors as well as community
management; processing and checking sensory products linked to individuals can
improve remote health care [5]. Such an information transformation procedure is
better illustrated using a “knowledge hierarchy”. We have adapted denitions for the
concepts of serotonin and semantics (see Fig.3.1).
3.1.2 Machine Learning
Machine studying (ML) has revolutionized how we do business. A disruptive cut-
ting edge technology which differentiates ML out of various other strategies to
hands free operation is actually a level far from the rules-based programming. ML
algorithms have made it possible for engineers to use information without having to
explicitly program models to examine specic parts of a problem. Rather, the
devices themselves return the proper responses, depending on the information they
have received [5]. This ability has caused manufacturers to reconsider the methods
they normally use to generate choices from the information.
Machine learning is used to generate predictions from new information by using
historical details as an instructive case in point. For example, you might wish to
determine a consumer lifetime valuation within an eCommerce retailer computing
website for an upcoming client meeting. If you currently have historical details on
customer interactions and net income related to the buyers, you might wish to make
use of machine learning. ML can determine the buyers who are prone to have the
greatest net benet, helping you to concentrate your efforts on them.
Among the strategies used to provide algorithms with information, the most
popular design is known as supervised learning. This chapter discusses this area of
information science and the reasons why it is regarded as low-hanging fruit for
companies that intend to venture into ML.
3 Enterprise IoT Modeling: Supervised, Unsupervised, andReinforcement Learning

58
3.1.3 Machine Learning Plus IoT
ML is being increasing explored by manufacturers because of the hoopla surround-
ing IoT.Many companies have recognized it as an important strategic area, whereas
others are launching pilot programs to determine the possibilities for IoT in their
business activities. Consequently, virtually every IT seller is suddenly announcing
IoT and consultation services expertise. However, increasing prots by means of IoT
is not simple. First, a possible lack of concrete goals is quite disconcerting.
Improvements in digitization and IoT locations are brand-new prerequisites with two
customers as well as sellers. Many commercial enterprises have neglected to deter-
mine how places are going to change together from the setup of an IoT technique.
Quite simply, well-dened, concrete intermediary goals are often absent. For
example, manufacturing businesses generate a tremendous quantity of information
every day. Nevertheless, businesses do not systematically gather and analyze these
data to enhance a procedure’s effectiveness or even create additional objectives. In
addition, very few vendors actually know how to explain, using concrete terminol-
ogy, to a prospect the best way to implement benecial IoT strategies. Just a prom-
ise associated with a cloud-based IoT wedge is not sufcient [6, 7].
According to Gartner[27], companies in Finland have advanced to a chapter in
which discussions on IoT involve specialized terminology rather than internet busi-
ness objectives. Consumers are offered revolutionary proposals and are also empow-
ered to take control of the project. However, vendors have to boost their abilities to
describe, using a lot more concrete terminology, how businesses can leverage the
use of IoT, as well as be inclined to help companies recognize the potential and
build practical blueprints. When a seller provides a response on an individual analy-
sis that is too general, alarm bells ought to be ringing.
3.1.3.1 Finding Solutions inIoT Data
A business’s brand-new facility was encountering serious production issues resulting
from a crucial turbine disaster. A third party was employed to resolve the situation.
After approximately 6weeks, the team of four specialists working with just a eld
evaluation had made very little progress. The facility required a lot of equipment
maintenance and time-stamped environmental data. Even with all of this specic,
readily available information, no one was able to transform it into usable data [8, 9].
We assisted the company with an analysis. When all the information was
enhanced through analytics, underlying issues were discovered, primarily with
much-needed oxygen feeds throughout the manufacturing process. Since this dis-
covery, the facility did not have any major problems. This is a key example of
exactly how machine learning can be used to attain much greater effectiveness. With
the correct algorithms, a device can be gradually trained to perform internal and
external production-related elements, improve the use of consumables, and enhance
the effectiveness of the whole output procedure.
R. K. Dhanaraj et al.

59
3.1.3.2 Machine Learning andIoT inaBusiness Domain Model
The whole planet is going crazy with information, both articial intelligence and
IoT.Many publications have discussed the quantity of information we produce each
and every working day, and countless statistics have demonstrated just how much
detail we will produce in the coming years. Thus, we want to discuss how ideas or
algorithms coming from other systems can be put into IoT information for optimi-
zation. In our previous publications, we discussed data science algorithms with IoT
information; here, we discuss machine learning.
On an extremely basic level, Machine learning techniques minimize the man power
using training. ML reads patterns in information as a stand-alone device and makes
autonomous choices without requiring a developer to create a brand-new group of
codes. When you use Siri on your iPhone, for example, you may notice that its replies
become more sophisticated and accurate as you use it more often. That is among the
fundamental uses of machine learning [10–12].
But exactly how would machine learning improve the IoT? Each time IoT recep-
tors collect information, someone has to be working on the backend to classify the
information, process it, and make sure the information is delivered back to the unit
for choice generation. When the information generated is huge, exactly how can an
analyst take care of the inux? Driverless automobiles, as an example, need to make
fast choices when on autopilot, so depending on people is not possible. That is
where machine learning becomes useful. To determine which algorithm needs to be
used for a specic group of projects, rst need determine the job. Several of the
activities involve discovering uncommon details, system ndings, predicting values
and groups, function extraction, and others [7, 13].
Classifying the information sets for various jobs make it much easier for a novice
to recognize the proper algorithm program [6]. For example, to focus on informa-
tion system ndings, clustering algorithms, including K means, can be used.
K-means was created to deal with substantial chunks of information that include
several detail types. In another example, one-class support vector machines and
Principal Component Analysis (PCA)-based anomaly detection algorithms are ideal
for instruction information coming from uncommon details.
Using IoT and machine learning in the same sentence is like playing buzzword
bingo. However, the two principles make good sense as a pair. By 2025, it is believed
that the IoT will produce more than 180 zettabytes of information yearly—that is
180trillion gigabytes. Machine learning is best when used with sizable datasets. To
understand why the IoT requires ML to dominate the planet, we begin by examing
the two principles individually in Fig.3.3. Then, we analyze four unique ML and
IoT real-life applications. The IoT is developing at an unprecedented speed. In fact,
there are 127 brand's -new gadget are connected to the web everyday. Improved
connectivity is only going to speed up the progression. McKinsey expects that, by
2022, 100% of the worldwide public will have access to low-power wide area net-
works (LPWANs) [3, 14, 15]. This can allow long-range marketing communica-
tions with attached products while optimizing each expense as well as power
consumption demands.
3 Enterprise IoT Modeling: Supervised, Unsupervised, andReinforcement Learning

60
3.2 Machine Learning Algorithms
Machine learning algorithms have specic and unique uses. The following list of
machine learning algorithms includes AI and developing machine learning sys-
tems [8]:
1. Linear Regression
2. Logistic Regression
3. Support Vector Machines
4. Random Forest
5. Naïve Bayes Classication
6. Ordinary Least Square Regression
7. K-means
8. Ensemble Methods
9. Apriori Algorithm
10. Principal Component Analysis
11. Singular Value Decomposition
12. Reinforcement or Semi-Supervised Machine Learning
13. Independent Component Analysis
An understanding of the algorithms will allow you to use them effectively in almost
any data problem and a variety of functional machine learning projects.
The following three categories apply to machine learning algorithms [16]:
1. Supervised Learning
2. Unsupervised Learning
3. Reinforcement Learning
It is important to understand each algorithm in order to select the correct one to
meet your problem and learning requirements, as shown in Fig.3.4.
3.2.1 Supervised Learning
Supervised learning techniques are used by optimum machine learning users. In
supervised learning, an algorithm’s learning process is completed with an instruc-
tion dataset. Even by making use of a training dataset, the task could be regarded as
Fig. 3.3 Data generation through an IoT system
R. K. Dhanaraj et al.

61
a mentor that is supervising the learning process. The algorithm can help within
generating predictions regarding the data in the training process and obtain the mod-
ications carried out by the teacher itself. A conclusion may indicate if the algo-
rithm has accomplished an appropriate amount or a certain degree of performance.
There are two kinds of supervised learning problems, which can be more grouped
generally as regression or classication issues:
• Classication Problems: A problem which generates adaptable, which moves
lower merely especially such as the “red” or perhaps “blue” or perhaps it may be
“disease” and “no disease”.
• Regression Problems: A regression concern is whether the variables are really
authentic, such as “dollars” or perhaps it might be “weight” [16, 17].
Several issues can be noted within the data type, including things like time series
prediction and recommendations. Supervised machine learning algorithms includ-
ing the following:
1. Decision Trees
2. Naive Bayes Classication
3. Support vector machines for classication problems
4. Random forest for classication and regression problems
5. Linear regression for regression problems
6. Ordinary Least Squares Regression
7. Logistic Regression
8. Ensemble Methods
Fig. 3.4 Types of machine learning
3 Enterprise IoT Modeling: Supervised, Unsupervised, andReinforcement Learning

62
3.2.2 Decision Trees
A great deal of information is apparent in the name decision tree; using easy phrases,
a decision tree can help you make a choice regarding the information at hand. For
example, a banker can make a decision on providing a mortgage to an individual
based on age, profession, and amount using a decision tree. When creating a deci-
sion tree, the task is launched in root node, responds to certain questions within each
node, and considers the area that corresponds to the specic solution. Adhering to
the procedure, we traverse to the root node, then to a leaf, then type conclusions
within the context of the information. An example is provided in Fig.3.5.
3.2.3 Unsupervised Learning
Unsupervised learning uses an algorithm in which you insert the information (X)
along with hardly, no any corresponding variables are getting established. The main
objective for unsupervised learning is to help model the underlying framework or in
an effort to assist the learners in understanding the information. Unsupervised learn-
ing is different from supervised learning because there are no any proper responses
are not mentioned the numbers to the instructor. Algorithms remain as their very
own products to help you nd the intriguing framework that is contained in the
information. Unsupervised learning may be grouped as clustering or connection
issues [7]:
Fig. 3.5 A tree showing
the survival of passengers
on the Titanic
R. K. Dhanaraj et al.

63
1. Clustering: A clustering shows what you would like to nd out and also assists
in determining the natural groups belonging to the information, such grouping
clients according to their buying behavior.
2. Association: A connection becomes the learning issue. It is exactly where you
will nd the guidelines that will explain the larger areas of your information. For
example, individuals who purchase X are also individuals who have a tendency
to purchase Y.
Some common unsupervised learning algorithms include the following:
• K means for clustering problems
• Apriori algorithm for connection guideline learning problems
• Principal Component Analysis
• Singular Value Decomposition
• Independent Component Analysis
3.2.4 Reinforcement or Semi-supervised Machine Learning
For some problems, you will need to enter a huge amount of information. In semi-
supervised machine learning, the information is rst labelled as (X), then later some
is labelled as (Y). This method is somewhere between supervised learning and unsu-
pervised learning. A great example is an image archive in which the subjects of only
some photos are labelled (e.g., dog, cat, most people, but a school is unlabelled) [4].
A great deal of practical society items learning related problems falls straight
into this specic group because they might be costly or time consuming. To label the
information, access may need to be obtained through the domain name profession-
als. The unlabeled information is low-cost also comparatively simple to gather as
well as nd. Unsupervised learning strategies can be used to accomplish great
things. They can help you learn as well as nd out the different legitimate compo-
nents that occur in the type of variable. Supervised learning methods help make the
very best of estimate predictions with unlabeled information. This information can
then be used in supervised learning algorithms as instructive information to make
predictions based on different details, as shown in Fig.3.6.
3.3 Machine Learning Applications inIoT
3.3.1 Price Savings Come toIndustrial Applications
Predictive abilities are incredibly benecial in a manufacturing environment. By
taking in information from several sources within and on devices, machine learning
algorithms can “learn” what is common for any device then identify when some-
thing abnormal starts to happen. “The collected information is delivered to the serv-
3 Enterprise IoT Modeling: Supervised, Unsupervised, andReinforcement Learning

64
Fig. 3.6 Overall classication of machine learning
R. K. Dhanaraj et al.

65
ers, exactly where it’s contrast to earlier details collected out of this printer, and
even information collected by using the same devices very much. The wedge of ours
is able to identify the smallest alterations as well as alert you of acquiring malfunc-
tions. This particular evaluation is actually carried out within real time and also the
outcomes are actually shown on the technician’s smart phone in just seconds.” [7]
Predicting if a machine requires upkeep can be quite important, converting
directly into money by saved expenses. An excellent case is Goldcorp, a mining
business that uses surprisingly impressive cars are dragged to the bay. When these
transporting automobiles decompose, Goldcorp is losing 2 billion dollars each day
from inefciency [13]. Goldcorp is currently making use of machine learning to
predict with more than 90% reliability when devices will require upkeep, which
results in a large cost savings.
3.3.2 Shaping Experiences toIndividuals
Almost everyone is familiar with machine learning in their daily lives. Netix and
Amazon use machine learning to understand our tastes and provide a much better
experience for their end users. Thus, they suggest products that you might want or
provide suggestions for lms and television shows to watch.
Likewise, IoT machine learning can be very important in shaping our environ-
ments to our personal preferences. The Nest Thermostat is a terric illustration, as
it uses machine learning to study your tastes for cooling and heating, ensuring that
the home is the perfect temperature whenever you return from work or awaken each
morning.
3.3.3 The New Innovative IoT Emerging Business Models
Like each main technological shift, the growth of the IoT has been both successful
and disastrous. The passion with which business owners, investors, and govern-
ments have adopted IoT is a testament to its deep cultural and economic
possibilities.
Nonetheless, passion has outpaced comprehension. Consequently, the IoT busi-
ness is rife with all sorts of errors and misapplications that typically plague early
adoption. As IoT moves beyond its infancy stage, the economic realities are becom-
ing more clear. Probably the most considerable of the revelations is the fact that
conventional hardware does not conveniently affect IoT systems [3, 15] because IoT
products offer recurring, infrastructure-related expenses unlike conventional hard-
ware. Consequently, IoT systems are only conrmed to be benecial when the soft-
ware leads to recurring, constant value for a client. This value is usually accomplished
by a number of methods that are distinct, including elevated advantages, decreased
operating expenses, and facilitation of compliance. With this section, we examine the
3 Enterprise IoT Modeling: Supervised, Unsupervised, andReinforcement Learning

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special economics of IoT and its most promising industrial uses. We also incorporate
particular use cases to help you understand the applications, as shown in Fig.3.7.
3.3.4 The Economics ofIoT Using Machine Learning
The standard hardware industry is based upon the traditional, product-at-a-margin
principal. A business creates a portion of hardware for X and also sells it to custom-
ers for X+Y. What complicates the unit inside IoT is the fact that the X value is
usually snowballing as well as recurring. That is, IoT items incur constant
infrastructure- related expenses that do not affect conventional hardware. Therefore,
to stay operational, IoT products need a network where to run, along with a system
wedge where to gather as well as control information. Consequently, conventional
device economics easily fail on a company’s scale or as equipment ages. IoT busi-
nesses are actually switching to business to shield their margins from erosion. These
designs frequently depend on recurring payments near the buyer, such as to the
“hardware as a service” industry version. Hayesis placed a unit whereby a hardware
company leased the item to clients for a monthly or annual fee. IoT wedge suppli-
ers, meanwhile, charge monthly fees because of the compilation, business, and cre-
ation of device-driven details. Due to these recurring expenses, IoT systems are
economically benecial whenever they provide recurring, constant worth to a client.
Thus, there may be a typical group of problems preventing a connected unit from
becoming successful:
• Model has an annuity (recurring earnings stream)
• Consistent or even improved sales through association with a consumable
• Direct and indirect operational effectiveness gains
• Exponential progress of perpetuity
A product has an incredibly scant, non-variable life cycle. Opening of various
earnings by the way of info put together. In other circumstances, an online business
offering linked treatments with an ordinary design will probably be unsuccessful.
That is because, with time, the recurring backend expenses will erode with the busi-
ness’s margins. Within these sections, we examine the most promising industrial
uses of IoT systems, as well as describe the apps with distinct use case examples [18].
Main Category
Retail
∑ Digital signage
∑ In-store offering &
promotions
∑ Adherence &
Support
∑ Clinical
∑ Virtual care
∑ Wellness &
Prevention
∑ Adherence &
Support
∑ Assisted &
autonomous driving
∑
∑
∑∑
∑
∑
∑
∑
∑
∑
∑
∑
∑
∑
∑
∑
∑
∑
∑ Fleet management
∑ In-vehicle
infotainment
∑ Shared mobility
∑ Smart navigation
∑ Vehicle assistance
∑ Clinical
∑ Virtual care
∑ Wellness &
Prevention
∑ Supply Chain
∑ Smart ordering &
payment
∑ Vending machines
Healthcare Healthcare
Internet of Things (IoT)
Buildings &
Living Smart Cities &
Energy
Connected
Industry
Insurance
Industries /
Applications
Energy efficiency &
HVAC
Home equipment &
appliances
Construction
Education Connected field
4.0
Digital factory
Product design &
engineering
Smart maintenance
Supply Chain
management
Energy
Environmental
Roads, traffic &
transport
Social & security
Water & Waste
Living assistance
Safety & security
Vehicle-to-
infrastructure
solutions
Workplace
operations
Fig. 3.7 Applications of the IoT
R. K. Dhanaraj et al.

67
3.3.5 Compliance Monitoring
Every year, American manufacturers invest an estimated $192billion to comply
with economic, environmental, and work safety regulations. IoT systems have
shown strong possibilities in this realm. By remotely overseeing very sensitive
assets, IoT products can enable companies to considerably reduce expenses related
to regulatory compliance. A good example is software used in the oil and gas
industry. Gas and oil extraction and processing are governed by strict compliance
requirements. Environmental and safety laws require constant vigilance to uphold.
However, internet-connected equipment makes compliance considerably less
complicated and cheaper. In the past, an area agent would have to actually exam-
ine an extraction or processing site to conrm regulatory compliance. Today, an
IoT unit can be used in the area to remotely check vital compliance metrics, such
as oil leaks and gasoline pollutants. Not only does this decrease the expenses for
onsite tracking, but it is also a lot more responsive. Although an area agent might
be in a position to go to a certain site occasionally, an IoT unit is able to offer regu-
lar, up-to-the-minute details in real time. This responsiveness also helps to limit
the scope of penalties when noncompliance occurs. For pollutants and leakages,
regulatory penalties are usually proportional to the quantity of materials produced.
IoT products might limit a business’s risk for penalties by allowing to to react
faster to leaks.
IoT systems also are proving to be helpful with facilitating compliance within
the insurance industry. Insurance companies are using IoT products to monitor
elements of policy adherence. A particularly intriguing illustration of this use
can be found in the homeowner’s insurance sector, where insurers are using IoT
products to monitor uids amounts within basements. When water starts to build
up in a basement, the IoT unit sends notications to the insurer and property
owner, so that they can correct the problem before substantial deterioration
occurs. This allows the insurer to decrease the price of policies and protects
homeowners by preventing damage to their homes. For these reasons, IoT sys-
tems may decrease the overhead associated with compliance and considerably
decrease a business’s costs associated with nes or policy payouts, for example
[19], as shown in Fig.3.8.
3.3.6 Preventative Maintenance
The expenses related to equipment malfunction have a tendency to cascade. Just one
malfunctioning device can impede production, interrupt a whole supply chain, or, as
discussed previously, lead to regulatory noncompliance. Fortunately, the same fea-
tures that allow IoT systems to facilitate compliance also help companies protect
invaluable assets. Returning to the use case of gasoline and petroleum extraction,
we can see how IoT systems help to revolutionize equipment upkeep. Petroleum
extraction sites are full of expensive and highly complex products. Typically, these
3 Enterprise IoT Modeling: Supervised, Unsupervised, andReinforcement Learning

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products were safeguarded exclusively by occasional and possibly imperfect eld
inspections. Because of their intricacy and scope, even small technological mal-
functions could to lead to decreased production and substantial monetary losses.
Catastrophic disasters, meanwhile, could cost companies countless dollars. Today,
a low-cost IoT unit can be used to remotely keep track of gear, monitor upkeep
schedules, and protect against malfunctions. When a machine part starts to under-
perform or maybe malfunction, the IoT unit will immediately issue an automatic
alert. Doing this will restrict losses caused by inefciency, as well as prevent large-
scale problems and injuries [9].
3.3.7 Remote Diagnostics
The remote monitoring abilities of IoT technology can be effectively used outside
of the area of physical upkeep. IoT products are now being used to obtain compre-
hensive and regular analytic details in several areas, ranging from pharmaceuticals
to agriculture. The compilation of data is allowing companies to automate some
procedures and remotely manage manufacturing. A good illustration can be found
in the agricultural sector. Forward-thinking farmers have used IoT products to mon-
itor crops. A connected unit can constantly monitor environmental conditions, such
as soil conditions, sunlight, humidity, and temperature. This data is then compiled
and analyzed to help farmers assess long-term environmental conditions. Additional
IoT products may be used to immediately react to this information. For example,
when a device registers very low moisture in the soil, it can instantly apply water to
x it. These features increase efciency and decrease overhead expenses. Just like
other IoT programs, the outcome is better, more effective, and less costly [20].
Fig. 3.8 IoT compliance monitoring
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3.3.8 Asset Tracking
Visibility is easily the most useful commodity within supply chain control. However,
more than 60% of businesses do not have complete supply chain visibility; the typi-
cal inventory precision threshold of U.S. merchants is only 63%. Fortunately,
low- cost IoT systems now allow companies to overcome this issue. A basic
3G-connected microcontroller can nd and monitor inventory in real time, from
anywhere on Earth. This unprecedented level of visibility will help businesses to
prevent theft and loss, enhance eet advantages, and improve forecasting. Monitoring
information can be shared immediately with other entities in a supply chain to addi-
tionally boost visibility and decrease inefciencies. Cisco and DHL, for example,
expect that IoT systems can have a $1.9trillion effect on their strategies and the
supply chain management industry. To help illustrate the possibilities, Fig. 3.9
shows IoT- based advantage monitoring that benets an simple, linear supply chain.
3.3.9 Automatic Fulllment
Improved visibility on the customer end of this resource chain also allows small busi-
nesses to carry out brand new, much more protable programs. Up-to-the- minute
inventory as well as usage information produces automated satisfaction and price
agreements for new buyers. This performance can help companies by improving need
forecasting, generating product sales, and informing marketing and advertising
efforts. In doing so, customers can benet by experiencing less merchandise short-
ages and more effective customer support. A fascinating example of this IoT program
is the Amazon Dash button, which is a small IoT unit that can be set up to reorder
certain products when pressed. For instance, a consumer can easily use their button to
Fig. 3.9 Asset tracking
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70
purchase a 12-pack of Bounty paper towels anytime it is pressed. Each switch is pro-
grammed for just one item and amount at any given time. The buttons can be mounted
with a user’s home, near each item’s location. Not only do the designs help to improve
product sales, but they supply priceless details on client consumption practices. These
system designs are still emerging, but their possibilities are clear. At the end of 2016,
Amazon announced that Dash gross sales had increased by more than 400% com-
pared with 2015, with 60 new brands signing on to participate in the program. Even
though the Dash design depends on client feedback to trigger replenishment, genu-
inely automated devices have started to gain traction, as shown in Fig.3.10.
3.3.10 Adding Value totheIoT withAI andML
Articial intelligence may be used hand in hand with a different technology,
machine learning. Often used interchangeably, the terms AI and ML represent the
basic principle of acquiring applications that have intelligence. This particular intel-
ligence enables them to evaluate information and generate choices much like the
human mind. The essence of IoT products is to gather information and generate uses
from it; information from physical products via AI and ML allows us to expand
these procedures. The Internet of Intelligent Things (IoIT) uses articial intelli-
gence takes importance over the IoT domain for higher interpreting information
about products that are connected [13].
The products within an IoT network are connected through receptors, actuators,
hardware, and software programs to provide people with rational inputs. The basis
Fig. 3.10 Example of automated fulllment
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71
of the IoT is AI and ML because it enables the products to interpret the information
collected. Whenever connected devices gather and compile raw details, the applica-
tions empowered with ML abilities can merge and assess the information [20].
3.3.11 Advantages ofUsing IoT andML Together
The IoIT produces IoT programs that recognize their potential. AI and ML allow for
much more comprehensive insight at a quicker speed. Businesses are implementing
IoIT to the benets discussed in the following sections.
3.3.12 Improved Accuracy Rate
If you have ever attempted to evaluate information in several different spreadsheets,
you know it can be a tiresome task. Human brains are restricted to performing par-
ticular duties at a particular speed, so when our brains are tired, we are likely to
make mistakes. The IoT has the capacity to process large amounts of information
and create an analysis. The entire process is machine and software driven; it can be
performed with no human intervention, which tends to make it error free and also
increases reliability [26].
E-commerce transactions, online payments, and ATM withdrawals, for example,
are very susceptible to fraud. Using the consolidated strength of human comprehen-
sion and IoT machine learning as well as Robotic Process Automation (RPA) meth-
ods of manmade intelligence, fraud may be prevented, therefore stopping some
monetary losses.
3.3.13 Predictive Analysis andMaintenance
Predictive analytics is a type of evaluation that examines pre-existing information
and predicts probable succeeding outcomes. It would not be an exaggeration to say
that IoT and AI are the basis of predictive maintenance. Presently, IoT products are
being used by businesses to monitors for malfunctions or concerns in an automatic
way with no human intervention. By using AI, this process can allow models to
perform a predictive evaluation, giving businesses the ability to identify possible
mishaps, malfunctions, and advance maintenance. For this reason, the risk of losses
is extremely reduced because problems are now being recognized in advance.
For example, delivery businesses can make use of predictive evaluation to exam-
ine and analyze their information to avoid unexpected downtime for shipping and
maintain shipping by way of frequent servicing [20].
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3.3.14 Improved Customer Satisfaction
The center of each company is client satisfaction. Presently, organizations such as
Amazon have gained recognition for being the best customer-centric businesses by
keeping the goals of their clients above all else. Nevertheless, human-based con-
sumer encounter may not be successful for a number of reasons, including language
barriers and time restrictions [20].
Businesses are realizing the advantages of AI by using chatbots to interact with
clients. Great quantities of client information can be used to supply them with a far
more personalized experience, as well as answering their queries appropriately.
3.3.15 Increased Operational Efciency
Predictions produced via manmade intelligence are extremely benecial for increas-
ing the operational effectiveness of a company. In-depth insights received via man-
made intelligence are often used to enhance a company’s processes, which may lead
to enhanced operational effectiveness plus reduced expenses.
With exact predictions, insights can be obtained concerning price consuming
matters as well as the period of the housing industry also automates these to enhance
the effective numbers. Additionally, for shipping businesses, the insights received
via man-made intelligence can help to improve procedures, equipment options, and
inventory to reduce needless expenditures [25].
• Improve the basic safety of equipment– The IoT can keep track of all machin-
ery, from compressors to high-speed engines, to quickly recognized potential
problems. This can result in greater safety for staff members and even the
environment.
• Decrease lost revenue related to downtime– Downtime is costly. By using ML
and the IoT, problems can be recognized in advance to considerably decrease
downtime, thus minimizing lost revenue.
• Allow for correct record keeping– The IIoT makes use of the information to
keep track of equipment, ag questionable components, and determine what
maintenance is needed.
• Predict equipment failure before it occurs– Real-time data allows a company
to assess what is occurring, but the IIoT is able to take action on your behalf.
Using man-made intelligence, algorithms, and much more, information analyz-
ers are now able to predict and alert you to potential failures.
• Provide real-time data– With ML analytics, a company can obtain a data evalu-
ation in real time to ensure that things are operating properly and that the busi-
ness is ourishing.
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3.4 Challenges inIoT Implementation
Problems in IoT implementation are related to network security expenses and infor-
mation evaluation. Most businesses may be ready to deal with sensors and data ana-
lytics, but they neglect to focus the same attention on network investments, system
integration, and security. This gap highlights a rift between technologies in addition
to excellent data transfer, where IT division is really sharp on buying the technical
feature needed having a prosperous IoT undertaking, it is the lack of really worth
sent, which is likely to create business experts look at the unsuccessful endeavor.
Capturing invaluable info that leads to phony attention could also work as the pri-
mary reason a great deal of business experts have a look at their IoT projects unsuc-
cessful. Problems experienced by businesses that create quality problems, resulting
in the venture disaster, which includes night conclusion as well as nances overruns
occasions [20].
3.4.1 Interoperability andCompatibility ofVarious IoT
Systems
According to the marketplace analysts with McKinsey, 40–60% of overall value is
based on the ability to attain interoperability between various IoT methods. With
many vendors, service providers, and OEMs, it becomes very difcult to preserve
interoperability between various IoT methods. Receptors and networking are the
essential parts of the IoT.However, not every single machine comes with complex
receptors and social networking abilities. Also, receptors with different energy con-
sumption and security requirements, built within history devices, might not be able
to offer the exact same outcomes.
A fast workaround might be to add external receptors. However, this is also dif-
cult because it must be determined what components and data will be communi-
cated to the network.
3.4.2 Authentication andIdentication ofTechnologies
Currently, there are approximately 20billion linked products in existence, as well as
in order to feel foundation of all the gear requires a good offer of safeguarding
implications without having just intricacy. Using many devices that are connected
on a single wedge requires formalization or a method to authenticate the gad-
gets [26].
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3.4.3 Integration ofIoT Products withIoT Platforms
For a protable setup of an IoT program, businesses have to incorporate a variety of
IoT connected goods with correct IoT platforms. Inappropriate integration could
result in irregularities in productivity along with executing to move really worth
towards the customers. A vice president with Gartner, Benoit Lheureux [27],
claimed that “Through 2018, 50% of the expense of applying IoT remedies will
probably be invested combining different IoT parts with every back-end and other
methods. It’s essential to be aware of integration is actually an important IoT
competency.”
However, there may be too many IoT endpoints to aggregate the sensor informa-
tion or transmit it to an IoT wedge. With rich integration, businesses are able to
mine the massive information by way of Big Data methods in order to produce
awareness and predict results [19].
3.4.4 Connectivity
Connectivity is a common marketing difculty because the Internet is not available
everywhere with the same speed. A worldwide satellite business, Inmarsat, dis-
closed that 24% of users reported connectivity problems as one of the greatest prob-
lems within an IoT deployment. Strong networks are needed to gather information,
but they may experience difculties in transmitting data.
The caliber of indicators collected by the receptors and transmitted to the net-
works mostly depend on the routers—MAN, LAN, and also WAN.These networks
have to be well connected via various systems to facilitate fast and high-quality
reception. Though the variety of gadgets that are connected to the networking cov-
erage, and that generates keeping track of as well as keeping track of issues.
3.4.5 Handling Unstructured Data
An increasing number of connected products also increase the difculties of dealing
with unstructured details on the volume, velocity, and type. However, an actual
struggle for businesses is determining what information is valuable, as only quality
information is actionable. Unstructured details cannot be kept in an SQL structure.
Unstructured information kept in a NoSQL structure makes the retrieval of informa-
tion a little complicated. With the launch of Big Data frameworks, such as Cassandra
and Hadoop, the problems with unstructured details have decreased. However,
merging the fundamental data with IoT causes problems. In addition, at this time,
there are not any common recommendations for the use and retention of informa-
tion or metadata.
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75
3.4.6 Data Capturing Capabilities
A challenge of recording information is to transform the information collected from dif-
ferent sources into a common structure that can be analyzed and automated. According
to a HubSpot article, sponsored by Par Stream[25], 86% of company stakeholders assert
that information is essential to their IoT tasks, but just 8% are actually able to shoot as
well as evaluate IoTin formation wearing a regular fashion, as shown in Fig.3.11.
The IoT depends on receptors for networks and indicators for that division, so
particular anomalies in deep runtime, like a power outage, could cause incorrect
data to be captured.
3.4.7 Intelligent Analytics
The intent behind IoT is to convert data into substantial information. Flaws in the
data or information design could result in false conclusions. We have to understand
the info inside itself is not a comprehension, type of right problems have to become
guided from the actual info reach the understanding.
According to an article by Hubspot[25], 42% of IoT stakeholders experience
issues with recorded information, and 30% stated that their analytic abilities were
not effective or adaptable, as shown in Fig.3.12.
Historical methods, including standard analytics programs in which only limited
information can be examined at any given time, can certainly restrict the ability to
control real-time details. The following are some difculties that prevent smart
analytics:
• Unpredictable activity of a computer throughout an incident.
• Traditional analytics software.
Fig. 3.11 Data capturing
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• Slow adoption of current engineering as a result of the excessive cost.
• Lack of competent workers in information mining, algorithms, machine learn-
ing, and complicated processing [26].
3.4.8 Data Security andPrivacy Issues
Even prominent companies such as Apple and visionaries such as Elon Musk have
not been spared by online hackers. Recent occurrences of ransomware strikes have
inhibited the condence of many companies. One analysis reported that by 2020,
25% of cyber-attacks will focus on IoT products[24]:
• Malware inltration: 24%
• Phishing attacks: 24%
• Social engineering attacks: 18%
• Device misconguration issues: 11%
• Privilege escalation: 9%
• Credential theft: 6%
Lapses in cyber-safety could occur in any organization and affect customers.
Therefore, it is crucial for every business to take measures to enhance security. A
report disclosed that 45% of IoT unit proprietors do not use any kind of third-party
protection application. In addition, 35% of these individuals do not change the
default password on their gadgets. Thus, buyers and businesses must collaborate to
implement effective security policies in IoT setup [20].
Difficult to capture useful data
Data is not captured reliably
Data is captured too slowly to be actionable
Too much data to analyze effectively
Analysis capabilities are not flexible enough to ask the questions we want
Data is analyzed too slowly to be actionable
We’re not sure what questions to ask
Other
Business processes are too rigid to allow us to act on the analysis
We have no challenges
36%
25%
19%
44%
30%
26%
27%
7%
6%
0%
Data captureData analysisActing on analysis
10% 20% 30% 40%
50%
45%25% 35%5% 15%
24%
Fig. 3.12 Challenges with collecting and analyzing data in an IoT project
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77
3.4.9 Consumer Awareness
Many individuals are not aware of the IoT, although they fully understand the
dependency on smart apps for information, stocks, and entertainment. It is not really
essential for customers to understand how elements function commercially.
However, a lack of fundamental knowledge may cause anxiety about price and
security, which may result in the slow adoption of technologies.
As outlined in a survey of 3000 Canadian and U.S. customers carried out by
Cisco, 53% of customers would not choose to individual information collected,
notwithstanding of this unit. This indicates that subscribers are reluctant to disclose
their information, which may serve as a deterrent to IoT implementation [20].
3.4.10 Delivering Value
Based on a Forbes Insights Survey[23], 29% of professionals are feeling a signi-
cant struggle in creating IoT abilities stands out as the quality of IoT. This informa-
tion indicates the challenge of IoT program growth businesses with obtaining
valuation for their customers. Thus, prior to plunging straight into the improvement
of IoT programs, a business needs to determine what value they are likely to provide
via what abilities, as well as how the solution will enhance effectiveness and ef-
ciency, while simultaneously producing customer satisfaction.
Because the IoT is about connected things, the IoT tasks also need a great degree
of help in this manner. Approximately 50% of businesses with IoT initiatives are
clearly associated with IT services, suppliers, or consultation services companies,
depending upon them to assist with shipping, delivery, and supply company recom-
mendations. Feel foundation, creating an IoT Development Company, that thinks
engineering away from labor as well as like function by merging all the components
of IoT with in a manner that is based on connectivity, improving recognition and
holding accuracy of all of the phases [20]. Nevertheless, continue a range of
enhancements of produce abilities to enhance the effectiveness of the item or maybe
system depending on the most recent engineering.
3.5 Conclusion
The modern era of the IoT and AI will greatly improve existing tasks. With hands-
free operation and in-depth evaluations, companies can enjoy the advantages of
progress while maximizing prots. The challenges of this era are to make better use
of AI and the IoT for the new generation. The distinctive, infrastructure-related
expenses of IoT systems is required for predicting the consistent hardware models.
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With these brand-new versions, IoT companies need to provide recurring value to
their clients in order to achieve success. Effective IoT businesses are delivering
recurring value to their customers by applying service- based internet business ver-
sions and monetizing consumer information. To ensure that the designs catch the
attention of clients, successful IoT companies should also supply recurring value in
exchange. The value can be made available in a number of ways. As the IoT busi-
ness matures, distinct products have started to appear. IoT products can supply
recurring value by improving operational efciencies, facilitating compliance,
enhancing sales, and opening new sales channels.
References
1. Kumar, D.R., Krishna, T.A., & Wahi, A. (2018). Health monitoring framework for in time
recognition of pulmonary embolism using internet of things. Journal of Computational and
Theoretical Nanoscience, 15(5), 1598–1602.
2. Anandhalli, M., & Baligar, V.P. (2017). A novel approach in real-time vehicle detection and
tracking using raspberry pi. Alexandria Engineering Journal, 57(3), 1597–1607.
3. Atzori, L., Iera, A., & Morabito, G. (2010). The internet of things: A survey. Computer
Networks, 54(15), 2787–2805.
4. Kamath, G., Agnihotri, P., Valero, M., Sarker, K., & Song, W. -Z. (2016). Pushing analytics to
the edge. In GLOBECOM, IEEE, pp1–6.
5. Deepak, S., & Anandakumar, H. (2019). AODV route discovery and route maintenance in
MANETs. In 2019 5th International Conference on Advanced Computing & Communication
Systems (ICACCS). https://doi.org/10.1109/icaccs.2019.8728456.
6. Nishiguchi Y, Yano A., Ohtani, T., Matsukura, R., & Kakuta, J. (2018). Iotfault management
platform with device virtualization. In 2018 IEEE4th World Forum on Internet of Things
(WF-IoT), pp.257–262.
7. Qiu, J., Wu, Q., Ding, G., Xu, Y., & Feng, S. (2016). A survey of machine learning for big data
processing. EURASIP Journal on Advances in Signal Processing, 2016, 67.
8. Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unied, real-
time object detection. In The IEEE conference on Computer Vision and Pattern Recognition
(CVPR), pp.779–788.
9. Kumar, R.N., Karthick, S., Valarmathi, R. S., & Kumar, D.R. (2018). Design and analysis
of multiply and accumulation units using low power adders. Journal of Computational and
Theoretical Nanoscience, 15(5), 1712–1718.
10. Anandakumar, H., & Umamaheswari, K. (2017). Supervised machine learning techniques in
cognitive radio networks during cooperative spectrum handovers. Cluster Computing, 20(2),
1505–1515.
11. Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M.,
& Adam, H. (2017). Mobilenets: Efcient convolutional neural networks for mobile vision
applications. Computer Vision and Pattern Recognition (cs.CV).
12. Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., & Keutzer, K. (2016).
Squeezenet: Alexnet-level accuracy with 50x fewer parameters and <05mbmodel size.
arXiv:1602.07360
13. Rajesh Kumar, D., & Shanmugam, A. (2017). A hyper heuristic localization based cloned node
detection technique using GSA based simulated annealing in sensor networks. In Cognitive
computing for big data systems over IoT, pp.307–335.
R. K. Dhanaraj et al.

79
14. Bansod, G., Raval, N., & Pisharoty, N. (2015). Implementation of a new light weight encryp-
tion design for embedded security. IEEE Transactions on Information Forensics and Security,
10, 142–151.
15. Cecchinel, C., Jimenez, M., Mosser, S., & Riveill, M. (2014). An architecture to support the
collection of big data in the internet of things. In: 2014 IEEE World congress on services,
IEEE, pp.442–449.
16. Anandakumar, H., & Umamaheswari, K. (2018). A bio-inspired swarm intelligence technique
for social aware cognitive radio handovers. Computers and Electrical Engineering, 71, 925–
937. https://doi.org/10.1016/j.compeleceng.2017.09.016.
17. Varghese, B., Wang, N., Barbhuiya, S., Kilpatrick, P., & Nikolopoulos, D.S. (2016, November
18–20). Challenges and opportunities in edge computing. In 2016 IEEE international confer-
ence on smart cloud, Smart Cloud 2016, NewYork.
18. Zhang, D., etal. (2018). Review on the research and practice of deep learning and reinforce-
ment learning in smart grids. CSEE Journal of Power and Energy Systems, 4, 362–370.
19. Chen, G., Parada, C., & Heigold, G. (2014). Small-footprint keyword spotting using deep
neural networks. In ICASSP, IEEE, pp4087–4091.
20. Doshi, R., Apthorpe, N., & Feamster, N. (2018, May). Machine learning DDos detection for
consumer internet of things devices. In 2018 IEEE Security and Privacy Workshops (SPW),
pp.29–35.
21. Dastjerdi, A.V., & Buyya, R. (2016). Fog computing: Helping the internet of things realize its
potential. IEEE Computer, 49(8), 112–116.
22. Dhiviya, S., Malathy, S., & Kumar, D.R. (2018). Internet of things (IoT) elements, trends and
applications. Journal of Computational and Theoretical Nanoscience, 15(5), 1639–1643.
23. https://www.forbes.com/forbes-insights/our-work/internet-of-things/
24. Ioannis Stellios, Panayiotis Kotzanikolaou, Mihalis Psarakis, Cristina Alcaraz, Javier Lopez,
A Survey of IoT-Enabled Cyberattacks: Assessing Attack Paths to Critical Infrastructures and
Services. IEEE Communications Surveys & Tutorials 20 (4):3453-3495
25. https://blog.hubspot.com/marketing/iot-retail
26. https://www.statista.com/statistics/607716/worldwide-articial-intelligence-market-revenues/
27. https://www.gartner.com/smarterwithgartner/ve-steps-to-address-iot-integration-challenges/
3 Enterprise IoT Modeling: Supervised, Unsupervised, andReinforcement Learning

81© Springer Nature Switzerland AG 2020
A. Haldorai etal. (eds.), Business Intelligence for Enterprise Internet of Things,
EAI/Springer Innovations in Communication and Computing,
https://doi.org/10.1007/978-3-030-44407-5_4
Chapter 4
An Overall Perspective onEstablishing
End-to-End Security inEnterprise IoT
(E-IoT)
VidyaRao, K.V.Prema, andShreyasSureshRao
4.1 Introduction
IoT is a vast network of networks consisting of physical and virtual interconnected
entities. These entities have unique addressing schemes and interact with each other
to provide certain customized or generic services. In 2012, the International
Telecommunication Union (ITU-T) recommended a standard denition of IoT as “a
global infrastructure for the information society, enabling advanced services by
interconnecting virtual and physical things based on existing and evolving interop-
erable information and communication technologies [1]”. Technically speaking,
IoT has its applications in diverse areas like healthcare, surveillance, transport,
security, manufacturing, environmental monitoring, and food processing, and it is
integrated with technologies like autonomic networking, decision making, machine-
to- machine communication, cloud computing, big data analytics, condentiality
protection, and security [2].
Enterprise Internet of Things (E-IoT) is the next level of sensor technology that
connects every physical object to form a vast network of embedded computing devices.
These devices are generally made up of tiny components. They have constrained pro-
cessing capabilities, low memory, and limited power resources. This emerging tech-
nology has reduced manual intervention and has increased business efcacy.
Gartner Press released an article in August 2019 showcasing that by 2020, there
would be about 5.8billion IoT endpoints, as compared to 4.8billion endpoints during
2019. That means there is almost a 21% increase in the addition of new endpoints.
V. Rao (*) · K. V. Prema
Manipal Institute of Technology, Manipal Academy of Higher Education,
Manipal, Karnataka, India
e-mail: prema.kv@manipal.edu
S. S. Rao
Sahyadri College of Engineering and Management, Mangalore, Karnataka, India

82
These endpoints are categorized under various use cases like utilities, government
buildings, automation, physical security, healthcare providers, manufacturing and
natural resources, information and transportation, retail, and wholesale. Among these
use cases, utilities have taken a major share of 17% with 1.33billion endpoints with
applications like electric smart grid, smart metering, and smart electricity supply.
Apart from this, physical security application surveillances, intruder systems, as well
as CCTVs have taken about 0.70billion endpoints.
These endpoints have generated a total revenue of about $389billion in countries
like North America (NA), Greater China (GC), and Western Europe (WE). Statistics
of Gartner’s study have shown that about 75% of the revenue would be generated by
electronic endpoints in the world, i.e., about $120billion revenue from NA and
$91billion and $82billion revenue from GC and WE, respectively, by 2020. It is
expected that the two main use cases that shall take a good share in the electronic
revenue are connected to consumer cars and networkable printing and photocopying
with $71billion and $38billion revenue, respectively. Then comes the government
indoor and outdoor surveillances that add on to the revenue as the government is
considering civilian security as its top priority.
These endpoints are enabled with various sensors like cameras, proximity sen-
sors, temperature sensors, air quality sensors, ow sensors, and many more sensors
that are unprotected. The reason is that the agent-based technologies do not protect
them from various attacks like distributed denial of service (DDoS), ransomware
attack, stealing of sensitive intellectual properties, cryptojacking attacks, etc. [3].
This is a major cause of concern as the data produced by these devices consists of
user health information, bank details, passwords, location information, and many
more. Hence these devices are subjected to security threats due to (a) malicious or
compromised node in the network, (b) defective manufacturing, and (c) presence of
an external adversary. There may also be threats to security initiated by nature.
These natural threats include earthquakes, oods, re, and hurricanes that cause
severe damage to the computer systems. As it is hard to safeguard against natural
calamities, it is advisable to reduce the damage by collecting backup of data through
a contingency plan. Similarly, there could be human threats that can be classied
under information-level attacks, adversary location attacks, access-level attacks,
and host-based attacks. To enable the security of the devices, it is essential to select
the hardware components that have the following properties: default authentication
capabilities, end-to-end trafc encryption, secure boot loading process, enforce-
ment of digital signatures during rmware update, and transparent transactions.
Also, it has been identied that there are almost 1.1billion data points created
every week, with 2.5billion GB of data being generated across the world. Likewise,
about 500GB of data is generated by offshore oil rigs and 100GB of data from oil
reneries per week. Also about 10,000GB of data is generated by jet engines every
30min. Overall, it is said that about 90% of the world’s data has been generated in
the last 2years. Thus, when such a huge amount of data points and data are available
on the public network like the Internet, they are susceptible to various attacks.
Hence, it is essential to identify the possible security safeguards at the earliest.
V. Rao etal.

83
With the growth of connected devices under IoT, there is an increase in the poten-
tial vulnerability on security, privacy, and governance. Though IoT can make people’s
life convenient, it might fail to ensure security and privacy of the user data leading to
a number of undesirable consequences. For example, in 2015, IoT baby monitors
were hacked through which the hackers were able to monitor the live feeds of the
baby, change the camera settings, and authorize other users remotely to view and
control the baby monitor [4]. During 2017, intruders could over-write the part of
Ukraine’s power grid that caused the rst cyber attack [2]. Even the Internet- connected
cars and wearable devices can also become a threat to the user’s security and privacy.
In [5] Atmali etal. have analyzed the impact of the above attacks on IoT applica-
tions like power management, smart car, and the smart healthcare system. Through
their study, they have projected that there is a need for security and privacy consid-
erations at the level of (a) actuators, (b) sensors, (c) RFID tags, and (d) the Internet/
network. Attack on actuators in power management applications can lead to nan-
cial loss due to excessive power consumption. Similarly, in smart cars, these com-
promised actuators may control the broken system costing a driver’s life. Also, in
the healthcare system, these compromised actuators can inject the wrong dosage of
medicine to a patient who is remotely monitored by the doctor. Likewise, a compro-
mised sensor can fake the data that may lead to the wrong diagnosis of a patient. At
the same time, these compromised nodes can reveal the personal information of the
patient or the data related to a user’s home through power management system.
Section 4.2 of this chapter explains the various security threats and attacks, fol-
lowed by elements of security in Sect. 4.3, some of the lightweight existing solu-
tions in Sect. 4.4, threat modeling tools in Sect. 4.5, Kali Linux-based ethical
hacking in Sect. 4.5.4, major IoT security practices of E-IoT in Sect. 4.6, and lastly,
conclusion in Sect. 4.7.
4.2 Security Threats andAttacks
Devices within the IoT communicate personalized data of many users. This data
consists of user health information, bank details, passwords, location information,
and many more. These devices are subjected to security threats like (a) malicious
node in the network, (b) defective manufacturer, and (c) external adversary [5].
These threats lead to security attacks that can be initiated either by nature or human.
The natural threats may include earthquake, oods, re, and hurricane that cause
severe damage to the computer system. Although it is hard to safeguard against
natural calamities, it is advisable to reduce the damage by collecting backup of data
through contingency plan. Accordingly, the security attacks caused by the humans
affect the node privacy [6, 7]. Such attacks can be classied as follows [5, 8]:
1. Information-level attacks: All IoT devices are enabled with sensors that record
the data from the physical environment and communicate the information over
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84
the Internet. As the Internet is an open domain, attackers can easily tamper the
information under following categories [7–9]:
(a) Denial of service (DoS): DoS is an attack over the network component that
makes it unavailable for an intended user.
(b) Masquerade: An intruder behaves as an intended user and tries to talk with
the network component.
(c) Modication of message: An intruder can alter or delete or fake a message
sent by a legitimate user.
(d) Man-in-the-middle (MITM): MITM is a kind of attack wherein a malicious
user takes control of the communication channel between two or more
endpoints.
(e) Message replay attack: It is a security breach in which the message is stored
by malicious node without the knowledge of intended users, and the mali-
cious node transmits an altered message that is forwarded to the receiver.
2. Adversary location attack: An intruder can be present in any part of the IoT eco-
system. He can either be within or outside the IoT environment [9]:
(a) Internal attack: An attack caused by the components within the IoT border.
It is also called as insider attack where the intruder tries to inject malicious
code toward the IoT components.
(b) External attack: An attack caused by an advisory that is located outside the
IoT environment in a remote place.
3. Access-level attack: Access-level attacks are broadly classied into active and
passive attacks [10]. In the passive attack, an attacker can read the packet that is
transmitted, but he/she cannot alter the packets like eavesdropping and trafc
analysis. On contradictory, in active attack, the attacker sees the data and then
alters the content of the data and transmits the altered data back to the network.
4. Host-based attack: Many devices in an IoT environment are made up of different
manufacturers [10]. These devices are subjected to user compromise attack, soft-
ware compromise attack, and hardware compromise attack. This is because the
manufacturer can hold the devices’ information which can be misused by him.
Hence the production of such poorly secured goods results in compromising the
user privacy. At the same time, any manufacturer can attack his competitors
through their devices.
4.2.1 IoT Four-Layered Architecture andAssociated Attacks
P.P. Ray [10] has surveyed various domain-based architectures that vary from RFID
to healthcare to security to cloud services. But in general, a four-layered design of
IoT is considered for different research as in Fig.4.1. Mainly it comprises of per-
ception layer, network layer, transport layer, and application layer. Each layer has its
own properties and protocols. Primarily, the perception layer forms the physical
V. Rao etal.

85
layer of the IoT ecosystem. It deals with sensors, devices, machines, actuators, and
movements of unprocessed raw data. In this layer, the data transmission medium
used is copper wire, coaxial cable, or radio wave. They have protocols like IEEE
802.3, Wi-Fi, LR-WPAN, 2G, 3G, 4G, and LTE networks [11].
Next is the Internet layer, which is also called the network layer. The main job of
this layer is to provide host identication and packet routing. IETF has proposed
many routing protocols that are suitable for low-powered device networks. Some of
the protocols are IPv6, IPv4, RPL, 6LoWPAN, multipath RPL (MRPL) [12],
energy-efcient probabilistic routing protocol (EEPR) [13], congestion avoidance
multipath routing protocol (CA-RPL) [14], movement-aided energy balance
(MABE) [15], least path interface beaconing protocol (LIBP) [16], and cognitive
machine-to-machine RPL (CoRPL) [17].
Then comes the transport layer which is considered for end-to-end message
transfer. The transmission can be either connection-oriented or connectionless with
protocols like transmission control protocol (TCP) and user datagram protocol
(UDP), respectively. This layer involves various processes like segmentation and
reassembly of packets, congestion control, error control, and ow control.
Lastly, the application layer interfaces with all the lower layers by establishing a
secure connection between the devices and servers. It uses standard port 80 and port 22
for most of HTTP and SSH protocols, respectively. Some of the protocols standardized
by IETF are constrained application protocol (CoAP), message queuing telemetry
transport protocol (MQTTP), extensible message and presence protocol (XMPP), data
distribution services (DSS), and advanced message queuing protocol (AMQP) [11].
Likewise there are various attacks based on layers of IoT as shown in Fig.4.2 [3,
18]. Sensing/perception layer is generally made up of sensors, RFIDs, NFCs,
ZigBee, Bluetooth, and other intelligent hardware devices. These devices are
exposed to more external attacks like node compromise attack, fake node injection,
Fig. 4.1 Generic
four- layered IoT
architecture
4 An Overall Perspective onEstablishing End-to-End Security inEnterprise IoT (E-IoT)

86
access control, and RF interference on RFIDs. The second layer is the Internet layer
and is subjected to attacks like address compromise attacks, routing information
attack, RFID spoong, and sinkhole attack. The next layer is the transport layer that
experiences attacks like denial of service (DoS), masquerade, distributed DoS
(DDoS), man-in-the-middle (MITM) attack, and session hijacking. And nally, the
application layer experiences attacks like phishing attack, viruses, worms, mali-
cious scripting, revealing of sensitive data, user authentication attacks, software vul-
nerability, and stealing of intellectual property.
4.2.2 Attacks Based onPhases ofIoT
IoT can also be dened as an interconnection of “factual and virtual” objects placed
across the globe that are attracting the attention of both “makers and hackers.” IoT
can be divided into ve different phases as mentioned in [19] by Jeyenthi as shown
in Fig.4.3. The rst phase is termed as the data collecting phase: primary interface
between physical environment and sensors. There can be either static objects like
body sensors or RFIDs or dynamic objects like sensors and chips on vehicles. The
second phase is the storage phase: as many IoT devices are having low self-storage
capability, IoT provides a server or cloud-based storage. Next is the intelligent pro-
Fig. 4.2 Attacks based on architecture
V. Rao etal.

87
cessing phase: it is where the analysis of stored data and later appropriate services
are provided to the users. IoT devices can be queried and controlled remotely using
the results obtained after processing of data. The fourth phase is data transmission:
it deals with processing of data communication between all of the above phases.
Last is the delivery phase: it is where the activity of delivering the processed data to
all the objects in time without being altered or hacked is performed.
Among the ve phases, the data perception phase is subjected to more attacks
like data leakage, data authentication, and data loss as the devices are easily avail-
able to users and hackers. Similarly, in storage phase, we can see attack on avail-
ability, modication of message, denial of service (DoS) attack, attack on integrity,
and data fabrication. Attacks on authentication are seen at the processing phase, and
channel security attack, session hijacking, routing protocol attack, and ooding are
seen at the transmission phase. Lastly, at the delivery phase, man- and machine-
made attacks are found as shown in Fig.4.3.
4.3 Elements ofSecurity
To ensure the IoT security, there are four elements of security [18]. They are device
authentication, secure connections, secure data storage, and lastly, secure code exe-
cution. The device authentication grants the access privilege of the devices to the
legitimate users. Secure connection enables the protection of the data that is travel-
ling across the network (data in motion). Secure storage provides protection for data
in rest using various lightweight encryption schemes. And lastly, the secure code
execution serves the intended host machines to use the data and process it in a
secure manner as in Fig.4.4.
Fig. 4.3 Phases of IoT and their possible attacks
4 An Overall Perspective onEstablishing End-to-End Security inEnterprise IoT (E-IoT)

88
As these poorly secured IoT devices can serve as means of entry point for cyber
attackers by allowing various malicious individuals to re-program a device and cause
malfunctioning, it becomes essential to provide security and privacy at the devices
level. In order to develop a safer IoT solution, it is required to consider three major
security requirements: (i) condentiality, (ii) integrity, and (iii) authentication [20].
• Condentiality means keeping information secret from the unauthorized user.
For example, when transmitting certain sensitive data like location of military
camp to the base station, it must be forwarded in secrecy to avoid intruders to
understand the information that is being transmitted.
• Data integrity ensures that the messages transmitted are reached at the destina-
tion unaltered. Data integrity certies the user that it has never been altered or
corrupted by protecting the data over a communication channel.
• Authentication is a process of determining whether the data is transmitted by
legitimate users or not. The user needs to identify the peer nodes that they need
to communicate.
4.4 Lightweight Secure Measures forIOT
Elliptic curve cryptography (ECC) was introduced in early 1985 by Neal Koblitz
and Victor Miller [21]. They stated that the hardness of ECC security depends on the
discrete logarithmic problem dened on the elliptic curve. Later, Gura etal. [22]
experimented ECC and RSA on an 8-bit CPU to compare their performance and
found that the use of ECC for a lower-bit processor provides the same level of secu-
Fig. 4.4 Elements of IoT security [18]
V. Rao etal.

89
rity as that of RSA.Later during 2013, Wenger [23] developed an ECC-based access
control scheme over a prime eld on 16-bit MSP430 micro-controller whereby the
results conrmed the feasibility of ECC for resource-constrained devices.
Basically, ECCs are often implemented by using a static public elliptic curve that
is shared among all the users in the network. In [24] the recommended elliptic curve
domain parameters are provided for the Weierstrass curve equation y2=x3+ax+b
that is accepted by various researchers [25].Liu etal. [26] have proposed software
and hardware architecture for resource-constrained embedded devices. Their work
has shown the feasibility of ECC on the embedded system. But the use of a xed
elliptic curve can be challenged on intensive cryptanalysis.Wang etal. [27] made a
study on using a xed prime eld to build a crypto-system for applications devel-
oped for different processors varying from 8 bits to 256 bits.
A lightweight multi-message and multi-receiver heterogeneous-based signcryp-
tion is proposed by Rahaman etal. [28]. They have used the hybrid elliptic curve to
generate signatures. The work is evaluated for various attacks like replay attack,
forward secrecy attack, and unforgeability using the AVISPA simulator tool. For the
heterogeneous environment, the attackers are inclined to impersonate legitimate
users. To solve such an issue, Jingwei Liu etal. [29] have proposed a novel authen-
tication scheme. They have provided a lightweight anonymous authentication and
key agreement scheme as proposed. Their scheme could toggle between the public
key infrastructure (PKI) and certicates analysis. Their method showed resistance
against replay and DoS attacks.
The combination of cloud-based services with IoT has raised the issue of limita-
tion regarding low latency and high mobility. To address such issues,Haldorai etal.
[30] have proposed the authentication and key agreement scheme for fog-based IoT
for the healthcare application. By using bilinear key agreement protocol, they have
proposed a protocol that showed resistance against MITM, replay attack, known-
session key attack, and intractability.
Recently, based on card shufing logic, a data condentiality algorithm is
designed using ECC, proposed by Khan [31]. The use of random card shufing has
shown double encryption and increased the security of the algorithm. The algorithm
can encrypt or decrypt any type of ASCII values. As the algorithm uses ECC, it is
suitable for resource-constrained devices. Li et al. [32] proposed a lightweight
mutual authentication protocol using public-key encryption schemes for smart city
applications. Their simulated work has shown a balance among ciphertext size,
usability, and efciency. The generation of online and ofine signatures created
overhead on the device storage. Diro etal. [33] have used ECC to provide lightweight
encryption for fog-based IoT applications. They have shown better efciency
regarding runtime, throughput, and ciphertext expansion. But they could only han-
dle a smaller data size.
An OTP-based end-to-end authentication scheme was proposed by Shivraj etal.
[34]. Their scheme used Lamport’s OTP scheme with ECC-based authentication
algorithm. Even though the scheme performed better than existing OTP-based sig-
nature schemes, they could not justify the implementation on a real-time scenario.
A security framework for IoT and cloud computing is proposed by Daisy Premila
4 An Overall Perspective onEstablishing End-to-End Security inEnterprise IoT (E-IoT)

90
etal. [20]. They used ECC-based message encryption and multi-factor authentica-
tion to ensure condentiality, integrity, privacy, and authentication. They have con-
cluded that the use of ECC-based security measures is better than RSA to eliminate
the ambiguity and enhance security. But the research to collaborate IoT and cloud
computing needs to depend on infrastructure.
During 2018, to address the usage of the static curve in ECC, Jia Wang etal.
[35] proposed a dynamic elliptic curve-based Internet of Vehicles (IoV) network.
Their work showed good computational efciency and security for a smaller key
size. But storing the elliptic curves as a plain text in embedded systems would lead
to security concern. To address the data integrity issue of Java card-based applica-
tion, Gayoso etal. [36] initiated the use of ECC-based encryption algorithm called
an elliptic curve integrated encryption scheme (ECIES) and concluded that ECIES-
based encryption is the best among encryption schemes for resource-constrained
devices.
4.5 Threat Modeling forIOT Security
Threat modeling (TM), whose lifecycle is depicted in Fig.4.6, is a process of iden-
tifying the potential threats, enumerating and prioritizing the threats, and providing
countermeasures to mitigate the threats. TM can be applied to any platform of a
working process like software, application, networks, IoT devices, or business pro-
cesses. Shostack [37] has summarized the reasons to incorporate a threat model in
SDL which are (i) to nd the bugs at the earliest, (ii) understand the security require-
ments, and (iii) engineer and deliver a better product. Basically, TM includes com-
ponents like target-of-evaluation (ToE) (a design or model of what type of platform
needs to be analyzed), a list of assumptions that can be threats on ToE, a list of
potential threats on ToE, possible countermeasures toward the identied threats,
and verication of success (VoS) that validates the threat model.
Before modeling a threat, there are four questions that need to be answered,
which are as follows:
1. What are we building? A detailed data ow diagram (DFD) is designed by speci-
fying various roles and responsibilities of each participant.
2. What can go wrong? The various possible threats are analyzed using methods
available in STRIDE, PASTA, STRIKE, or VAST.
3. What are we going to do about that? Potential mitigation strategies against the
threats are framed.
4. Did we do a good job? Once the mitigation is applied, the system is validated for
the stability and security against the threats.
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91
4.5.1 Microsoft Security Development Lifecycle
Microsoft SDL was introduced during 2008 to ensure security and privacy consid-
erations throughout all the phases of the development process. This helped develop-
ers to build highly secure software, address security compliance requirements, and
reduce development cost. The core of Microsoft SDL is threat modeling. Threat
modeling helps in shaping the application design and meeting the security objec-
tives of the company by reducing the risk severity. The ve major steps of threat
modeling involve (Fig.4.5) the following:
1. Dening security requirements: To understand the ecosystem of the device, i.e.,
analysis of the ToE by framing various use cases. In this process the external and
internal assets are identied.
2. Creating an application diagram: Here a detailed data ow diagram of the pro-
posed ToE is framed with appropriate trust boundaries and security requirements
for each participant.
3. Identifying the threats: Microsoft TMT follows STRIDE-based threat modeling
where the threats are identied. Potential adversaries are identied under four
categories called remote software attacker, network attacker, malicious insider
attacker, and advance hardware attacker.
4. Mitigating the threats: For the threat identied, relevant countermeasures are
established.
5. Validating that threats have been mitigated: Finally, the verication of the threat
model against the mitigation is performed to check the stability of the proposed
system.
4.5.2 STRIDE Framework Methodology
It is important to develop a secure design for any software application or system.
Failing to do so may cost about 30 times higher than estimated cost [38]. Hence
threat modeling plays a vital role in the software development lifecycle. Among
various threat modeling methods like STRIDE, PASTA, VAST, and STRIKE,
Fig. 4.5 Microsoft
Security Development
Lifecycle (SDL) using
TMT
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STRIDE has taken a major share among the industrial development processes [39,
40]. STRIDE is developed by Microsoft as a part of their Security Development
Lifecycle. STRIDE is an acronym for spoong, tampering, repudiation, information
disclosure, denial of service, and elevation of privilege [41]. The security properties
and attack types associated with STRIDE are summarized in Table4.1 [38].
4.5.3 Overview ofThreat Modeling Tool (TMT)
Microsoft TMT is used to provide assistance in analyzing the design of a system or
an application in order to check for security risks and provide solution for the threat
found. Figure4.4 displays the initial page of TMT when launched. This page has
two partitions; the top part is used to create the threat model of the user’s choice
using the templates provided by the Microsoft, while the bottom part helps the user
to customize his own template on the default Microsoft Security Development
Lifecycle (SDL) template as in Fig.4.6.
4.5.4 Kali Linux-Based Ethical Hacking
Kali Linux was developed by Mati Aharoni and Devon Kearns of Offensive Security
and was mainly suitable for digital forensic and penetration testing under ethical
hacking [42]. Kali Linux has approximately 300 hacking tools that are broadly cat-
egorized under information gathering, vulnerability analysis, wireless attacks, web
application, exploitation tools, forensic tools, snifng and spoong tools, password
attacks, maintaining access, reverse engineering, and hardware hacking tools.
Among these, the most commonly used tools are Metasploit framework, dsniff, tcp-
dump, Nmap, Wireshark, Aircrack-ng, Armitage, Burp Suite, BeEF, and so on [42].
Table 4.1 STRIDE threat model with associated security properties
Threats
Security
property Denition
Spoong Authentication Unauthorized access
Using another user’s identity
Tampering Integrity Malicious modication
Unauthorized information changes
Repudiation Non-repudiation Denying to perform action
Information
disclosure
Condentiality Unprivileged user gains access and compromises the
system
Denial of service Availability Denying services to valid users
Threats to system availability and reliability
Elevation of
privilege
Authorization Exposure of information to individuals not supposed
to access
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93
Featuring the rapid growth of smart cities,Barghuthi etal. [43] have made a
study of how the increase in the population of smart cities shall add to an increase
in the security breach and damage to businesses by 2050. Thus, they have proposed
Kali Linux-based vulnerability assessment and penetration testing solution using
low-cost Raspberry Pi 3 devices. Through their results, it has been concluded that
Raspberry Pi 3 can be used as a machine to check the vulnerability similar to any
traditional PC or laptop-based Kali Linux machine.
To replace the expensive and resource-intensive devices used for industrial vul-
nerability and assessment tests,Hu etal. [44] proposed an automated vulnerability
assessment using OpenVAS and Raspberry Pi 3 device. They have detailed methods
for analyzing the vulnerability assessment of distributed architecture. They made
the study on variables like CPU temperature, CPU usage, and CPU memory of the
device at the time of vulnerability assessment.
Visoottiviseth et al. [45] developed a GUI-based penetration testing tool called
PENTOS used for IoT devices. PENTOS runs on Kali Linux and is specically
designed for the ethical hacking of wireless communication like Wi-Fi and Bluetooth.
PENTOS enables the analysis of password attack, web attack, and wireless attack that
ensure to gain access privilege of the various algorithms. They also have explained the
Open Web Application Security Project (OWASP) specied ten vulnerabilities of IoT
applications.
Finally, they have given the recommendations for the secure deployment of the
IoT environment.Denis etal. [46] performed various penetration tests using tools
available on Kali Linux. They were able to set up a private network and generate
Fig. 4.6 Microsoft TMT initial screen
4 An Overall Perspective onEstablishing End-to-End Security inEnterprise IoT (E-IoT)

94
attack reports and visualize the reports using Kali Linux tools. The attacks they
performed were hacking phones, MITM attack, smartphone penetration testing,
spying, hacking phones’ Bluetooth, and hacking WPA-protected access, and then
they hacked the remote PC using IP and open ports.
Liang etal. [47] experimented on different methods of doing DoS attack using
Raspberry Pi-based Kali Linux. They have provided an attack framework and com-
pared various DoS attacks on their framework. They have used Hping3 with random
IP, SYN ood with spoofed IP, and TCP connection ood tools. The comparison
was made under the parameters like CPU utilization, memory utility, time for the
success of an attack, and packet loss rate. Ryan Murray [48] has proposed a forward-
looking approach for a secure eHealth solution called HealthShare. It could share
data among various organizations that were hosting the patient’s data over the cloud.
Detailed steps as to conduction of MITM and DoS attack using tools like Ettercap,
Pexpect, manual SET, threads using the timer and Nmap timer, and Scapy have also
been provided.
4.6 Major E-IOT Security Practices
As E-IoT is deployed on a larger scale with heterogeneous business applications,
the cybersecurity space has obtained an intense research spectrum. Some of the
important security practices that should be followed by enterprise IoT are
explained below.
(a) Understand your endpoints: Every endpoint of the business network is assem-
bled by various manufacturers using different open-source operating systems.
These devices are potential entry points for cybercriminals. Thereby, it is essen-
tial to deploy devices in a tamper-proof environment using secure hardware and
software resources.
(b) Track and manage the endpoints: Business enterprise poses the responsibility of
constant check on the devices that are deployed under their network and should
be updated with frequent rmware and security patches. As it is infeasible to
monitor each device physically, Earl Perkins of Gartner Solutions has recom-
mended “rolling out an asset discovery, tracking, and management strategy” to
be implemented before the IoT project begins.
(c) Change the default passwords and other credentials: The manufacturers set
their devices with a common default password, which has to be updated by the
enterprise ofcials frequently. This is because, most of the time, hackers are
well aware of default passwords and sneak into your network by brute force
attacks.
(d) Execute risk-driven strategies: IoT projects need to be analyzed for risk possi-
bility using various threat modeling tools. Such tools help to identify the risks
in the network and guide the network administrator to take corrective actions.
Also, performing regular pen-testing at the hardware and software levels shall
ensure the attack resistivity of the network.
V. Rao etal.

95
(e) Consideration of the latest encryption protocols: Business enterprises should
encrypt the data passing from and to their network using the updated and latest
encryption schemes. If in case a single device is accessed by multiple users,
then the focus should be on user authentication, identity-level control, and pro-
viding data integrity.
4.7 Summary
IoT is a rapidly growing network that has its major contribution in making the busi-
ness enterprise smarter. E-IoT could connect to a diverse domain of applications and
devices across the globe thus leading to various levels of attacks and threats. Various
levels of hardware and software issues are studied with possible lightweight solu-
tions. A generalized layer of security architecture is discussed, followed by a brief
description on threat modeling tool. In addition, Kali Linux-based pen-testing on a
real-time E-IoT is also studied. Finally, the major E-IoT practices are generalized
that help future researchers to concentrate on the specic issues in E-IoT.
References
1. Telecommunication Standardization Sector of ITU. (2012). Series Y.Global information infra-
structure, internet protocol aspects and next-generation networks (pp.1–6). Geneva: ITU.
2. Vermesan, O., & Friess, P. (2014). Internet of things-from research and innovation to market
deployment (Vol. 29). Aalborg: River Publishers.
3. Ahemd, M.M., Shah, M.A., & Wahid, A. (2017). IoT security: A layered approach for attacks
and defenses. In 2017 international conference on Communication Technologies (ComTech),
IEEE, pp.104–110.
4. Khan, R., Maynard, P., McLaughlin, K., Laverty, D., & Sezer, S. (2016). Threat analysis of
blackenergy malware for synchrophasor based real-time control and monitoring in smart grid.
In ICS-CSR.
5. Atamli, A.W., & Martin, A. (2014). Threat-based security analysis for the internet of things.
In 2014 international workshop on Secure Internet of Things (SIoT), IEEE, pp.35–43.
6. Stankovic, J.A. (2014). Research directions for the internet-of-things. IEEE Internet of Things
Journal, 1(1), 3–9.
7. AbdAllah, E.G., Hassanein, H. S., & Zulkernine, M. (2015). A survey of security attacks
in information-centric networking. IEEE Communication Surveys and Tutorials, 17(3),
1441–1454.
8. Abomhara, M., & Kien, G.M. (2015). Cyber security and the internet-of-things: Vulnerabilities,
threats, intruders and attacks. Journal of Cyber Security, 4, 65–88.
9. Nawir, M., Amir, A., Yaakob, N., & Lynn, O.B. (2016). Internet of things (IOT): Taxonomy
of security attacks. In 3rd International Conference on Electronic Design (ICED), IEEE,
pp.321–326.
10. Ray, P.P. (2018). A survey on internet of things architectures. Journal of King Saud University-
Computer and Information Sciences, 30(3), 291–319.
4 An Overall Perspective onEstablishing End-to-End Security inEnterprise IoT (E-IoT)

96
11. Arulmurugan, R., Sabarmathi, K.R., & Anandakumar, H. (2017). Classication of sentence
level sentiment analysis using cloud machine learning techniques. Cluster Computing, 22(S1),
1199–1209.
12. Quynh, T.N., LeManh, N., & Nguyen, K.N. (2015). Multipath RPL protocols for greenhouse
environment monitoring system based on internet of things. In 2015 12th international confer-
ence on Electrical Engineering/Electronics, Computer, Telecommunications and Information
Technology (ECTI-CON), IEEE.
13. Park, S. -H., Cho, S., & Lee, J. -R. (2014). Energy-efcient probabilistic routing algorithm
for internet of things. Journal of Applied Mathematics, Hindawi Publishing Corporation, Vol.
2014, Article ID 213106, 7 pages.
14. Haldorai, A., & Ramu, A. (2019). Cognitive social mining applications in data analytics and
forensics (Advances in social networking and online communities). Hershey: IGI Global.
https://doi.org/10.4018/978-1-5225-7522-1.
15. Haoru, S., Wang, Z., & An, S. (2013). MAEB: Routing protocol for IoT healthcare. Advances
in Internet of Things, 3, 8–15. Scientic Research.
16. Ngqakaza, L., & Bagula, A. (2014, May 26–28). Least Path Interference Beaconing Protocol
(LIBP): A frugal routing protocol for the internet-of-things. In 12th international confer-
ence proceedings, Wired/Wireless Internet Communications- WWIC2014, Paris, France,
pp.148–161.
17. Aijaz, A., & Aghvami, A. H. (2015). Cognitive machine-to-machine communications for
internet- of-things: A protocol stack perspective. IEEE Internet of Things Journal, 2(2),
103–112.
18. Miller, L., & Johnson, C.A. (Eds.). (2016). IoT security for dummies. Chichester: Wiley.
19. Anandakumar, H., & Umamaheswari, K. (2017). Supervised machine learning techniques in
cognitive radio networks during cooperative spectrum handovers. Cluster Computing, 20(2),
1505–1515.
20. Bai, T.D. P., Rabara, S.A., & Jerald, A.V. (2015). Elliptic curve cryptography based security
framework for Internet of Things and cloud computing. In Conference on recent advances on
computer engineering by WSEAS, pp.65–73.
21. Miller, V.S. (1985). Use of elliptic curves in cryptography. In Conference on the theory and
application of cryptographic techniques (pp.417–426). NewYork: Springer.
22. Gura, N., Patel, A., Wander, A., Eberle, H., & Shantz, S.C. (2004). Comparing elliptic curve
cryptography and RSA on 8-bit CPUs. In International workshop on cryptographic hardware
and embedded systems (pp.119–132). Berlin/Heidelberg: Springer.
23. Wenger, E. (2013). Hardware architectures for MSP430-based wireless sensor nodes perform-
ing elliptic curve cryptography. In International conference on applied cryptography and net-
work security (pp.290–306). Berlin: Springer.
24. SEC, S. (2000). SEC 2: Recommended elliptic curve domain parameters. Standards for
Efcient Cryptography Group, Certicom Corp.
25. Silverman, J.H. (2009). The arithmetic of elliptic curves (Vol. 106). NewYork: Springer.
26. Liu, A., & Ning, P. (2008). TinyECC: A congurable library for elliptic curve cryptography in
wireless sensor networks. In Proceedings of the 7th international conference on Information
processing in sensor networks, IEEE Computer Society, pp.245–256.
27. Wang, J., & Cheng, L. M. (2017). Dynamic scalable ECC scheme and its application to
encryption workow design. In Proceedings of the international conference on Security and
Management (SAM), pp.261–262.
28. Rahman, A.U., Ullah, I., Naeem, M., Anwar, R., Noor-ul-Amin, Khattak, H., & Ullah, S.
(2018). A lightweight multi-message and multi-receiver heterogeneous hybrid signcryption
scheme based on hyper elliptic curve. International Journal of Advanced Computer Science
and Applications, 9(5), 160–167.
29. Liu, J., Ren, A., Zhang, L., Sun, R., Du, X., & Guizani, M.A. (2019). Novel secure authentica-
tion scheme for heterogeneous internet of thing. arXiv preprint arXiv:1902.03562.
V. Rao etal.

97
30. Haldorai, A., Ramu, A., & Murugan, S. (2019). Smart sensor networking and green technolo-
gies in urban areas. In Computing and communication systems in urban development (pp.205–
224). Cham: Springer. https://doi.org/10.1007/978-3-030-26013-2_10.
31. Khan, M.A. (2018). Multidisciplinary Journal of European University of Bangladesh. Cell,
1713(006814), 01914–098494.
32. Li, N., Liu, D., & Nepal, S. (2017). Lightweight mutual authentication for IoT and its applica-
tions. IEEE Transactions on Sustainable Computing, 2(4), 359–370.
33. Diro, A.A., Chilamkurti, N., & Nam, Y. (2018). Analysis of lightweight encryption scheme for
fog-to-things communication. IEEE Access, 6, 26,820–26,830.
34. Shivraj, V., Rajan, M., Singh, M., & Balamuralidhar, P. (2015). One time password authentica-
tion scheme based on elliptic curves for Internet-of-Things (IoT). In 5th National Symposium
on Information Technology: Towards New Smart World (NSITNSW), IEEE, pp.1–6.
35. Wang, J., Li, J., Wang, H., Zhang, L. Y., Cheng, L.M., & Lin, Q. (2018). Dynamic scalable
elliptic curve cryptographic scheme and its application to in-vehicle security. IEEE Internet of
Things Journal, 6(4), 5892–5901.
36. Gayoso Martínez, V., Hernández Álvarez, F., Hernández Encinas, L., & Sánchez, C. (2011).
Analysis of ECIES and other cryptosystems based on elliptic curves. Journal of Information
Assurance and Security, 6(4), 285–293.
37. Shostack, A. (2014). Threat modeling: Designing for security. Indianapolis: Wiley.
38. Verheyden, L. (2018). Effectiveness of threat modelling tools. Master thesis.
39. Bodeau, D., McCollum, C., & Fox, D. (2018). Cyber threat modeling: Survey, assessment, and
representative framework. HSSEDI, The Mitre Corporation.
40. Meghanathan, N., Boumerdassi, S., Chaki, N., & Nagamalai, D. (2010, July 23–25). Recent
trends in network security and applications. In Third international conference, CNSA-2010,
Chennai, India, 2010 proceedings, Vol. 89. Springer.
41. Khan, R., McLaughlin, K., Laverty, D., & Sezer, S. (2017). Stride-based threat modeling for
cyber-physical systems. In 2017 IEEE PES Innovative Smart Grid Technologies conference
Europe (ISGT-Europe), IEEE, pp.1–6.
42. K.Linux. (2016). Kali linux tools listing. https://www.kali.org
43. Al Barghuthi, N.B., Saleh, M., Alsuwaidi, S., & Alhammadi, S. (2017). Evaluation of portable
penetration testing on smart cities applications using raspberry pi III.In 2017 fourth HCT
Information Technology Trends (ITT), IEEE, pp.67–72.
44. Hu, Y., Sulek, D., Carella, A., Cox, J., Frame, A., & Cipriano, K. (2016). Employing miniatur-
ized computers for distributed vulnerability assessment. In 2016 11th International Conference
for Internet Technology and Secured Transactions (ICITST), IEEE, pp.57–61.
45. Visoottiviseth, V., Akarasiriwong, P., Chaiyasart, S., & Chotivatunyu, S. (2017). PENTOS:
penetration testing tool for internet of thing devices. In TENCON 2017–2017. IEEE region 10
conference, IEEE, pp.2279–2284.
46. Denis, M., Zena, C., & Hayajneh, T. (2016). Penetration testing: Concepts, attack methods,
and defense strategies. In 2016 IEEE Long Island Systems, Applications and Technology
Conference (LISAT), IEEE, pp.1–6.
47. Liang, L., Zheng, K., Sheng, Q., & Huang, X. (2016). A denial of service attack method for an
IOT system. In 2016 8th international conference on Information Technology in Medicine and
Education (ITME), IEEE, pp.360–364.
48. Murray, R. (2017). A raspberry pi attacking guide. Birmingham: Packt Publishing.
4 An Overall Perspective onEstablishing End-to-End Security inEnterprise IoT (E-IoT)

99© Springer Nature Switzerland AG 2020
A. Haldorai et al. (eds.), Business Intelligence for Enterprise Internet of Things,
EAI/Springer Innovations in Communication and Computing,
https://doi.org/10.1007/978-3-030-44407-5_5
Chapter 5
Advanced Machine Learning
forEnterprise IoT Modeling
N.Deepa andB.Prabadevi
5.1 Introduction
The Internet of Things (IoT) which is going to be the future enables every object
(any living or non-living thing) to interact through the Internet without any form of
human intervention like human-to-human or human-to-computer interactions.
Ultimately like the Internet, IoT will also become a fundamental need. It is also
termed as the Internet of Objects as it is an inter-connection of real-world entities
[1]. Any object to be a part of the IoT system must have a unique identier and
capability to process the information. In terms of IoT, sensors and actuators allow
every object in the IoT system to communicate. IoT makes everything smart; here,
smart refers to making all the application-specic objects interact without any
human intervention. Some of the IoT applications are smart cities, smart medicines,
smart home, smart farming, and so on [2].
IoT makes activities smart by joining hands with various other technologies like
embedded systems (supporting hardware and software communication), sensors
(sensing live application-specic accurate information), big data analytics (manag-
ing colossal volume of data), cloud computing (data storage and scalability), and
machine learning (making machines learn and predict from experiences). IoT has
achieved its most signicant impact on business through its variant named enter-
prise IoT (E-IoT). The term enterprise refers to a business that encompasses various
front-ofce and back-ofce activities. IoT in convergence with business automates
all the activities of an enterprise by embedding computing devices in enterprise
objects thus enabling them to communicate for accomplishing the business
N. Deepa (*) · B. Prabadevi (*)
School of Information Technology and Engineering, VIT University,
Vellore, Tamil Nadu, India
e-mail: deepa.rajesh@vit.ac.in; prabadevi.b@vit.ac.in

100
processes without or with less intervention of humans. This, in turn, will reduce
manual operations thereby enhancing the efciency of the business.
Enterprise resource planning (ERP) software is already in place for automizing
all the business activities both front ofce and back end. Many ERP software such
as Oracle, SAP, PeopleSoft, Fedena, Odoo, and CloudERP are providing better
enterprise solutions to respective different business domains such as education,
nance, manufacturing, and so on. So this will also be a part of an E-ToT environ-
ment. The emergence of E-IoT applications in the enterprise will automize the busi-
ness processes. The various components of E-IoT are depicted in Fig.5.1.
Though enterprise-specic business processes exist in each business, in general,
most of the business processes respond based on the signals or commands from
robust machines and other types of equipment. Also, these business processes can
react based on the predened business rules embodied in the E-IoT application.
Fig.5.2 depicts how the business processes interact with other entities and how the
solution is retrieved by them. To deploy IoT applications on a large scale, the enter-
prises must have sufcient capital, good reach, enormous resources, and appropriate
reasons for deploying it. Subsequently, it will be easier for businesses to attain
greater business value, as well to utilize it for further adoption and business invest-
ment from the return of investment (ROI).
The greatest challenge for E-IoT is how the heterogenous business data will be
handled. So, E-IoT bridges with technologies like mining to extract useful informa-
tion from data stored and processed in the cloud and predicts different trends and
patterns from knowledge gained through mining using machine learning and big
data analytics. Also, real-time stream analytics have a more signicant role to play
with live data involved in business processes. Real-time streaming helps the enter-
prise decision makers to visualize data involved in every transaction and other busi-
ness activities before taking an operational decision[3]. These technologies help in
ERP Systems Enterprise IoT
(Internet Cloud)
People
Value Chain
IoT devices and
Enterprise Assests
Business
Applications
sensors
Fig. 5.1 Components of enterprise IoT
N. Deepa and B. Prabadevi

101
predictive maintenance of equipment, predicting market trends, determining pro-
duction styles through customer buying pattern prediction, predicting the stock
market, forecasting market demand to assist in demand-based manufacturing, pre-
dicting marketing trends, deciding prices for stock depending on different market-
ing stages of a product (i.e., from product promotion to stabilized brand product),
and so on.
Integrating IoT devices with enterprise processes requires tiring work. This
includes custom activity in various functionalities like hardware conguration, its
deployment, and middleware conguration. So it is better to understand the existing
business before proceeding for integration. Business process modeling (BPM) is the
most popular technique in developing business models and accomplishing complex
business processes in enterprises. Though several tools on BPM exist, those tools do
not resolve the challenges in IoT-based business processes. Some of the challenges
are adaptive event-driven business processes, distributed processes, and business
processes that deal with unreliable data and resources [4].
5.2 Enterprise Forecasting
Business forecast refers to predicting the probable outcome in almost all the busi-
ness processes or future situations of an enterprise and acting accordingly to attain
success. Henceforth a better business forecasting facility of an enterprise deter-
mines its success in the short term. Most of the activities in the business are uncer-
tain. So to be on the safer side and to react better in uncertain situations, forecasting
Fig. 5.2 Business process interaction with other entities in an enterprise
5 Advanced Machine Lea rning forEnterprise IoT Modeling

102
is a must in all the activities we do.There are various models for enterprise forecast-
ing [4] as depicted in Fig.5.3. The models are classied based on term: long-term
forecasts are those that determine the activities in the long run, whereas short-term
forecasts determine the immediate future, i.e., short period. Therefore top manage-
ment will be focusing on long-term forecast while intermediary level or basic level
of management in an enterprise will be focusing on short-term forecast to decide
their short-term plans.
The forecasts are classied based on the position or level of people in an enter-
prise hierarchy. At each level, management will focus on different stages of the
micro- and macro-continuum. For instance, the production manager will be inter-
ested in forecasting the number of assembly parts, the number of employees, and
the number of plants to be operated to complete the current tender while the top
management will be bothered about the effective utilization of plants over a while
(for a year). The forecasting can be classied in the way it is determined as qualita-
tive or quantitative. The qualitative forecast is based on expert opinions or input
from the enterprise’s customers. Most enterprise practitioners will prefer this when
having very meager or no information about past or historical data or when the
market is facing a new scenario. In such situations, experts will help to predict the
future based on prior experiences. Some of the examples of this approach are mar-
ket research where predictions are done based on a survey with a certain group of
people and Delphi approach where the experts are forced to give forecasts. The
quantitative forecasting is just opposite to the former one discussed. It is merely
based on accurate historical data and omits the human involvement in the analysis.
Enterprise Forecast
Enterprise Forecast
Based on term Based on Position
of the people
Based on nature
of forecast
Based on nature
of output
Long
term
Micro Quantitative Point
Interval
forecast
Density
forecast
forecast
Qualitative
continuum
Macro
continuum
Short
term
Types
Fig. 5.3 Types of enterprise forecasts
N. Deepa and B. Prabadevi

103
Thus predictions are done based on the previous data and not considering the expert
opinions (i.e., manipulation based on human judgments). One of the commonly
used quantitative approaches is the indicator approach which considers the relation-
ship among certain indicators (the positive indicators) and determines the lagging
one’s performance by leading indicator’s previous performance results. The other
methods are econometric and time series. The econometric model is purely based
on mathematical or statistical approaches like regression techniques such as simple
linear regression, multiple regression, and regression with time-series data. Instead
of relying on the static data members relationship, it will calculate the consistency
of the variables in dataset over time and then determine the relationship strength.
The time-series models generally refer to the set of models that predict based on
past data. It differs from others by considering more weight for recent data and
omitting the outliers. The enterprises over the other models best prefer this approach.
The forecast can be classied based on the output as a point, an interval, or a density
forecast. If the forecast output is the single best value, then it is a point forecast,
whereas if a forecast output falls between a range of values, then it is an interval
forecast, or if the output is probably distributed for future value, then it is a density
forecast. Choosing one best forecast method among many available methods is
determined based on various factors such as type of product (new vs. old), goals
(short term or long term), and constraints (cost and time factors). However, it is
infrequent that one single model ts for all the enterprise forecasts. Always a hybrid
combination of forecasts will give the best results. In general, forecasting is done in
ve steps, namely, problem analysis and dataset collection, data preprocessing,
building and evaluation of the model, implementation of the model, and evaluation
of the results. Specically, the performance of the forecast depends on the type of
data and the source from which it was collected. Predicting the future can be better
assisted with machine learning algorithms.
5.3 Machine Learning Algorithms andTheir Application
Machine learning is a technology in which the computer will have the capability to
learn from the available dataset and implement the learning to predict unique pat-
terns from the data. It helps in developing models for data analytics. It identies
hidden knowledge or interesting patterns from the available dataset and predicts the
future in most of the problems. The two popularly used methods in machine learn-
ing are supervised learning and unsupervised learning.
The supervised machine learning algorithms are allowed to perform learning on
the dataset in which class labels or outcomes are known. These kinds of algorithms
are used in credit card companies where the past fraudulent dataset is used for train-
ing the model and the learning helps to predict the fraud in the future. Some of the
popularly used supervised algorithms are support vector machine (SVM), decision
tree classier, naive Bayes algorithm, linear regression method, logistic regression
method, K-nearest neighbor algorithm, etc. The unsupervised machine learning
5 Advanced Machine Lea rning forEnterprise IoT Modeling

104
algorithms learn from the dataset in which class labels or outcomes are not known.
The main aim of these kinds of unsupervised algorithms is to analyze the data and
produce interesting hidden patterns in the data. For example, these algorithms can
be used to group customers with similar purchasing behavior in retail marketing.
Some of the unsupervised learning algorithms are hierarchical clustering algorithm,
K-means clustering algorithm, self-organizing maps, etc.
The various steps in the development of machine learning models are shown in
Fig.5.4. The data relevant to the problem should be gathered either from real life or
from past historical data. As the raw data collected may contain missing information
or may be erroneous and inconsistent, statistical methods can be applied to clean the
data. The raw data collected may be in a different unit of measurement, and it should
be normalized to the same unit of measurement which helps to improve the accu-
racy of the developed model. If the dataset consists of high-dimensional data, fea-
ture selection/dimension reduction can be applied to reduce the dimension of the
data or to identify the useful set of features. Then the dataset needs to be divided for
training (learning) and testing purpose. The model is developed by applying appro-
priate classication or clustering algorithms. From the results, knowledge
Fig. 5.4 The steps in the
development of machine
learning models
N. Deepa and B. Prabadevi

105
prediction is obtained to make reliable decisions for the identied problem. The
results can be visualized with the help of graphs for further analysis and comparison
with the state-of-the-art techniques.
As most of the enterprises are working with a large amount of data nowadays,
machine learning algorithms have become popular in the business domain. They
develop models using machine learning algorithms to identify the interesting pat-
terns in their huge dataset. They make rapid decisions based on the knowledge
acquired through machine learning models on their dataset which helps them to
obtain maximum prot. Machine learning algorithms are applied to the dataset
acquired from IoT sensor devices to predict the knowledge in various domains.
As there is a rapid increase in the application of IoT devices, the amount of data
generated by them also increases. In order to process and analyze the data collected
from IoT devices, models should be developed which can process the huge amount
of data. Machine learning algorithms have proved to provide better results in pro-
cessing data obtained from IoT sensors. Machine learning algorithms have globally
become increasingly popular, and they have been providing various benets in our
day-to-day life. The various application domains of machine learning algorithms
are shown in Fig.5.5.
Fig. 5.5 Application domains of machine learning algorithms
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Machine learning algorithms have been applied to develop classication and pre-
diction models for the development of agriculture such as land suitability analysis
and crop classication based on multiple parameters [5–11]. A new disease risk
prediction algorithm based on convolutional neural network was developed to pre-
dict the chronic diseases in patients. Experimentation was done using the hospital
data collected from China. Missing data in the dataset was handled by latent factor
model. The results of developed algorithm were compared with existing methods
and showed 94.8% prediction accuracy [12]. A framework was developed to detect
the fake and abusive records in the claims received from the customers in healthcare
insurance agencies. In the framework, pairwise comparison using analytical hierar-
chy process method was applied for the calculation of weights of criteria involved.
Expectation maximization was used for clustering similar records. The developed
framework was validated using a real dataset with six different fraudulent
records [13].
Supervised learning algorithms were used to identify the optimal land location
for starting a new retail store. The real dataset collected from NewYork was used
for studying the prediction accuracies of various machine learning methods [14]. A
non-linear non-parametric forecast model was built using machine learning algo-
rithms such as linear regression to predict the credit risk of the consumers of a com-
mercial bank [15]. A model was developed to classify and predict the claim amount
from the automobile insurance agency.
Machine learning algorithms such as the hierarchical clustering method, heuris-
tic method, and regression algorithm were used to build the model. Clustering algo-
rithms were applied to group the policyholders with similar factors, and the
regression method was used for classication and prediction of claim amount [16].
A machine learning model was developed using an enhanced AdaBoost algorithm
for classication and prediction of sales data in the retail business. The model pro-
cesses the data obtained from daily transactions and utilizes it to predict future sales
for inventory management. Accordingly, proper decisions can be made to improve
the sales of the retail business [17]. A study was conducted by the Korea
Transportation Safety Authority to nd the most suitable parameters that affect the
safety measures of the agencies, and based on that, a strategy for promotion can be
suggested. Cluster-based negative binomial regression method was used for the
selection of factors such as socioeconomic factors, demographic factors, conditions
of roadways, driver behavior, and trafc rule violations. Negative binomial regres-
sion models were built to identify the unique parameters for the prediction of deadly
crashes [18].
A machine learning model was built for the prediction of rainfall under the
hydrological research with non-linear pattern of data which is very difcult to pro-
cess. Machine learning algorithms such as recurrent articial neural network algo-
rithm, support vector machine method, and particle swarm optimization approach
were used to develop the prediction model. Recurrent articial neural network algo-
rithm was used to predict the rainfall, and support vector machine is used to nd the
solution for the non-linear regression and the problems in time-series data. Particle
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swarm optimization method is used to select the relevant parameters for the devel-
opment of the model [18]. The survey of applications of various machine learning
methods in different problems is summarized in Table5.1.
5.4 Applications ofMachine Learning inE-IoT
From the past few years, the costs of IoT sensors and chips used to store the data
have been diminishing. The data collected by IoT sensors are very huge in the cloud
and nowadays it is very fast also. In order to analyze the data stored in IoT, machine
learning plays a major role. This can be possible by integrating the physical devices,
cloud, and machine learning models which in turn will increase the performance
and efciency of decisions taken in the business enterprise [20]. Enterprise IoT is
becoming popular due to the technological advancement where the physical sensor
devices are integrated with software modules to take rapid and accurate decisions in
business applications. Enterprise IoT reduces manpower and helps to improve the
overall productivity of businesses. The physical devices are interconnected with the
Table 5.1 Applications of machine learning algorithms in different domains
S.
no. Purpose Technologies/methods used
1 To assist farmers to take decision on
agriculture crop cultivated in their land for
agriculture development
Machine learning algorithms [5–8]
2 Multi-class classication model for
agriculture land suitability analysis
Multi-layer perceptron, IoT sensors [11]
3 Prediction algorithm to forecast the chronic
diseases in patients
A convolutional neural network, latent factor
model [12]
4 A framework for the detection of fake and
abusive insurance records for the claims
submitted by the customers
Analytical hierarchy process [13]
5 A model to nd the optimal site location
for starting a new retail shop
Machine learning algorithms [14]
6 Non-linear non-parameteric forecast model
to predict the credit risk of bank customers
Linear regression method [15]
7 A decision model to classify and predict
the insurance claim amount for automobile
insurance agency
Hierarchical clustering method, heuristic
method, and regression algorithms [16]
8 A model to classify and predict the sales
data for a retail business
AdaBoost algorithm [17]
9 A model to nd suitable attributes for
safety measures in transportation agencies
Cluster-based negative binomial regression
method [18]
10 A machine learning model to predict the
rainfall with non-linear pattern of data
Recurrent articial neural network algorithm,
support vector machine, and particle swarm
optimization approach [19]
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Internet thus transforming the required data to the cloud for data analysis which
helps to improve the enterprise.The architecture diagram depicting the application
of machine learning models in enterprise IoT is shown in Fig.5.6.
IoT devices have been recently used in the manufacturing divisions of industries
where the data collected from various sensors are used for predicting some useful
information. A predictive model was developed using autoregressive integrated
moving average method. The data collected from various sensor devices xed in a
slitting machine were applied to the developed predictive model in order to forecast
the time-series data. Thus machine learning has proved its importance in enterprise
IoT [21]. A machine learning model was built using multistage meta-classier for
network trafc analysis. This model classies and identies the IoT devices which
are connected to a specic network. The model initially distinguishes between the
trafc data generated by the IoT devices and non-IoT devices. Later it classies and
identies the correct IoT device from the chosen nine IoT devices. The model has
proved to provide an accuracy of 99.28% [22].
A multi-class model was built using a supervised machine learning algorithm,
namely, random forest, for the identication of fake IoT devices connected to the
organization network. The model extracts the trafc data from the network to iden-
tify the type of IoT device from the given list of devices. In order to train the clas-
sication model, the trafc data collected from 17 IoT devices have been used with
9 class labels. The multi-class model has given 99.4% accuracy in detecting fake
IoT devices [23]. The water crisis is a major problem nowadays for sustainable
agriculture development. There is a need for the effective utilization and
Fig. 5.6 Applications of machine learning models in E-IoT applications
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management of water. A machine learning-based model was developed using mul-
tiple linear regression algorithm for monitoring the water level in the agriculture
eld. IoT devices were xed in the irrigation eld which will continuously collect
details about the underground water level and other details such as sunlight, air pres-
sure, rainfall, temperature, etc.; this will help the farmers to get details about the
water scarcity which will hit the land in the future [24].
A model was developed using hidden Markov method to predict the disease in
grapefruits and give an alert to the farmers to apply pesticides through SMS to their
mobile phones. In order to develop the model, various sensors to collect details such
as moisture, humidity, temperature, and leaf wetness were xed for data collection
[25]. A model was built using a machine learning algorithm to alert about the health
condition of a person. The heartbeat of the person was measured using IoT sensor
and taken as a parameter to predict the stress behavior of the person in this model
[26]. A business intelligent system was proposed to track the emotion and behavior
of the customers using IoT devices which runs on Apache Spark cluster in the retail
industry. An intelligent trolley, namely, EmoMetric, was developed to provide
insights on customer behavior by using the customer’s face. The results were com-
pared with other popular techniques and showed 95% accuracy [27].
A classication model was developed using a supervised neural network algo-
rithm, namely, multi-layer perceptron, to classify the threats in IoT sensor network.
The results of the developed model produced 99% accuracy and proved to give bet-
ter solution in the detection of distributed denial-of-service attacks [28]. A frame-
work was presented for the development of the decision support system to work
inside the ecosystem of IoT. Network communication data related to quality for
electric smart meter is analyzed to make decisions on whether a technician needs to
be sent to the customer address to solve the issue in the electric smart meter. The
framework was implemented using a Bayesian network, and the results were com-
pared with the results obtained from naive Bayes, decision tree, and random forest
algorithms [29].
An articial intelligence-based bot, namely, SamBot, was developed for a mar-
keting purpose which is to provide interactive answers to customers’ questions in
the corporate website of Samsung IoT. SamBot is integrated with the marketing
domain knowledge such as frequently asked questions, product promotions, etc.,
Sometimes the bot gives random answers if the knowledge is not available. In order
to increase the efciency of the bot, a supervised machine learning algorithm was
applied to improve the knowledge of the developed SamBot [30]. A big data-based
cloud system architecture was proposed to provide recommendations on a list of
products to the customers. The architecture includes cloud to store the data relevant
to products and purchases ranking. It analyzes the big data from the private and
hybrid cloud for ranking the products to improve the retention in marketing [31].
An integrated system model was developed to monitor the environment and cli-
matic changes. The system integrates cloud platform, Internet of Things, remote
sensing, global positioning system, geographic information system, and articial
intelligence. Web services, various sensors, and private and public networks have
been used in this model to collect the data from various sources and transport it to
5 Advanced Machine Lea rning forEnterprise IoT Modeling

110
the destination network. IoT-based data collection is done using multiple sensor
devices, satellites, radar, balloons, meteorological instruments, mobile devices,
Bluetooth, ZigBee, Wi-Fi, and RFID. Decision making and planning is scheduled in
the application layer of the developed system architecture. The main functionalities
of this layer are the storage, organization, processing, and distributing of the data
related to climate and environment which are acquired from various sensor devices.
And it also monitors ecological factors, pollution, resource management, and
weather and forecasts disaster. It accomplishes the task of decision making related
to the environment and weather conditions. A case study was conducted by collect-
ing data from China to prove the efciency of the system developed [32].
Internet of Things-based production system for processing electrical and elec-
tronic equipment waste was developed for a particular type of manufacturing unit
for recycling purpose. In this system, a cloud-based architecture was implemented
to provide services for the production department. The cloud platform is deployed
to deliver computing units, software products, and storage space to the destination
through the network. In the manufacturing division of an industry, it is not possible
to provide resources such as tools, materials, and machines through the network.
Hence Internet of Things is essential to connect and integrate the physical devices
to the cloud platform and named as the Internet of Manufacturing Things (IoMT).
This IoMT is used to exchange the resources and data but in various formats.
Therefore there is a necessity to build an integrated method to assist the industrial
information system among various service shareholders. In the cloud platform,
electrical and electronic equipment waste can be combined with industrial informat-
ics through IoMT [33].
A study is conducted on how to provide service through the development of the
Internet to the manufacturing division of an organization by deploying Internet of
Things platform. The study also analyzes how various benets of the Internet of
Things can be applied to improve product-service systems. Three case studies have
been taken in which the Internet of Things was successfully implemented in three
manufacturing companies in different sectors of industry such as power distribution
and generation and metal processing units. The empirical results provided an idea to
create different values of providing services to the industrial sectors in the manufac-
turing division [34].
The various enterprise applications of machine learning and the Internet of
Things are summarized in Table5.2.
5.5 Issues inEnterprise Internet ofThings
There are several issues in integrating Internet of Things with enterprise. The major
challenges faced by enterprise IoT are shown in Fig.5.7 [35].
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Table 5.2 Application of machine learning algorithms on enterprise Internet of Things
S.
no. Purpose Technologies/methods used
1 The predictive model to forecast
time-series data
IoT, autoregressive integrated moving average
method [22]
2 Machine learning model for network
trafc analysis and to distinguish between
IoT and non-IoT devices
Multistage meta-classier, IoT [23]
3 Multi-class model to identify fake IoT
devices in organization network
Random forest algorithm, IoT sensors
4 A decision model to monitor the
groundwater level in sustainable
agriculture development
Multiple linear regression algorithm, IoT
sensor devices [24]
5 A model to predict the disease in
grapefruits and recommend suitable
pesticides to the farmers
IoT sensor devices, machine learning
algorithm, namely, hidden Markov method
[25]
6 An approach to predict the stress behavior
of a person
Machine learning algorithms, sensor devices
[26]
7 Business intelligent system to predict the
emotion and behavior of the customers
IoT devices, Apache Spark cluster, machine
learning methods [27]
8 A classication model to classify the
threats in IoT sensor networks in
distributed denial-of-service attacks
Multi-layer perceptron, IoT sensors [28]
9 A decision support system to provide
technical support for smart electric meter
Internet of Things, Bayesian classier,
decision tree, random forest [29]
10 Articial intelligence-based SamBot to
provide online help for Samsung
customers
The supervised machine learning algorithm,
IoT devices [30]
11 A recommendation model using cloud
system architecture to give product
suggestions for the customers
Cloud platform, IoT, machine learning
algorithms [31]
12 An integrated model to monitor the
environment and climate change
IoT, multi-sensor devices, cloud platform,
articial intelligence, remote sensing, global
positioning system, geographic information
system, web services [32]
13 IoT-based production system to process
electrical and electronic equipment waste
in manufacturing division for recycling
purpose
Cloud platform, IoT, machine learning
algorithms, Internet of Manufacturing Things
[33]
14 A system to provide services to the
manufacturing division of the
organization with the implementation of
the Internet of Things
Internet of Things, cloud platform, machine
learning algorithms [34]
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112
5.5.1 Data Security Issues
It has been found that most of the Internet attacks are targeted toward IoT devices.
Topmost enterprises also face these security issues. Both users and enterprise have
to face the problem of issues in privacy and security. Hence proper measure needs
to be taken to enhance the privacy and security solutions in enterprise Internet
of things.
5.5.2 Issues intheNetwork Connection
The major challenge in Internet connectivity is it is not possible to utilize the Internet
facility at the same speed in all the places. It has been revealed that one of the major
challenges in IoT implementation is the Internet connectivity issue. When IoT
devices are xed in remote locations, it is not possible to transmit the data with high
speed. There may be disruption in Internet connection. Furthermore, based on the
router, the signal quality collected by various sensors which transmit data to the
network differs. The networks should be connected with the latest technologies to
enhance the speed and quality of data transmission in the IoT platform. Moreover,
Fig. 5.7 Major challenges in integrating Internet of Things with enterprise
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the number of IoT devices connected to the network should also be monitored to
obtain good network coverage.
5.5.3 IoT Platform Compatibility withHeterogeneous Network
IoT sensors and network are the major components in implementing IoT.Not every
computer connected to a network is capable of connecting to advanced sensors. And
also not every network has the capability to communicate and share the data effec-
tively. Different sensors have the distinct power-consuming capability and may not
be able to produce the same results. It is mandatory to deploy the latest technologies
to integrate the different sensors to the heterogeneous network which will commu-
nicate data in the same manner.
5.5.4 Integration ofIoT Devices withAppropriate
IoT Platform
In order to incorporate the IoT devices with enterprises, the IoT physical devices
should be integrated to correct IoT platforms. If it is not properly connected to
appropriate IoT platform, functional abnormalities and inefciency in the delivery
of solutions to the customers may happen. It has been found that nearly half of the
implementation cost should be spent for the integration of IoT devices with appro-
priate back-end systems and IoT platforms. Only then the enterprises can explore
the data through big data tools and predict the knowledge. Therefore appropriate
IoT devices and platform for integration of application-specic data should be
devised before integration. This will help in avoiding the issues.
5.5.5 Issues inData Collection
The main purpose of integrating IoT devices with the enterprise is to capture the
data from various physical devices, convert to some standard format, apply machine
learning models, and predict meaningful information for business purposes. If there
are anomalies in the real environment where IoT devices are xed, say for example
power shutdown or problems due to natural calamities, wrong data may be collected
by the IoT sensor devices.
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5.5.6 Issues inIntelligent Predictive Analytics
The major contribution of enterprise Internet of Things is that it transforms the data
to supply knowledge. If there are aws in the collected data, the prediction capabili-
ties will not be correct. In most of the places, IoT stakeholders nd it difcult to
collect correct data, and they conrm that the analytics made by them are not exi-
ble and strong. Therefore the latest analytic software like machine learning models
can be developed to check the integrity and consistency of the data before intelligent
predictive analytics can be made.
5.5.7 Integration ofBig Data withIoT forHandling
Unstructured Data
As there is an increase in connected devices, the problems in handling unstructured
data also increase. The other major challenge for the enterprises is to decide which
data is useful because only valuable data can be used for data analytics. The data
collected from various physical devices consists of unstructured data. Unstructured
data cannot be stored in SQL databases. When unstructured data is saved in NoSQL
format, it is a little complex to access the data. Big data frameworks such as
Cassandra and Hadoop reduce the complexity of handling unstructured data. But
the integration of IoT with big data is itself a major challenge.
5.5.8 Lack ofAwareness inUsers
Many customers use mobile apps, but they are aware of the Internet of Things incor-
porated in it. Even though it is not necessary for the consumers to have technical
awareness about IoT devices, the lack of IoT knowledge may lead to hesitation with
respect to cost and security of such devices. And that may slow down the usage of
such technologies. In a survey conducted by Cisco, it has been found that 53% of
customers do not want their data to be collected from their devices. This shows the
lack of awareness about the IoT platform among the consumers.
5.6 Advanced Machine Learning Techniques inE-IoT
As discussed priorly, machine learning assists in various business activities and
allows the decision makers to make a wise decision in different aspects of business
solutions. Based on the historical data, the machines are trained and then test data is
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given to test the system’s behavior without human intervention. In this section, we
provide a brief overview of advanced machine learning techniques for E-IoT.
5.6.1 Application ofMachine Learning forDemand Forecast
inEnterprise Supply Chain
One of the major activities in an enterprise is a business forecast. The supply chain
is the chain that connects the supplier from whom raw material is procured, the
manufacturer who produces the product from raw material procured, the dealer, the
wholesaler, the retailer, and nally the end buyer or the user. To stay connected and
to retain good relationship with various roles in this chain, an enterprise must make
wise decisions in all the activities in this chain. A supplier needs to forecast manu-
facturer demands, and in turn, a manufacturer must forecast various other processes
in predicting the customer. Although customer demand is predictable, the manufac-
turer’s demand will uctuate randomly. So the biggest challenge is predicting man-
ufacturer’s demand in this collaborative forecasting environment. It has now become
mandatory for an enterprise to realize the integrated information across various
stakeholders in its supply chain. This determines the value of an enterprise. Fig.5.8
Customer
Replenishment
Supply Chain
Customer order
Procurement
Manufacturing
Manufacturer
Disributor
Supplier
Fig. 5.8 A supply chain
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116
shows various stakeholders in a supply chain. A major challenge in forecasting the
demand of a manufacturer is the demand signal distortion through the supply chain.
This distortion may be due to inaccurate information passed into the supply chain
[36]. The accurate result on forecasting can be attained through supply chain col-
laboration, i.e., by involving all the stakeholders of the supply chain. Since it is not
always possible to involve all the participants every time, distorted demand signal
should be observed in case of full collaboration and partial collaboration. The dis-
torted demand signal and bullwhip effect in manufacturing are shown in Fig.5.9.
Though various forecasting techniques can be used as mentioned in the prior
section, it is important to analyze these techniques to observe which induces the
bullwhip effect.
The bullwhip effect is a distribution channel phenomenon. Bullwhip effect on the
supply chain will be observed when there are changes in customer demand causing
the companies involved in the supply chain to make more orders until the new
evolved demands of the customer are met [37]. In a multi-level supply chain, if we
move from right to left, that determines the ow of information for customers; on
other side, it is the demand owas shown in Fig.5.8 (Fig.5.9).
R.Carbonneau etal. had carried out an analysis of various advanced ML tech-
niques for supply chain demand forecast and compared them with traditional basic
methods that can be used for forecast [38]. The advanced ML techniques considered
are neural network (NN), feed-forward error backpropagation NN, support vector
machine (SVM), and recurrent neural network (RNN). The basic forecast tech-
niques used are naïve forest, moving average method, average method, trend analy-
sis, and multiple linear regression. They observed that machine learning algorithms
provide better accurate forecasts than traditional methods. They suggest that trend
60
50
40
Orders Placed
Actual Sales
Time
a. BullWhip Effect
b. Distorted Demand Signal
30
20
10
Quantity
0
Fig. 5.9 Bullwhip effect
and distorted
demand signal
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117
analysis and naïve forest for demand signal processing show more errors in fore-
casts. Also Bontempi etal. suggest that the machine learning algorithms like arti-
cial neural networks (ANNs), decision trees, nearest neighbor, and SVM outperform
the traditional models such as Box-Jenkins and linear regression for time-series
forecasting [39].
5.6.2 Application ofMachine Learning forEnhancing
Customer Value inE-IoT
It is a well-known fact that “a product with good features and excellent performance
but with no customer is equivalent to a failed product.” So a successful enterprise is
one with valued customers. Therefore, customer value is more important in any
enterprises. Some of the IoT applications focus on enhancing customer value in an
enterprise. Understanding the mandatory need for customer value in an enterprise is
considered to be a requirement for IoT adoption in an enterprise. Based on research,
three categories of IoT applications were identied to enhance the customer value.
They are monitoring and controlling, information sharing and collaboration, and big
data analytics and business analytics [40]. Monitoring and control is dealt with han-
dling data maintenance. It handles data collection of equipment’s operational condi-
tion and its environment. Decision makers can track and control the performance of
the equipment at any time from any place. I.Lee and K.Lee clarify how these three
categories assist in attaining customer value [41]. Based on how the system is per-
forming and how the customer is reacting to various operations of the pieces of
equipment in the IoT environment, enterprises can decide how they can enhance
their customers’ needs. As IoT is connected with more number of devices, an enor-
mous amount of data need to be collected from various actuators and sensors. These
data will be transmitted to data analytic and business intelligence tools to make
them appear simple for decision makers. These data can be used for forecasting
customer demands in the market and their behavior. The last category of informa-
tion sharing and collaboration helps in creating a better forecast. As discussed
regarding collaboration in the supply chain, here information sharing happens
through people to device, device to device, and people to people. So, collaboration
among all the entities participating in the communication must be considered. When
all the people/entities are connected collaboratively via IoT devices, here communi-
cation will be transparent and tasks will be accomplished on time. Bose and
Mahapatra have done a brief survey on various applications utilizing machine learn-
ing algorithms in business data mining [42]. Ampazis had tried to predict the
demand of customers in the basic and multi-level supply chain using machine learn-
ing algorithms [43]. He used trained ANN and SVM for regression. He found less
uncertainty while using these advanced machine learning algorithms on three differ-
ent huge datasets, namely, Netix (to predict demand of movie rentals during the
holiday period), Rotten Tomatoes (a movie review aggregator), and Flixster
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118
(movies- based social network). He has also suggested a computational intelligence
approach for supply chain demand prediction [44].
5.6.3 Application ofMachine Learning forVarious Other
Activities inE-IoT
There is no enterprise without a customer. Similarly, business partners are also
equally important as customers. So Mori etal. have used machine learning algo-
rithms for predicting best business partners using their enterprise proles and their
transactional dealings with various enterprises [45]. To predict the customer-
supplier relationship, they have to build a machine learning-based prediction model.
They suggest that SVM performs well in modeling the customer-supplier relation-
ship. So this approach can be employed in E-IoT architecture to nd plausible busi-
ness partners based on their employee count, ranking, and foundation date and to
better classify the customer-supplier relationships. Cumby etal. have proposed a
machine learning-based prediction model for determining customer’s shopping list
from PoS purchase data. They have used decision trees (C4.5) for predicting class
labels, and for learning each class, they have applied perceptron, winnow, and naïve
Bayes [46]. They conclude that their tool can be used to build better customer satis-
faction and can increase the revenue to up to 11%. So this can be employed in E-IoT
for enhancing customer satisfaction.
To retain the customer, better customer relationship management is mandatory in
any enterprise. Buckinx etal. proposed a multi-linear regression model to predict
customer loyalty based on the transactional data available [47]. They have com-
pared their model with other machine learning algorithms like random forest and
relevance determination neural network and obtained better prediction rates from
their models. So this model can be employed for E-IoT for predicting their customer
loyalty.
5.7 Summary
This chapter discusses about enterprise IoT and its uses, challenges and issues,
components, and applications in the real world. Various technologies that can be
used for assisting E-IoT to sort various issues and challenges of E-IoT were pre-
sented. Brief details on machine learning and its importance, functionality, applica-
tions, and various algorithms had been carried out. Specically how E-IoT is
benetted with the help of machine learning algorithms was addressed. As a con-
cluding note, machine learning algorithms serve better with best accuracy results
than traditional models existing for prediction of demand in supply chain, customer
value, nancial transaction, and business partners.
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References
1. Xia, F., Yang, L.T., Wang, L., & Vinel, A. (2012). Internet of things. International Journal of
Communication Systems, 25(9), 1101.
2. Gharami, S., Prabadevi, B., & Bhimnath, A. (2019). Semantic analysis-internet of things,
study of past, present and future of IoT. Electronic Government, an International Journal,
15(2), 144–165.
3. Haller, S., & Magerkurth, C. (2011). The real-time enterprise: Iot-enabled business processes.
In IETF IAB workshop on interconnecting smart objects with the internet, pp.1–3.
4. Hanke, J.E., Reitsch, A.G., & Wichern, D.W. (2001). Business forecasting (Vol. 9). Prentice
Hall: Upper Saddle River.
5. Haldorai, A., & Kandaswamy, U. (2019). Cooperative spectrum handovers in cognitive radio
networks. In EAI/Springer innovations in communication and computing (pp.1–18). Cham:
Springer. https://doi.org/10.1007/978-3-030-15416-5_1.
6. Deepa, N., & Ganesan, K. (2019). Decision-making tool for crop selection for agriculture
development. Neural Computing and Applications, 31(4), 1215–1225.
7. Deepa, N., Ganesan, K., & Sethuramasamyraja, B. (2019). Predictive mathematical model for
solving multi-criteria decision-making problems. Neural Computing and Applications, 31(10),
6733–6746.
8. Haldorai, A., Ramu, A., & Murugan, S. (2019). Social relationship ranking on the smart inter-
net. In Computing and communication systems in urban development (pp.141–159). Cham:
Springer. https://doi.org/10.1007/978-3-030-26013-2_7.
9. Deepa, N., Ganesan, K., Srinivasan, K., & Chang, C.Y. (2019). Realizing sustainable devel-
opment via modied integrated weighting MCDM model for ranking agrarian dataset.
Sustainability, 11(21), 6060.
10. Deepa, N., Srinivasan, K., Chang, C. Y., & Bashir, A. K. (2019). An efcient ensemble
vtopes multi-criteria decision-making model for sustainable sugarcane farms. Sustainability,
11(16), 4288.
11. Vincent, D. R., Deepa, N., Elavarasan, D., Srinivasan, K., Chauhdary, S. H., & Iwendi,
C. (2019). Sensors driven AI-based agriculture recommendation model for assessing land suit-
ability. Sensors, 19(17), 3667.
12. Chen, M., Hao, Y., Hwang, K., Wang, L., & Wang, L. (2017). Disease prediction by machine
learning over big data from healthcare communities. IEEE Access, 5, 8869–8879.
13. Kose, I., Gokturk, M., & Kilic, K. (2015). An interactive machine-learning-based electronic
fraud and abuse detection system in healthcare insurance. Applied Soft Computing, 36,
283–299.
14. Karamshuk, D., Noulas, A., Scellato, S., Nicosia, V., & Mascolo, C. (2013, August). Geo-
spotting: Mining online location-based services for optimal retail store placement. In
Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and
data mining, ACM, pp.793–801.
15. Khandani, A.E., Kim, A.J., & Lo, A.W. (2010). Consumer credit-risk models via machine-
learning algorithms. Journal of Banking & Finance, 34(11), 2767–2787.
16. Yeo, A. C., Smith, K. A., Willis, R.J., & Brooks, M. (2001). Clustering technique for risk
classication and prediction of claim costs in the automobile insurance industry. Intelligent
Systems in Accounting, Finance & Management, 10(1), 39–50.
17. Von Kirby, P., Gerardo, B.D., & Medina, R. P. (2017). Implementing enhanced AdaBoost
algorithm for sales classication and prediction. International Journal of Trade, Economics
and Finance, 8(6), 270–273.
18. Jung, S., Qin, X., & Oh, C. (2019). Developing targeted safety strategies based on trafc
safety culture indexes identied in stratied fatality prediction models. KSCE Journal of Civil
Engineering, 2019, 1–8.
19. Hong, W.C. (2008). Rainfall forecasting by technological machine learning models. Applied
Mathematics and Computation, 200(1), 41–57.
5 Advanced Machine Lea rning forEnterprise IoT Modeling

120
20. Patel, P., Kaulgud, V., Chandra, P., & Kumar, A. (2015, December). Building enterprise-
grade internet of things applications. In 2015 Asia-Pacic Software Engineering Conference
(APSEC), IEEE, pp.4–5.
21. Kanawaday, A., & Sane, A. (2017, November). Machine learning for predictive maintenance
of industrial machines using iot sensor data. In 2017 8th IEEE International Conference on
Software Engineering and Service Science (ICSESS), IEEE, pp.87–90.
22. Meidan, Y., Bohadana, M., Shabtai, A., Guarnizo, J.D., Ochoa, M., Tippenhauer, N.O., &
Elovici, Y. (2017, April). ProlIoT: A machine learning approach for IoT device identication
based on network trafc analysis. In Proceedings of the symposium on applied computing,
ACM, pp.506–509.
23. Meidan, Y., Bohadana, M., Shabtai, A., Ochoa, M., Tippenhauer, N.O., Guarnizo, J.D., &
Elovici, Y. (2017). Detection of unauthorized iot devices using machine learning techniques.
arXiv preprint arXiv:1709.04647.
24. Ahmed, F. (2017, October). An IoT-big data based machine learning technique for forecasting
water requirement in irrigation eld. In International conference on research and practical
issues of enterprise information systems, Springer, pp.67–77.
25. Patil, S.S., & Thorat, S.A. (2016, August). Early detection of grapes diseases using machine
learning and IoT. In 2016 second international conference on Cognitive Computing and
Information Processing (CCIP), IEEE, pp.1–5.
26. Pandey, P.S. (2017, July). Machine learning and IoT for prediction and detection of stress. In
2017 17th International Conference on Computational Science and Its Applications (ICCSA),
IEEE, pp.1–5.
27. Tallapragada, V.S., Rao, N.A., & Kanapala, S. (2017). EMOMETRIC: An IOT integrated big
data analytic system for real time retail customer’s emotion tracking and analysis. International
Journal of Computational Intelligence Research, 13(5), 673–669.
28. Haldorai, A., & Kandaswamy, U. (2018). Cooperative spectrum handovers in cognitive radio
networks. In EAI/Springer innovations in communication and computing (pp.47–63). Cham:
Springer. https://doi.org/10.1007/978-3-319-91002-4_3.
29. Siryani, J., Tanju, B., & Eveleigh, T.J. (2017). A machine learning decision-support system
improves the internet of things’ smart meter operations. IEEE Internet of Things Journal, 4(4),
1056–1066.
30. Pradana, A.D. I.T. Y.A., Goh, O.S., & Kumar, Y.J. (2018). Intelligent conversational bot for
interactive marketing. Journal of Telecommunication, Electronic and Computer Engineering
(JTEC), 10(1–7), 1–4.
31. Galletta, A., Carnevale, L., Celesti, A., Fazio, M., & Villari, M. (2017). A cloud-based system
for improving retention marketing loyalty programs in industry 4.0: A study on big data stor-
age implications. IEEE Access, 6, 5485–5492.
32. Fang, S., Da Xu, L., Zhu, Y., Ahati, J., Pei, H., Yan, J., & Liu, Z. (2014). An integrated system
for regional environmental monitoring and management based on internet of things. IEEE
Transactions on Industrial Informatics, 10(2), 1596–1605.
33. Wang, X.V., & Wang, L. (2017). A cloud-based production system for information and ser-
vice integration: An internet of things case study on waste electronics. Enterprise Information
Systems, 11(7), 952–968.
34. Rymaszewska, A., Helo, P., & Gunasekaran, A. (2017). IoT powered servitization of man-
ufacturing – An exploratory case study. International Journal of Production Economics,
192, 92–105.
35. https://www.noit.com/blog/enterprise-challenges-in-iot/
36. Heikkila¨, J. (2002). From supply to demand chain management: Efciency and customer
satisfaction. Journal of Operations Management, 20(6), 747–767.
37. Dejonckheere, J., Disney, S.M., Lambrecht, M.R., & Towill, D.R. (2003). Measuring and
avoiding the bullwhip effect: A control theoretic approach. European Journal of Operational
Research, 147(3), 567–590.
N. Deepa and B. Prabadevi

121
38. Carbonneau, R., Laframboise, K., & Vahidov, R. (2008). Application of machine learning
techniques for supply chain demand forecasting. European Journal of Operational Research,
184(3), 1140–1154. https://doi.org/10.1016/j.ejor.2006.12.004.
39. Bontempi, G., Ben, T.S., & Le Borgne, Y. A. (2013). Machine learning strategies for time
series forecasting. In M.A. Aufaure & E.Zimányi (Eds.), Business intelligence. eBISS 2012
(Lecture notes in business information processing) (Vol. 138). Berlin/Heidelberg: Springer.
40. Chui, M., Lo¨fer, M., & Roberts, R. (2010). The internet of things. McKinsey &
Company. Retrieved from http://www.mckinsey.com/insights/high_tech_telecoms_internet/
the_internet_of_things
41. Lee, I., & Lee, K. (2015). The internet of things (IoT): Applications, investments, and
challenges for enterprises. Business Horizons, 58(4), 431–440. https://doi.org/10.1016/j.
bushor.2015.03.008.
42. Bose, I., & Mahapatra, R.K. (2001). Business data mining– A machine learning perspective.
Information & Management, 39(3), 211–225. https://doi.org/10.1016/s0378-7206(01)00091-x.
43. Ampazis, N. (2015). Forecasting demand in supply chain using machine learning algorithms.
International Journal of Articial Life Research (IJALR), 5(1), 56–73.
44. Ampazis, N. (2012). A computational intelligence approach to supply chain demand forecast-
ing. In Machine learning: Concepts, methodologies, tools and applications (pp.1551–1565).
Hershey: IGI Global.
45. Mori, J., Kajikawa, Y., Kashima, H., & Sakata, I. (2012). Machine learning approach for nd-
ing business partners and building reciprocal relationships. Expert Systems with Applications,
39(12), 10402–10407. https://doi.org/10.1016/j.eswa.2012.01.202.
46. Cumby, C., Fano, A., Ghani, R., & Krema, M. (2004). Predicting customer shopping
lists from point-of-sale purchase data. In Proceedings of the 2004 ACM SIGKDD inter-
national conference on Knowledge Discovery and Data Mining – KDD’04. https://doi.
org/10.1145/1014052.1014098.
47. Buckinx, W., & Van den Poel, D. (2005). Customer base analysis: partial defection of behav-
iourally loyal clients in a non-contractual FMCG retail setting. European journal of operational
research, 164(1), 252–268.
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123© Springer Nature Switzerland AG 2020
A. Haldorai et al. (eds.), Business Intelligence for Enterprise Internet of Things,
EAI/Springer Innovations in Communication and Computing,
https://doi.org/10.1007/978-3-030-44407-5_6
Chapter 6
Enterprise Architecture forIoT:
Challenges andBusiness Trends
A.Haldorai , A.Ramu , andM.Suriya
6.1 Introduction
The industrial architecture is categorized as a discipline used for holistically and
proactively initiating industrial responses to disruptive forces. As such, this form of
architecture is vital for analyzing and identifying the relevant execution transition to
obtain the desired outcomes and visions. This paradigm determines the manner in
which enterprises can attain its present and future industrial missions and visions.
Industrial architecture includes the management principles, which are behind the
present discussions concerning the enterprises strategies and the manner it is pre-
sented via the IoTs. The enterprise architecture (EA) was established as an approach
to enhance the specication of a logical blueprint, which explains the operation and
structure of an enterprise. The purpose of the EA is to determine the manner in
which enterprises can effectively attain their present and future missions and visions.
EA aligns technology architecture capabilities with business architecture processes
and services [1]. Historically, this alignment embraced monolithic structures,
including operational systems and data warehouses supported by relational data-
bases and at les. With today’s Internet of Things (IoT) and all the connected
devices it involves, solution design offers new opportunities, even though designs
can be more challenging. The emerging solution environment has the following
characteristics:
A. Haldorai (*)
Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India
A. Ramu
Presidency University, Bangalore, India
M. Suriya
KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India

124
• New services and processes for business commerce, lifestyle, social interaction,
culture diversity, and environment management
• A broad range of user platforms such as wearables, smartphones, and a multitude
of appliances
• An expanding use of sensors that replicate human sight, hearing, smell, taste, and
touch and implement real-time concepts.
• Better timing dependency among and between services and processes using large
data volumes and new data analytics to drive the process of decision-making.
This executive update explores the impact of the IoT on traditional business and
technology architectures and the role of EA as an effective methodology for devel-
oping and implementing IoT strategies. We examine business architecture and how
it integrates IoT-driven processes with traditional processes. The IoT is character-
ized by “things”– many of them small in physical size, which can connect to other
things, generating a large network of collaborative IoT and non-IoT things. In this
paradigm, business process management (BPM) extends to accommodate innova-
tive workows that are more functionally robust with “thing-to-thing” linkages. The
architects behind the EA are the experts who manage every structure to ensure that
IT frameworks are designed based on the present enterprise standards and strate-
gies. The IoTs has proposed a new prototype whereby the global networks of
devices and machines are capable of sharing to establish a digitalized innovation in
industries. As such, the sector of the IoTs has signicantly developed to become the
largest sector.
IoTs is large when both viewed as an opportunity for enterprise, including the
kind of data it creates. Approximately 90% of data in the whole world has been
formed over the past 2years. Presently, there is an output data of about 2.5quintil-
lion bytes produced daily. Due to the movement of enterprises from the initial phase
of experimentation to complete deployment of the IoTs application, the deluge of
data will progressively be termed as plague to the organizations since they focus on
processing, capturing, and acting on the big data.
6.2 IoT Trends andChallenges
For the purpose of determining the dependability and reliability of the IoTs, a num-
ber of minimal segments of the measures have to be accomplished to attain an
interoperability and integration in the sector of IoTs.
Contract decoupling: The IoTs framework includes the heterogeneous network-
ing devices with the disparate communication paradigms [1]. The integration frame-
work has to be competent to effectively deal with contract decoupling.
Scalability: Provided the developing condition of the IoTs and calculation and
prediction by [2, 3], an effective incorporation system has to be evolvable and scal-
able to efciently support a lot of things linked to the network.
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Ease of testing: The integration segment must support the ease of debugging and
testing. Moreover, it must promise the support for debugging failures and defects,
incorporation testing, system testing, component testing, installation testing, com-
patibility testing, non-functional testing, security testing, and performance testing.
Ease of development: The IoTs integration system has to promise a form of ease
of development for Internet developers. The system should potentially eliminate all
the unnecessary complexities to assure an efcient documentation form for both the
developers and the non-developers who have to apply the basic programming skills.
This enables users to easily understand the inner network systems.
Fault tolerance: The IoTs framework should be resilient and dependable. The
smart integration system has to effectively deal with any faults as the IoTs devices
can possibly toggle over the online and ofine states.
Lightweight implementation: The incorporation frameworks must be lightweight
overheads in the deployment and development stages. Moreover, it must be easier to
install, activate, uninstall, update, deactivate, and adapt and for versioning [4].
Internal domain operability: The system must be extensible to effectively support
the inner domain communication. For instance, in the intelligent vehicle domain,
the integration system must promise the support of interaction and communication
with the necessary road closures and trafc lights that belong to the intelligent town
domains.
6.3 Enterprise IoT– Overview
The enterprise IoTs is the upcoming sector in technology since it will enhance the
development of the physical things comprising embedded computer devices to
effectively be applied in various business processes. As such, this enables the reduc-
tion of manual work while advancing the general organizational efcacy [5]. With
the application of the various technological advancements that range from the
embedded networking devices with actuators and sensors to the network-based
cloud platforms and communication, the enterprise IoTs applications can poten-
tially automate the various enterprise processes. These procedures depend on the
contextual data that is produced from the programming devices like vehicles, equip-
ment, and machines. The enterprise IoTs is purposed to be the next advancement in
technology, which included the physical things comprising embedded computing
networking devices which are used in organizational processing [6]. This process is
applicable in reducing manual tasks hence enhancing the overall enterprise efcacy.
Based on the combination of a number of technologies that range from the embed-
ded devices with actuators and sensors to the Internet-based communication and the
cloud platforms, the industrial IoTs application can effectively automate the organi-
zational processes, which are dependent on contextual data produced by the pro-
gramming devices like vehicles and machines. Moreover, these enterprise IoTs
applications can transfer control instructions to the devices dependent on the rele-
vant enterprise guidelines. The IoTs is a crucial novel networking concept that
6 Enterprise Architecture forIoT: Challenges andBusiness Trends

126
permits the previously unlinked physical devices to link up with the Internet. In the
future, the IoTs will become the Internet itself due to the possibility of availing a lot
of trials and opportunities to users [7].
The IoTs is projected to develop the efciency of enterprises [3] through facilita-
tive novel business models to align various physical processes with digitalized
assets on actual-time basis. The big data and cloud data technologies are vital since
they support the IoTs in ensuring smart insights and scalability. The main objective
of the technology is to formulate an insight that allows a faster, appropriate, and
accurate decision-making process in a more client-centric enterprise.
Some of the basic features of every IoTs stack include:
• Loosely coupled: Three critical IoTs stacks have been recognized. However, it is
fundamental that every stack is applied separately from the other. It has to be
possible to apply an IoTs cloud platform from a single distributor with an IoTs
gateway from another supplier and device stacks from a third distributor.
• Modular: Every stack has to permit for the characteristics to be sourced from
various distributors [8].
• Platform independent: Every stack has to have its own entire cloud and hardware
infrastructure. For instance, the device stacks have to be available on a lot of
MCUs and the IoTs cloud platforms, which have to be operated on various
cloud PaaS.
• Dependent on available standards: Information transfer between different stacks
has to be centered on available standards to accomplish interoperability.
• Dened APIs: Every stack has to contain a dened API, which permits an easy
incorporation with the present integration and application of the relevant IoTs
solutions [9].
6.3.1 Enterprise IoT Platform: Key Attributes
To take the pulse of enterprise IoT, you need an IoT platform whose key attributes
align with the requirements of your enterprise solutions. Your enterprise IoT plat-
form should provide you with the following capabilities:
1. Managing a large number of devices –Your enterprise IoT solution requires a
exible device management system that can easily be on-board or off-board and
manage a large number of devices in bulk. Plus, it should provide a mechanism
to support a variety of contemporary and emerging protocols to deliver the full
control of devices, regardless of their make, model, or manufacturer. Finally,
your IoT platform should scale to meet the pace and magnitude of growth of
connected devices. This kind of platform can store a warehouse of data and man-
age billions of transactions in real time.
2. Multi-tenancy and ne-grained access control– We should emphasize a major
difference between creating several customers, a.k.a. tenants, on the platform
and creating several users with different permissions within a single tenant.
A. Haldorai et al.

127
Tenants are physically separated data spaces with own users, separate applica-
tion management, and no sharing of data by default. Your enterprise solution
needs a natively multi-tenant platform which would allow you to host countless
customers running differently branded digital applications without the risk of
data leakage. Within a single tenant, you also need ne-grained control over your
users and their access rights. For example, not all employees should have access
to every device or its data, nor should they have the ability to change the settings.
That means such devices should have ways to display what data exactly they are
sharing and who they are sharing it with.
3. Integration with enterprise systems– Enterprise IoT platform should provide
you with an easy way to add new integrations to enterprise systems. By integrat-
ing with existing enterprise applications, you can create a master application that
would let you streamline your work and get the most value from your data.
4. Security and privacy at every level of the stack– Security should be embedded at
every level of the platform. Your platform should provide end-to-end data protec-
tion ensuring that personal data is not accessible to unauthorized viewers. Most
importantly, it should support per-device authentication and authorization to
enforce enhanced security. Also, the platform needs to offer a robust and proven
security model that will keep your system secure and data safe. Finally, it should
be ISO 27001 accredited, which demonstrates that you have taken all the neces-
sary steps to implement internal security practices and protect your business.
5. Data analysis– Data analytics should be an integral part of the enterprise con-
nected solution. Why? Through data analysis, raw data is transformed into mean-
ingful information that will help end users to draw key insights to make their
decisions move forward.
6.3.2 Enterprise IoT Architecture
The IoT technology stack is given in Fig.6.1 which contains three tiers: gateways,
data center or cloud IoT platform, and sensor devices. According to [10], “a typical
IoT solution is characterized by many devices (i.e., things) that may use some form
of gateway to communicate through a network to an enterprise back-end server that
is running an IoT platform that helps integrate the IoT information into the existing
enterprise.”
The devices tier concentrates on data collections through the sensors, which can
be embedded in various forms of devices. These devices include the mobile comput-
ing devices, autonomous appliances, machines, and wearable technologies. These
devices have the potential to capture the details concerning the physical environ-
ments like light, humidity, chemistry, vibration, and pressure. The standard- centered
wireless and wired network paradigms are applicable in the transfer of telemetry
information ranging from the cloud via the gateways to the networking devices. The
layers of devices form the foundations of the IoTs stacks. The legacy networking
devices, which have been available for many years with the intelligent, modern, and
6 Enterprise Architecture forIoT: Challenges andBusiness Trends

128
interlinked devices, are the vital foundation in IoTs. Every device is fundamental for
obtaining information from the different sensors, which have the potential to track
the crucial parameters. These devices can also be applied on controlling the condi-
tion of equipment, for instance, switching off the faulty machines or rectifying the
RPM of a revolving gear. These device layers have the capacity to assure the nal
mile connection to the remote equipment. It therefore presents the present status of
network devices alongside the capability to remotely control these devices.
The gateways, which are also known as the control tiers, are known as the inter-
mediaries that potentially facilitate the transfer of information, ofoad processing
actions, and drive functions. Due to the fact that some sensors produce a lot of data
points in every second, these gateways assure a place for pre-processing of data
locally before being sent to the relevant cloud tiers. Whenever information has been
aggregated in the gateways, it is summarized and strategically evaluated. Limiting
the amount of information can have a signicant implication on the networking
transmission costs, mostly on mobile networks. Moreover, this permits a crucial
organization rule, which is applicable depending on the amount of data streaming
in. The control tier is considered bidirectional. This can possibly provide control
data to the relevant devices to facilitate conguration transitions. During the same
moment, it can possibly respond to information-tier command-and-control requests,
like the security requests used in the process of authentication.
The low-power and legacy devices cannot be used to directly register or com-
municate data to the various IoTs platforms. In this process, the gateways are con-
sidered in the entire networking picture. This is sometimes referred to as the edge
devices, whereby the gateways are used as a proxy to the various networking
Business
M2M / IoT
Multi-
Field
Data
Service
Gateway
M2M / IoT
Protocol
Integration
Platform
Enterprise Interfaces
Integrate
Act
Store
Connect & Control
Collect
Elaborate
Communicate
Field Interfaces
+
Applications
Fig. 6.1 Enterprise IoT architecture model
A. Haldorai et al.

129
devices. These gateways are accepted to route the commands that are transferred
from the back end to the relevant networking devices. An edge device or the locally
available gateways present a signicant market chance. The networking and hard-
ware vendors are focusing to capture their own share of the market through the
process of augmenting their switches, routers, edge devices, and rewalls meant to
double up the various IoT gateways. The inclusivity of the novel IoT devices, edge
devices, and legacy machines forms a crucial device layer of the IoTs stacks.
The cloud tier, data center, and the IoTs platform are obliged to undertake a
large-scale computation of information to produce the relevant insight, which is
vital in the enterprise. It is therefore relevant in offering a back-end enterprise ana-
lytics to fully execute a complex event processing like the process of analyzing
information that is adaptable in the business world with rules that are inclined to
historical trends. Moreover, this process includes the dissemination of organiza-
tional guidelines downstream. The process requires scalability both horizontally
and vertically to effectively support an increasing number of linked devices. These
are meant to address the various IoTs problems. The vital functions of the IoTs
information center and cloud platform include the messaging routing and connec-
tivity, data storage, event analysis, device management, and application enablement
and integration [11]. The functioning capacity of the industrial IoTs depends on the
software platforms, which manage the status of devices, analyze them, store them,
and present the correct insights to enhance the process of decision-making.
Moreover, it acts as the middleware, which is meant to orchestrate the complete
ow of work. Provided the attributes of the cloud like reliability, scale, and elastic-
ity, it is currently becoming a crucial deployment environment of the IoTs platform.
The device layer denotes the cloud gateways, which are responsible for authoriz-
ing and authenticating devices meant to manage the ow of works. Moreover, it
ensures that secure communication is maintained between the various devices and
the centralized command centers. These gateways are purposeful in dealing with a
lot of protocols and the information formats. The heterogeneous device and locally
available gateways with disparate protocols are used in the process of registering the
cloud gateways. For instance, the locally available gateways and the devices can
possibly communicate with the cloud gateways via the SOAP, REST, AMQP,
XMPP, CoAP, MQTT, and WebSocket. Despite the inbound protocols, the cloud
gateways are signicant for assuring a consistent view of devices to the remaining
components. An average enterprise IoTs deployment is responsible for thousands of
devices and sensors that have to be deployed in a lot of sites. Every device requires
registration and maintenance in the centralized repository that serves as the authori-
tative inventory that acts in place of the present condition of deployment. The device
registry represents a centralized inventory where each device is registered into a
system. Every device alongside the metadata is available in the individual registry.
Any component in a platform can query the network device registry to evaluate the
present status of a device alongside its capability.
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6.4 IoT Business Model
Progressively, all forms of businesses are purposed to replace their one-off and dis-
crete sales frameworks with the present subscription-centered framework, which
links up products and services within a framework for a long-term relation. These
subscription models are effective and relevant to both the customers and businesses.
The business is designated to more recurrent and consistent streams of revenues.
Clients are no longer subjected to massive capital investments but they are being
offered opportunities of scaling their capacities meant to access superior services
and support. In the current years, advance cloud computing, technologies, and con-
nectivity have made the process possible since the advent of the “as-a-service”
enterprise model. This process began with the software-as-a-service (SaaS) before
becoming adjusted software. The IoTs has assured a powerful novel enterprise
framework, which is considered as a shot in the arm. Moreover, the low-cost, inter-
linked IoTs sensors and the monitored devices, including the sold products, enhance
the kind of services produced in an enterprise. The actual-time and historical infor-
mation generated by the IoTs devices permits users to exercise preventive mainte-
nance. For instance, the automated alerting and the predictive maintenance have
become possible in the modern age. These developments are useful in enhancing the
technological investment connected to the IoTs [12].
6.4.1 Types ofIoT Architecture Models
Device-to-device: “IoT devices within the same network that generally connect
using wireless PAN protocols, such as Bluetooth and ZigBee, are device-to-device
architectures,” the report illustrates.
Device-to-cloud: In such architectures, “IoT devices connect directly to the
cloud, typically using a long range communications network, such as cellular. For
example, IoT-enabled vehicle monitoring devices (such as those provided by car
insurance companies to drivers) collect data on the vehicle, such as distances and
speeds driven, and acceleration and braking rates. These data are then transmitted to
the cloud, analyzed in the cloud, and used by insurance companies to create tailored
insurance rates based on the driving data.”
Device-to-gateway: “Device-to-gateway architectures transfer information from
sensors to the cloud via a gateway device. The gateway collects the data and then
communicates the data to the cloud through additional network connectivity, such
as Wi-Fi or cellular connection.”
Cloud-to-cloud: “Cloud-to-cloud architecture, also known as back-end data
sharing, enables third parties to access uploaded data from IoT devices. For exam-
ple, smart buildings receiving data from smart thermostats and smart light bulbs can
send the data to a cloud via Wi-Fi. The collected data are then aggregated in cloud
1, which may be owned by the building as the conduit, a user can set a smart
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thermostat to activate when the user’s car approaches their house (the conditional
trigger in this example being the location sensor within the car).”
IOT-enabled enterprise architecture given in the gure is a blueprint for the
deployment of IT resources that support business services and processes. It is a
high-level view of six fundamental domains: service, process, information, applica-
tion, data, and infrastructure.
EA aligns new technology capabilities with innovative business architecture pro-
cesses and services. In Fig.6.2, new architecture elements associated with the IoT
are accented in red. In the business architecture with IoT, commodity services with
standard processes and operational data are augmented with custom services deliv-
ered through congurable processes. These processes provide on-demand informa-
tion tailored to user preferences or customized processes delivering unique services
associated with multiple, diverse connected devices. Due to larger consumer mar-
kets, more congurable processes will be required to meet broader customer prefer-
ences [13].
In the emerging EA [14] in which the IoT is a force, the architecture is more
diverse and complicated. New data platforms, including Hadoop, NoSQL, and
cloud data, will be used to house huge volumes of data. The IoT will enable people
in their daily lives to access custom services implemented using congurable
processes. For example, a smart refrigerator user can set food expiration policies
and rely upon automated leading signals before actual expiration dates are realized.
Fig. 6.2 Types of IoT architecture models
6 Enterprise Architecture forIoT: Challenges andBusiness Trends

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A congurable process minimizes food loss and reduces consumer grocery cost. In
another example, a food manufacturer can signal consumers if a product has been
found to pose public health risks (e.g., the possible presence of E. coli) to minimize
consumer exposure to this risk. With real-time design, a food producer might search
purchased smart refrigerator data, identify the location of tainted food, and send an
urgent message to affected consumers, which can be broadcast among a cluster of
product users, increasing the speed and reach of the message within the targeted
customer sector. These innovative workows are enabled through smart refrigerator
integration with applications and data located across selected infrastructure
platforms.
6.5 Enterprise IoT Monetization
One of the most vital objectives of the IoTs is to transform the mindset of doing
things in an enterprise. This mindset is relevant in the process of determining if
users can use a product-based monetizing approach or a service-based approach.
CSPs, ISVs, OEMs, and other relevant stakeholders are invited to contribute their
intellectual properties in advancing a signicant interlinked ecosystem. This pro-
cess necessitates monetization of a framework meant to permit all the contributors
to effectively leverage the novel IoTs organizational models to assure the agility to
deploy novel application to accomplish quicker ROI.The application enablement
platforms such as SensorLogic are vital for bridging this necessity with a competent
device pre-built and on-boarding to the IoTs services.
6.5.1 Enable Flexible Monetization Models
The IoT is developing a novel enterprise framework, for instance, the PaaS (product-
as- a-service), whereby the OEM provides the device, but in the instance of charging
the client upfront, the OEM permits them to pay via exible frameworks (monthly,
pay-per-use or metered, etc.). The Sentinel Software Monetization resolutions per-
mit the CSP and OEM to effectively apply the exible monetization frameworks via
tested technologies.
6.5.2 License andEntitlement Management
The objective of software is gradually developing and will continue progressing
since there are a lot of interlinked applications which have emerged in the recent
years. ISVs have, for a long time, handled various problems to enhance the moneti-
zation of their smart properties, i.e., software. This involves challenges such as
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reverse engineering and piracy. Whereas the software industries have increased, the
incumbent and emerging device producers are still new to their pending concerns.
To effectively monetize the intellectual properties, there should be effective enforce-
ment of the licensing policies for all the devices, which must also be broken down
to the individual features in the devices. The Sentinel Embedded and Cloud Service
Monetization resolutions permit the OEM and cloud service vendors to effectively
enforce the licensing frameworks.
6.5.3 Software Upgrades
Due to the rapid development of the IoTs services, the OEM will necessitate a
robust infrastructure for the purpose of remote upgrading of features and software
on the devices. However, these upgrades necessitate advanced levels of reliability
and security to assure that the devices in the elds cannot be tampered with making
them safe. The Sentinel Embedded resolutions assure a system for secure down-
loading of the software upgrades, which make sure that the features can be deployed
for the purpose of generating new streams of revenues from the interlinked devices.
6.5.4 Device Management
As a result of proliferation of different endpoints, the difculty of different device
protocols has gradually enhanced. Ranging from the standard-based to the proprie-
tary implementation, the device integration into the IoTs environment is considered
as a time-consuming process. To assist devices to communicate with the business or
cloud resources, the SensorLogic platform is relevant as an orchestrator. Based on
the application of the device translators, the device on-boarding is considered easier,
which reduces the set-up process of the devices in the network.
6.5.5 IoT Application Development
The SensorLogic platform promises a system that can be used for the rapid develop-
ment of novel applications for IoTs use cases. This system involves (but is not lim-
ited to) a wide range of web services for the purpose of administration, authentication,
alarm notication, and location identication. Moreover, it assures a pre-integration
of a third-party service such as connectivity and geo-fencing resolutions. The pre-
developed building blocks are used by IoTs developers to enhance the formulation
of new use cases, in addition to the present blocks. Developer kits, such as the
Cinterion Concept Board, and application platforms, such as Java, are relevant for
prototyping and designing. For the client, this represents a quicker timeframe of
deployment and ROI for the purpose of IoTs application.
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6.6 Case Study
6.6.1 Waste Management
M1 Limited (M1), one of Singapore’s leading full-service communications provid-
ers, is working with OTTO Waste Systems Singapore Pte. Ltd. (OTTO), to provide
a litter bin management system to the National Environment Agency (NEA). The
new system is designed to enable the NEA to leverage technology to better manage
the deployment of litter bins, as well as to optimize cleaning resources. The NEA
has been exploring how data can be used to enhance the effectiveness and efciency
of public cleaning. The new system utilizes IoT sensors tted within litter bins to
track how full they are, so that the cleaning crew can be notied when they need to
empty these litter bins. The NEA can also monitor the usage of litter bins to gauge
if there are adequate bins in a particular area to serve the public. OTTO aims to
deploy up to 500 of these smart bins during the rst quarter of 2019. M1 says the
reliable and secure city-wide coverage provided by its NB-IoT, together with its
support for industry standards, makes the technology well suited for large-scale
smart city applications, such as the proposed litter bin management system. Smart
city solutions can also benet from NB-IoT’s power efciency, which makes it via-
ble to use batteries in connected devices, thus reducing infrastructure and mainte-
nance costs. M1 developed the litter bin monitoring solution together with OTTO,
who supplies the litter bin receptacles, and SmartCity, who provides the centralized
management system. “The collaboration with M1, using their NB-IoT network for
smart waste management, allows our customers to enjoy easy access to useful real-
time data for smarter planning and resourcing on waste management nationwide,”
says Christopher Lopez, Managing Director of OTTO Waste Systems. “We also see
the potential of such implementations to help consumers have a greener environ-
ment to live in.” “Extensive research and development were carried out to produce
the hardware and the methodology of installation to maximise the accuracy of the
measurement in waste level and pollution in the environment,” adds P.Renganathan,
Director of SmartCity. “Through the strategic cooperation with M1, we will help
companies to achieve greater cost savings and reach higher productivity.”
6.6.2 Smart Cold Chain– Tracking Temperature
During Transit
In Thailand, mobile operator AIS has developed a mobile IoT-based solution for
monitoring the temperature of perishable goods during transportation as depicted in
Fig.6.3. Fresh food, frozen food, medicine, and some other goods need to be kept
at a constant temperature during distribution to ensure that they don’t decay and that
they reach end customers in a pristine condition. Connected “cold chain” solutions
can be used to monitor the temperature of a cold storage container during transit and
maintain the quality of goods, reducing the number of claims from customers that
goods have decayed or been damaged during transportation.
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These solutions can be congured to send a notication to the supplier if the
temperature rises beyond a specic threshold. To meet the demand for a low-cost
solution that can be installed easily without impacting logistics companies’ existing
systems, AIS is using NB-IoT to connect on-board thermometers to its IoT plat-
form, which can be used to record, analyze, and display the resulting temperature
data. AIS says the compact battery-powered thermometer is cost-effective enough
to be deployed at scale, while its small size and independent power supply mean it
can be quickly and easily installed or moved to another location. The device can
measure the temperature between −50 and 20 degree Celsius. It can be congured
to transfer temperature data to AIS IoT platform every x interval such as every
3min and alert when the temperature changes by more than x degree Celsius such
as one degree Celsius. AIS says it is also using NB-IoT to monitor the electrical
supply of the cooling system, allowing it to ensure there is sufcient power to cool
the goods being transported. If the power supply is not working properly, the system
is designed to relay the relevant data to the AIS IoT platform, thereby allowing the
logistics company to proactively resolve the issues before any serious damage is
caused. “By installing the temperature-measuring devices in cold chain logistic sys-
tems, the quality of perishable goods can be assured– Mobile IoT connectivity can
be used to notify the operating parties when issues arise and take necessary actions
to prevent any damage to the goods,” explains Asnee Wipatawate, Head of Enterprise
Product and International Service of AIS. “The quality of IoT solutions becomes
critical to mitigate this problem and therefore yield competitive advantages.”
6.6.3 Smart Security
The remote access management and monitoring of high-value, distributed infra-
structure assets is a historically difcult, labor-intensive, disconnected, and non-
scaling burden. Singtel’s vision of a frictionless, smart, highly scalable perimeter
access control solution begins today with the planned commercial launch of
igloohome’s connected digital lock system as shown in Fig. 6.4. Singtel and
Fig. 6.3 Smart cold chain model
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igloohome (a Singtel-funded start-up) are excited to announce the upcoming com-
mercial launch of their connected perimeter access solution, based on igloohome’s
connected lock technology.
The resolutions assure an actual-time management, scalable remote control, and
monitoring of the distributed infrastructure perimeter accessibility. igloohome
works with leading property developers throughout Asia, including but not limited
to Sansiri (Thailand), Capitaland (Vietnam), and Mitsubishi (Japan). Matthew Ng,
VP of Product of igloohome says: “We have adopted carrier-grade IoT network
technologies like LTE-M and NB-IoT as they are increasingly prevalent among
global operator IoT solutions deployments. In Singapore, we rely on Singtel’s
NB-IoT cellular network because of its wide coverage and high availability. These
high-quality public IoT networks give us faster time-to-market, and obviate the
need to deploy our own private network, or implement discrete connectivity hubs/
gateways. LTE-M and NB-IoT are very power efcient, making our battery- powered
smart locks more appealing to end users.” igloohome is a worldwide partner of
Airbnb, Booking.com, and Agoda, works with over 50 distributors, and ships to
more than 90 countries. A global operator-deployed standard like NB-IoT is an
essential element in support of igloohome’s global business.
Rahul Mehta, IoT Product Lead of igloohome comments: “We have extensively
tested our NB-IoT locks across many countries in Asia, and they have performed
well among all deployment scenarios – from deep indoor, to outdoor, and even
remote locations. We are excited to meet the global market demand for our con-
nected smart locks solution, a task that’s simplied by a global IoT network stan-
dard like NB-IoT.” igloohome rst proved its technology in the vacation rental
space, partnering with Airbnb to simplify host-controlled guest access without the
need for a physical key exchange. The solution further enhanced host peace of mind
with on-demand, detailed visibility of guest-specic room, site, or location access.
igloohome then broadened its offering to address the needs of different categories of
home and property owners, addressing the operational and security limitations of
physical keys and enabling use cases like time-sensitive, remote-monitored, and
controlled access for delivery and trade services and access expiration for former
tenants.
Fig. 6.4 Smart
security lock
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6.7 Summary
Enterprise architecture is an important methodology for improving and cultivating
the business value of IT.The emerging capabilities of the IoT are valuable process-
ing of data and analytical contributions to new enterprise services and processes.
Consequently, the business value of the IoT can be realized and validated when an
EA framework is applied to IoT strategy and projects. When an EA framework for
IoT is constructed, it is not isolated from non-IoT components. Architectures should
integrate non-IoT and IoT into a unied blueprint. This framework is composed of
reference models, standards, and principles that encompass the expanded function-
ality of new non-IoT and IoT products and services. Today, new data technologies
and exible platform congurations can be implemented to manage this data. From
a market perspective, user-enabled products are a major growth sector targeted by
many companies. Popular user-enabled products, including smartphones and wear-
ables, are in demand in broad consumer markets. User-enabled products are increas-
ingly powered by the IoT.
References
1. Shroff, G. (n.d.). Enterprise architecture: Role and evolution. Enterprise Cloud Computing,
39–48. https://doi.org/10.1017/cbo9780511778476.006.
2. Kale, V. (2019). Internet of things (IoT) computing. In Digital transformation of enterprise
architecture (pp.413–435). Milton: CRC Press. https://doi.org/10.1201/9781351029148-22.
3. Tambo, T. (2017). Enterprise architecture beyond the enterprise– Extended enterprise archi-
tecture revisited. In Proceedings of the 19th international conference on enterprise informa-
tion systems. https://doi.org/10.5220/0006277103810390.
4. Anandakumar, H., & Umamaheswari, K. (2017, March). Supervised machine learning tech-
niques in cognitive radio networks during cooperative spectrum handovers. Cluster Computing,
20(2), 1505–1515.
5. Anandakumar, H., & Umamaheswari, K. (2018, October). A bio-inspired swarm intelligence
technique for social aware cognitive radio handovers. Computers & Electrical Engineering,
71, 925–937. https://doi.org/10.1016/j.compeleceng.2017.09.016.
6. Arulmurugan, R., & Anandakumar, H. (2018). Early detection of lung cancer using wavelet
feature descriptor and feed forward back propagation neural networks classier. In Lecture
notes in computational vision and biomechanics (pp.103–110). Cham: Springer. https://doi.
org/10.1007/978-3-319-71767-8_9.
7. Haldorai, A. R., & Murugan, S. Social aware cognitive radio networks. In Social net-
work analytics for contemporary business organizations (pp. 188–202). https://doi.
org/10.4018/978-1-5225-5097-6.ch010.
8. Arulmurugan, R., & Anandakumar, H. (2018). Region-based seed point cell segmentation and
detection for biomedical image analysis. International Journal of Biomedical Engineering and
Technology, 27(4), 273.
9. Suganya, M.,& Anandakumar, H.(2013,December). Handover based spectrum allocation in
cognitive radio networks. 2013 International Conference on Green Computing, Communication
and Conservation of Energy (ICGCE). https://doi.org/10.1109/icgce.2013.6823431, https://
doi.org/10.4018/978-1-5225-5246-8.ch012.
6 Enterprise Architecture forIoT: Challenges andBusiness Trends

138
10. Implementation: Developing Enterprise Architecture. (n.d.). From enterprise architecture to IT
governance, 167–194. https://doi.org/10.1007/978-3-8348-9011-5_7.
11. Rana, M. M., & Bo, R. (2019). IoT-based cyber-physical communication architecture:
Challenges and research directions. IET Cyber-Physical Systems: Theory & Applications.
https://doi.org/10.1049/iet-cps.2019.0028.
12. Al-Qaseemi, S.A., Almulhim, H.A., Almulhim, M.F., & Chaudhry, S.R. (2016). IoT archi-
tecture challenges and issues: Lack of standardization. 2016 Future Technologies Conference
(FTC). https://doi.org/10.1109/ftc.2016.7821686.
13. Planning: Creating Enterprise Architecture. (n.d.). From enterprise architecture to IT gover-
nance, 153–166. https://doi.org/10.1007/978-3-8348-9011-5_6.
14. Armour, F., Kaisler, S., & Bitner, J. (2007). Enterprise architecture: Challenges and implemen-
tations. 2007 40th annual Hawaii International Conference on System Sciences (HICSS’07).
https://doi.org/10.1109/hicss.2007.211.
A. Haldorai et al.

139© Springer Nature Switzerland AG 2020
A. Haldorai et al. (eds.), Business Intelligence for Enterprise Internet of Things,
EAI/Springer Innovations in Communication and Computing,
https://doi.org/10.1007/978-3-030-44407-5_7
Chapter 7
Semi-Supervised Machine Learning
Algorithm forPredicting Diabetes Using
Big Data Analytics
SenthilkumarSubramaniyan, R.Regan, ThiyagarajanPerumal,
andK.Venkatachalam
7.1 Introduction
This chapter introduces the concept of Big Data for predicting complications of
diabetes mellitus. The discussion includes descriptive and predictive investigations,
knowledge discovery and information disclosure, cloud processing, Hadoop
MapReduce, and machine learning strategies.
7.1.1 Machine Learning andKnowledge Discovery
1. Big Data
Big Data uses an assortment of components: social systems administration,
mobile computing, data analytics, and the cloud, which are collectively known as
SMAC. Modern research areas require huge amounts of information that are dif-
cult to store and process quickly in a reasonable amount of time using customary
approaches. Thus, such information requires an innovative approach, such as Big
Data [1]: “Big data alludes to the instruments, procedures, and strategies enabling
an association to make, control, and oversee gigantic informational indexes and
storage facilities” [2].
S. Subramaniyan · T. Perumal
Dept of CSE, University College of Engineering, Pattukkottai, Rajamadam, India
R. Regan
Computer Science and Engineering, University College of Engineering, Villupuram, India
K. Venkatachalam (*)
School of Computer Science and Engineering, IT Bhopal University, Bhopal, India

140
2. Big Data in Healthcare
The rapid growth of information in healthcare applications is related to the digi-
tization of patient information, installed frameworks in social insurance, and the
need for accessible and efcient mobile systems. Breaking down and deciphering
the enormous and complex datasets in healthcare is a challenge for specialists and
is known as a Big Data issue.
Big Data management is a signicant job in healthcare. To a great extent, Big
Data analytics enables patients Well-Being of mind, framework and legitimate man-
agement. Big Data methodology considers issues related to the 4Vs: volume, veloc-
ity, variety, and veracity.
3. Diabetes Mellitus
Diabetes mellitus (DM) is a metabolic issue with abnormal insulin regulation.
Insulin insufciency results in higher blood glucose levels and impaired absorption
of sugar, fat, and proteins.
DM is a common endocrine issue, affecting approximately 200million individu-
als worldwide. The incidence of DM is expected to increase drastically in the com-
ing years. DM can be generally classied as one of two types: type 1 diabetes (T1D)
and type 2 diabetes (T2D), based on etiology. T2D is the most common type, chiey
characterized by insulin resistance. Fundamental causes of T2D include lifestyle,
physical activity, diet, and genetics. T1D is believed to result from the autoimmuno-
logical devastation of Langerhans islets facilitating pancreatic-β cells. T1D accounts
for 10% of diabetes cases around the world. Other types of diabetes can be charac-
terized by their insulin emission proles and include gestational diabetes, endocri-
nopathies, maturity onset diabetes of the young (MODY), neonatal diabetes, and
mitochondrial diabetes. The main complications of DM include polyurea, polydip-
sia, and critical weight loss. Diagnosis relies upon blood glucose levels (fasting
plasma glucose=7.0mmol/L).
7.2 Related Work
DM has been unequivocally associated with a few complications, mostly because of
the constant hyperglycemia. DM has a broad scope of different pathophysiologies.
Many well-known complications have been identied, including vascular issues,
diabetic nephropathy, retinopathy, neuropathy, diabetic coma, and cardiovascular
disease. Because of the great morbidity and mortality associated with DM, preven-
tion and management have attracted considerable attention. Insulin is a fundamental
treatment for T1D, but is only given in specic instances of T2D, such as when
hyperglycemia cannot be controlled through diet, weight loss, exercise, and oral
medication. The targets of medication aim to
(a) Spare a patient’s life and mitigate complications.
(b) Prevent complications and thus extend life span.
S. Subramaniyan et al.

141
The most widely recognized anti-diabetic medications include sulfonylurea,
metformin, alpha-glucosidase inhibitors, peptide analogs, and non-sulfonylurea
secretagogues. Most of these medications have various side effects. Furthermore,
insulin treatment is associated with weight gain and hypoglycemic episodes. Hence,
antidiabetic medications and treatments are of extraordinary importance and simul-
taneously a clincial challenge.
1. Big Data Analytics
An immense amount of information is created each day by specialists, treatment
plans, medical reports, imaging, and body sensor devices, such as IoT gadgets,
which result in Big Data. Creating Big Data is certainly not a signicant challenge
for healthcare experts, but obtaining helpful information from Big Data is.
Investigations are required to contribute Big Data to medical services. Accessible
medical information in a Big Data investigation enable health care providers to
provide efcient results, from which patients can be prescribed the best medications
or treatments. Currently, Cloud-based Big Data analytic environment is needed to
analyze semi structured, structured and unstructured Big Data from healthcare sector.
2. Descriptive/Predictive Analytics
Insightful investigations are fundamental to predict disease in a patient based on
the patient’s health parameters. Using machine learning, such as support vector
machine (SVM) calculations with data frameworks such as Hadoop, it is conceiv-
able to execute a huge volume of data sets effectively and quickly to predict the
likelihood of diabetes based on a patient’s lifestyle. Along these lines, a Hadoop
distribution in a conveyed condition is the most recent innovation.
3. Hadoop MapReduce
A lot of processing capacity is needed to separate helpful information in Big
Data. Distribution and/or circulation enable us to take quick calculations by utiliz-
ing the numerous CPUs or CPU centers to run a few calculations simultaneously.
Although parallel and disseminated registering are not identical, they ll a similar
principal need of separating huge problems into smaller ones that can be analyzed
simultaneously. In distribution or parallelization, parameters such as execution
speed, memory, and simultaneousness are signicant elements. The memory limit
of a few machines can be used for distribution and/or parallel or circulated handling;
however, this can be overcome by using a single device to process the enormous
data collections.
4. Machine Learning Techniques
In computer science, articial intelligence (AI), sometimes called machine intel-
ligence, is intelligence demonstrated by machines, in contrast to the natural intelli-
gence displayed by humans and animals. Machine learning technique takes over the
activity involving automated learning from data set. It initially learns the knowledge
and applies this knowledge to distribute for predictions. A wide area of articial
intelligence enabled devices are implemented through information.
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142
The general meaning of machine learning was denoted by Mitchel:
A computer program is said to learn from experience Ex with respect to some class of tasks
Ta and performance measure Pe, if its performance at tasks in Ta, as measured by Pe,
improves with experience Ex [3].
Knowledge discovery in databases (KDD) is an area associating theories, tools, and
techniques to create data and mine useful patterns or knowledge. The many steps of
KDD (gathering, feature selection, transformation, knowledge discovery, pattern
evaluation and creation) are shown in Fig.7.1. A full overview of KDD was pro-
vided by Fayyad etal., who dened it as follows:
KDD is the nontrivial process identifying valid, novel, potentially useful, and ultimately
understandable patterns in data.
Types of Machine Learning Tasks
Machine learning [4] tasks can be categorized into three main types, as shown in
Fig.7.2:
1. Supervised learning– The system can learn the data by labeling 30% as train-
ing data,
2. Unsupervised learning– The system can adapt by unlabeling the data
3. Reinforcement learning – The system can communicate with static/dynamic
environments.
1. Supervised Learning
In supervised learning, a framework can “learn” things inductively with a capac-
ity known as target work, which is represented as an outow of an output model
depicting the data and information. A target work can be utilized to estimate an
input variable, also known as a subordinate instance/variable or a yield instance/
variable, depicted from autonomous factors, information factors/attributes, or char-
acteristics. The arrangement for conceivable information estimations of the
Selection Preprocessing Transformation Data Mining Interpretation /
Selected Preprocessed Data Transformed
Patterns
Data
Data Subset
Knowledge
Evaluation
Data
Fig. 7.1 The general ow of a KDD process
S. Subramaniyan et al.

143
capacity, such as its area, are known as occurrences. Each case can be portrayed
with many of the qualities (traits/characteristics). The subset of everything being
equal; in which, it provides each and every variable plays an important role. The
implementation of data was denoted as a models. Therefore, to derive an objective
capacity, learning framework, or preparation subset, theory/elective capacities and
known theories, signied by a hypothesis, must be considered. Supervised learning
consists of two types of learning methods:
• Grouping
• Relapse
Arrangement techniques attempt to anticipate particular methods, such as blood
collections, whereas relapse techniques anticipate numerical qualities. The most
commonly used supervised systems are decision trees (DT), rule learning, and
instance-based learning (IBL), such as k-nearest neighbors (k-NN), genetic algo-
rithms (GA), articial neural networks (ANN), and support vector machines (SVM).
2. Unsupervised Learning
In unsupervised learning, the machine is used to provide the hidden variable and
structure of the input data/associations among the different variables. Therefore, the
training data consists of variables without the target labels.
7.3 Association Rule Learning
Association rule mining denotes an interrelationship between data items that is used
in many techniques. It is receiving more attention in research on databases [5, 6].
This approach was proposed in the 1990s by Rakesh Agrawal in a market bin
Fig. 7.2 Machine learning tasks
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144
examination. In this shopping/marketing basket model, association rules were rep-
resented as the structure of {A1, …, An}→B, which implies that when you nd all
of A1, …, An in a truck, then it is conceivable to also nd B.The Apriori algorithm
was proposed in 1994 by Rakesh Agrawal.
Association rule mining is mainly used for market bin investigations, including
bioinformatics. Another application in science and bioinformatics incorporates an
organic succession investigation, the examination of high-quality information, and
others. A careful survey of nding regular examples and association rules originates
from organic information, calculations and unsupervised applications.
7.3.1 Clustering
Clusters are educational examples that occur through grouping, such as the partition
of an entire dataset into groups of information [7]. The goal is to gather similar
examples for comparison under a particular circumstance.
3. Reinforcement Learning
Reinforcement learning is a type of machine learning used to group methods
where the framework covers to learn and act through direct contact with nature for
enhancing the general ides for execution. Reinforcement learning is predominantly
applicable to self-ruling frameworks, because of the freedom to connect its condi-
tions [8].
7.4 Diabetic Mellitus andMachine Learning
The next section describes the signicant processes for DM [9].
7.4.1 Biomarker Identication andPrediction ofDM
An enormous number of components are regarded as signicant in DM.Obesity is
considered to be signicant hazard impact, particularly in T2D, providing a very
strong connection among the types of DM.The diagnosis of DM can be aided by a
few tests. In the early stages, the ndings in both T1D and T2D can affect the
following:
(a) Slowing progression of the disease.
(b) Directed choice of medication.
(c) Preventing future complications.
(d) Identifying related problems.
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Biomarkers (BM) (e.g., organic atoms) are quantiable markers of a specic
condition associated with healthy and unhealthy states. Normally, BMs are described
as follows.
(a) Found in bodily uids (blood, saliva, or urine).
(b) Experienced data and information was decided based on the autonomous etio-
pathogenic robotic pathway.
(c) Used to screen for disease status and response to medical treatment.
Biomarkers will be the immediate conclusion of the use of infection itself with
different inconveniences. Current advances, including metabolomics, proteomics,
and heredity, will improve the use of BMs. In cases of DM, BMs can indicate hyper-
glycemia and other relevant issues in diabetes.
The following sections discuss diagnostic and predictive markers, as well as dis-
ease prediction.
7.4.2 Diagnostic andPredictive Markers
The primary class manages biomarker disclosure, which is a task for the most part
that performed through component choice methods. Following an element determi-
nation step, an order calculation is used to evaluate the expected precision of the
chosen characteristics [10–16].
Primarily, setup techniques are utilized in biomarker assessment. A medical
dataset containing 803 prediabetic females with 55 characteristics was used for a
few basic element determination calculations to predict DcMs. It presumed that the
better presentation that has been accomplished with wrapper strategies. In addition,
among the channel strategies utilized, balanced vulnerability showed the best out-
comes. It consolidated electromagnetic (E-M) calculations of the closest classier
and an inverse sign test (IST). As a totally unique methodology to manage charac-
teristics in a medical dataset for diabetes, cluster-based (progressive grouping)
includes an extraction system using data on disease symptoms. Their approach
delivered groups to be used as characteristics for disease severity and patient read-
mission hazard forecasts.
7.5 Prediction ofDM
The second class manages disease expectation and analysis. Various calculations
and methodologies have been applied, such as conventional AI calculations, outt
learning methods, and association rule methods, to accomplish the order exactness.
In addition to the previously mentioned technique, Calisir and Dogantekin imple-
mented LDA–MWSVM, a framework used to diagnosis diabetes. This framework
7 Semi-Supervised Machine Learning Algorithm for Predicting Diabetes Using Big…

146
includes feature extraction techniques, such as Linear Discriminant Analysis (LDA),
followed by a Morlet Wavelet Support Vector Machine (MWSVM) classier.
High-dimensional datasets may be used. For example, one dataset consisted of
4.1 million people and 42,000 factors from regulatory cases, pharmacy records,
social insurance use, and laboratory results for the years from 2005 to 2009. This
was used to assemble prescient models (in light of calculated relapses) for various
onsets of T2D. Various learning calculations appear to be viable methods for
improving arrangement exactness. The methodologies have additionally been used
in DM prediction.
7.5.1 Diabetic Complications
Complications from elevated glucose levels are an extraordinary eld. The negative
effects of hyperglycemia can be classied as follows:
(a) Macrovascular complications, such as coronary supply route infection, fringe
blood vessel malady, and stroke.
(b) Microvascular complications, including diabetic neuropathy, nephropathy, and
retinopathy.
Hyperglycemia is considered to be a fundamental contributor to morbidity and
mortality in T1D and T2D.Many studies have demonstrated an association between
glycemia and diabetic microvascular complications in both T1D and T2D.Diabetic
complications can likewise be grouped by their severity and onset.
Additionally, both insulin resistance and hyperglycemia are involved in the
pathogenesis of diabetic dyslipidemia. This is signicant because DM affects indi-
viduals with well-controlled blood glucose levels. Many of these instances have
been considered in AI and information mining applications [17–24].
Huang etal. [19] used a decision tree-based expectation device that joined both
genetic and clinical characteristics to identify diabetic nephropathy in individuals
with T2D.Leung etal. [20] examined a few AI strategies that incorporated halfway
least squares relapse, characterization and relapse trees, the C5.0 Decision Tree,
random forest, innocent Bayes, neural systems, and bolster vector machines. The
dataset was composed of both genetic (single nucleotide polymorphisms) and clini-
cal information. Age, time of diagnosis, systolic blood pressure, and hereditary
polymorphisms of uteroglobin and lipid digestion emerged as the most useful
indicators.
DuBrava etal. [21] used random forest to select specic characteristics for pre-
dicting diabetic peripheral neuropathy. In order of importance, the characteristics
selected were the Charlson Comorbidity Index score (100%), age (37.11%), num-
ber of pre-record methodology and administrations (29.701%), number of pre-le
outpatient treatments (24.223%), number of pre-list outpatient visits (18.302%),
number of pre-le research center visits (16.912%), number of pre-list outpatient
ofce visits (12.12%), number of inpatient treatments (5.913%), and number of
S. Subramaniyan et al.

147
torment related medicine remedies (4.404%). The general precision of the model
arrived at 89.01%. The Diabetes Preprocessing Research Activity (DiScRi) data-
base was used to predict cardiovascular autonomic neuropathy (CAN).
Staniery etal., [22] used choice trees and ideal choice way discoverer (ODPF) to
locate the ideal succession of tests to diagnose CAN.Abawajy etal. used relapse
and meta-relapse, in combination with the Ewing equation, to distinguish the
classesofn CAN, while conquering the issue of missing information. Cardiac abnor-
malities are common diabetic complications. They are considered to be a critical
connection between diabetes, coronary disease, and stroke.
Hypoglycemia (low glucose levels) mainly occurs from anti-diabetic medica-
tions and has an extraordinary effect on individuals with DM.AI strategies such as
random forest, bolster vector machines (SVM), k-closest neighbor, and innocent
Bayes were used by Wang etal., [24] to predict hypoglycemia in individuals with
T2D. Bolster vector relapse was used by Georga etal. for a similar investigation.
Furthermore, effectively distributed calculations were used by Jensen in a simi-
lar study.
Diabetic retinopathy (DR) is an eye disorder that occurs in individuals with T1D
or T2D.The longer a patient has diabetes, the higher is the risk of acquiring this
particular pathophysiological condition. D_R is a rule shows early warning sign &
it is portrayed with signicant diabetic confusion. DR has two primary stages: non-
proliferative (NPDR) and proliferative (PDR) Information mining and AI have been
used to predict DR, principally by imaging. A comprehensive review of computa-
tional strategies for DR was published in 2013. A two-stage technique, diabetic
fundus image recuperation (DFIR), has been used to predict DR. The rst stage
includes an analysis of advanced retinal fundus images. The subsequent stage uses
a help vector machine for the forecast. This resulted in an approach to determine a
patient’s need for referral based on evidence of DR-related injuries on retinal
imaging.
7.5.2 Classication
For semi-supervised learning strategies, the objective is to create a model that
results in one of a variety of potential classes using named and unlabeled datasets.
The easiest class of semi-supervised learning methods trains one model using one
learning calculation with many characteristics. For example, self-preparing rst
trains a model on the marked models. The model is then applied to all unlabeled
information, whereby the models are positioned by the condence in their predic-
tions. The most certain predictions are then included in the named models. This
procedure repeats until all unlabeled models are marked.
Another class prepares different classiers by inspecting the preparation infor-
mation several times and preparing a model for each example. For example, tri-
preparing rst trains three models on the marked models using bagging for the outt
learning calculation. At that point, each model is refreshed iteratively, whereby the
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148
other two models make predictions on the unlabeled models. Only models with
similar forecasts are used to with the rst named guides to re-train the model. The
cycle stops when no model changes. Finally, the unlabeled models are marked uti-
lizing lion’s share casting a ballot in which at any rate two models must concur with
one another.
7.6 Summary
This chapter focused on predictive analytics with machine learning to analyze Big
Data in order to predict future complications in diabetic patients. A dataset of
4.101million people and 42,000 factors from case management, pharmacy records,
medical records, and laboratory results between 2005 and 2009 was used to create
prescient models (to calculate relapse) for various onsets of T2D.The most certain
predictions were then included in the marked models. This procedure repeated until
all unlabeled models were named. The following class prepared different classiers
by inspecting the preparation information several times and preparing a model for
each example. At that point, each model was refreshed iteratively, whereby the other
two models made predictions on the unlabeled models; only the models with similar
forecasts were used with the rst named guides to re-train the model. The cycle
stopped when no model changes. Finally, the unlabeled models were named utiliz-
ing dominant part casting a ballot in which at any rate two models must concur with
one another.
References
1. American Diabetes Association. (2009). Diagnosis and classication of diabetes mellitus.
Diabetes Care, 32(Suppl. 1), S62–S67.
2. Bagherzadeh, F., Khiabani, A., Ramezankhani, F., Azizi, F., Hadaegh, E.W., & Steyerberg,
D. (2016, March). A tutorial on variable selection for medical prediction models: Feature
selection methods in data mining could improve the results. Journal of Clinical Epidemiology,
71, 76–85. https://doi.org/10.1016/j.jclinepi.2015.10.002.
3. Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules in large
databases. In Proceedings of the 20th international conference on very large databases
(pp.478–499).
4. Cai, L., Wu, L., Li, D., Zhou, K., & Zou, F. (2015). Type 2 diabetes biomarkers of human
gut microbiota selected via iterative sure independent screening method. PLoS One, 10(10),
e0140827. https://doi.org/10.1371/journal.pone.0140827.
5. Haldorai, A. R., & Murugan, S. Social aware cognitive radio networks. In Social net-
work analytics for contemporary business organizations (pp. 188–202). https://doi.
org/10.4018/978-1-5225-5097-6.ch010
6. Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge dis-
covery in databases. AI Magazine, 17, 37–54.
7. Abawajy, J., Kelarev, A., Chowdhury, M., Stranieri, A., & Jelinek, H.F. (2013). Predicting car-
diac autonomic neuropathy category for diabetic data with missing values. Computers in Biology
and Medicine, 43(10), 1328–1333. https://doi.org/10.1016/j.compbiomed.2013.07.002.
S. Subramaniyan et al.

149
8. Agrawal, R., Imielinski, T., & Swami, A. (1993). Mining association rules between sets of
items in large databases. In Proceedings of the ACM SIGMOD conference on management of
data (pp.207–216).
9. Georga, E.I., Protopappas, V.C., Polyzos, D., & Fotiadis, D.I. (2015). Evaluation of short-
term predictors of glucose concentration in type 1 diabetes combining feature ranking with
regression models. Medical & Biological Engineering & Computing, 53(12), 1305–1318.
https://doi.org/10.1007/s11517–015–1263-1.
10. Anandakumar, H., & Umamaheswari, K. (2017, September). An efcient optimized handover
in cognitive radio networks using cooperative spectrum sensing. Intelligent Automation & Soft
Computing, 1–8. https://doi.org/10.1080/10798587.2017.1364931.
11. Huang, G.-M., Huang, T.-Y., & Lee, J. (2015). An interpretable rule-based diagnostic clas-
sication of diabetic nephropathy among type 2 diabetes patients. BMC Bioinformatics,
16(S-1), S5.
12. Jelinek, H.F., Stranieri, A., Yatsko, A., & Venkatraman, S. (2016). Data analytics identify gly-
cated haemoglobin co-markers for type 2 diabetes mellitus diagnosis. Computers in Biology
and Medicine, 75, 90–97. https://doi.org/10.1016/j.compbiomed.2016.05.005.
13. Lagani, V., Chiarugi, F., Thomson, S., Fursse, J., Lakasing, E., Jones, R.W., et al. (2015,
May–June). Development and validation of risk assessment models for diabetes-related com-
plications based on the DCCT/EDIC data. Journal of Diabetes and its Complications, 29(4),
479–487. https://doi.org/10.1016/j.jdiacomp.2015.03.001.
14. Lagani, V., Chiarugi, F., Manousos, D., Verma, V., Fursse, J., Marias, K., etal. (2015, July).
Realization of a service for the long-term risk assessment of diabetes-related complica-
tions. Journal of Diabetes and its Complications, 29(5), 691–698. https://doi.org/10.1016/j.
jdiacomp.2015.03.011.
15. Lee, B.J., & Kim, J.Y. (2016). Identication of type 2 diabetes risk factors using phenotypes
consisting of anthropometry and triglycerides based on machine learning. IEEE Journal of
Biomedical and Health Informatics, 20(1), 39–46. https://doi.org/10.1109/JBHI.2015.2396520.
16. Leung, R.K., Wang, Y., Ma, R.C., Luk, A.O., Lam, V., Ng, M., etal. (2013). Using a multi-
staged strategy based on machine learning and mathematical modeling to predict genotype–
phenotype risk patterns in diabetic kidney disease: A prospective case–control cohort analysis.
BMC Nephrology, 14, 162. https://doi.org/10.1186/1471-2369-14-162.
17. Anandakumar, H., & Umamaheswari, K. (2017, March). Supervised machine learning tech-
niques in cognitive radio networks during cooperative spectrum handovers. Cluster Computing,
20(2), 1505–1515.
18. Marling, C.R., Struble, N.W., Bunescu, R.C., Shubrook, J.H., & Schwartz, F.L. (2013). A
consensus perceived glycemic variability metric. Journal of Diabetes Science and Technology,
7(4), 871–879.
19. Marx, V. (2013, June 13). Biology: The big challenges of big data. Nature, 498(7453),
255–260. https://doi.org/10.1038/498255a.
20. Mitchell, T. (1997). Machine learning (p.2). Singapore: McGraw Hill. ISBN:0-07-042807-7.
21. Haldorai, A. R., & Murugan, S. Social aware cognitive radio networks. In Social net-
work analytics for contemporary business organizations (pp. 188–202). https://doi.
org/10.4018/978-1-5225-5097-6.ch010.
22. Sacchi, L., Dagliati, A., Segagni, D., Leporati, P., Chiovato, L., & Bellazzi, R. (2015). Improving
risk-stratication of diabetes complications using temporal data mining. In Conference pro-
ceedings– IEEE engineering in medicine and biology society (pp.2131–2134). https://doi.
org/10.1109/EMBC.2015.7318810.
23. Stranieri, A., Abawajy, J., Kelarev, A., Huda, S., Chowdhury, M., & Jelinek, H.F. (2013). An
approach for Ewing test selection to support the clinical assessment of cardiac autonomic
neuropathy. Articial Intelligence in Medicine, 58(3), 185–193. https://doi.org/10.1016/j.
artmed.2013.04.007.
24. Wang, K.J., Adrian, A.M., Chen, K.H., & Wang, K.M. (2015). An improved electromagnetism-
like mechanism algorithm and its application to the prediction of diabetes mellitus. Journal of
Biomedical Informatics, 54, 220–229. https://doi.org/10.1016/j.jbi.2015.02.001.
7 Semi-Supervised Machine Learning Algorithm for Predicting Diabetes Using Big…

151© Springer Nature Switzerland AG 2020
A. Haldorai etal. (eds.), Business Intelligence for Enterprise Internet of Things,
EAI/Springer Innovations in Communication and Computing,
https://doi.org/10.1007/978-3-030-44407-5_8
Chapter 8
On-the-Go Network Establishment ofIoT
Devices toMeet theNeed ofProcessing Big
Data Using Machine Learning Algorithms
S.Sountharrajan, E.Suganya, M.Karthiga, S.S.Nandhini,
B.Vishnupriya, andB.Sathiskumar
8.1 Introduction
With the advancement of technologies, a lot of devices are coupled together, gener-
ating data, and that data is very much bigger and complex. This huge volume of data
is termed as “big data.” Conventional method of data processing cannot manage
such huge voluminous data. A lot of business-related queries are answered using
this massive collection of data sets. So, this big data has to be processed in some
manner to solve our business problems. Recent developments in open-source plat-
form, namely, Spark and Hadoop, paved a way to handle and store this big data in a
cheaper and more efcient way. Machines apart from humans are solely responsible
in the generation of this massive data which is increasing day by day. Also with the
evolution of Internet of Things (IoT) and machine learning, the generation of mas-
sive data has skyrocketed. With the growth of big data increasing day by day, the
usefulness of this data is just underpinning. Cloud computing with its scalability
and elasticity expands the usefulness of big data and provides a path for the devel-
opers to build ad hoc clusters for small data subsets and to test it.
S. Sountharrajan (*)
VIT Bhopal University, Bhopal, India
e-mail: s.sountharrajans@vitbhopal.ac.in
E. Suganya
Anna University, Chennai, India
M. Karthiga · S. S. Nandhini · B. Vishnupriya
Bannari Amman Institute of Technology, Sathyamangalam, India
e-mail: karthigam@bitsathy.ac.in; nandhiniss@bitsathy.ac.in; vishnupriya@bitsathy.ac.in
B. Sathiskumar
VIT University, Chennai, India
e-mail: sathiskumar.b@vit.ac.in

152
8.2 Business Use Cases ofBig Data
The massive big data helps in addressing a wide range of business actions from
customer feedback to good analytics. Some of the use cases are listed below.
8.2.1 New Product Establishment
Top companies use big data to understand the needs of their customers. By analyz-
ing the past and latest services, key attributes are classied and predictive models
are created by understanding the relationship between these previous and current
attributes which in turn builds new products/services. Any plan to produce and
launch new products is made after specic analytics of the data from focused
groups, media, markets, and stores.
8.2.2 Predictions About Mechanical Failures
By analyzing structured and unstructured data hidden in organizations such as
equipment manufactured year, model, entries of the log, errors, and engine and
room temperature, mechanical failure of the machines can be predicted earlier
before a problem breaks out. This prediction helps the company to reduce the
amount spent for maintenance and to uplift their income.
8.2.3 Experience oftheCustomers
The experience of the customers can be known well than before with the availability
of big data. With the help of social media, log les, web surng, and other data, big
data improves the customer experience knowledge and the delivery value. It helps
in handling the issues positively and thereby diminishes the customer’s stress.
8.2.4 Security Breaches
Security has become a big landscape nowadays. Complaints and security issues are
growing constantly. Data patterns related to fraudulence are easily identied from
big data and the valuable information is alienated to induce fast reporting.
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8.3 Handing Huge Data
Each day 2.5billion gigabytes of data are generated with an increase in complex
patterns to interpret. To process all those, we need an intelligent system. For exam-
ple, data from social media and sensory information collected by satellite in space
need to be recorded for analysis. The following primitive systems can be utilized to
break the enormous amount of data, which takes more time for human analysis [15].
Intelligent learning– is a journey, which indicates maturity level and capability of
automation of the business process to improve efciency and reduce operational
cost. The system can do, think, learn, and adapt which in turn lead to articial
intelligence.
Articial intelligence– is where a machine performs the task that requires human
judgment, learning, or problem-solving skills using techniques like NLP, speech
recognition, machine learning, and many more.
Machine learning– is a technique to implement articial intelligence, which uses
algorithms to understand patterns in a huge amount of data.
Deep learning– is a technique of learning complicated patterns in a huge amount of
data using different types of neural networking concepts.
Cognitive learning– is a combination of all these techniques to automate a task that
involves unstructured content decision-making or any complex challenge.
Among these buzzing techniques, the widely used techniques are machine learn-
ing and cognitive computing or learning.
8.4 Cognitive Learning (CL)
Efcient steering through a ood of unstructured information requires a new era of
computing called cognitive computing. Before understanding cognitive learning,
one should rst know the meaning of cognition, which means context and reasoning
form the basis of the cognition system, which mimics humans’ reason and the pro-
cess through which they analyze information and draw an inference. Cognitive
learning plays a vital role in fetching data from the rational business aspects and
implements the solution in an efcient way [1]. CL is used in the eld of analyzing
social media data and is the perfect assistant for treating a serious medical condition
as it speeds up interaction and decision-making accordingly. CL can merge data
from various sourcesofinformation upon balancing context and conicting evi-
dence to suggest the best possible answers. To process the above-said data, cogni-
tive learning includes some learning technologies that use data mining, pattern
recognition, and NLP to mimic the way the human brain works, and these were used
to achieve the learning systems. To enact the above-said technologies, cognitive
learning uses its adjacent computing technique called machine learning.
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Machine learning algorithms are used to perform typical tasks and solve differ-
ent types of problems, which require a vast amount of structured and unstructured
data [2]. A rened way of identifying patterns and processing data leads the cogni-
tive learning system to move to the next level of anticipating new problems and
modeling possible solutions. To attain all these capabilities, it must have the follow-
ing ve key attributes/features:
8.5 Machine Learning
Machine learning is a data-driven approach with a class of self-learning algorithms.
In traditional programming, the data is given as an input and the output is predicted.
In the machine learning approach, the data is provided and output is predicted by the
machines; in turn, machines learn from the data, nd the hidden insights, and create
a model. Examples of machine learning in the industry are found in Google Maps
and Facebook – face recognition, virtual personal assistant, speech recognition,
natural language processing, and so on. The objectives of machine learning include
greater accuracy, greater coverage of problems, greater economy in obtaining solu-
tions, and greater simplicity of knowledge representation. Machine learning, like
human learning, has two aspects: a behavioral component and knowledge acquisi-
tion component [5].
The following are the features of the machine learning technique:
1. It adjusts the program action making use of the data to detect patterns in a data
set.
2. It provides an efcient way of implementing complex programs.
3. It uses iterative algorithms to explicitly nd the hidden perspectives.
4. It automates analytical model building.
A typical machine learning life cycle has six steps:
1. Collecting data– relevant data is collected from various sources.
2. Data wrangling– it is the process of cleaning and converting data into a specic
format.
3. Analyzing data – it is the process of selecting and ltering the required data
using machine learning algorithms.
4. Train and test algorithm– use the appropriate algorithm.
5. Prediction– a new set of input is given during testing and the model will classify
the input according to the training data set.
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8.6 Cognitive Computing andMachine Learning
Cognitive computing and machine learning can be used to support decision-making,
deliver highly relevant information, and optimize the available attention to avoid
missing key developments. Cognitive machine learning can perform operations
analogous to learning and decision-making in humans [6]. Intelligent personal
assistants can recognize voice commands and queries, respond with information, or
take desired actions quickly, efciently, and effectively.
Implementing cognitive computing can help achieve these desirable results:
• It helps people make better decisions, take action more quickly, and achieve
more successful outcomes.
• It delivers relevant information and advice at the time of need.
• It reduces information overload and optimizes people’s available attention span.
• It allows people to act more efciently and effectively.
• It reduces errors, minimizes loss and damage, and improves health and safety.
Cognitive computing tools such as IBM Watson, articial intelligence tools such
as expert systems, and intelligent personal assistant tools such as Amazon Echo,
Apple Siri, Google Assistant, and Microsoft Cortana can be used to extend the abil-
ity of humans to understand, decide, act, learn, and avoid problems. Using these
approaches, one can enhance the capabilities of humans by augmenting their pow-
ers of observation, analysis, decision-making, processing, and responding to other
people and to routine or challenging situations. Cognitive learning principles are
ways to apply sentient thought to learning activities and in turn control learning
behavior and motivate toward more protable results.
The following are the three basic cognitive principles of learning strategies [3]:
1. Effective learning– rather than responding to stimuli, its focus is on what you
know.
2. Emphasize structure– connect new information and sort the same.
3. Effective learning strategy– validate the information and act accordingly.
8.7 Cognitive-Machine Learning Algorithms
Machine learning deals with articial intelligence algorithms to enable the com-
puter to think and learn as efciently as humans. The main goal of machine learning
algorithm over cognitive computing is to design the process that helps in recogniz-
ing patterns and compose decisions based on input data. Machine learning is applied
for training cognitive networks.
One of the best machine learning techniques is supervised learning which leads
cognitive learning to work efciently [4]. The feature of machine learning algorithm
design depends on the appropriate selection of one or more basic principles, which
are selected based on its learning tasks. The combination of these basic principles is
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156
suitable for learning tasks if only a short time is available for the solution and if an
optimal solution is not necessary, which should be solved by the algorithm. Ordered
version space search with the aid of score function and reduction of the number of
concept versions solves a convoluted learning task with a large number of training
examples that include the following:
8.7.1 Particle Swarm Optimization (PSO)
In 1995, James Kennedy and Russell Eberhart rst described a nondeterministic
optimization technique named particle swarm optimization (PSO), which is a
swarm-intelligence-based approximate technique. In the area of cognitive comput-
ing, optimization techniques can be used to nd the parameters/objectives that pro-
vide the maximum (or minimum) value of a target function for classication
algorithms such as articial neural networks and support vector machines. These
classication algorithms often require the user to dene certain coefcients, which
are to be found by trial and error or exhaustive search [9].
The following are the perceptions of particle swarm optimization:
• The PSO algorithm retains multiple hidden solutions at one time.
• Objective functions determine the tness value at each iteration.
• At the search space, each solution is represented by a particle in the tness.
• The particles “y” or “swarm” through the search space and determine the maxi-
mum value returned by the objective function.
8.7.2 Bayesian Cognitive Learning Method
1. Bayes theorem is a linear classication to analyze feature probabilities and
assumes feature independence. It learns and predicts very fast and it does not
require lots of storage. The prevailing applications of Bayes algorithms are real-
time prediction, multiclass prediction, text classication, recommendation sys-
tem, cognitive systems, and image processing [7]. Cognitive machine learning is
sympathetic in the best hypothesis h from some space H, given observed training
data. Bayesian learning is relevant for two reasons:
• Explicit manipulation of probabilities
• Perspective for understanding learning methodology
2. The following is a feature of Bayesian learning methods:
• Each observed training sample that could incrementally decrease or increase
the estimated probability is correct.
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3. Final probability of a hypothesis is determined from the prior knowledge from
the observed data.
• Weighted probabilities with combined predictions of multiple hypotheses
were used to classify a new instance.
4. Bayes theorem and cognitive learning can be used for designing straightforward
learning algorithms, which is achieved through a brute-force MAP learning
algorithm. A Bayesian algorithm is a way to distinguish the behavior of learning
algorithms. The advantage of using the Bayesian approach is that it uses inde-
pendence assumption, which might not hold true in certain databases that could
contain many interactions between the predictor (independent) variables. It has
been applied to a variety of data mining analyses in a large number of domains.
8.7.3 Hill Climbing-GA (HCA-GA)
5. Hill climbing is a heuristic approach used for numerical optimization problems
in the eld of cognitive learning. The features of hill climbing include (1) vari-
ants of test algorithms as it takes the feedback from the test procedures. It is an
iterative calculation that begins with an arbitrary answer for an issue, and after
those attempts, it improves the answer by making changes. The cognitive engine
has three major functions: optimization, decision-making, and learning.
6. HCA is good for nding a local minimum but not for a global solution [6]. Hill
climbing is coupled with the genetic algorithm to nd a global solution in the
cognitive system. GA is a computer science search technique used to discover
approximate alternatives for issues of optimization and search.
7. In particular, it falls within the category of local search methods and is thus usu-
ally an unnished search. HCA-GA is a method for hybrid optimization. In each
genetic iterative, after the genetic operation, we get an ideal person from the
entire population. Then the hill climbing algorithm optimizes the optimal indi-
vidual again.
8.7.4 Associative Memory Algorithm
8. Associative memory is a framework of content-addressable memory that maps
particular representations of inputs to particular depictions of output. Associative
memories store content in such a manner that information can be recovered later
by adding a tiny part of the material to the memory instead of giving the memory
an address. Associative memories are used in database engine algorithms, anom-
aly detection systems, compression algorithms, cognitive learning, and face rec-
ognition systems as construction blocks. Hopeld’s neural network is a classic
illustration of associative memory [8].
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It is a system that “associates” two patterns (X, Y) such that when one is encoun-
tered, the other can be recalled. Typically, XÎ {−1, +1} m, Y Î {−1, +1} n and m and
n are the length of vectors X and Y, respectively. The vector components can be
thought of as pixels when the two patterns are considered as bitmap images. There
are two associative memory classes: auto-associative and hetero-associative. An
auto-associative memory is used to collect a pattern earlier stored that is most simi-
lar to the present pattern, i.e., X= Y. On the other hand, in a hetero-associative
memory, the retrieved pattern is, in general, different from the input pattern not only
in content but possibly also different in type and format, i.e., X 1Y.
8.8 Big Data: AChallenge
Though big data offers plenty of use cases, there are lots of challenges in utilizing
it. First and foremost the size of the data is big. In spite of the advancement in new
technologies in storing and managing the data, the size of the data is increasing
rapidly day by day. Yet organizations are trying to determine the best solution to
store and retrieve their data in an efcient manner. Challenges not only occur in data
storage but also in using the valuable information the data hold according to the
needs. Data cleaning and data organization in a meaningful manner with respect to
the needs of the clients are still a challenging task. Nearly 60–70% of the time is
spent on the data preparation task itself. In spite of the challenges in storing and
retrieving big data, the technologies used in handling the big data are varying rap-
idly. Apache Hadoop technology is utilized a few years back and then Spark tech-
nology is introduced. Presently both Apache Hadoop and Spark are used together.
8.9 Complexity ofBig Data inInternet ofThings (IoT)
One of the huge revolutions brought in the technological world is the Internet of
Things (IoT) [10–12]. IoT helps to know about the things happening around us
using sensors and to infer useful information from the sensors to utilize our world
in a smart manner. Thus the life of the people has improved a lot in a smarter way
using these real-world applications. Smart city is one of the use cases of IoT where
a lot of applications operate together to make the city smart [13, 14, 16, 17]. The
biggest challenge in IoT is its huge collection of data. Data collected from the appli-
cations cannot be utilized as it has to be processed to obtain useful information.
Three approaches are utilized in data processing of the big data collected from
IoT applications:
• Cloud computing
• Edge processing
• Local computing
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8.9.1 Local Computing
In this approach processing of data will be done in the place where data is com-
posed. No data will be communicated to the servers located in remote places. Useful
information if necessary to take advance decisions will only be communicated to
remote places. This in turn enhances the whole efciency and performance and also
diminishes the added burden of transmission. The technology of local processing is
used in smart sensors. Smart sensors are sensors with inbuilt computation and com-
munication paradigm. A smart sensor collects data and then processes the collected
data to make smart decisions and also store the data for future reference. It also
provides two-way communication. It has become nowadays an integral tool in intel-
ligent systems and an important perspective in advance IoT applications. One of the
best examples is “smart wearables”. These wearables obtain information from the
environment, process and produce the user’s required information, and also com-
municate the relevant information to the external platforms if needed.
The new implementation of intelligent systems in industries using IoT brought
revolutionary changes thereby leading to the next phase of industrial revolution
through “industrial IoT (IIoT)” [19]. Network virtualization plays a noteworthy role
in providing exible and manageable network connections [20]. Thus the intricacy
of the infrastructure is reduced because the resources of the network are utilized as
logical services rather than physical services. This facilitates the execution of set-
ting up smart methodologies to calculate the network usability and data ows from
an IoT application. But this resource utilization has to be properly carried out to
enhance the effectiveness and efciency. This is a challenging task to the network
operators since active monitoring will produce an added overhead in the network.
Yet, a promising methodology has to be implemented in an intelligent manner based
on monitoring data partially and not as a whole.
8.9.2 Edge Processing
It is one of the emerging paradigms nowadays. It involves deploying storage and
processing capability at the border or at the edge of the network. It is created
between the data sensor area and the data cloud centers. Computation capability,
storage ability, and resources for network are deployed. This capability of gaining
computation capability near the data centers helps in attaining low latency, more
bandwidth, and less jitter to services [21]. Some of the approaches that are imple-
mented on account of edge processing are as follows:
1. Fog computing: It provides storage and computation capability by using fog
nodes which consist of devices like routers, switches, and gateways of the net-
work. These fog nodes with devices are considered as virtual nodes thereby con-
tributing to network virtualization facility. This capability leads to the larger
usage of fog processing in mobiles as well as in IoT devices.
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2. Mobile edge computing (MEC): It involves deploying cloud computation in the
base stations [22].
3. Cloudlets: It involves cloud computation facility without the facility of wide area
networks (WANs). Local data acquisition network area contains the servers
which are known as cloudlets.
8.9.3 Cloud Computing
It provides exibility in yielding computation resources on pay basis. Currently, it
is the most widely available disruptive technology. The data centers are deployed in
large numbers and they are used by the users virtually with greater efciency. This
helps the usage of IoT use cases to operate in various environments in a lively man-
ner without changing the infrastructure [40]. This facility increases the usage of IoT
as a service [23]. It leads to efcient computation by integrating various IoT devices
and other embedded systems thereby outsourcing advanced services and use cases
based on collected data. The big data that is accumulated in the cloud is analyzed
and useful information is gathered and outsourced to the stakeholders using data
mining techniques. Complexity and constraint arising during operation are resolved
and more favorable dynamic solutions are obtained.
8.9.4 Context-Aware Computing
Context-aware computing systems have a variety of functions like gathering context
information from sensor devices and application interface and storing in a reposi-
tory. Then context ltering of raw data is done to obtain useful information and
model it into a useful context. This context information is utilized by the system to
react and make appropriate decisions to the users via the interface. A recent advance-
ment in context-aware computing is web-service-based context-aware systems [18].
A wide variety of middleware are also available in providing context-aware compu-
tations. Context-responsive applications that are aware about the different situations
are designed as objects in RCSM [24]. The modeled objects gather data from differ-
ent situations and accumulate it into object containers where analysis in respect to
the situations is made. Context-aware applications are created using the infrastruc-
ture and programming framework Java Context Awareness Framework (JCAF)
[25]. The issues related to big data analytics are also addressed by JCAF. User-
preferred decision supporting system with a programming framework is provided
by the middleware PACE [26]. Network-operated intelligent bots are represented in
CAMPUS infrastructure for supporting context reasoning related to users and envi-
ronment [27]. CoWSAMI [28] supports context-aware computing in pervasive
environments. Context awareness related to disaster management is supported by
web-service-related middleware ESCAPE [29]. It has both front end and back end.
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Front-end components support context data sensing and data sharing for contextual
information processing through web services. Back-end components support stor-
age and processing of data from different front ends. Another web-service-related
intensive working environment for context sensing and reasoning is provided by
InContext [29]. Context-aware computing systems not only analyze and respond
according to the behavior of the environment but also perk up the response of the
software. It is a dynamic infrastructure with self-adaptable and conguring ability.
8.10 Issues inIoT Things andBig Data
Things in Internet of Things communicate with themselves and with surroundings
for data aggregation and to respond unaccompanied to the real world without any
human intrusion when taking important business decisions and social processes. It
also creates services for interactions with the smart devices connected over the
Internet and also changes their states when security breaches are accounted in main-
taining the privacy of the users. The sensor devices in IoT operate anywhere and
anytime. They gather different kinds of data for which it is meant for, like collecting
humidity data for monitoring temperature, gathering noise level in case of environ-
mental monitoring, obtaining biomedical data for patient monitoring system, etc.
These data are big data and there are a lot of research issues related to the data col-
lected. Sensor data are used by context-aware use cases to acclimatize to the changes
and respond accordingly. Sensor data not only are complex, highly dynamic, and
inaccurate and alter according to the time, but also these data have to be integrated
in a repository and the information has to be analyzed specic to the domain.
To determine the complex relations between situations and data from sensor,
appropriate machine learning techniques are employed. But the performance is
highly dependable upon the complexity of the sensor data. Hidden Markov model
(HMM) is utilized in most of the context-aware use cases where a Markov chain
method (series of events) is used to model the system [30]. The system contains lot
of nite hidden states and responses generated from these states. SVM (support vec-
tor machine) is a machine learning technique utilized for linear and non-linear data
classication [31]. The protective nature of SVM against over-tting helps in man-
aging large feature spaces irrespective of the number of features.
With the wide knowledge of the environment and the needed context and how it
is to be used, developers decide which features from data are needed to support their
applications. But moving to the actual implementation from a design is a big task.
Help from different services is needed by the developers to achieve this task.
Webinos EU [32] is a funded project which provides a platform for a wide variety
of wireless applications. Multi-platform, application-specic systems related to
web technology are provided by Webinos for connected things. To do so, Webinos
at any time makes use of the knowledge about the recent state of the IoT device and
users to provide decisions based on the context. Thus Webinos is a good example in
providing cross-platform environment for third-party context-aware services.
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8.11 Opportunistic Data Distribution Maintenance forIoT
Access of data content from many devices has widely increased nowadays. The
most widely accessed data by most of the people via mobile devices are short text
messages, voice messages, and contacts. People access their SMS, mails, social
network accounts, calendar data, photos, videos, music, games, and apps from any-
where and anytime using Wi-Fi. These accesses via wireless media produce an ava-
lanche of data which has to be managed and ltered using big data platforms. In
opportunistic networks, mobile devices are used for communication to achieve
larger throughputs during failure of direct communication. These opportunistic net-
works are used for short-range communication when users are nearer to each other.
The network routes are dynamically created. If a node wishes to transfer data, it rst
stores it and then passes the data across the network till it reaches the destination
node that is in closer proximity to the sender node. From this node the data has to
be relayed and this is one of the biggest challenges in opportunistic networks. A lot
of solutions have been proposed in determining the suitable relay node such as
choosing the highest centrality node [33], predicting the nodes that follow the same
interaction pattern everyday [34], and deciding upon contact and inter-contact time
of the node.
Data distribution is an imperative research area in opportunistic networks. In
opportunistic networks, the topology is unstable due to its dynamic nature. New
techniques based on design models in distributing data are proposed by different
authors where data is distributed from the sources to the really interested receivers
because of the unawareness of the sources and receivers previously. A lot of research
elds have emerged based on delay-tolerant networks (DTNs) and opportunistic
networks (ONs) for IoT use cases. These networks do not have a permanent infra-
structure as like traditional networks whose nodes are assumed as permanent and
topology is known beforehand. In traditional network, if a node likes to transmit a
message, it embeds the message into the network path, and through the path, the
message will reach its destination. In DTNs and ONs, the technique of message
transfer is different. No two nodes are neighbors at a different time period. Nodes
that are in close proximity can come in contact while there is data exchange. The
buffer space is not available for the nodes in DTNs and ONs as they are mobile
devices, so all the data cannot be exchanged at a time. It is relayed on store-carry-
forward paradigm. These DTNs’ and ONs’ techniques are used mostly in IoT sce-
narios where the communication via traditional network approach is difcult
and costly.
Nowadays online mobile advertisers generate revenues by targeting a users’ wish
and preference. For revealing appropriate ads based on users’ preferences, precious
information about the users is collected by these advertisers. The security and pri-
vacy of the users’ data is not guaranteed. MobiAd is a solution provided for ensur-
ing users’ privacy by revealing the users’ preferences to the advertisers yet preserving
the users’ privacy [35, 36]. An example of a context-aware mobile application solu-
tion is CAPIM (context-aware platform using integrated mobile services) [37].
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It is a next-generation application integrated with a lot of services to gather con-
text data. It uses the sensing ability of modern smartphones and other external sen-
sors while dynamically loading the needed smart services. For smart city application,
DTNs and ONs are the best methods. In smart city application, the sensors are
meant for monitoring and integrating the signicant infrastructure of the cities, for
optimizing the usage of resources, and for planning future maintenance activities.
The changes in the environment are guessed and reacted upon automatically by the
sensors. The cumulative intelligence of cities is employed through smart city use
case which connects the physical, economic, social, and business activities of the
cities. The communication by smart city sensors is done through ONs and DTNs.
The data collected from all these sensors are gathered and processed efciently
through big data platforms.
8.12 Data Distribution Techniques inOpportunistic
Networks
Data distribution in an opportunistic network is carried out through four popular
techniques. First, socio-aware overlay algorithm develops an overlay that consists
of nodes with high centrality values and high visibility [38]. Before starting the
communication, the technique assumes that an infrastructure exists and forms an
overlay with visible nodes from each community sector. The nodes are interacted by
starting gossiping after community detection. The gossiping distribution begins by
sending the message to random node groups. The node centrality measurement unit
is used to choose a hub in a network by the socio-aware overlay algorithm. Bluetooth
and Wi-Fi devices are utilized for discovering the nodes. Subscribe approach or
unsubscribe approach is followed by the socio-aware method. During communica-
tion, broker node information along with the centrality list and timestamp informa-
tion is passed to the nodes for subscriptions or unsubscriptions. During change of a
broker node due to loss of closeness, a new subscription list is exchanged. Updates
about the new broker nodes are transferred to all the brokers. Community brokers
help in propagating the information about subscriptions during gossiping. When a
broker receives a publication, it forwards it to all the other brokers, and then all the
brokers update their own publication list. When there are lots of members in its
community, the information is ooded to all the members through proper channels.
The community detection methodology is provided by community-related algo-
rithms such as socio-aware algorithm. Depending upon the knowledge of standpoint
of each and every node in a network, the socio-aware algorithm classies the nodes
and builds a community structure. In this community structure, the rst level of
nodes belongs to the same community, and these nodes have a large number of con-
tacts and a stable standpoint duration. The next level of nodes is familiar stranger
nodes; these also have a larger number of contacts, but the standpoint duration of
these nodes is short. The third level is completely stranger nodes which have a
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smaller number of contacts with shorter contact duration. Finally, the nodes with
smaller contacts but with higher standpoint duration are friend nodes. Community
detection of nodes is done in a decentralized manner due to the unreliable and
unxed structure of the opportunistic networks. Each node should determine its
own local community. Two algorithms are there for community detection in distrib-
uted environments, namely, simple algorithm and k-clique algorithm. To identify
the local community, the nodes interact with the nearby devices and run a detection
algorithm. This methodology is used in the data exchange level between the nodes.
Each node maintains the content detail about the nearby confront nodes, the dura-
tion of their contacts, and the identied local community. In a wireless environment,
infrastructure is not possible, so a wireless podcasting method is used to facilitate
the content distribution among the podcasting devices in that wireless range [39].
In resource-constrained networks, the content is made available to all the users in
contact without high usage of resources by ContentPlace method [41]. This method
utilizes the knowledge of relationships among the users to decide upon the area to
position the user data. The method of design is based on two preferences: one is
grouping the users according to their interests and the other one is based on their
social relationships.
8.13 Research Path inBig Data Platforms forIoT
The actual research path leads to the evolution of Internet of Things which provides
a huge amount of data by connecting all things in the environment thereby raising
the demand requirements in a complex way. The increasing growth of data volume
day by day requires a highly scalable platform for processing the data, for managing
the trafc in the network, and for efciently storing the data. For maintaining the
connection between the things during poor connection of Wi-Fi/wireless links, good
communication protocols are desired to enable the connection and to maintain the
network trafc. For storing, processing, and manipulating the data in a procient
manner, new algorithms are needed. IoT helps in developing a lot of applications
that connect the people and the things around the environment. They help the urban
population in gathering information at their ngertips and also in mounting their
activities to improve the competitiveness.
In addition to this, IoT also enables outdoor-computing facility and user purpose
approaches to boost the lifestyle of the urban population like smart transportation,
social services, anytime healthcare applications, agency administrations, and smart
education. IoT is not a single technique; it is a combination of multiple technologies
which tend to modify the society in the forthcoming years. The advancement of IoT
depends on the growth of a huge number of research projects and journal publica-
tions. As estimated, the growth of IoT will accompany more than 50,000billion
things of different types. To accompany the growth of IoT, new protocols for com-
munication and standard methodologies should be invented for communication
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between device and people. Web services can be used to embed the smart IoT
devices and their applications. By increasing the consumption of resource time,
response time, throughput time, and reliability, the quality of providing services
could be enhanced. The availability of a lot of services and instant publishing/sub-
scribing and notifying capability pave a path for easy management of the complex
architecture of IoT.
The information obtained from IoT devices should be well processed so that the
quality of the information is improved. The number of IoT devices is increasing, so
proper management is required to provide a highly robust, autonomous, and intel-
ligent operation. Solutions for self-organization, self-healing, self-protection, and
self-optimization of IoT devices are needed. For distributed information storage and
sharing, new computational services are available. For providing security and pri-
vacy to all IoT devices, new protocol mechanisms are essential. For context-aware
IoT models, stronger security measures are essential. For saving the energy and for
developing self-sustainable models, a new methodology is required.
A lot of research projects are going on to make the smart IoT objects gain their
required energy from the environment itself, thereby facilitating smart energy man-
agement. In addition to this, new platforms and advanced techniques are essential
for real-time storage and processing of big data to accomplish guaranteed data
availability and provisioning in real time. One available technique for big data is
cloud computing which simplies the construction of big data infrastructure. It also
facilitates massive big data storage and processing thereby satisfying the customer
demands. New cloud computing capabilities are required for global collection of
different kinds of big data and for distributed processing and secure communication
among different platforms throughout the globe. When interoperability of IoT
devices is considered, a huge variety of technologies and design procedures are
involved. One approach is using standardized protocols for enabling inter-
communication, and in addition to this, the self-conguring and self-managing
abilities of smart devices are essential for inter-operability and inter-communication
within the smart devices and with the surroundings. This methodology is highly
preferable than centralized management of complex IoT infrastructure. The autono-
mous nature of IoT devices is preferred in their operational level. These automatic
respondents of smart devices enable the building of complex infrastructure accord-
ing to the changes in the environment. Special methodologies are needed for
enabling low-power-consumption devices. Power management should be done
starting at the operational level of devices to network routing level. This would
enable us to develop complex expandable infrastructure at a low cost rate.
Due to the distributed nature of the surroundings, different issues may arise in
the operations and decision-making of the collaborated devices. The major issue
lies in device convergence and facilitating a quality solution. The other issue lies in
aiming to provide a correct solution thereby avoiding duplication and malpractice.
Altogether, things should be capable of preserving condentiality, integrity, and
data availability.
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8.14 Summary
The evolution of Internet of Things leads to the growth of highly complex environ-
ments thereby raising demand requirements toward current and future research. The
tremendous growth of data from IoT things requires highly reliable and scalable
environments for supporting network trafc and storage and processing of data as
needed by the consumers. For continuous connectivity of devices in wired and wire-
less links, standard communication protocols are essential to manage the high traf-
c in the network as well. In addition to this, new solutions are required for efcient
storage, fetching, and searching of data in these complex environments. Such a
solution would include forming dynamic networks of IoT devices based on the
needs to process big data using any of the listed machine learning algorithms to
provide useful information to the enterprise, which in turn helps in making the right
decisions.
References
1. Ahmed, E., & Rehmani, M.H. (2017). Mobile edge computing: Opportunities, solutions, and
challenges. Future Generation Computer Systems, 70, 59–63.
2. Anandakumar, H., & Umamaheswari, K. (2017, March). Supervised machine learning tech-
niques in cognitive radio networks during cooperative spectrum handovers. Cluster Computing,
20(2), 1505–1515.
3. Anandakumar, H., & Umamaheswari, K. (2017, September). An efcient optimized handover
in cognitive radio networks using cooperative spectrum sensing. Intelligent Automation & Soft
Computing, 1–8. https://doi.org/10.1080/10798587.2017.1364931.
4. Arulmurugan, R., & Anandakumar, H. (2018). Early detection of lung cancer using wave-
let feature descriptor and feed forward back propagation neural networks classier. In
Lecture notes in computational vision and biomechanics (pp. 103–110). https://doi.
org/10.1007/978-3-319-71767-8_9.
5. Athanasopoulos, D., etal. (2008). CoWSAMI: Interface-aware context gathering in ambient
intelligence environments. Pervasive and Mobile Computing, 4(3), 360–389.
6. Boldrini, C., Marco, C., & Andrea, P. (2008). ContentPlace: Social-aware data dissemination
in opportunistic networks. In Proceedings of the 11th international symposium on Modeling,
analysis and simulation of wireless and mobile systems. ACM, Vancouver, British Columbia,
Canada.
7. Catherine, F., & Tamilarasi, J.S. (2018). Articial intelligence– Deep learning-based cognitive
ontology model. International Journal of Innovative Technology and Exploring Engineering
(ITITEE), 8(2S2).
8. Celesti, A., etal. (2016). Exploring container virtualization in IoT clouds. In 2016 IEEE inter-
national conference on Smart Computing (SMARTCOMP). IEEE.
9. Ciobanu, R.I., & Ciprian, D. (2012). Predicting encounters in opportunistic networks. In
Proceedings of the 1st ACM workshop on high performance mobile opportunistic systems.
ACM.
10. Dobre, C., Flavius, M., & Valentin, C. (2011). CAPIM: A context-aware platform using inte-
grated mobile services. In 2011 IEEE 7th international conference on intelligent computer
communication and processing. IEEE, Cluj-Napoca, Romania.
S. Sountharrajan etal.

167
11. Doukas, C., etal. (2007). Patient fall detection using support vector machines. In IFIP inter-
national conference on articial intelligence applications and innovations. Boston: Springer.
12. Giacobbe, M., etal. (2018). Evaluating information quality in delivering IoT-as-a-service. In
2018 IEEE international conference on Smart Computing (SMARTCOMP). IEEE, Taormina,
Sicily, Italy.
13. Gil, D., etal. (2016). Internet of things: A review of surveys based on context aware intelligent
services. Sensors, 16(7), 1069.
14. Haddadi, H., et al. (2011). Targeted advertising on the handset: Privacy and security chal-
lenges. In Pervasive advertising (pp.119–137). London: Springer.
15. Haldorai, A., Ramu, A., & Murugan, S.Social aware cognitive radio networks. In Social
network analytics for contemporary business organizations (pp. 188–202). https://doi.
org/10.4018/978-1-5225-5097-6.ch010.
16. Henricksen, K., & Ricky, R. (2006). A survey of middleware for sensor networks: State-of-
the-art and future directions. In Proceedings of the international workshop on Middleware for
sensor networks. ACM, Melbourne, Australia.
17. Hui, P., Jon, C., & Eiko, Y. (2010). Bubble rap: Social-based forwarding in delay-tolerant
networks. IEEE Transactions on Mobile Computing, 10(11), 1576–1589.
18. Kalyan, A., Srividya, G., & Sridhar, V. (2005). Hybrid context model based on multilevel
situation theory and ontology for contact centers. In Third IEEE international conference on
pervasive computing and communications workshops. IEEE, Kauai Island, HI, USA.
19. Kanda, T., etal. (2008). Who will be the customer?: A social robot that anticipates people’s
behavior from their trajectories. In Proceedings of the 10th international conference on
Ubiquitous computing. ACM, Seoul, Korea.
20. Kim, H., Young-Jo, C., & Sang-Rok, O. (2005). CAMUS: A middleware supporting context-
aware services for network-based robots. In IEEE workshop on advanced robotics and its
social impacts, 2005. IEEE, Nagoya, Japan.
21. Kristina, M., & Jan, P. (2003). Basic principles of cognitive algorithms design. In Proc. of the
IEEE International Conference Computational Cybernetics, Siófok, Hungary (pp.245–247).
22. Lenders, V., Gunnar, K., & Martin, M. (2007). Wireless ad hoc podcasting. In 20074th annual
IEEE communications society conference on sensor, mesh and Ad Hoc communications and
networks. IEEE.
23. Macke, J., etal. (2018). Smart city and quality of life: Citizens’ perception in a Brazilian case
study. Journal of Cleaner Production, 182, 717–726.
24. March, H. (2018). The Smart City and other ICT-led techno-imaginaries: Any room for dia-
logue with Degrowth? Journal of Cleaner Production, 197, 1694–1703.
25. Mayank, A., Joshua, C. P., & Thomas, L. G. (2019). Using Machine Learning to guide cogni-
tive modeling: A case study in moral reasoning. arXiv preprint arXiv:1902.06744.
26. Mohammadi, M. (2018). Enabling cognitive smart cities using big data and machine learning:
Approaches and challenges. IEEE.
27. Ng, I.C. L., & Susan, Y.L. (2017). The Internet-of-Things: Review and research directions.
International Journal of Research in Marketing, 34(1), 3–21.
28. Saarikko, T., Ulrika, H.W., & Tomas, B. (2017). The Internet of Things: Are you ready for
what’s coming? Business Horizons, 60(5), 667–676.
29. Samir, M. (2017). Cognitive computing architectures for machine (deep) learning at scale.
MDPI, Presented at the IS4SI 2017 Summit DIGITALISATION FOR A SUSTAINABLE
SOCIETY, Gothenburg, Sweden.
30. Satyanarayanan, M. (2017). The emergence of edge computing. Computer, 50(1), 30–39.
31. Su, Y., etal. (2018). Dynamic virtual network reconguration method for hybrid multiple
failures based on weighted relative entropy. Entropy, 20(9), 711.
32. Tomáš, K., Štěpán, B., & Johannes, F. (2018, April).A review of possible effects of cognitive
biases on the interpretation of rule-based machine learning models. arXiv:1804.02969v1 [stat.
ML].
8 On-the-Go Network Establishment of IoT Devices to Meet the Need of Processing…

168
33. Truong, H., & Schahram, D. (2009). A survey on context-aware web service systems.
International Journal of Web Information Systems, 5(1), 5–31.
34. Van Kasteren, T., etal. (2008). Accurate activity recognition in a home setting. In Proceedings
of the 10th international conference on Ubiquitous computing. ACM.
35. Vergori, P., etal. (2012). The webinos architecture: A developer’s point of view. In International
conference on mobile computing, applications, and services. Berlin/Heidelberg: Springer.
36. Williams, J.A., Weakley, A., & Cook, D.J. (2013). Machine learning techniques for diagnos-
tic differentiation of mild cognitive impairment and dementia. AAAI Workshop.
37. Yau, S. S., & Fariaz, K. (2004). A context-sensitive middleware for dynamic integration of
mobile devices with network infrastructures. Journal of Parallel and Distributed Computing,
64(2), 301–317.
38. Yingxu,W. (2017).Novel machine learning algorithm for cognitive concept elicitation by cog-
nitive robots. IJCINI.
39. Yoneki, E., etal. (2007). A socio-aware overlay for publish/subscribe communication in delay
tolerant networks. In Proceedings of the 10th ACM symposium on modeling, analysis, and
simulation of wireless and mobile systems. ACM, Chania, Crete Island, Greece.
40. Zhihan, L., etal. (2018). Government affairs service platform for smart city. Future Generation
Computer Systems, 81, 443–451.
41. Zhong, R.Y., etal. (2017). Intelligent manufacturing in the context of industry 4.0: A review.
Engineering, 3(5), 616–630.
S. Sountharrajan etal.

169© Springer Nature Switzerland AG 2020
A. Haldorai et al. (eds.), Business Intelligence for Enterprise Internet of Things,
EAI/Springer Innovations in Communication and Computing,
https://doi.org/10.1007/978-3-030-44407-5_9
Chapter 9
Analysis ofVirtual Machine Placement
andOptimization Using Swarm
Intelligence Algorithms
R.B.Madhumala andHarshvardhanTiwari
9.1 Introduction
Virtualization is one of the powerful techniques that introduced a wide variety of
applications to the real world. Cloud computing is considered as a computer para-
digm for computing and delivering services over the Internet with many exciting
features and provides three major service frameworks such as the software as a
service (SaaS), the infrastructure as a service (IaaS), and the platform as a service
(PaaS). Consumers are always concerned about the performance of the application
that they are using and are very much interested in providing efcient resources.
The predominant issues in effective cloud service provisioning are optimal resource
utilization and energy conservation over the network. Due to their emerging growth,
cloud providers also provide infrastructure as a service (IaaS) to their main opera-
tions. In cloud service models, the IaaS model provides virtualized hardware
resources where a single physical server is partitioned into multiple logical servers;
in turn, each logical server acts as a single physical server for problem execution.
The total energy used and the cost utilization in cloud services mainly depend on
virtual machine scheduling. Efcient resource management through virtual machine
placement is a great concern in data centers. Allocating resources for cloud data
centers to formulate a virtual machine from a physical machine makes it a big prob-
lem. While allocating, there is a need to consider the basic parameters to optimize
resource allocation. We are mainly concentrating on the energy consumption as well
as the cost minimization of cloud data centers.
R. B. Madhumala (*)
Department of Computer Science Engineering, Jain University, Bangalore, India
H. Tiwari
CIIRC, Jyothy Institute of Technology, Bangalore, India

170
The cloud service providers provide a group of computational memory and stor-
age units in the form of virtual machines. Each physical bare-metal unit is divided
into a number of VMs. The main concern of the cloud provider is to identify a
proper physical machine to deploy the VMs meant at reducing the physical
machine’s running while maintaining service quality. This process is termed as vir-
tual machine placement optimization.
The researchers continuously carried out the effective optimization of VMs. Few
kinds of research used heuristic methods while others used metaheuristic methods
with a avor of articial intelligence.
Optimization helps to completely utilize all the resources of a currently running
physical machine (Fig. 9.1). Optimization helps in reducing power consumption
thereby improving the green computing. In real-life situations, there will be many
constraints for optimizing the cloud center. Maximizing the efciency of the cloud
center and minimizing the undesired factors is a challenging task to solve.
Optimization is a trial and error method. Many new algorithms will be proposed
and their results are tested against the desired metrics and the successful ones are
continuously modied to get better results. Broadly we can classify the optimiza-
tion algorithms as conventional and non-conventional algorithms. Much of the work
is already done in the conventional algorithms, but very few are proposed in non-
conventional algorithms. The non-conventional algorithms are primarily the bio- or
nature-inspired algorithms. These nature-inspired algorithms need intelligence and
the AI tools are used for inducing the required intelligence into the algorithms. Few
Fig. 9.1 Virtual machine placement in cloud computing
R. B. Madhumala and H. Tiwari

171
optimization algorithms are listed under heuristic methods as well as metaheuristic
methods. In virtual machine selection, the overloaded host is detected and VMs are
further selected to ofoad the host mainly to address the performance issue [1].
After selecting VMs, the host once again is checked if it still remains overloaded. In
the next step, it is applicable to select another VM, which is purposed to migrate
from its host [2]. This process is thus repeated unless the host is considered as
unoverlooked. The aspect of virtualization is a vital concept of cloud computing
which determines the manner in which a suitable host for the virtual machine is
analyzed. As indicated above, the inspiration behind the placement of VM is
inscribed in the trafc-aware, application-aware, topology-aware, and energy-aware
prospects in Fig.9.2.
Energy management—minimizing the cost at the level of hardware and server
consolidation: green computing. Resource management—resources should be
available based on the need and cannot be allocated statistically based on the peak
workload elasticity of the cloud trafc engineering: to maintain the data center
application efciency and also for accurate planning of the network architecture.
Resources are limited to cloud data centers simply because cloud providers try to
minimize over-utilized resources. This helps them to have a minimum number of
servers as well as reduce the cost for both server hardware and software. Resources
like CPU, memory, and hard disk need to be kept at minimum to avoid unnecessary
costs. So some improvements are needed to reduce energy cost. This research has
analyzed the recent researches concerning the provision of resources in the process
of virtualizing computing ecological segment. It reduces response time and maxi-
mizes resource utilization but QoS factors are not considered [3]. It achieves sched-
ule with a lower makespan, but its time complexity is more. It minimizes the total
cost and high risk in resource management. Existing methods cannot operate on the
issues of non-coordinate framework like resolution to power eld.
Optimization
algorithms
Exact methods Approximate
methods
Ad-hoc
heuristics
Constructive
heuristics Local search Trajectory Population
based
Metaheuristics
Iterative
methods
Enumerative
methods
Fig. 9.2 Optimization algorithm classication
9 Analysis ofVirtual Machine Placement andOptimization Using Swarm Intelligence…

172
9.2 Literature Survey
This section provides insight into existing algorithms developed which can be used
for optimal virtual selection of machines and the inclusivity of cloud computing,
which is placed in the group of issues known as the NP-hard issues because of large
resolution spaces. Cloud computing includes tasks for mapping on an unlimited
computing resource, nature-inspired algorithm in ant colony optimization (ACO),
particle swarm optimization (PSO), and gravitational search algorithm (GSA),
which are vital for dealing with the NP-hard issues mentioned in this chapter. The
condition-motivated algorithm in the area of privacy is meant to safeguard the data
connected with cloud computing and task computing.
• Kumar etal. [4] presents a critical survey of nature clever algorithms that are
used in articial intelligence and automation in real-life domains. Nature-
inspired algorithms are an emerging area of research on an algorithm based on
physics and biology. This paper explains the idea of duplication in articial
frameworks. The condition-inspired computing and computation intelligence
will assure maximum resolution to issues on a new venue of development and
research.
• Nature with its massively adverse, robust, dynamic, and complex idea is a great
source of information for solving problems in computer science. Biology-
inspired computing has a wide eld for research; in particular, there are great
opportunities in exploring a new approach. Nature-inspired algorithm is a vital
algorithm which increases the eld of future-generation computing. Binitha etal.
[5] presents a broad view of biology-inspired optimization algorithms which in
turn increases the areas where these algorithms can be successfully applied.
• Cloud computing needs optimal resource utilization of cloud resources which in
turn needs a certain novel scheme with enhanced dynamic resource allocation,
collective control, resource management, and maximum distribution in network-
ing and computing resources. The present trend is using virtualization in resource
mobilization for data centers through machine migration techniques. Theja and
Babu [6] discusses proposed approaches for the optimization of resources for
cloud infrastructure. Virtual machines in association with physical machines can
be an effective solution for resource optimization by considering certain increased
predictive schemes, load balancing, and mapping which could be an increasing
factor in virtualization for better performance.
• The VM placement in the cloud is enhanced based on objects such as VM time
allotment, power consumption, SLA violation utility of resources, etc. [7]. This
includes proposed algorithms such as the multiplied hybrid ACO-PSO algo-
rithms that minimize the resource wastage and consumption of power to assure a
more balanced server. It helps in the reduction of server costs.
• Usmani and Singh [8] deals with details regarding VM placement algorithm aim-
ing at maximum utilization to reach optimal solution for minimization of power
consumption. These algorithms aim at studying the workload variability and
transforming the application demands, including the minimization of trade- off
R. B. Madhumala and H. Tiwari

173
between power consumption and better performance by using a hybrid technique
for server energy efciency. This is designed to be a two-staged procedure that is
comprised of green computing and overload avoidance.
• VM migration is a source-intensive process to address VMs’ progressive demand
which is effective for the CPU cycles, communication bandwidth, and cache
memory capability. The continuous movement becomes essential in managing
the efciency of data centers and in smoothing the application service. Choudhary
etal. [9] deal with the problem faced in VM migration by having them shifted
while there is a progressive operation making it possible for the VMs to be
changed with zero downtimes. This shows the various forms of content, which
include migration of the CPU states, storage contents, and memory contents. This
analyzes the post-copy, pre-copy, and hybrid method of VM migration. The VM
migration methods are sub-divided into two categories: models and frameworks.
• Cloud computing has attained remarkable growth in every eld; provisioning,
scaling, and maintenance of applications are achieved and it serves in a breeze.
Rani and Bhardwaj [10] focuses on task scheduling using ant colony optimiza-
tion genetic algorithm, PSO, and GSA.This survey deals with task scheduling in
cloud computing based on current information and sources to build a good map-
ping relationship between tasks and resources. It compares the ant colony opti-
mization with other techniques to prove the former is better in comparison.
• The main goal of [11] is to provide an understanding of the present algorithms
and approaches that ensure an effective VM placement in the contest of cloud
identication and computing that will be applied in future systems. The sophis-
ticated VM placement optimization aims to reduce work, power, and cost and
prevent congestion of data ow. The migration of VM requires a secure connec-
tion between the source and target servers. This aims to make a way for further
work to address this problem for establishing and managing better
communication.
• Cloud computing which is an important development in sharing and pooling of
resources over Internet services is still in its infancy to achieve improvement;
much research is required in various directions: one is proceduring the objective
of scheduling to trace enough resources. This is placed in the segment of prob-
lems known as the NP-hard problems. There is no algorithm that provides opti-
mized remedies within the polynomial timeframe to resolve this issue.
Metaheuristic-based methods provide some remedies within the speculated
timeframe. The metaheuristic techniques like ACO, GA, and PSO and two novel
approaches like the League Championship Algorithm (LCA) and bat algorithm
(getting inspiration from the echolocation behavior of bats, Yang introduced the
bat algorithm) form these techniques. The comparative framework of these algo-
rithms is centered on metaheuristic methods utilized for optimizing methods,
nature of obligations, and ecology where these algorithms are applied. Son and
Buyya [12] proposes priority-aware VM allocation (PAVA) which uses network
topology information to allocate VM on the host which is nearest to the requester
of the resource. The priority of the task is also considered as a parameter.
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174
9.3 Optimization Techniques
9.3.1 Ant Colony Optimization Algorithm (ACO)
The ant colony optimization (ACO) algorithm is considered as one of the most
recent algorithms that is competitive to other forms of algorithms [13]. The ACO
algorithm is developed based on its combination with the ABC algorithm. The
hybrid algorithm is formulated to link up the advantages of the global research abil-
ity of the ABC algorithm and the localized search ability of the ACO algorithm
alongside the merits provided by the maximum and minimum algorithms. The algo-
rithm initially utilizes the advanced max and min algorithm meant to schedule
requests, whereas balancing is performed by ACO that is enhanced by the ABC
algorithm. The vital aim of using multiple-objective ant colony system algorithm
for the virtual machine placement problem is to obtain a collection of non- dominated
resolution, the Pareto set, which minimizes the complete resource wastage and the
consumption of energy [14]. The projected algorithm is analyzed with the instances
recognized in literature. The remedies are performed and compared to the present
multi-purpose genetic algorithm and the two-objective algorithms referred to as the
bin-packing algorithm and the maximum and minimum ant system algorithm in
Fig.9.3.
Whenever the ant has to locate the shortened path between the colonies to the
feeding sources, the moving ant produces some pheromone on the ground, which is
responsible for forming paths by a trail of substances. Whereas the isolated ant
migrates at a fundamentally randomized way, the ant that encounters the laid trail
may potentially determine and decide based on a high probability to embrace it. As
such, this reinforces a trail with the pheromone. This inclusive behavior that pops up
has created an autocatalytic condition whereby more ants following the trail are
attracted thus becoming a followed path. The procedure is grouped based on a posi-
Fig. 9.3 Ant colony
behavior in the search
space
R. B. Madhumala and H. Tiwari

175
tive feedback loop, whereas the probability of the ant to select the path is developed
through the number of ants which have chosen a single path.
9.3.2 Variants ofAnt Colony Optimization (ACO)
Ant system: The initial ACO algorithm promised was the ant system (AS). This seg-
ment was applied to a number of small cases of travelling salesman issues in
about 75 urban environments. On November 15, 1991, Dorigo submitted a man-
uscript for the ACO which was again revised on September 3, 1993, July 2, 1994,
and December 28, 1994, and it was nally published on IEEE in 1996. It was an
attempt by Dorigo to extend the travelling salesman (TSP) problem to the asym-
metric travelling salesman problem (ATSP) [15].
Dorigo proposed the following algorithm:
1. Initialize the time counter, cycle counter, and trail intensity to zero.
2. Place m ants in n nodes.
3. Make the ants move to different towns with a probability function.
4. Each time a path is retraced, update the trail counter and prepare a tabu list with
the values.
5. Steps 2 to 4 are repeated till the tabu list is full (all the towns are covered for each
ant).
6. Steps 2 to 5 are repeated until the cycle counter reaches a preset value.
7. From the table, nd the path with the highest trail intensity, and this is considered
as the shortest path.
The complexity of the algorithms is determined by:
NC = number of cycles
n=number of towns
m=number of ants
Dorigo has even proposed two other extended versions of the algorithm, namely,
ant-density and ant-quantity algorithms, but there was not enough research done as
they didn’t become more popular like the original ant system. The main problem of
the ant colony algorithm is stagnation. When many ants travel in the problem space,
a lot of pheromones will be put on that path, and since the path with a lot of phero-
mones is considered as the best path, there is a chance that the other available paths
get ignored and this creates the stagnation in Fig.9.4.
One issue which pops up based on the algorithms is stagnation. Whenever many
ants travel in this issue space, they are placed down on pheromones. However, as
many of them gravitate over the best resolution to locate it, it possibly creates star-
vation to the remaining datasets. Though few ants may take other paths, their phero-
mones will get evaporated eventually making that path as an unreliable path. This
means that only the path where many ants travel will have pheromone replenish-
9 Analysis ofVirtual Machine Placement andOptimization Using Swarm Intelligence…

176
Fig. 9.4 Basic ow chart of ant colony optimization algorithm (ACO)
R. B. Madhumala and H. Tiwari

177
ment and all other paths will be ignored. When this happens, there is no possibility
of further optimization, and to combat this, many modied and advanced versions
of the ant system are engineered.
Elitist ant system: In the elitist AS, they have formulated the specialist ants along-
side the normal ant system. In the elite ant system, the bonus pheromones are
provided to the most relevant resolutions by the basis of multiplication of some
pheromones provided by the specialist ants. At each moment, the ultimate path
found is based on massive deposition of the pheromones that is initiated for a
relevantly long duration of time. While the usual paths degrade as time goes, the
elitist paths are rendered as a huge concentration of pheromones that stay and are
a validated option for a long duration of time. As such, one elitist path is suf-
cient enough to diverge from the present issue space therefore preventing any
possibility of stagnation.
Rank-based ant system: Ranked AS utilizes a form of a signicant ant as seen in the
case of the elitist ant system. But in this case, they spread the pheromones on
various promising paths instead of providing one best path. Each path is ranked
according to its length, whereby the best-ranked one gets more pheromones and
the one that has been ranked the least obtains a few pheromones by the specialist
ants. In the ranked AS, in case there are less specialists than the usual ants, the
worst-ranked path shall not be assigned any pheromones.
Min-max ant system: In the minimum and maximum ant system, there exists no
specialist ant. It is utilizing the normal ants. In this framework, there is a cap
reecting the max and min values of the pheromone, which can be placed on a
certain path. Since the algorithm places the cap on a minimum pheromone
amount, the path pheromone cannot be dropped as low to make the path so abso-
lute. In the same case, the maximum amount of pheromone on paths is xed as
paths cannot get saturated in the process of overshadowing all the other paths.
Whenever one or more paths are closer to the min or max levels, the pheromone
levels are considered smooth. This therefore promotes the paths with low levels
of pheromone while maintaining the standings over the paths. In this methodol-
ogy, the pheromones are applicable on the most effective path while ignoring the
others. All these additions make the basic ant system a powerful and competitive
algorithm.
9.4 Particle Swarm Optimization Algorithm (PSO)
9.4.1 Introduction
In 1995, J.Kennedy and R.Eberhart based on their earlier investigations suggested
that the group members can benet not only from their unidirectional memories but
also from the collective memory with a multidimensional group. Particle swarm
optimization (PSO) method is based on bird ocking. It is a metaheuristic optimiza-
9 Analysis ofVirtual Machine Placement andOptimization Using Swarm Intelligence…

178
tion algorithm. The major PSO algorithm operates based on a population or swarm
of candidate resolutions known as particles. All these particles revolve around the
issue search space as per the simple formula [16].
This is the concept behind the particle swarm optimization algorithm. We start
with randomly grouped particles on the points of the forms K=<k1, k2,…,kn> and
each is characterized with a randomized selected speed V=<v1, v2,…,vn>. Every
particle is composed of three elements such as inertia, memory, and group memo-
ries in Fig.9.5.
The movements of the particles are guided by:
1. Their individually known positions in the searching spaces (pbest)
2. The complete swarm’s best-known position (gbest)
3. The velocity V which is generally capped to some maximum value Vmax to avoid
searching out of the search space
Basic pseudo-code for PSO algorithm is provided below:
Step 1: Initialization
For each particle i
Initialize the particle’s position with a uniform distribution and represent the
lower and upper bounds of the search space.
(a) Initialize pbest to its initial positions: pbest.
(b) Initialize gbest to the minimal value of the swarm: gbest.
(c) Initialize velocity: V.
Step 2: Repeat until a termination criterion is met.
Swarm Member-j
Best Location in the History
Diversity Vector
Inertia
Explorative
Exploitive
New Velocity of Member-j
Best Swarm Member
of Member-j
Fig. 9.5 Particles move around the problem search
R. B. Madhumala and H. Tiwari

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For each particle i
Pick random numbers: r1, r2.
Update the particle’s velocity. See formula (2).
(a) Update the particle’s position. See formula (3).
(b) Update the best-known position of particle i: pbest.
Step 3: Output gbest(t) holds the best position found in a given problem search
space.
The initial segment is termed as inertia that signies the previous speed of par-
ticles. The following part is referred to as the cognitive element that is determined
by every particle that signies the other, including the third parameter that signies
the collaborative inuence of the particles to locate the novel resolutions [17].
9.4.2 Basic Variants ofPSO
Jau etal. [18] projected a modied QPSO that is applicable to the high-breakdown
regression estimators and the minimally trimmed square methodology. Jamalipour
etal. recommended the QPSO based on various mutation operators used to optimize
the WWER-1000 core fuel processing. Tang etal. [19] projected a memetic algo-
rithm and the memory technique. The memetic algorithm gives some basic experi-
ence to the particles in their local space, and after gaining experience in the local
space, they are released to search the global space. Davoodi etal. [20] proposed a
new approach, by combining improved QPSO and simplex algorithms. Li and Xiao
[21] recommended the encoding technique that is centered on qubits described on
the Bloch sphere. Yumin and Li [22] combined the articial sh swarm to the QPSO
and exploited adaptive constraints meant to be overlooked. Jia etal. [23] projected
a developed QPSO centered on GA to evaluate the asynchronous optimization of
the various sensor arrays and classiers. Gholizadeh and Moghadas [24] focused
and developed QPSO metaheuristic algorithm purposed to initiate a performance-
based optimized designing process. The two number samples have been initiated to
represent the most effective methodology. The two numerical examples have been
included to represent the effectiveness of the proposed methodology. J. Kennedy
proposed bare bones particle swarm optimization (BBPSO) in the year 2003.
Modied the BBPSO algorithm by utilizing the crossover and mutation operator of
the DE algorithms. In the year 2015 projected a binary BBPSO by formulating a
reinforced memory technique to update all the local leader particles targeting to
evade degradation of pending genes in particles to solve optimal feature subset and
classication problems. Chaotic PSO (CPSO) was implemented by integrating the
chaotic theory with the PSO.Many combinations were done with the CPSO algo-
rithm like combining it with the K2 algorithm as done by Zhang etal. Pluhacek
etal. [25] used various chaotic frameworks as a pseudorandom gure generator for
the calculation of PSO algorithm. Juang etal. [26] projected the adaptive FPSO
9 Analysis ofVirtual Machine Placement andOptimization Using Swarm Intelligence…

180
(AFPSO) algorithm that uses fuzzy logic to enhance the model. PSO with time-
varying acceleration coefcients (TVAC) was anticipated to enhance the perfor-
mance of standard PSO by Cai etal.
9.5 Comparison oftheParticle Swarm Optimization
Algorithms
Author and
year Parameter used Limitations Advantages Conclusion
Singh and
Chana [26]
Execution time It does not consider
multiple levels of
QoS requirements
QoS-aware
resource
scheduling in
cloud environment
Dashti and
Rahmani
[27]
Makespan It is more efcient
for scheduling
The modied PSO
algorithm
Lalwani
etal. [28]
CPU-based and
GPU-based
strategies
Comparison
between different
PSO approaches
Study of variations
of PSO algorithm
Xu and Yu
[29]
Contraction-
expansion
coefcient, the
wave function
It does not fully
address the issue
related to
convergence rate and
running time
Markov properties
of SPSO are
analyzed
The convergence
of SPSO is studied
using Martingale
theory
Selvaraj
etal. [30]
Turnaround time,
waiting time, and
CPU utilization
It is efcient when
compared to
SPSO, GA, and
DPACO models
VM selection
using swarm
intelligence
approach
9.6 Applications ofPSO
Particle swarm is more than just a collection of particles wherein these particles
have no power to solve any type of problem and progress occurs only when they
interact with each other. The diagram represents the different applications of PSO in
various domains as shown in Fig.9.6.
PSO does not utilize the gradient of the issue but is centered on optimizing the
necessary optimization issues that are dened by the classical optimization method-
ologies. PSO can be utilized in the process of optimizing the irregular issues. The
US military is investigating swarm intelligence techniques for controlling unmanned
vehicles. NASA is also investigating the use of PSO technology for planetary map-
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181
ping. PSO is also applied in computer graphics and visualization. It is also used in
designing and optimizing engines as well as electrical motors.
9.7 Summary
This chapter surveys different nature-inspired algorithms and their applications in
different domains. We have also surveyed different algorithms used in optimal
placement and selection of virtual machines in the cloud-centered ecological sys-
tems. All the algorithms discussed have considered various parameters like memory
optimization, bandwidth optimization, computation time and cost, etc. As noted
each algorithm has its limitations, and we need to develop a hybrid solution for
optimizing the placement of virtual machines in the cloud.
References
1. Abdelsamea, A., etal.(2017, November). Virtual machine consolidation enhancement using
hybrid regression algorithms. Egyptian Informatics Journal, ,18(3), 161–170.
2. Guo, Z., et al. (2017). Heuristic algorithms for energy and performance dynamic optimiza-
tion in cloud computing. Computing and Informatics, 36, 1335–1360. https://doi.org/10.4149/
cai201761335.
3. Dhanda, M., & Sangwan, O. P (2019). Quality of Service in Dynamic Resource Provisioning:
Literature Review. In: Minz S., Karmakar S., Kharb L. (eds) Information, Communication
and Computing Technology. ICICCT 2018. Communications in Computer and Information
Science, vol 835. Springer, Singapore.
4. Kumar, M.S., Sindhuja, P., & Ramamoorthy, P. (2018). A brief survey on nature inspired
algorithms: Clever algorithms for optimization. Asian Journal of Computer Science and
Technology, 7, 27–32.
5. Binitha, S., & Sathya, S. S. (2012). A survey of bio inspired optimization algorithms.
International Journal of Soft Computing and Engineering, 2(2), 137–151.
6. Theja, P. R., & Babu, S. K. (2014). Resource optimization for dynamic cloud comput-
ing environment: A survey. International Journal of Applied Engineering Research, 9(24),
26029–26042.
Load Balancing
Task scheduling
Workflow Scheduling
Cost Optimization
Others
Fig. 9.6 Applications of PSO in cloud computing
9 Analysis ofVirtual Machine Placement andOptimization Using Swarm Intelligence…

182
7. Suseela, B. B. J., & Jeyakrishnan, V. (2014). A multi-objective hybrid ACO-PSO optimiza-
tion algorithm for virtual machine placement in cloud computing. International Journal of
Research in Engineering and Technology, 3(4), 474–476.
8. Usmani, Z. & S. Singh (2016). “A survey of virtual machine placement techniques in a cloud
data center.” Procedia Computer Science 78: 491–498.
9. Choudhary, A., Govil, M. C., Singh, G., Awasthi, L. K., Pilli, E. S., & Kapil, D. (2017). A criti-
cal survey of live virtual machine migration techniques. Journal of Cloud Computing, 6(1), 23.
10. Rani, P. and A. K. Bhardwaj (2017). “A Review: Metaheuristic Technique in Cloud Computing.”
International Research Journal of Engineering and Technology (IRJET) Volume 4.
11. St́ephanie, C., Paraiso, F., Merle, P. (2017). A Study of Virtual Machine Placement
Optimization in Data Centers. 7th International Conference on Cloud Computing and Services
Science.
12. Son, J., & Buyya, R. (2019). Priority-aware VM allocation and network bandwidth provi-
sioning in Software-Dened Networking (SDN)-enabled clouds. IEEE Transactions on
Sustainable Computing, 4(1), 17–28.
13. Blum, C. (2005). Ant colony optimization: Introduction and recent trends. Physics of Life
Reviews, 2(4), 353–373.
14. Gao, Y., Guan, H., Qi, Z., Hou, Y., &Liu, L. (2013, December). A multi-objective ant colony
system algorithm for virtual machine placement in cloud computing. Journal of Computer and
System Sciences,79(8), 1230–1242.
15. Dorigo, M., & Gambardella, L. M. (1997). Ant colony system: A cooperative learning approach
to the traveling salesman problem. IEEE Transactions on Evolutionary Computation, 1(1),
53–66.
16. Bin, W., Qinke, P., Jing, Z., & Xiao, C. (2012, June 1). A binary particle swarm optimiza-
tion algorithm inspired by multi-level organizational learning behavior. European Journal of
Operational Research, 219(2), 224–233.
17. Zhang, S., etal. (2017). Numerical study on attenuation of bubble pulse through tuning the air-
gun array with the particle swarm optimization method. Applied Ocean Research, 66, 13–22.
18. Jau, Y.-M., Su, K.-L., Wu, C.-J., & Jeng, J.-T. (2013). Modied quantum-behaved particle
swarm optimization for parameters estimation of generalized nonlinear multi-regressions
model based on Choquet integral with outliers. Applied Mathematics and Computation, 221,
282–295. https://doi.org/10.1016/j.amc.2013.06.050.
19. Tang, D., Cai, Y., Zhao, J., & Xue, Y. (2014). A quantum-behaved particle swarm optimiza-
tion with memetic algorithm and memory for continuous non-linear large scale problems.
Information Sciences, 289, 162–189. https://doi.org/10.1016/j.ins.2014.08.030.
20. Davoodi, E., TarafdarHagh, M., & GhassemZadeh, S. (2014). A hybrid Improved Quantum-
behaved Particle Swarm Optimization–Simplex method (IQPSOS) to solve power sys-
tem load ow problems. Applied Soft Computing, 21, 171–179. https://doi.org/10.1016/j.
asoc.2014.03.004.
21. Li, P. (2008). Quantum genetic algorithm based on Bloch coordinates of qubits and its applica-
tion. Control Theory & Applications, 25(6), 985–989.
22. Yumin, D., & Li, Z. (2014). Quantum behaved particle swarm optimization algorithm based
on articial sh swarm. Mathematical Problems in Engineering, 2014, 1–10. https://doi.
org/10.1155/2014/592682.
23. Jia, P., Tian, F., Fan, S., He, Q., Feng, J., & Yang, S. (2014). A novel sensor array and classi-
er optimization method of the electronic nose based on enhanced quantum-behaved particle
swarm optimization. Sensor Review, 34(3), 304–311. https://doi.org/10.1108/SR-02-2013-630.
24. Gholizadeh, S., & Moghadas, R. (2014). Performance-based optimum design of steel frames
by an improved quantum particle swarm optimization. Advances in Structural Engineering,
17, 143–156. https://doi.org/10.1260/1369-4332.17.2.143.
25. Pluhacek, M., Senkerik, R., & Zelinka, I. (2014). Particle swarm optimization algorithm
is driven by multichaotic number generator. Soft Computing, 18, 631–639. https://doi.
org/10.1007/s00500-014-1222-z.
R. B. Madhumala and H. Tiwari

183
26. Singh, S. and I. Chana (2015). “QRSF: QoS-aware resource scheduling framework in cloud
computing.” The Journal of Supercomputing 71(1), 241–292.
27. Dashti, S.E., & Rahmani, A.M. (2016). Dynamic VMs placement for energy efciency by
PSO in cloud computing. Journal of Experimental & Theoretical Articial Intelligence, 28(1–
2), 97–112.
28. Lalwani, S., H. Sharma, S. C. Satapathy, K. Deep and J. C. Bansal (2019). “A Survey on Parallel
Particle Swarm Optimization Algorithms.” Arabian Journal for Science and Engineering
44(4), 2899–2923.
29. Xu, G. and G. Yu (2018). “Reprint of: On convergence analysis of particle swarm optimization
algorithm.” Journal of Computational and Applied Mathematics 340, 709–717.
30. Selvaraj, A., R. Patan, A. H. Gandomi, G. G. Deverajan and M. Pushparaj (2019). “Optimal
virtual machine selection for anomaly detection using a swarm intelligence approach.” Applied
Soft Computing 84, 105686.
9 Analysis ofVirtual Machine Placement andOptimization Using Swarm Intelligence…

185© Springer Nature Switzerland AG 2020
A. Haldorai et al. (eds.), Business Intelligence for Enterprise Internet of Things,
EAI/Springer Innovations in Communication and Computing,
https://doi.org/10.1007/978-3-030-44407-5_10
Chapter 10
Performance Evaluation ofDifferent
Neural Network Classiers forSanskrit
Character Recognition
R.DineshKumar, C.Sridhathan, andM.SenthilKumar
10.1 Introduction
Classifying the input characters according to the predened character data set is a
challenging task. Many researchers using the image processing techniques have
contributed their work on English character recognition. There is an increasing
interest for computer applications which ease the human jobs and solve complex
problems. Optical character recognition is used for classifying the alphanumeric
characters which are in the digital image format. Classical methods for handwritten
character recognition have drawbacks. First, same characters will differ in style,
shape, and size from person to person. Second, the characters will vary from time to
time for the same person. Third, visual characters are affected with noise near the
edges while reading through OCR techniques. Optical character recognition system
has a variety of commercial and practical applications such as in banks, postal ser-
vice, license plate recognition system, smart card processing system, and so
on [1–3].
The third most commonly utilized language after English and Chinese is Hindi.
There are around 500 billion people all over the world who write and speak in
Hindi. Hindi is the fundamental script of many Indian languages that originated
from Sanskrit. Sanskrit being an ancient language is no longer spoken. It is a very
expressive language, which has been enriched and inuenced by Farsi, Turkish,
Dravidian, Portuguese, English, and Arabic.
Printed Sanskrit characters are effortlessly identied by computer machines.
But, handwritten Sanskrit characters are not being identied accurately and
R. D. Kumar (*)
Siddhartha Institute of Technology & Science, Hyderabad, Telangana, India
C. Sridhathan · M. S. Kumar
Nalla Malla Reddy Engineering College, Hyderabad, Telangana, India

186
efciently by the computer machine. Numerous researchers have proposed different
kinds of methods and algorithms for recognizing the Sanskrit characters. Several
software are used for optical Sanskrit character recognition. However, for recogniz-
ing the handwritten Sanskrit characters, several procedures are to be adopted; no
single machine or single process can perform Sanskrit character recognition.
Articial neural network (ANN) can be used for character recognition because of its
versatility and simplicity of working design.
10.2 Literature Review
The functioning of the human brain has inspired many scientists to come up with
computing systems termed as neural networks. Some researchers applied optical
character recognition using feed-forward neural networks, backpropagation algo-
rithm model, and other techniques for pattern recognition [1, 3, 4].
Recognition of handwritten characters is the most mesmerizing research area in
the eld of image processing and pattern recognition. In recent years, character
recognition has been a popular research area for many researchers. This is because
of its potential application in several elds like number plate recognition, sorting
letter based on postal codes, etc. The rst research on Devanagari characters was
published in 1977[5]. At present researchers are working on handwritten character
recognition of many languages such as English, Chinese, Hindi, Tamil, Bangla, and
Telugu. Few researchers are focusing on neural network and articial intelligence
technique for reduced processing time with higher recognition accuracy [1–3, 6–9].
Character recognition is mainly classied into online and ofine which are further
classied into clustering template matching, etc. as shown in Fig.10.1 [8]. Existing
Character
Recognition
ANN
Feature Extraction
Clustering
Template Matching
Off lineOnline
KNN
Direction Based
Fig. 10.1 Classication of character recognition system
R. D. Kumar et al.

187
methods for handwriting identications are template matching, statistical and struc-
tural techniques, fuzzy logic, support vector machines, and neural networks (NNs).
Recognition of handwritten character involves (1) image acquisition, (2) image
pre-processing, (3) image segmentation, (4) feature extraction, and (5) classica-
tion. Classication stage is the decision-making part of image processing systems.
Feature extraction and classication play a signicant role in handwritten character
recognition [1, 2, 6].
10.3 Character Recognition Process
Character recognition process for ofine handwritten patterns consists of the fol-
lowing stages as shown in Fig.10.2.
10.3.1 Image Acquisition
The handwritten Sanskrit characters are scanned as an input as shown in Fig.10.3.
10.3.2 Pre-processing
Scanned handwritten Sanskrit characters are transferred as an image. Pre-
processing involves binarization, denoising, thinning, skew detection, and correc-
tion. In binarization, color images are converted into the gray-scale image with the
help of the balanced histogram thresholding technique. Based on the histogram
Pre-processing
Input Document
Recognised Document
Training Data
Classification
SegmentationFeature Extraction
Image Matrix Mapping
Training Network
Fig. 10.2 Stages of Sanskrit handwritten character recognition system
Fig. 10.3 Scanned Sanskrit handwritten character
10 Performance Evaluation of Different Neural Network Classiers for Sanskrit…

188
weights, this method divides the images into two: foreground and background.
Denoising is performed using non-local median lters for eliminating and sup-
pressing the noise level. Zhang-Suen thinning algorithm has been utilized to pro-
duce another binary image with a thickness of one pixel for better edge detection.
This minimizes the noises without losing the quality of the image. The nal step in
pre-processing is skew detection where the orientation of the character is adjusted
with respect to the true horizontal axis. The angle of orientation of the character is
rotated such that it is ±15 degrees. Pre-processed Sanskrit handwritten character is
shown in Fig.10.4.
10.3.3 Segmentation
In segmentation, the required region of the image is separated. Here, improved
multi-scale segmentation approach is used to divide the characters into the
horizontal and vertical projections with the help of the fragmentation process.
After the fragmentation process, the texts are split into the paragraphs and then
lines, words, and characters with the help of the histogram approach. Line, word,
and character segmentation is carried out and segmented character images are
shown in Fig.10.5.
Fig. 10.4 Pre-processed Sanskrit handwritten character
Fig. 10.5 Character segmentation
R. D. Kumar et al.

189
10.3.4 Feature Extraction
The statistical and structural procedure can be used for extracting features such as
height, width, horizontal lines, vertical lines, circles, slope lines, and arcs. Here the
height and width of the characters are estimated with the glyph and related bound-
ary values. These detected line values are decomposed by applying the wavelet
function described in Eq.10.1.
Fabfxabxdx
,,
()
=∫
()
∗
()()
∞
−∞
ψ
(10.1)
where ∗ is the complex conjugated and ψ is the Daubechies wavelet function.
In addition mean, variance, standard deviation, entropy, and energy are also cal-
culated and their corresponding formulae are given in Eqs.10.2, 10.3, and 10.4.
Mean ip i
n
i
xy
=∑
()
−
()
=
+
21
0
.
(10.2)
Varianceipij
n
i
n
j
=∑∑−
()
()
−
=
−
=
1
0
1
02
µ
.,
(10.3)
Entropy
pp
n
ij
ij ij
=∑−
()
−
=
1
0,
ln
(10.4)
10.4 Classication
Feature extracted characters are used in the classication stage for decision-making.
The classication methods can be statistical methods, articial neural networks
(ANNs), kernel methods, and multiple classier combination. In this work RCS
with BPNN, BPNN with RBF, and multilayer perceptron model are used for
classication.
10.4.1 RCS withBPNN
Random candidate selection (RCS) with backpropagation neural network (BPNN)
is one of the efcient supervised learning models which maps each input to the
particular output. The main goal of the RCS with BPNN is to train the network in
10 Performance Evaluation of Different Neural Network Classiers for Sanskrit…

190
an efcient manner that can learn the appropriate internal representations, which
allows the arbitrary mapping of the input to the output with minimum error rate.
Steps involved in the proposed algorithm are as follows:
Step 1: Initialize all weights.
Step 2: Input vector consists of Fourier descriptors and border transition values
which are used as classiers.
Step 3: By using non-linear sigmoid function, the weights are adjusted to obtain the
outputs yj.
Step 4: The weights are adjusted such that all the training inputs are used for weight
stabilization.
The structure of the network and the training algorithm used in RCS improve the
recognition of Sanskrit character and reduce the error rate based on the learning rate.
10.4.2 BPNN withRBF Network
The backpropagation neural network with radial basis function is one of the better
classication algorithms with training process to get the desired output. RBFN con-
sists of input layer, hidden layer, and output layer. Each layer produces the linear
output even for non-linear inputs. In radial basis function neural network, extracted
features like character height, weight, direction, mean, standard deviation, and vari-
ance are fed as inputs and transferred to the hidden layer that uses the radial basis
(radbas) as the activation function. K-mean clustering algorithm is used to obtain
the center value as given in Eq.10.5.
argmin Xi
XSk
i
i
=−
∈
=
∑∑
µ
12
(10.5)
In the recognition phase, the characters are identied based on the shape and the
distance between the centers. Finally, the features of the handwritten character are
stored as a template in the database and compared with the new character as given
in Eq.10.6.
yx
Wx
k
p
M
j
kj j
p
()
== ∑
()
=0
ϕ
(10.6)
10.4.3 Multilayer Perceptron Neural Network
Multilayer perceptron neural network’s working principle is relatively based on the
human brain. Normally a human brain stores the information as a pattern and gains
the knowledge to solve the complex problems by experience. The multilayer
R. D. Kumar et al.

191
perceptron neural network recognizes the patterns by supervised training algorithm
with feed-forward from input to output layers. Activation function is computed as
given in Eq.10.7.
xnfWxn
i
m
M
jN
ij
m
j
m
+=…
()
=∑
()





1
1
(10.7)
Based on the error rate, the weightage factor is modied as given in Eq.10.8.
neww
wnxn
ji
m
ij
m
T
t
i
m
j
m
.=+∑
()
()
−=−1
1
1
γδ
(10.8)
10.5 Performance Analysis
The performance of RCS with BPNN, BPNN with RBF, and multilayer perceptron
neural network models is evaluated based on the following metrics:
10.5.1 Accuracy
Accuracy is a statistical measure which is used to analyze how well the classier
recognizes the Sanskrit character with optimized way in Eq.10.9.
Accuracy =
+Noof True PositiveNoofTrueNegative
TotalNoofSample
)
ss
(10.9)
Accuracy can also be expressed in terms of sensitivity and specicity as given in
Eq.10.10.
Accuracy sensitivity prevalence specificity prevalen=
()()
+
()
−
1c
ce
()
(10.10)
10.5.2 Sensitivity
Sensitivity is a measure of how the proposed system correctly classies the hand-
written characters with efcient manner. The sensitivity is measured using Eq.10.11.
Sensitivity True Positive
True PositiveFalse Negative
=
+
(10.11)
10 Performance Evaluation of Different Neural Network Classiers for Sanskrit…

192
10.5.3 Specicity
Specicity measures how the proposed system correctly identies the negative clas-
siers during the character recognition process. It is expressed as shown in Eq.10.12.
Specificity True Negative
True NegativeFalse Positive
=
+
(10.12)
The minimal error rate and precision recognition are used so that the efciency
of the Sanskrit character recognition system is increased.
10.6 Results
The experimental results of the random candidate selection with backpropagation
neural networks, backpropagation neural networks with radial basis function net-
works, and multilayer perceptron neural network models are evaluated in terms of
error rate, sensitivity, specicity, and accuracy measures. The results of the pro-
posed classiers are compared with the traditional classiers such as SVM, SOM,
etc., and the comparison results are shown below in Tables 10.1, 10.2, and 10.3.
Table 10.1 Performance evaluation of proposed RCS with BPNN and existing methods
Different classiers Sensitivity Specicity Accuracy
SVM 85 87 93
SOM 86.5 87.43 94.23
RBFN 88.23 91.23 94.34
Fuzzy NN 89.46 94.23 95.35
Proposed RCS with BPNN 92.13 95.63 96.45
Table 10.2 Performance evaluation of BPNN with RBF and existing methods
Different classiers Sensitivity Specicity Accuracy
SVM 85 86 92
SOM 83.5 86.43 93.23
RBFN 87.23 90.23 94.34
Fuzzy NN 88.46 93.23 95.35
BPNN with RBF 93.13 96.63 97.45
Table 10.4 Efciency comparison for proposed character recognition methods
Metrics RCS with BPNN BPNN with RBF MLP networks
Sensitivity 92.13 93.15 94.13
Specicity 95.63 96.63 96.73
Accuracy 96.45 97.45 98.46
R. D. Kumar et al.

193
The experimental metrics results of the MLP network models were compared
with other models such as RCS with BPNN and BPNN with RBF; it had higher
overall efciency. The comparison results are shown in Table10.4 below. It can be
seen from Table10.4 that the sensitivity, specicity, and accuracy levels of MLP
level are higher. The methods used for recognizing the handwritten Sanskrit charac-
ters produce minimum error rate and high accuracy when compared to other exist-
ing methods.
10.7 Summary
Handwritten Sanskrit character recognition was done using the following methods:
RCS with BPNN, BPNN with RBF, and MLP network. The MLP network gave an
effective and efcient result in both feature extraction and recognition. The accu-
racy level of MLP level was 98% and execution time required was very less. This
work can be extended in face recognition also.
References
1. Abbas, M. (2016). Ofine handwriting recognition using neural networks. Thesis.
2. Aicha, E., Mohamed, K.K., & Hacene, B. (2015). Ontologies and bigram-based approach for
isolated non-word errors correction in OCR system. International Journal of Electrical and
Computer Engineering (IJECE), 5(6), 1458–1467.
3. Chirag, I.P., Ripal, P., & Palak, P. (2011). Handwritten character recognition using neural
network. International Journal of Scientic & Engineering Research, 2(5), 1–6.
4. Khushbu, S.M. (2013). Image pre-processing on character recognition using neural networks.
International Journal of Computer Applications, 82(13), 11–15.
5. Dimple, B., Gulshan, G., & Maitreyee, D. (2014). Design of an effective preprocessing
approach for ofine handwritten images. International Journal of Computer Applications,
98(1), 17–23.
6. Shoba, R., Sanjay, K. V., & Anitta, J. (2016). A zone based approach for classication and
recognition of telugu handwritten characters. International Journal of Electrical and Computer
Engineering, 6(4), 1647–1653.
Table 10.3 Performance evaluation of proposed MLP and existing methods
Different classiers Sensitivity Specicity Accuracy
SVM 86 87 97
SOM 87.5 86.42 96.89
RBFN 89.23 95.23 97.54
Fuzzy NN 91.46 91.43 97.77
Proposed MLP 94.13 96.73 98.46
10 Performance Evaluation of Different Neural Network Classiers for Sanskrit…

194
7. Shobha, R., Neethu, O.P., & Nila, P. (2015). Automatic vehicle tracking system based on xed
thresholding and histogram based edge processing. International Journal of Electrical and
Computer Engineering, 5(4), 869–878.
8. Shruti, S.K., Hoshank, J.M., Sakshi, S.G., Sarang, S.S., & Chitre, D.K. (2017). Handwriting
recognition using neural network. International Journal of Engineering Development and
Research, 5(4), 1179–1181.
9. Sudarshan, S., & Seema, B. (2016). Handwritten character and word recognition using
their geometrical features through neural network. International Journal of Application or
Innovation in Engineering & Management, 5(7), 77–85.
R. D. Kumar et al.

195© Springer Nature Switzerland AG 2020
A. Haldorai etal. (eds.), Business Intelligence for Enterprise Internet of Things,
EAI/Springer Innovations in Communication and Computing,
https://doi.org/10.1007/978-3-030-44407-5_11
Chapter 11
GA withRepeated Crossover
forRectifying Optimization Problems
MayankJha andSunitaSinghal
11.1 Introduction
Optimization is at the core of many processes that solve problems in the real world.
Nonetheless, identifying the most optimal resolution for these issues is normally
tiresome, mostly in the availability of non-linear, high-dimensionality, and multiple
modalities. The evolving algorithm has indicated a more considerable success
recorded over the past few years to deal with the complex issues of optimization.
Whereas the EA family includes a number of various algorithms, the most promi-
nent and commonly used in practice is the genetic algorithm (GA). There has been
an increase in studies relating to the actual-parameter genetic algorithm (GA) in the
past decades irrespective of the presence of one similar actual-parameter evolution-
ary algorithm, such as evolutionary technique and the differential evolution. In con-
trast, in theory, the actual-parameter GA studies indicated the same theoretical
behavior in tness landscapes with appropriate parameter tuning, as shown in the
previous study.
Crossover and mutation include the vital operation utilized in genetic algorithm.
The genetic algorithm (GA) is signicantly utilized in dealing with the practical
optimization issues. GA has the capability of handling both the discrete and con-
tinuous variables, which is suited for the purpose of handling the parallel computing
and tness environments that are extremely complex. Some researchers have desig-
nated that the genetic algorithms have a better adaptability to effectively deal with
the various models instead of ancient mathematics programming techniques. In this
M. Jha (*)
Citicorp Services India Limited, Pune, India
S. Singhal (*)
School of Computing and Information Technology, Manipal University Jaipur, Rajasthan,
India
e-mail: sunita.singhal@jaipur.manipal.edu

196
chapter, the main aim is to enhance the status of task scheduling based on the
comparison of the proposed genetic algorithm with the present techniques to obtain
the accuracy, tness value, and runtime and know the turnaround time of the task
allocated by various task schedulers like FCFS, RR, etc.
11.2 Proposed System
In the proposed system, we have purposed a genetic algorithm to calculate and mea-
sure the tness value from the assigned tasks in the scheduler. The tasks schedulers
could be in the form of FCFS, RR, etc. By using GA we will come to know the mini-
mum turnaround time or execution time required for processing the tasks as shown
in Fig.11.1.
11.2.1 General Arrangement ofaGenetic Algorithm
Generally, GA includes ve elements:
start
Initial
N
New Populationstop
best solution
roulette wheel
Y
terminating Solution candidates
fitness computationcondition?
Population P(t)
1100101010
1011101110
0011011001 0011011001
0011001001
1100110001
1100101010
1011101110
1100101110
1011101010
▪
▪
▪
chromosome
decoding
decoding
evaluation
selection
mutation
Offspring C(t)
CC(t
)
CM(t)
P(t)+C(t)
chromosome
solutions
encoding crossover
t¬0
t¬t+1
Fig. 11.1 The general structure of a genetic algorithm
M. Jha and S. Singhal

197
1. A genetic account of probable resolutions to the issue
2. A technique of formulating a population (a preliminary assortment of possible
solutions)
3. Testing functions in terms of tness ranking solutions
4. Genetic operators altering offspring’s genetic composition (crossover, mutation,
selection, etc.)
5. The constraint values utilized by the genetic algorithm (population size, genetic
operator probabilities, etc.)
11.2.2 Algorithm Techniques
The proposed algorithm technique for task scheduling is genetic algorithm as
given below:
Algorithm: Genetic Algorithm
1 START
2 Generate the initial population
3 Compute tness
4 REPEAT
5 Selection
6 Crossover
7 Mutation
8 Compute tness
9 UNTIL secluding has converged
10 STOP
The given algorithm shows the execution process of the tasks allocated to the
CPU.The GA includes primarily the initialization, discovery, crossover, and muta-
tion phases. Initially all the tasks are given for processing. A set of parameters
(variables) known as genes describe a person. The tness function determines how
competitive a person is (an individual’s ability to compete with other people). The
probability that an individual will be selected for processing is based on its tness
score. The whole process is repeated until the t individuals are not obtained. Next
step is for tness score. The tness element refers to the tness of someone’s state
(an individual’s ability to compete with other folks). It gives a tness score to each
individual. Next step is selection. Fitness-related selection, also known as selec-
tion of roulette wheels, is a genetic operator used in genetic algorithms to select
potentially useful recombination solutions. In tness-related selection, as in all
selection strategies, the tness function assigns tness to possible solutions. A
crossover is a simple operation wherein a pair of strings randomly swaps their
substrings with each other. This operator’s role in GA is more critical. It imple-
ments the principle of evolution. Next step is mutation. An operation of mutation
11 GA withRepeated Crossover forRectifying Optimization Problems

198
is formulated to diminish the processors’ idle time that is spent waiting for the
other processors’ information. Again the tness of the process is computed for the
selection procedure until the scheduling process is converged.
11.3 Literature Review
Shigeyoshi Tsutsui etal. [1] projected simplex crossover (SPX), a new multi-par-
ent recombination operator for real-coded GAs. The SPX is considered as a simpli-
ed crossover operator, which utilizes the search space property of the simplex.
The SPX is characterized with a balance between the development and discovery
and is distinct in the production of offspring from coordinate systems. The results
of the experiment showed high performance with multimodality and/or epistasis
on test functions.
Isao Ono etal. [2] proposed a novel actual-coded genetic algorithm that utilizes
the unimodal normal distribution crossover (UNDX) improved by the uniform
crossover (UX). The UNDX is characterized by an advantage that is not found in
other crossover operators. As such, this is effective for the process of optimizing the
functions in operators with rm epistasis. UNDX is assured with the most crossover
operators; hence, it is vital for optimizing various functions in the parameters with
rm epistasis.
Zhu Can etal. [3] applied the genetic algorithms with actual codes to resolve
issues meant to nd the novel individual promised by the crossover operators of the
seeds in various localized minimal points disrupting schemata of parents. This was
responsible in acting as mutation operators. With reference to this, the idea is sepa-
rated from the optimal sub-population and proposed by the population division cen-
tered on the Euclidean distances between the present optimal individuals. Kalyanmoy
Deb etal. [4] recommended the standardized parent-centered recombination opera-
tors and the steady-state, scalable, elite-conserving, and rapid population shift mod-
els. The performance of the G3 system, including the PCX operator, is evaluated on
three extensively utilized test issues as contrasted with various classical and evolu-
tionary optimization algorithms, which include other actual-parameter GAs com-
posed of uniform modal normal distribution crossover (UNDX) and simplex
crossover (SPX) operators and the related self-adaptive evolutionary algorithm. The
technique of covariance matrix adaptation evolution (CMA-ES), the methodology
of differential evolution, and the quasi-Newton method are some of the initiatives.
It was noticed in the analysis that the recommended algorithm performed an effec-
tive, reliable, and consistent approach. Saber M.Elsayed et al. [5] presented an
objective meant to enhance the evaluation of the genetic algorithm through the pro-
cess of initiating novel crossover with random operator targeted at replacing muta-
tion. The projected crossover uses three parents vital for generating three new
M. Jha and S. Singhal

199
offspring relevant for exploitation, whereas a third offspring is purposed to enhance
exploration. The randomized operator is helpful to facilitate an escape and prema-
ture convergence.
Can et al. [6] analyzed the unfair use of fair selection laws to overcome
genetic algorithm restricted optimization. A form of population framework with
the ideology of Pareto dominance, including the sequence of user factory
designs. As a result, this balances the feasible limits and regions along an opti-
mal search path of the resolution that makes the genetic algorithm of constrained
optimization on all sides of the region feasible to consider the optimal resolu-
tion. There is a relation between the feasible part in demes and the revolutionary
generation. It considers both the algorithmic search quality and efciency opti-
mization. The example of computational research and engineering has shown
that the improved genetic algorithm of constrained optimization includes a sim-
plied algorithm structure with a signicant quality of resolutions. Tetsuyuki
Takahama etal. [7] recommended a constrained DE with the archive and gradi-
ent-based mutation (εDEag) to enhance the stability, usability, and efciency of
the εDEg.
K.Sunitha etal. [8] designed an algorithm to schedule the DAG tasks on hetero-
geneous processors in such a way that minimizes the total completion time (makes-
pan). Makespan is a measure of the throughput of the heterogeneous computing
system (execution time+waiting time or idle time). Comparing makespan for dif-
ferent number of processors, number of tasks, population size, and number of gen-
erations to be done. Yan Kang etal. [9] have shown the use of GAs in the process of
dealing with task scheduling issues through various means. The two vital techniques
appear to be the methodologies that apply the GA in the linking of other list sched-
uling methods and techniques that apply the GA to produce the real assignment of
transforming tasks to processors.
11.3.1 Expected Results
This section contrasts simulation results of our proposed algorithm with the results
of several other algorithms. Table11.1 lists the parameters of our proposed algo-
rithm and the other algorithms used in the performance analysis. Table 11.2 indi-
cates the detailed specications.
By studying various techniques in literature survey, we will improve the methods
and techniques in Figs.11.2, 11.3, and 11.4. We also applied our algorithm to a task
scheduling problem [10]. Task scheduling’s primary goal is to schedule tasks on
processors and reduce the make-up of the program, i.e., the accomplishment period
of the last task compared to the start time of the rst task. The output of the problem
is the assignment of tasks to processors.
11 GA withRepeated Crossover forRectifying Optimization Problems

200
Table 11.1 Comparison of all algorithms along with their parameters and values
Algorithm Parameter Value
GGA P-CROSSOVER 0.8
P-MUTATION 0.8
NUMBER OF GENERATION VARIABLE
SCGA P-CROSSOVER 0.8
P-MUTATION 0.8
NUMBER OF GENERATION VARIABLE
SSGA P-CROSSOVER 0.8
P-MUTATION 0.8
NUMBER OF GENERATION VARIABLE
GA-RA P-CROSSOVER 0.8
P-MUTATION 0.8
NUMBER OF GENERATION VARIABLE
CLOUDSIM MIPS(VM) 20(incremented by 10)
CLOUDLET LENGTH 4000(incremented by 50)
SCHEDULING POLICY VARIED
Table 11.2 Algorithm’s runtime and objective value for OneMax function
OneMax
gGA GA-RA
Iterations Runtime Value Runtime Value
5000 158 315 332 512
10000 372 332 550 512
15000 563 350 808 512
3.85
3.75
3.65
3.55
3.45
3.350102030405060
SpeedUP
SpeedUP
Number of Tasks
3.8
3.7
3.6
3.5
3.4
Fig. 11.2 Speedup vs. number of tasks
M. Jha and S. Singhal

201
11.4 Summary
GA is a heuristic remedy search method motivated by the physical evolution. It
belongs to the class of exible and robust approaches. GA can be used in a wide
range of optimization and learning issues. This is one of the major reasons for its
enormous popularity. It is certainly suited to issues whereby traditional optimiza-
tion systems break down due to irregular structure of search spaces or due to
searches becoming computationally intractable. In this research, we have proposed
a genetic algorithm for solving optimization problems. We also tested the algorithm
on task scheduling problem and found it efcient in reducing the makespan time.
The experimental examination presented that the algorithm congregates quicker
than its counterparts to the optimal solution. Also, the results produced were effec-
tive based on the projected value, thus exhibiting a superior performance.
1.2
0.8
RR
GA
TS-GA
0.6
0
25 7550
number of Task
100
Speedup
0.4
0.2
1
Fig. 11.3 The comparison speedup of three algorithms RR, GA, and TS-GA
RR
GA
TS-GA
1.2
0.8
0.6
0
25 7550
number of Task
100
Efficiency
0.4
0.2
1
Fig. 11.4 The comparison efciency of three algorithms RR, GA, and TS-GA
11 GA withRepeated Crossover forRectifying Optimization Problems

202
References
1. Shigeyoshi Tsutsui, Masayuki Yamamura, & Higuchi, T.. (1999). Multi-parent recombination
with simplex crossover in real coded genetic algorithms. In IEEE (pp657–664).
2. Ono, I., Kita, H., & Kobayashi, S. (1999). A Robust real-coded genetic algorithm using
unimodal normal distribution crossover augmented by uniform crossover: Effects of self-
adaptation of crossover probabilities. IEEE, 1, 496–503.
3. Zhu Can, & Liang Xi-Ming. (2009). Improved genetic algorithms to solving constrained opti-
mization problems, international conference on computational intelligence and natural com-
puting. In IEEE (pp486–489).
4. Deb, K., Anand, A., & Joshi, D. (2002, December). A computationally efcient evolutionary
algorithm for real-parameter optimization, evolutionary computation. IEEE, 10(4), 371–395.
5. Elsayed, S.M., Sarker, R.A., & Essam, D.L. (2011) GA with a new multi-parent crossover
for constrained optimization. In 2011 IEEE Congress of Evolutionary Computation (CEC)
(pp.857–864). New Orleans.
6. Can, Z., Xi-Ming, L., & Shu-renhu, Z. (2009). Improved genetic algorithms to solving con-
strained optimization problems. In 2009 International conference on computational intelli-
gence and natural computing (pp.486–489). IEEE.
7. Takahama, T., & Sakai, S. (2010). Constrained optimization by the ε constrained differen-
tial evolution with an archive and gradient-based mutation. In Congress on Evolutionary
Computation (pp.1–9). Barcelona: IEEE.
8. Sunita, K., & Sudha, P. V. (2013). An efcient task scheduling in distributed computing
systems by improved genetic algorithm. International Journal of Communication Network
Security, IEEE, 2, 24–30.
9. Kang, Y., & Zhang, Z. (2011). An activity-based genetic algorithm approach to multiproces-
sor scheduling. In 2011 Seventh international conference on natural computation (pp.1048–
1052). Shanghai: IEEE.
10. Anandakumar, H., & Umamaheswari, K. (2017). Supervised machine learning techniques in
cognitive radio networks during cooperative spectrum handovers. Cluster Computing, 20(2),
1505–1515.
M. Jha and S. Singhal

203© Springer Nature Switzerland AG 2020
A. Haldorai et al. (eds.), Business Intelligence for Enterprise Internet of Things,
EAI/Springer Innovations in Communication and Computing,
https://doi.org/10.1007/978-3-030-44407-5_12
Chapter 12
An Algorithmic Approach toSystem
Identication intheDelta Domain Using
FAdFPA Algorithm
SouvikGanguli , GagandeepKaur, PrasantaSarkar, andS.SumanRajest
12.1 Introduction
System identication relates to the eld of modeling dynamic systems from experi-
mental data [1]. This process requires a huge amount of information regarding its
input/output which may not always be available. In the classical methods, a model
structure is rst selected, and the unknown model parameters are identied by mini-
mizing an objective function. However, conventional techniques are typically based
on gradient descent techniques [2].
Metaheuristic algorithms and their hybridizations have already made inroads in
identication and control literature [3, 4]. Wiener and Hammerstein systems are two
commonly used prototypes used for the identication of systems [5]. Parameter
assessment of these systems is conducted in the discrete time based on the applica-
tion of the time domain shift operator and the complex domain z-transform using
various soft computing techniques [6–10]. A large volume of works also exists in
the continuous-time system using classical techniques [2]. Hammerstein and Wiener
model identication in a continuous domain is based on the application of the meta-
heuristic method approaches which are rarely being investigated.
There are several methods built on discrete time systems using the ability of
computers in system identication and control. Almost contemporaneously, there
S. Ganguli (*) · G. Kaur
Thapar Institute of Engineering & Technology, Patiala, Punjab, India
e-mail: souvik.ganguli@thapar.edu; gagandeep@thapar.edu
P. Sarkar
National Institute of Technical Teachers’ Training & Research,
Bidhannagar, West Bengal, India
e-mail: psarkar@nitttrkol.ac.in
S. S. Rajest
Vels Institute of Science, Technology & Advanced Studies, Chennai, Tamil Nadu, India

204
has been an analogous endeavor in evolving methods in systems theory because the
physical signals are progressively by default. Modeling, control, and identication
using the delta operator is continuous based on the application of the delta operator
as comprehensive methodology whereby the systems and signals are structured in
the discrete domain and leads to convergence to its corresponding continuous-time
signals and systems at a high sampling frequency therefore unifying both the con-
tinuous- and discrete-time systems and signals [11].
Although, in the discrete-time domain, Hammerstein and Wiener model identi-
cation with metaheuristic approaches is absolutely familiar, similar analyses for
continuous-time systems are rarely reported. Therefore, system classication with
hybrid metaheuristic techniques can be considered for unication of discrete and
continuous systems using the delta operator’s properties. In order to estimate the
unknown Hammerstein and Wiener model parameters in the delta domain, a hybrid
algorithm, namely, FAdFPA, proposed by Ganguli etal. [12] was used. The remain-
der of the chapter is constituted as follows. Section 2 discusses the problem of
identication in the delta domain. Section 3 gives an overview of the FAdFPA algo-
rithm. Section 4 highlights the results, while Sect. 5 infers the paper.
12.2 Statement oftheProblem
Hammerstein and Wiener models are two common models used in dynamic system
identication. Ganguli etal. [13] formulated the simulation of the delta operator to
estimate the unknown structure and parameters of polynomial nonlinearity using
GWOCFA algorithm. Interested readers may go through their modeling equations
that this chapter avoids. The authors of this chapter focused on developing a new
heuristic approach to parameter calculation by minimizing the error resulting from
the difference between real and expected values as described in Eq.12.1.
JNyk yk
k
N
=
()
−
()



=
∑
1
1
2
ˆ
(12.1)
where y(k) and ˆ
yk
()
represent the actual and expected model performance
responses. Therefore, in the next section, the proposed hybrid technique used to
identify parameters in the delta domain is deliberated.
12.2.1 FAdFPA Algorithm
Previously, the authors developed a hybrid algorithm called FAdFPA to solve
unconstrained problems of optimization [12]. By using FA as a global optimizer, the
balance for exploration was achieved, whereas FPA was used to perform exploitation.
To improve upon the local search abilities of FPA, its switch probability formulation
has been made adaptive by the formula in Eq.12.2
S. Ganguli et al.

205
pp pp
tT
=− −
()
×
()
maxmax min / (12.2)
Here, pmaxand pmin are two user-dened parameters, considered as 0.9 and 0.4,
respectively, in the experiments as reported in the literature. “T” denotes the maxi-
mum number of iterations while “t” is the present iteration. The steps for system
identication algorithms using the abovementioned hybrid technique are shown in
Fig.12.1 listed below.
Step 1: Ignite the system with PRBS sequence adulterated with white noise.
Step 2: Yield random solutions for parameters of the linear and nonlinear parts in the
specied search domain.
Step 3: Calculate the tness function J for all possible solutions produced in Step 2.
Step 4: Using FAdFPA algorithm, nd the best possible solution and its position.
Step 5: Compare the best position with the previous best position.
Step 6: Update position for the new tness function.
Step 7: Repeat Step 4 until the maximum iteration or best tness value is produced.
12.3 Results andDiscussions
The transfer function plant model [2] in the continuous time is represented as
Gbb
aa aa
ρ
ρ
ρρρρ
ρ
ρρ ρρ
()
=
+
++ ++
=−+
++
++
01
4
1
3
2
2
34
43 2
5
23 185 800 2500
(12.3)
The corresponding delta model at 100Hz sampling frequency is given by
Gbb
aa aa
δ
δ
δδδδ
δ
δδ δδ
()
=
+
++ ++
=−+
++ +
01
4
1
3
2
2
34
43 2
08 45
22 180 760
..
++ 2200
(12.4)
Two separate continuous nonlinearities, viz.,
Step1: Ignite the system with PRBS sequence adulterated with white noise.
Step2: Yield random solutions for parameters of the linear and nonlinear parts in
the specified search domain.
Step3: Calculate the fitness function J for all possible solutions produced in Step 2.
Step4: Using FAdFPA algorithm find the best possible solution and its position.
Step5: Compare the best position with the previous best position.
Step6: Update position for the new fitness function.
Step7: Repeat Step 4 until the maximum iteration or best fitness value is produced.
Fig. 12.1 Steps for identication algorithm using FAdFPA
12 An Algorithmic Approach toSystem Identication intheDelta Domain Using…

206
yk cx kcxk cx kxkx
kx
k
()
=
()
+
()
+
()
=
()
+
()
+
()
12
2
3
32
3
05 025
..
(12.5)
and
yk xk
ccxk
xk
xk
()
=
()
+
()
=
()
+
()
12
22
010090..
(12.6)
are taken up, respectively, for identifying Wiener and Hammerstein model parame-
ters in the delta domain. For the PRBS input signal, a sample size of 255 was con-
sidered. The input signal has been polluted with an unbiased signal, viz., white
Gaussian noise of SNR 50dB.For each of the experiments, a population segment
of about 20 and a maximum number of iterations of a hundred are considered. The
parent algorithms FA and FPA and the latest heuristics such as MFO, MVO, SCA,
and SSA have been compared. For all the algorithms listed, standard parameter
values are considered for comparison with the hybrid technique.
As metaheuristic algorithms are fundamentally stochastic, they have to be exe-
cuted multiple times to generate meaningful numerical measures. Hence 20 test
runs are executed for every algorithm to get signicant statistical results. Wilcoxon
rank-sum test [14] is also performed to validate the ndings. The identied and
actual parameters for the Hammerstein and Wiener frameworks in the delta domains
are indicated in Tables 12.1 and 12.2, respectively. The results are compared with
sufcient number of heuristic techniques reported in the literature. The closer
parameter estimates to the actual values are marked with the help of bold letters.
From Tables 12.1 and 12.2, it is quite fair to say that the hybrid technique proves
better than the several metaheuristic algorithms in the delta domain. Table12.3 also
provides the statistical measurements of the Wiener and Hammerstein model param-
eters in the delta domain. The best results obtained in each column of the table are
highlighted with bold letters.
The hybrid method yields the minimum tness function as compared to other
algorithms. Since standard deviation with the hybrid approach turns out to be the
least, it can be inferred that the algorithm is more robust than those considered for
comparison. Moreover, to verify the validity of the results obtained, few additional
statistical tests need to be done, verifying that the results did not happen all of a
sudden. The non-parametric Wilcoxon rank-sum analysis is therefore conducted to
verify the signicance of the obtained results, and the measured p-values are cited
as relevant metrics. A few selected p-values are reported in Table12.4.
Table 12.4 suggests that the results of the hybrid method are meaningful for all
other algorithms. The convergence curve is drawn in Fig.12.2 for the test system
used for Wiener model identication in the delta domain. The y-axis of the plot
represents the normalized tness function while the x-axis depicts the number of
iterations. Further, the parent algorithms FA and FPA as well as other popular algo-
S. Ganguli et al.

207
Table 12.1 Comparison of Wiener system model parameters
Types of values Algorithms b0b1a1a2a3a4c1c2c3
Actual −0.8 4.5 22 180 760 2200 1 0.5 0.25
estimated FAdFPA −0.7993 4.4856 21.9989 179.8492 759.8219 2206.2981 1.0018 0.5101 0.2494
FA −0.7572 4.3832 21.7958 172.1841 763.0585 2258.0174 1.0442 0.4592 0.2620
FPA −0.8344 4.5947 22.0104 175.9721 766.0472 2370.6045 1.0576 0.5189 0.2565
MFO −0.7265 4.7379 23.1151 169.4181 734.5583 2251.8777 0.9507 0.4694 0.2538
MVO −0.7271 4.7374 23.1263 169.4236 734.5486 2251.898 0.9422 0.4492 0.3143
SCA −0.7753 4.2673 21.7239 164.1472 741.4551 2312.0279 0.9608 0.4675 0.2636
SSA −0.8429 4.4526 22.2516 172.5593 699.8514 2309.6354 1.0326 0.5385 0.2485
12 An Algorithmic Approach toSystem Identication intheDelta Domain Using…

208
Table 12.2 Comparison of Hammerstein system model parameters
Types of values Algorithms c1c2b0b1a1a2a3a4
Actual 0.1 0.9 −0.8 4.5 22 180 760 2200
estimated FAdFPA 0.9993 0.9023 −0.7943 4.4928 22.4527 179.8753 759.6725 2196.7754
FA 0.1009 0.9099 −0.8623 4.7801 20.6720 188.4102 763.1495 2031.5233
FPA 0.0909 0.8992 −0.8045 4.4909 23.1408 164.1544 825.5786 2031.3154
MFO 0.0992 0.8659 −0.7829 4.5010 21.4638 188.7328 804.1881 2237.4300
MVO 0.0994 0.8758 −0.7927 4.5210 21.4936 188.7438 805.1892 2237.4538
SCA 0.0983 0.9848 −0.7929 4.6235 20.1478 165.2356 793.3553 2275.4090
SSA 0.1063 0.9206 −0.8153 4.4029 20.0600 166.9278 808.3730 2076.1786
S. Ganguli et al.

209
Table 12.3 Statistical analysis of the tness function
Test systems Test methods Minimum Maximum Mean Standard deviation
Wiener model FAdFPA 0.0016 0.0017 0.0016 3.6635e-05
FA 0.0018 0.0020 0.0019 5.8361e-05
FPA 0.0019 0.0021 0.0020 7.4325e-05
MFO 0.0018 0.0020 0.0019 5.8296e-05
MVO 0.0018 0.0021 0.0019 5.8356e-05
SCA 0.0018 0.0019 0.0019 6.7876e-05
SSA 0.0018 0.0020 0.0019 5.5030e-05
Hammerstein model FAdFPA 0.0183 0.0186 0.0184 4.9132e-05
FA 0.0190 0.0196 0.0193 5.9423e-05
FPA 0.0190 0.0196 0.0193 7.7482e-05
MFO 0.0191 0.0197 0.0193 5.9376e-05
MVO 0.0191 0.0198 0.0194 5.9772e-05
SCA 0.0191 0.0195 0.0193 7.8523e-05
SSA 0.0190 0.0195 0.0193 9.7813e-05
Table 12.4 Selected signicant p-values using non-parametric test
Test systems Proposed method FA F PA MFO SCA
Wiener system FAdFPA 7.9026E-05 5.6379E-05 1.2893E-06 2.9131E-06
Hammerstein system FAdFPA 4.9017E-05 5.3492E-05 1.1300E-02 1.2000E-02
0 10 20 30 40 50 60 70 80 90 10
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Convergence plot of wiener model with FAdFPA in the delta domain
No. of iterations
Normalized function value
FAdFPA
FA
FPA
MFO
MVO
SCA
SSA
Fig. 12.2 Convergence curve of Wiener system with FAdFPA
12 An Algorithmic Approach toSystem Identication intheDelta Domain Using…

210
rithms like MFO, MVO, SCA, and SSA are used for comparison with the pro-
posed method.
It is obvious from Fig.12.2 that the planned process congregates at a faster rate
as compared to the other algorithms considered. In addition, the proposed method
provides better accuracy in terms of the tness function value.
12.4 Summary
The hybrid topology assimilating FA and FPA is used to recognize Hammerstein
and Wiener model parameters in the delta domain by minimizing the tness func-
tion. Delta operator modeling unies continuous and discrete-delta domain results.
The hybrid technique’s tness value not only outperforms that obtained by some
latest metaheuristic algorithms but also the heuristics that it constitutes for the test
systems considered. Also, the Wilcoxon test veries the signicance of the hybrid
approach results. Similar to other algorithms, the hybrid approach shows superior
convergence characteristic in the delta domain. Therefore, in the future, the algo-
rithm can also be used to estimate fractional-order systems in the delta domain.
References
1. CLjung, L. (1987). System identication: Theory for the user. Upper Saddle River:
Prentice-hall.
2. Sinha, N.K., & Rao, G.P. (2012). Identication of continuous-time systems: Methodology and
computer implementation (Vol. 7). Dordrecht: Springer Science & Business Media.
3. Bhushan, B., & Singh, M. (2011). Adaptive control of DC motor using bacterial foraging
algorithm. Applied Soft Computing, 11(8), 4913–4920.
4. Xu, X., Wang, F., &Qian, F.(2011). Study on method of nonlinear system identication. In
International Conference on Intelligent Computation Technology and Automation (ICICTA)
(Vol. 1, pp.944–947).
5. Al-Duwaish, H.N. (2000). A genetic approach to the identication of linear dynamical sys-
tems with static nonlinearities. International Journal of Systems Science, 31(3), 307–313.
6. Pal, P. S., Kar, R., Mandal, D., & Ghoshal, S. P. (2016). Identication of NARMAX
Hammerstein models with performance assessment using brain storm optimization algorithm.
International Journal of Adaptive Control and Signal Processing, 30(7), 1043–1070.
7. Sun, J., & Liu, X. (2013). A novel APSO-aided maximum likelihood identication method for
Hammerstein systems. Nonlinear Dynamics, 73(1–2), 449–462.
8. Raja, M.A. Z., Shah, A.A., Mehmood, A., Chaudhary, N.I., & Aslam, M.S. (2016). Bio-
inspired computational heuristics for parameter estimation of nonlinear Hammerstein con-
trolled autoregressive system. Neural Computing and Applications, 29(12), 1455–1474.
(2018).
9. Cuevas, E., Díaz, P., Avalos, O., Zaldívar, D., & Pérez-Cisneros, M. (2018). Nonlinear sys-
tem identication based on ANFIS-Hammerstein model using gravitational search algorithm.
Applied Intelligence, 48(1), 182–203.
S. Ganguli et al.

211
10. Garnier, H., Mensler, M., & Richard, A. (2003). Continuous-time model identication from
sampled data: Implementation issues and performance evaluation. International Journal of
Control, 76(13), 1337–1357.
11. Middleton, R.H., & Goodwin, G.C. (1990). Digital control and estimation: A unied approach
(prentice hall information and system sciences series). Prentice Hall: Englewood Cliffs, NJ.
12. Ganguli, S., Kaur, G., & Sarkar, P. (2016). A novel hybrid metaheuristic algorithm to solve
unconstrained optimization problems. International Journal of Advanced Computational
Engineering and Networking, 4(5), 40–43.
13. Ganguli, S., Kaur, G., & Sarkar, P. (2019). A hybrid intelligent technique for model order
reduction in the delta domain: A unied approach. Soft Computing, 23(13), 4801–4814.
14. Wilcoxon, F., Katti, S.K., & Wilcox, R.A. (1970). Critical values and probability levels for
the Wilcoxon rank sum test and the Wilcoxon signed rank test. Selected tables in mathematical
statistics, 1, 171–259.
12 An Algorithmic Approach toSystem Identication intheDelta Domain Using…

213© Springer Nature Switzerland AG 2020
A. Haldorai et al. (eds.), Business Intelligence for Enterprise Internet of Things,
EAI/Springer Innovations in Communication and Computing,
https://doi.org/10.1007/978-3-030-44407-5_13
Chapter 13
An IoT-Based Controller Realization
forPV System Monitoring andControl
JyotiGupta, ManishKumarSingla, ParagNijhawan, SouvikGanguli ,
andS.SumanRajest
13.1 Introduction
In the recent era, the rapid increase in the consumption of electricity and issues
regarding environmental concerns are the main reasons behind the development of
renewable energy sources (RESs). The electricity generation from RES has been
derived for its economic benets and reliability [1]. Micro-grid includes distributed
generation resources such as photovoltaic, wind, diesel generators, etc. The imple-
mentation of micro-grid in the distribution network was introduced in the early
years of 2000 and encouraged by various agencies and utilities. Micro-grid has
increased the operation of power electronics converter in the power system for ef-
cient and effective power quality [2]. The micro-grid in a distribution system can be
installed near the substation or at the end of the feeder. Micro-grid has an essential
and useful feature, i.e., it can be operated in two different modes, islanded and grid-
connected mode, to give essential support during the time of grid failure or mainte-
nance of grid system by supplying constant power. Due to an increase in the
interconnection of micro-grid in the distribution system, it raises some problems
that include uctuation in the voltage, steady-state over-voltage, increases in the
system loss, and issues related to the voltage regulation devices and protection;
therefore, appropriate allocation of micro-grid is desirable.
There are various types of micro-grids, namely, DC micro-grid, AC micro-grid,
and hybrid micro-grid, which combines both AC and DC micro-grids [3]. Solar
energy systems are dependent on two factors: temperature and irradiance. The max-
J. Gupta · M. K. Singla · P. Nijhawan · S. Ganguli (*)
Thapar Institute of Engineering and Technology, Patiala, India
e-mail: jgupta_phd19@thapar.edu; souvik.ganguli@thapar.edu
S. S. Rajest
Vels Institute of Science, Technology & Advanced Studies, Chennai, India

214
imum power point (MPP) is the point where maximum power output is produced by
PV array. The control scheme is developed using a neural network. A neural net-
work (NN) is designed to provide a gate pulse signal to the inverter circuit to
improve the performance of the system. The articial neural network (ANN) is
based on a machine learning approach and contains the number of articial neurons
to perform the specic task in the system [4]. To investigate its impact in different
conditions, the inverter control scheme is used to improve the efciency of the
power transfer between the PV system and the grid. An IEEE 13-node test feeder
system is considered as a grid system and is commonly used to test features of a
standard distribution network of a power system, operating at 4.16 kV.
The main problem that arises in monitoring the output power of the photovoltaic
system is the accuracy and time duration for the detection of the fault and the appro-
priate solution to it. The best approach for dealing with such kind of issues is the
Internet of Things (IoT) [5, 6]. It is the new concept which has emerged recently and
has gained a lot of attention in a few years. It can be generally explained as an infor-
mation sharing environment where elements of the system are attached to a wireless
and wired network. These days, this concept is not only applicable in the eld of
electronics but also in the area of home appliances, smart cars, industrial secu-
rity, etc.
For the individuals and companies associated with solar panels, the IoT makes it
possible to increase the MPPT reliability and performance of the system [7, 8]. For the
controlling or monitoring of the system, the proposed method is discussed in the chapter.
13.2 Micro-grid Model Description
An IEEE 13-node test feeder system is considered as a grid system and is com-
monly used to test features of a standard distribution network of a power system,
operating at 4.16 kV.
13.2.1 Photovoltaic Module
The photovoltaic system operates on the principle of the photovoltaic effect, that is, when
sunlight is radiated upon the semiconductor diode, there is a movement of electrons from
P-type to N-type side of the semiconductor which produces the current in the system [10].
The photovoltaic module is simulated in MATLAB/Simulink using a photovol-
taic array. To generate power of 341.65 kW, the SunPower SPR-445NX-WHT-D
model is used, selected from the module block in the PV array block. Also, there are
8 series-connected modules and 96 parallel-connected modules per string. The
module parameters and data are shown in Table13.1. These values are calculated
standard temperature and irradiance, i.e., 25°C and 1000 W/m2 irradiance.
J. Gupta et al.

215
13.2.2 DC-Link Capacitor andInverter Circuit
A three-phase inverter circuit is connected across the DC link. DC-link capaci-
tors are employed to stabilize the DC-link voltage of the grid-connected inverter.
Due to temperature and irradiance variation to the total resistance within PV
cells leading to non-linear output efciency, capacitance is needed to allow elec-
tronic control methods to maintain maximum output power. The three-phase
inverter converts the DC output voltage, i.e., 590V, from micro-grid into AC out-
put, i.e., 240V.
13.2.3 RL Filter, Transformer, andLoad
RL lter is connected at the three-phase inverter circuit output, to reduce the
total harmonics distortion in the current. They prevent overcurrent condition in
the system. A three-phase coupling transformer is between the micro-grid sys-
tem and grid. Three-phase coupling transformer, star to the delta, is connected to
step-up the voltage from 240V to 4160V at 50Hz frequency. It has a winding
resistance of 0.01 per unit. The load is the distributed load of IEEE 13-node
feeder systems.
13.2.4 Point ofCommon Coupling andGrid
Point of common coupling is described by using circuit breakers between PV sys-
tem and grid system; it plays a vital role in the proposed method by isolating the PV
system from grid system, i.e., in the islanded mode at the time of grid blackout or
when grid is under maintenance and to supply power to particular load in case of
decit energy produced by micro-grid. Grid is a standard IEEE 13-node test feeder
system, as displayed in Fig.13.1.
Table 13.1 Model parameters
of PV array
Name of the parameters Number/rating
Maximum power (watt) 444.86
Cell per module 128
Light generated current Ip(A) 6.2167
Diode saturation current ID (A) 1.3552e-11
Shunt resistance Rsh (ohm) 508.2463
Series resistance Rse (ohm) 0.54861
13 An IoT-Based Controller Realization forPV System Monitoring andControl

216
13.3 Proposed Control Features
Micro-grid is mainly operated in two modes, i.e., islanded and grid-connected. The
proper controlling action of micro-grid is an essential condition for steady and ef-
cient operation in every mode. The controller performs the following functions:
• Control of the power ow between the grid system and micro-grid.
• Proper synchronization of micro-grid with the grid system.
• Proper regulation of load sharing between the grid system and micro-grid.
• Regulation of the frequency and voltage for both operating modes, namely, grid-
connected and islanded mode.
• Re-optimization of the operation cost of the micro-grid.
• While operating during the switching modes, proper handling of transients and
restoration of the desired condition are necessary.
13.3.1 MPPT Controller
Maximum power point tracking (MPPT) is the algorithm which extracts the maxi-
mum power from the photovoltaic system generated by photovoltaic cell or module
at the specic environmental condition. The specic voltage at which solar device
generates maximum power output is called maximum power point. There are various
types of techniques used for maximum power point tracking, out of which perturb and
observe algorithm is used in this work for determining the maximum power point of
Fig. 13.1 Block representation of micro-grid-connected grid system [9]
J. Gupta et al.

217
the photovoltaic module. This technique uses only one sensor to detect the maximum
power point that is a voltage sensor. It also reduces the cost and complexity of the
system. This technique is quite easy as it has the least time complexity [11, 12].
13.3.2 Inverter Controllers
13.3.2.1 PI Controller
The inverter controller operates on the double closed-loop current algorithm. There are
two loops, outer and inner loop. The voltage control loop is referred to as the outer loop,
and its primary duty is to maintain the DC voltage of PV array. A current control loop is
referred to as the inner loop, and its primary duty is to keep the active and reactive current
signal of the grid (Id and Iq). For stabilizing the power factor at unity for efcient grid
interconnection, the voltage control loop provides the output of Id, which is the reference
of current, while keeping Iq value regulated to minimum, i.e., zero, using a PI controller
to minimize error. The inner control loop provides the voltage output Vd and Vq. The
tracking error can be reduced, and tracking speed can be increased by regulating the PI
controller in the inner current control loop. Forward-feed compensation output is included
in the loop to decrease the disadvantages to the perturbation of the voltage of grid.
The phase-locked loop (PLL) algorithm is applied to regulate the values of the
active power and reactive power injected into the IEEE 13-node feeder systems. The
advantage of a phase-locked loop is that it helps in locking and synchronizing the
frequency and phase angle output of the voltage with reference to the current of the
grid using transformations. The output of the current control loop and voltage con-
trol loop is feed to the converter block whose function is to sense the vector values
of current and voltage, respectively and convert theses values into the d-q reference
frame as DC quantities. The output of the converter block is then feed to over modu-
lation in order to increase the linear region of a three- phase PWM modulator by
approximately 15%. The output is transferred to PWM three-level pulse generators
to produce the input pulse to the inverter.
13.3.2.2 Articial Neural Network Controller
An articial neural network is a massively parallel distributed processor consisting
of simple processing units, which has an inherent property to store knowledge of
experiments and make it available for use. In two respects it resembles the brain as
follows:
• The strengths of the interneuron connection, known as synaptic weights, are used
to store the knowledge gained.
• The network acquires knowledge from its environment through a learning
process.
• Mathematically, neuron modeling can be represented by the following Eqs.13.1,
13.2 and 13.3:
13 An IoT-Based Controller Realization forPV System Monitoring andControl

218
uw
x
k
j
M
kj j
=
=
∑
1
(13.1)
yub
kk
k
=+
()
ϕ
(13.2)
vub
kkk
=+
(13.3)
where xj denotes the inputs, wkj represents the synaptic weights of the neuron k,
uk is the combined output due to the inputs, bk is the bias, φ(.) is the activation
function, and yk is the output signal of the neuron. The use of bias has the effect
of applying an afne transformation to the linear combiner output uk in the mod-
eling process.
Levenberg-Marquardt algorithm is used to train the neural network, which is the
second-order optimization of the backpropagation algorithm. This algorithm typi-
cally requires less time and is robotic. This algorithm typically requires less time
and is robust, which are the advantages of this algorithm over the Gauss-Newton
algorithm and gradient descent method. The ow chart explaining the process of a
neural network is shown in Fig.13.2.
13.3.2.3 Monitoring System
Mainly two main MPPT designing variables, perturbation period (Tp) and perturba-
tion magnitude (x), can be used as metrics. For the higher-resolution system, a
smaller metric has greater acceptability [13–15]. The primary constraint of the men-
tioned system is the speed at which it receives and regulates the desired information.
The constraints of the MPPT variable Tp are described in Eq.13.4.
Tp
n
>=
−
∈





ln
.
2
δω
(13.4)
where wn is the natural frequency of the dened inverter system, δ is the damping
factor of the system, ∈ is the controlling variable of the system, and mainly, 0.1 is
the assigned value. The constraints of variable (x) are described in Eq.13.5.
xAKphGTp>
1
µ
..
..
(13.5)
Here, μ is the static gain of the inverter, Kph is the PV material constant related
to the spectral-averaged responsiveness, G is the average of the irradiance slope,
and A is the combination of dependent variable irradiance [16, 17]. Figure 13.3
represents the block diagram of the IoT-based MPPT system of the modules.
J. Gupta et al.

219
13.4 Results andDiscussions
The system is simulated in Simulink for 0.75s with the following parameters: fre-
quency of 50Hz at standard irradiance and temperature and 590V.The allocation of
PV system between the load bus 632 and 631in the IEEE 13-node feeder system is
decided by the calculation of the central position in the grid using line segment data.
The inverter circuit is controlled by using a standard PI controller and neural
network. The power quality results using both the controllers are compared. The
power quality results include power output, voltage deviation, total harmonic distor-
tion, and phase angle deviation, which are observed as shown in Figs.13.4 (a),
13.4(b), 13.5(a), 13.5(b), and 13.6, respectively.
Data Base Formation
Training, Validation and
Train the Network using LM
Increase NN Size NO
MSE and R
goal met?
Generate Simulink Deployment
Save Results
Stop
YES
Algorithm
Select NN Architecture
Testing Data set
Start
Fig. 13.2 Flow diagram of ANN
13 An IoT-Based Controller Realization forPV System Monitoring andControl

220
(a) Power output across the load
(b) Voltage deviation at different buses
Voltage using Ps
Voltage using nn
Fig. 13.4 (a) Power output across the load. (b) Voltage deviation at different buses
Read Sensors
Algorithm
API Call
Switch Driving
Fig. 13.3 IoT-based
MPPT system [17]
J. Gupta et al.

221
The output graphs of the power quality are divided into three half-sections:
1. The rst part is from 0s to 0.25 s; at this time, system load is met by both PV
system and grid, i.e., the system is operated at grid-connected PV mode.
2. The second part is from 0.25s to 0.5s; at this time, system load is met only by
grid, i.e., PV output is not sufcient to meet the required load.
3. The third part is from 0.5s to 0.75s; at this time, system load is achieved only
by PV system, i.e., PV output is sufcient to satisfy load, and the system is oper-
ated at islanded mode.
Fig. 13.5 (a) FFT using PI and (b) FFT using NN
13 An IoT-Based Controller Realization forPV System Monitoring andControl

222
13.5 Summary
It is presented that the interconnection of a photovoltaic system as a micro-grid
improves the reliability of electric supply and power quality by regulating the IoT-
based inverter controller systems. The IoT-based ANN inverter control scheme for
the efcient and reliable power transfer between the grid system and micro-grid to
satisfy the desired load is being demonstrated in this work. The IoT-based ANN-
based controller response has also been compared with IoT-based PI controller,
which justies its technical feasibility.
References
1. Sadeghian, Hamidreza, Mir HadiAthari, & Zhifang Wang. (2017). Optimized solar photovol-
taic generation in a real local distribution network. In 2017 IEEE Power & Energy Society
Innovative Smart Grid Technologies Conference (ISGT) (pp.1–5). IEEE.
2. Lasseter, R.H. (2002). Micro-grids. In 2002 IEEE Power engineering society winter meeting.
Conference Proceedings (Cat. No.02CH37309) 1 (pp.305–308). IEEE.
3. Pham, D.H., Hunter, G., LiLi, & Zhu, J.. (2015). Advanced microgrid power control through
grid-connected inverters. In 2015 IEEE PES Asia-Pacic Power and Energy Engineering
Conference (APPEEC) (pp.1–6). IEEE.
Fig. 13.6 Phase angle at different buses
J. Gupta et al.

223
4. Singh, K., Swathi, P., &Ugender Reddy, M.. (2014). Performance analysis of PV inverter
in microgrid connected with PV system employing ANN control. In 2014 International
Conference on Green Computing Communication and Electrical Engineering (ICGCCEE)
(pp.1–6). IEEE.
5. Chen, X., Sun, L., Zhu, H., Zhen, Y., & Chen, H.. (2012). Application of internet of things in
power-line monitoring. In 2012 International conference on cyber-enabled distributed com-
puting and knowledge discovery (pp.423–426). IEEE.
6. Kang, B., Park, S., Lee, T., & Park, S.. (2015). IoT-based monitoring system using tri-level
context making model for smart home services.In 2015 IEEE International Conference on
Consumer Electronics (ICCE) (pp.198–199). IEEE.
7. Woyte, A., Richter, M., Moser, D., Mau, S., Reich, N., & Jahn, U.. (2013). Monitoring of
photovoltaic systems: good practices and systematic analysis. In Proceedings of the 28th
European photovoltaic solar energy conference (pp.3686–3694).
8. Belghith, O.B., & Sbita, L.. (2014). Remote GSM module monitoring and photovoltaic system
control.In 2014 First International Conference on Green Energy ICGE 2014 (pp.188–192).
IEEE.
9. Kersting, W.H.(2009). Distribution feeder voltage regulation control. In 2009 IEEE rural elec-
tric power conference (pp. C1–C1). IEEE.
10. Villalva, M.G., Gazoli, J.R., & Filho, E.R. (2009). Comprehensive approach to modeling and
simulation of photovoltaic arrays. IEEE Transactions on power electronics, 24(5), 1198–1208.
11. Chin, C.S., Tan, M.K., Neelakantan, P., Chua, B.L., & Teo, K.T. K. (2011). Optimization of
partially shaded PV array using fuzzy MPPT.In 2011 IEEE colloquium on humanities, sci-
ence and engineering (pp.481–486). Penang: IEEE.
12. Esram, T., & Chapman, P.L. (2007). Comparison of photovoltaic array maximum power point
tracking techniques. IEEE Transactions on Energy Conversion, 22(2), 439–449.
13. De Brito, M.A. G., Galotto, L., Sampaio, L.P., de Guilherme, A. e. M., & Canesin, C. A.
(2012). Evaluation of the main MPPT techniques for photovoltaic applications. IEEE
Transactions on Industrial Electronics, 60(3), 1156–1167.
14. Esram, T., & Chapman, P.L. (2007). Comparison of photovoltaic array maximum power point
tracking techniques. IEEE Transactions on Energy Conversion, 22(2), 439–449.
15. Beevi, S.Shanifa, J.M., & Vincent, G.. (2016). A high performance instantaneous resistance
maximum power point tracking algorithm. In 20167th India International Conference on
Power Electronics (IICPE) (pp.1–5). IEEE.
16. Ma, J., Man, K.L., Ting, T.O., Zhang, N., Guan, S.U., & Wong, P.W. H. (2014). Estimation
and revision: A framework for maximum power point tracking on partially shaded photovol-
taic arrays. In 2014 International symposium on computer, consumer and control (pp.162–
165). Taichung: IEEE.
17. Bardwell, M., Wong, J., Zhang, S., & Musilek, P. (2018). Design considerations for IoT-based
PV charge controllers. In 2018 IEEE world congress on services (SERVICES) (pp.59–60).
San Francisco: IEEE.
13 An IoT-Based Controller Realization forPV System Monitoring andControl

225© Springer Nature Switzerland AG 2020
A. Haldorai et al. (eds.), Business Intelligence for Enterprise Internet of Things,
EAI/Springer Innovations in Communication and Computing,
https://doi.org/10.1007/978-3-030-44407-5_14
Chapter 14
Development ofanEfcient, Cheap,
andFlexible IoT-Based Wind Turbine
Emulator
ManishKumarSingla, JyotiGupta, ParagNijhawan, SouvikGanguli ,
andS.SumanRajest
14.1 Introduction
Energy is always the most crucial need for growth and development of a nation.
Due to the rapid growth of the world population, changes in technology, and
other political and economic scenarios, the energy demand is increased
immensely. As of now, though more than 80% of the energy is generated by
conventional sources of energy, it is gradually depleting. So, the development of
an efcient and reliable renewable system becomes the prime motive [1]. Wind
technology is rapidly improving in efciency and scope. Energy researchers
have high future goals for wind energy development. The main difculty for the
development of the wind energy system is the natural disruption of these
resources. If units are too large to meet demand, severe imbalances can endanger
system security. IoT has emerged as a smart and effective solution for monitor-
ing the renewable energy system. Internet of Things (IoT) can be explained as a
digital network that connects various elements of a particular system and regu-
lates the data according to the situation using advanced embedded systems,
including controllers, meters, and sensors. IoT brought the third revolution in
the eld of technology [2]. In the near future, IoT is expected to be widespread
and will cover all aspects of human life, including the production and manage-
ment of renewable energy.
An advanced version of IoT is introduced named as the Internet of Energy (IoE),
which includes the arrangement of energy ecosystem and ICT.The implementation
M. K. Singla · J. Gupta · P. Nijhawan · S. Ganguli (*)
Thapar Institute of Engineering and Technology, Patiala, India
e-mail: jgupta_phd19@thapar.edu; souvik.ganguli@thapar.edu
S. S. Rajest
Vels Institute of Science, Technology & Advanced Studies, Chennai, India

226
of IoT as a monitoring system in the smart grid is described in this study [3].
Nowadays, IoT is considered a vital part of the designing and execution of smart
building schemes and smart cities, as shown in Figure14.1. With the advancement
in the eld of IoT, a new theory is propounded dened as smart grid 2.0 which is
also known as the second generation of smart grids. It refers to the improved model
of the smart hybrid system with the IoT and is expected to be implemented in the
next few years [4].
In the new concept (smart grid 2.0), the power exchange between the hybrid
energy system and the power grid is regulated using the advanced-level smart
electric metering system. The amount of energy exchange, the plug and play
capability, and the vital information about the parameter between seller and buyer
could be shared using informatics infrastructure. Plug and play feature indicates
that a demand-side electricity source can inject power to the grid as easy as insert-
ing a plug into an outlet, i.e., ability to inject even a very minimal power genera-
tion into the power grid like vehicle-to-grid (V2G) power transfer. Such types of
consumers which have the ability to deliver power to another system are dened
as prosumers [5]. Still, now there are no technologies that can disconnect and
connect the distributed generation system with a grid at each desired moment. It
is sensible to study wind energy conversion system (WECS) with respect to
cybersecurity. The overall effect and signicance of cybersecurity on the reliabil-
ity of WECS have been studied [6, 7].
Fig. 14.1 Application of IoT in smart grid [4]
M. K. Singla et al.

227
14.2 Wind Energy System withIoT
Wind energy is nowadays emerging as one of the most promising technologies.
However, the output of the wind energy conversion system (WECS) is dependent on
wind ow, which is sometimes erratic and at times unpredictable. By this time,
developing a control scheme and ensuring a reliable WECS has been a signicant
area of focus. However, the efcacy of the control scheme can be assured by several
experiments with an actual wind turbine (WT). The actual wind turbine is ulti-
mately dependent on environmental conditions. Such dependency may cause inde-
nite delays. Moreover, several worst cases for which system needs to be tested may
never occur, and thus performance and reliability of controllers for those cases are
always questionable. Another way is to set up a WT in the laboratory. But it is dif-
cult to set up a WT in the laboratory because of challenges like space and con-
trolled environment, for example, wind tunnel.
A solution to these problems can be found in a wind turbine emulator. Wind
turbine emulator mimics the behavior of actual wind turbine under controlled man-
ner. Line diagram of wind turbine emulator and power conditioning unit is shown in
Fig.14.2. Essentially it simulates the same operating pattern at hardware level in
real time similar to what an actual wind turbine does at given operating parameters
of wind speed and pitch angle. The term emulation is coined for simulation prac-
tices which involve hardware platform. It provides a fast-congurable testing plat-
form. Usually these are performed in real time. Emulator is similar to
hardware-in-the-loop simulation concepts. Wind turbine emulator can nd applica-
tions in numerous elds. It provides a exible testing platform for the study of
dynamic and steady-state behavior of wind turbine. Beginners can learn about
power/wind speed, torque/turbine speed, and power/turbine speed characteristics of
a wind turbine and can perform comparative studies about how changes in param-
eters of wind turbine affect the behavior. For other applications, this emulator can
be coupled to the generator (induction, PMSG, DFIG) followed by power electron-
ics in place of an actual wind turbine. So, researchers would not have to rely on
environmental conditions which are appropriate for driving the wind turbine at
some desired operating point. Since the operating point of wind emulator can be
controlled, researchers can simulate all possible scenarios of operation of a wind
turbine and accordingly can modify the power electronics and control algorithms.
Consequently, it will improve the product quality and reliability. The operating
point of wind emulator can be controlled using IoT.The block diagram of wind
operating system with IoT system is shown in Figure14.3.
IoT topology in combination with ICT infrastructure allows the wind power pro-
ducers to regulate the output power at maximum efciency and provides the accu-
rate maintenance reminder of each component at regular interval in order to avoid
any huge disaster. There are various algorithms which help in formulating the
desired schedule for maintenance like machine learning, fuzzy logic, neural net-
work, etc. For instance, on-time maintenance can reduce the index of levelized
14 Development ofanEfcient, Cheap, andFlexible IoT-Based Wind Turbine Emulator

228
Fig. 14.2 Line diagram of wind turbine emulator and power conditioning unit
Wind AC-DC
IoT
system
power converter
DC-AC Grid
Load
converter
generation
Fig. 14.3 Block representation of wind operating system with IoT system
M. K. Singla et al.

229
energy costs (LCoE) for wind assets that denotes the net present value of the unit
power cost over the lifetime of the turbines [8].
In digital communication system, the beneciary data of each element of system
is collected and processed using machine learning algorithm [9]. The main two
problems related with the IoT system are the delay in information transfer espe-
cially in offshore wind farms and limitation on the bandwidth for exchanging infor-
mation. Therefore, if essential information could be received and processed at a
faster rate, then corrective measures for the system at the time of failure can be
automated like shutting down of turbine system in case of turbulence. Therefore, we
need the deployment of more IoT systems with improved algorithm for monitoring
the wind power generation system [10].
14.3 Results
The wind emulator system developed is run at varying motor buck duties in order to
obtain the effect on the various output parameters such as generator voltage, genera-
tor current, generator speed, battery voltage, DC link voltage, and motor armature
current. The following results are also obtained using LabVIEW software. Their
variation with time is shown in Table14.1.
Thus, all the output parameters are obtained in the graph with respect to time
which are shown in Figure14.4 and are presented in two parts.
Speed is varying with change in the duty ratio while eld and armature voltages
remain invariant. The graph shown in Fig.14.4 provides the variation of the motor
output parameters versus the time. Thus, an efcient, economical, and exible IoT-
based wind turbine emulator is successfully developed in this chapter.
Table 14.1 Results of motor parameters
Motor buck duty (%) Field voltage (V) Armature voltage (V) Speed (rpm)
2 230 229 60
4 230 229 74
6 230 229 96
8 230 229 116
10 230 229 131
12 230 229 146
14 230 229 164
16 230 229 180
18 230 229 199
20 230 229 215
22 230 229 229
14 Development ofanEfcient, Cheap, andFlexible IoT-Based Wind Turbine Emulator

230
14.4 Summary
The proposal for a highly efcient, cost-effective, and exible IoT-based wind tur-
bine emulator is thus presented in this chapter. The monitoring can be performed
using a web-based service which would receive the wind turbine data and convert it
into useful information. The challenge however still lies with the feasibility of the
amount of data that can be accumulated from each and every element of a wind
power system in a real-time environment.
Fig. 14.4 Variation of motor output parameters with time
M. K. Singla et al.

231
References
1. Kalaiarasi, D., Anusha, A., Berslin Jeni, D., & Monisha, M.. (2016, April). Enhancement
of hybrid power systems using IoT. International Journal of Advanced Research Trends in
Engineering and Technology (IJARTET), 3(19).
2. Buyya, R., Calheiros, R.N., & Dastjerdi, A.V. (Eds.). (2016). Big data: principles and para-
digms. Cambridge: Morgan Kaufmann.
3. Byun, J., Kim, S., Sa, J., Kim, S., Shin, Y.-T., & Kim, J.-B. (2016). Smart city implementa-
tion models based on IoT technology. Advanced Science and Technology Letters, 129(41),
209–212.
4. Cao, J., & Yang, M. (2013). Energy internet—towards smart grid 2.0. In 2013 Fourth interna-
tional conference on networking and distributed computing (pp.105–110). IEEE.
5. Ritzer, G. (2015). Automating prosumption: The decline of the prosumer and the rise of the
prosuming machines. Journal of Consumer Culture, 15(3), 407–424.
6. Zhang, Y., Xiang, Y., & Wang, L. (2016). Power system reliability assessment incorporating
cyber-attacks against wind farm energy management systems. IEEE Transactions on Smart
Grid, 8(5), 2343–2357.
7. Hatzivasilis, G., Fysarakis, K., Soultatos, O., Askoxylakis, I., Papaefstathiou, I., & Demetriou,
G. (2018). The industrial internet of things as an enabler for a circular economy Hy-LP: A
novel IIoT protocol, evaluated on a wind park’s SDN/NFV-enabled 5G industrial network.
Computer Communications, 119, 127–137.
8. Lot, H., & Khodaei, A. (2016). Levelized cost of energy calculations for microgrids. In 2016
IEEE Power and Energy Society General Meeting (PESGM) (pp.1–5). IEEE.
9. Alhmoud, L., & Al-Zoubi, H. IoTapplications in wind energy conversion systems. Open
Engineering, 9(1), 490–499.
10. Moness, M., & Moustafa, A.M. (2015). A survey of cyber-physical advances and challenges
of wind energy conversion systems: prospects for internet of energy. IEEE Internet of Things
Journal, 3(2), 134–145.
14 Development ofanEfcient, Cheap, andFlexible IoT-Based Wind Turbine Emulator

233© Springer Nature Switzerland AG 2020
A. Haldorai etal. (eds.), Business Intelligence for Enterprise Internet of Things,
EAI/Springer Innovations in Communication and Computing,
https://doi.org/10.1007/978-3-030-44407-5_15
Chapter 15
An Application ofIoT toDevelop Concept
ofSmart Remote Monitoring System
MeeraSharma, ManishKumarSingla, ParagNijhawan, SouvikGanguli ,
andS.SumanRajest
15.1 Introduction
One of the main contributors of clean energy all over the world, currently, is the
solar photovoltaic system. A PV system’s power generation potential is the main
factor to be determined [1]. This potential may vary depending upon various con-
straints such as the technology employed as well as the location considered. The
expense of every kWh pay backs the return on investment on the electricity potential
[2]. Hence, before the installation of PV systems, certain measures need to be taken,
so as to have larger energy potentials. There may be chances of collapse and main-
tenance issues at the time of operation of the system despite the efforts made during
or before the installation of the PV systems. Such problems are occurring more
often where the installations are in remote locations. Therefore, to mitigate such
problems, a suitable approach is desired. The most accurate way to avoid such
issues is the frequent check that is almost impossible for a person to carry out. Even
problems involving concentration, giving full attention, and identication of solu-
tion cannot be encountered [3].
The best way to cater such a problem is to adopt the most emergent method
known as the “Internet of Things” (IoT) for remote evaluation of models. IoT helps
us to interact with objects used in our daily life in a much quicker and easy way with
the usage of communication devices following network protocols [4, 5].
IoT has a vast range of applications including infrastructure, industrial automa-
tion, healthcare support, home power management and the renewable energy frame-
work, trafc maintenance, automotive enterprise, micro-grids, and intelligent drive
M. Sharma · M. K. Singla · P. Nijhawan · S. Ganguli (*)
Thapar Institute of Engineering and Technology, Patiala, India
e-mail: souvik.ganguli@thapar.edu
S. S. Rajest
Vels Institute of Science, Technology & Advanced Studies, Chennai, India

234
systems, among others [5, 6]. Based on the advent of the IoT, solar PV systems are
the latest targets being focused upon. This is because of their increasing usage in the
current energy sector trends especially in energy distribution. Solar PV system’s
popularity would be a major breakthrough for the implementation of IoT systems in
combination with it, thereby giving an edge for the IoT service suppliers as well as
the consumers.
15.2 Photovoltaic Systems
A PV system consists of the arrangement of a PV module, power converters, and
storage devices. In essence it is the power harvester, which changes sunlight into
electricity [7]. This technique is quite different from the traditional process which
involves fossil fuels for power generation. Although power transmission and distri-
bution embraces similar traditional methods, PV arrays are formed by grouping PV
modules; such PV arrays are known as PV generators when arranged in series and
parallel congurations [8–10]. They are then installed in a manner such that they are
exposed to direct sunlight. The DC electricity generated from the PV generator is
changed into AC with the assistance of the inverters. This power can be self-
consumed or distributed to the energy grid through the transmission network [11].
Nonetheless, the energy can be stored using the batteries instead of being trans-
ferred. The PV models are grouped into two forms, i.e., the off-grid and the on-grid
PV models, centered on various forms of functional components. An illustration in
Fig.15.1 provides a layout of the operation of the photovoltaic system.
Sun
PV Array
AC Load
Chopper
Smart Meter
Battery
Inverter
Electric Grid
DC
AC
DC
DC
Fig. 15.1 Layout of a
typical photovoltaic system
[12]
M. Sharma etal.

235
15.2.1 IoT andIts Requirement inPhotovoltaic Systems
Internet of Things, abbreviated as IoT, is a technology that is developed by grouping
together “wireless technologies, micro-electromechanical systems, and the Internet”
[3–6]. The mechanical/digital machines, computing devices and objects, solitary
identiers, and other such analogous things coordinating together constitute
IoT.Because of this synchronization, which transmits the data across the network,
the distance between the operational systems and information technology is closed
without the aid of human-to-human and human-to-machine interaction.
The contemporary science and engineering systems cannot solve the most intri-
cate issues which IoT can solve. The operational behavior of various components of
the PV systems, which are used for generating power, varies. In short, a constant
generation of power is not achievable throughout because of the solar intensity
being weather dependent and time varying [12]. This has an indirect effect on the
working of other components of the device such as voltage levels of power convert-
ers, status of battery charging, and load energy demands. Some environmental con-
ditions, such as accumulation of dust, are also sometimes responsible for the poor
performance of the PV system. Nevertheless, these problems can lead to the col-
lapse of the whole system in longer terms. In order to maintain the operating data
log, humans face difculties to monitoring since it requires visits to the plant site
time and time again. Henceforth, humans consume a lot of time in addressing these
failures because of system breakdown or bad performance [3]. To check the param-
eters of the system and store them in cloud, a continuous monitoring system together
with the PV system is to be equipped. This stored data will provide the performance
parameters and the causes of poor performance and will make troubleshooting and
maintenance operation much easier and faster. Therefore, the need for IoT is neces-
sary to optimize the device parameters with the option of remote control.
15.2.2 IoT-Based Photovoltaic System Architecture
The IoT architecture of a photovoltaic system consists of three distinct layers.
Figure15.2 clearly displays the IoT photovoltaic system architecture. The initial
layer incorporates the PV model design ambience and is connected for user satis-
faction according to the required congurations. In this particular case, the Arduino
server is interlinked with the components of the PV device, creating the second
layer of the IoT architecture. Along with an Internet rewall option, using a router,
the web server can be inter-connected with the hardware projects of the PV scheme,
hence forming the gateway linkage in this second layer. The Arduino server is
majorly responsible for this integration. It carries out the main functions of con-
trolling, monitoring, and managing the PV scheme hardware constituents. The
server collects the data from the third and last layer known as the remote monitor-
ing and control layer. This information is transmitted to the storage devices that
15 An Application ofIoT toDevelop Concept ofSmart Remote Monitoring System

236
help generate periodic reports. Using an Android interface with cloud data storage
via a Wi-Fi network, these data can be drawn up in the form of visual graphs and
reports, and then the users can access it accordingly.
15.2.3 Proposed Concept forIoT-Aided System
forPhotovoltaic Monitoring
This research proposes a device that evaluates the condition of the PV framework
based on the IoT-centered network with the purpose of remotely controlling it. For
the transmission of sensor information, a mobile radio network is used. The remote
server data is sent through a GPRS module [13]. Figure15.3 displays IoT technol-
ogy schematics for a solar power plant.
A three-layered schematic diagram having the bottom layer as the sensing layer
consists of current and voltage sensors, irradiance-measuring device (pyranome-
ter), and other sensors. The sensing layer also consists of a microcontroller-based
data processing which is acquired by the sensors. A wireless module is utilized to
communicate with the microcontroller in order to initialize and start transmitting
data to the server.
The second layer, known as the system layer, is where information logging is
done from the plant for real-time transmission and includes database storage as
Fig. 15.2 IoT-aided layout for photovoltaic system [12]
M. Sharma etal.

237
well [14]. The application layer further uses this stored and processed data from
the network layer. Based on the collected data’s processing and storage, the web-
based services are hence designed smartly. In order to help in monitoring the
plant’s performance, graphical user interfaces are employed. With the console, the
decision- making time is shortened, indicating the administrator with historical
data-based decision.
A remote monitoring system based on IoT makes it much easier to supervise the
solar power plant’s performance as a whole using a web-based technique as shown
in Fig.15.4.
15.3 Summary
The main advantage of using IoT photovoltaic technology is that we can accurately
view the status of our property from the central control panel. Through connecting
the computer to the cloud network, we can even pinpoint where the problem lies
and allow technicians to repair it long before the entire system breaks down. The
network is less vulnerable to the production issues (due to power outages) and
potential security threats through the use of the Internet of Things. Through install-
ing an IoT solution directly and linking solar devices, we can monitor our solar
Plant Monitoring Maintenance
Fault Monitoring Inspection Scheduling
Internet Database
Sensors
Sensing Layer
Wireless Module
Microcontroller
Network Layer
Data Analytics Historical Data analysis
Generation Monitoring Other Decision Making
Application Layer
Fig. 15.3 IoT-based solar power plant [7]
15 An Application ofIoT toDevelop Concept ofSmart Remote Monitoring System

238
power system’s broadband even when there are thousands of devices connected to
the network. In addition to real-time business notications, the solar industry’s
Internet of Things increases energy efciency and protability by gathering histori-
cal modeling data. This makes energy generation more efcient in terms of both
costs and logistics.
References
1. Cota, O. D., & Kumar, N.M. (2015). Solar energy: a solution for street lighting and water
pumping in rural areas of Nigeria. Proceedings of International Conference on Modelling,
Simulation and Control (ICMSC-2015), 2, 1073–1077.
2. Pezzotta, G., Pinto, R., Pirola, F., & Ouretani, M.Z. (2014). Balancing product-service pro-
vider’s performance and customer’s value: the service engineering methodology (SEEM). In
6th CIRP conference on industrial product-service systems (Vol. 16, pp.50–55). Elsevier.
3. Internet of Things (IoT). IoT Agenda. http://internetofthingsagenda.techtarget.com/denition/
Internet-ofThings-IoT.
4. Atzori, L., Iera, A., & Morabito, G. (2010). The internet of things: A survey. Computer
Networks, 54(15), 2787–2805.
5. Zanella, A., Bui, N., Castellani, A., Vangelista, L., & Zorzi, M. (2014). Internet of things for
smart cities. IEEE Internet of Things Journal, 1(1), 22–32.
6. Bellavista, P., Cardone, G., Corradi, A., & Foschini, L. (2013). Convergence of MANET and
WSN in IoT urban scenarios. IEEE Sensors Journal, 13(10), 3558–3567.
7. Adhya, S., Saha, D., Das, A., Jana, J., & Saha, H. (2016). An IoT based smart solar photo-
voltaic remote monitoring and control unit. In 2016 2nd international conference on Control,
Instrumentation, Energy & Communication (CIEC) (pp.432–436). IEEE.
8. Reddy, B.V., Sata, S. P., Reddy, S.K., & Babu, B.J. (2016). Solar energy analytics using
internet of things. International Journal of Applied Engineering Research, 11(7), 4803–4806.
Fig. 15.4 Complete schematic of the smart remote monitoring system [7]
M. Sharma etal.

239
9. Nadpurohit, B., Kulkarni, R., Matager, K., Devar, N., Karnawadi, R., & Carvalho, E. (2017).
Iot enabled smart solar pv system. International Journal of Innovative Research in Computer
and Communication Engineering, 5(6), 11324–11328.
10. Guamán, J., Guevara, D., Vargas, C., Ríos, A., & Nogales, R. (2017). Solar manager:
Acquisition, treatment and isolated photovoltaic system information visualization cloud plat-
form. International Journal of Renewable Energy Research, 7(1), 214–223.
11. Vignesh, R., & Samydurai, A. (2017). Automatic monitoring and lifetime detection of solar
panels using internet of things. International Journal of Innovative Research in Computer and
Communication Engineering, 5(4), 7014–7020.
12. Kim, J., Byun, J., Jeong, D., Choi, M.-i., Kang, B., & Park, S. (2015). An IoT-based home
energy management system over dynamic home area networks. International Journal of
Distributed Sensor Networks, 11(10), 828023.
13. Chen, X., Sun, L., Zhu, H., Zhen, Y., & Chen, H. (2012). Application of internet of things in
power-line monitoring. In 2012 International conference on cyber-enabled distributed com-
puting and knowledge discovery (pp.423–426). IEEE.
14. Peijiang, C., & Xuehua, J. (2008). Design and Implementation of Remote monitoring system
based on GSM.In 2008 IEEE Pacic-Asia workshop on computational intelligence and indus-
trial application (Vol. 1, pp.678–681). IEEE.
15 An Application ofIoT toDevelop Concept ofSmart Remote Monitoring System

241© Springer Nature Switzerland AG 2020
A. Haldorai et al. (eds.), Business Intelligence for Enterprise Internet of Things,
EAI/Springer Innovations in Communication and Computing,
https://doi.org/10.1007/978-3-030-44407-5_16
Chapter 16
Heat Maps forHuman Group Activity
inAcademic Blocks
RajkumarRajasekaran, FizaRasool, SparshSrivastava, JollyMasih,
andS.SumanRajest
16.1 Introduction
Sensing and identifying human group activities has garnered a rising research inter-
est in many practical applications like video surveillance and crime detection.
Multifarious algorithms have been designed and proposed for the recognition of
group activities or interactions. Though many of the already prevailing algorithms
have unearthed the wide-ranging features among the activities such as the full
movement data, they were unsuccessful in obtaining information about the temporal
motion (e.g., they failed to depict the time and place of a person) [1]. Hence, they
suffered from restrictions in case of detecting and identifying more convoluted
group activities. Though these other techniques include and study the temporal data
by using the chain models like HMM (hidden Markov models), they still suffer from
one disadvantage which is the requirement of enormous amounts of training data,
along with managing the variations in motion operation and behavior. Also, several
uncertainties also play a challenging concern in recognition of group activity. Due
to inherent variation in the motion of people in cluster activities, the precision and
correctness of recognition might be prominently inuenced by the unsure changing
characteristic of group motion [2].
The ultimate goal of the system is optimization of targeted digital advertising. It
is the most effective form of advertising [3]. Most advertising done these days is
online, and almost all of it is targeted. Targeted advertising is a method by which
R. Rajasekaran (*) · F. Rasool · S. Srivastava
Vellore Institute of Technology, Vellore, India
J. Masih
Erasmus School of Economics, Rotterdam, Netherlands
S. S. Rajest
Vels Institute of Science, Technology & Advanced Studies, Chennai, India

242
advertisers are able to provide more specic and relevant advertisements to users
based on data they have collected on the users from various sources– services like
Google AdWords and Facebook advertising. The reason why this type of advertis-
ing is extremely effective is because it utilizes data that is most pertinent to the user
or taker. Through this chapter, we put forth a very innovative heat map algorithm for
the recognition of human group activity in academic buildings and hostel blocks.
The main objectives of this chapter can be stated as the following points:
1. This chapter presents a completely new heat map (HM) aspect to characterize
group activities in academic buildings and hostel blocks. This proposed HM will
be able to efciently fetch the temporal motion data of the crowd activities based
on the temporal and cultural factors [4].
2. This chapter proposes to implement a thermal diffusion method to generate the
heat map. In this technique, we will be able to efciently address the motion
uncertainty from different people.
3. We also intend to perform a new SF (surface tting) method which can also
identify the group activities. This presented process can essentially study the
features of the heat map and can provide us with recognition results effectively.
16.2 Literature Survey
Anandakumar and Umamaheswari [5], Arizona State University, USA, applied a
dynamic, actual-time heat map data capture, and for two military simulations, they
generated tools which include unmanned aerial vehicle sensor operator scenario and
ground-based combat scenario. Arulmurugan and Anandakumar [6] have developed
SURV or Smart Urban Visualization system. SURV is a new framework developed
by applying HTML, CSS, as well as Mapbox.js and CARTO.js JavaScript libraries
in order to generate dynamic visualizations and interactive maps. Python, the most
popular versatile language of these times, is also integrated and used with this sys-
tem due to its excellent performance in data processing. SURV offers an interface or
a web app where the end-user will be able to perform three-dimensional (3D) visu-
alizations along with data animation to deliver better data analysis.
Hirenkumar Gami, Miami University, USA, has only used a single PIR sensor
combined with machine learning algorithms and signal processing to clearly study
and predict the presence, proximity, and direction of the crowd movements on the
oors of a building. [7], PSUT, Jordan, presented the signicance of visualization
techniques when implemented on huge real-time data sets from a certain corpora-
tion in Jordan as detailed tools for analysis of data. The name of the software uti-
lized to analyze and visualize this data is known as Tableau. [8] in his research paper
perceives the pairwise actions by fetching the causality features from the bitrajecto-
ries. Ni etal. further extended the cause and effect characteristics into three different
kinds. Among these, the rst is individuals, the second is pairs, and the third is
groups. [9] discovers and studies crowd activities by presenting connected operating
R. Rajasekaran et al.

243
segmentations in order to depict and display the corresponding congruence among
the people. Cheng etal. propose to study the pattern for group activity by taking into
consideration the Gaussian parameters and creating trajectories that are determined
by the calculation from many people.
16.2.1 Detailed Working
The system is composed of two components– the hardware detectors and the soft-
ware backend. The hardware components have been prototyped using an Arduino
development board with PIR sensors. These are xed at certain key positions in the
academic buildings, and based on the factors we considered, a number of people
visiting the academic buildings are stored in a software database. And using the
surveillance cameras in the academic buildings, the activities of students and staff
are continuously recorded. As discussed before, if the overall characteristics are
straight away being obtained from the motion data, most of the temporal data/infor-
mation will be lost. To avoid this loss of information, it is proposed that the trajec-
tory is represented as a sequence of heat sources. So, for converting the trajectory to
the series of heat sources, we need to rstly dissect the full video scene into tiny
patches which are non-overlaying. In the case wherein a trajectory goes falling into
a patch, this case will be regarded as a heat source. In this way, we can convert a
trajectory into several heat source series, and the thermal values pertaining to the
series of heat sources can be organized in an increasing order following the course
of the trajectory [10]. The obtained temporal data converted into some useful infor-
mation is effectively inserted.
Besides, as the trajectories of people might show huge deviations, straight away
utilizing the series of heat sources by way of characteristics shall be signicantly
inuenced by the motion uctuation. Therefore, to reduce the uctuation in motion,
a consecutive technique can be implemented by introducing a further process in the
form of thermal diffusion which disperses the heat from the series of heat sources.
This is called diffusion result of the heat map. Although this process has been used
in the past, this is the rst time we are using it for human activity recognition in
academic buildings and hostel blocks. With our proposed heat map feature, we can
describe the activities’ information of motion with the help of 3D surfaces. But
again, the problem occurs in choosing an appropriate technique for implementing
recognition which uses this feature of HM.Therefore, we additionally proposed the
SF (surface tting) method for recognition of activities [11]. In the proposed SF
technique, rstly, a bunch of prevailing classes of surfaces are recognized in order
to represent the diverse activities. Secondly, the various parallels concerning the
input surface HM and the standard surface classes can be easily determined. In the
nal step, the standard surface class that almost matches will be selected, and its
equivalent human activity will be declared as the activity which is most recognized
for the inputted heat map. Therefore, the data that is generated from HMs is passed
16 Heat Maps forHuman Group Activity inAcademic Blocks

244
onto the digital advertising partners who then utilize this information to serve the
targeted advertising.
16.2.2 Hmb Method
The working of the heat map-based algorithm is shown below. Firstly, the input
trajectories are converted into a series of heat sources, followed by the process of
thermal diffusion to generate the HM characteristic which calculates the group
activity input [12]. After this, the surface tting technique is useful for implement-
ing the activity recognition. The thermal diffusion and the transfer of heat source
series, along with the SF method, are the prime components of the presented algo-
rithm shown in Fig.16.1.
16.3 Experimental Results
The experimental outcome for the proposed heat map-based algorithm is discussed
here. In the experiment, the HMB algorithm is compared with three other methods:
the WF-SVM algorithm, the GRAD algorithm, and the PGTB algorithm. Here, we
performed four individual experiments and the average of the results is calculated.
Further, we have determined miss and false alarm (FA) for the four different meth-
ods and then calculated their total as shown in Table16.1.
From the above table, it is clear that the PGTB algorithm fails to yield good
results. Comparatively, the WF-SVM and the GRAD algorithms provide better
results and performance with the help of many features which are distinguishable.
When a comparison is made among these mentioned methods, the proposed HMB
algorithm shows the nest performance. And, as activities are detected, we can now
send the data to the advertising partner who can advertise based on the people’s
interests and activities (Fig.16.2).
16.3.1 Analysis ofHuman Footfall inanAcademic
Building(in Terms ofDensity)
From the above table, we can see that the density of human footfall is high toward
the morning of each day. This density in the early morning stays maximum for the
entire day except in the case of Monday, where 10am–2pm has increased density
over the morning 6am–10am slot. For almost all the days, human density dwindles
as the day progresses with least amount of density at nighttime. One more notice-
able change takes place on Wednesday, 10am–2 pm slot, where the density falls
R. Rajasekaran et al.

245
sharply as no classes are scheduled during this reserved slot meant for non- academic
activities as shown in Table16.2.
The following can be visualized better using the following heat map:
Thus, this heat map shown in Fig.16.3 helps us to visualize that the density of
people in the academic building is maximum during the working hours which is
indicated by the bright red shades. As the day progresses, the pattern of the heat map
turns toward lighter pastel colors when almost all the students have returned to their
hostels and the footfall is negligible.
Group Activity Trajectories
Heat Source Transfer Series
Diffuse to Create Heat
Maps
Recognize Group Activity
Surface Filtering
Method
Recognition Result
Fig. 16.1 Heat map method
Table 16.1 Heat map result
HMB WF-SVM PGTB GRAD
Miss 0.9% 11.8% 33.1% 10.7%
FA 1.2% 0.4% 4.2% 1.4%
Miss 5.6% 8.5% 8.1% 16.5%
FA 0.8% 2.1% 34.5% 0.9%
Miss 4.7% 8.8% 31.9% 13.9%
FA 0.8% 1.9% 0.1% 2.1%
Miss 3.2% 5.2% 34.8% 8.5%
FA 0.3% 0.3% 6.8% 0.7%
Miss 2.9% 4.7% 45.1% 9.9%
FA 1.3% 1.7% 0.02% 2.8%
Miss 5.2% 3.1% 59.8% 2.7%
FA 0.5% 2.0% 0.3% 1.1%
3.8% 6.9% 36.9% 11.2%
16 Heat Maps forHuman Group Activity inAcademic Blocks

246
The bar graphs and the line graphs presented in Fig.16.4 provide a better picture
in visualizing the footfall of the students in the building. These can prove to be
really helpful to target the temporal aspects of the situation and to tackle any mishap
as shown in Fig.16.5 Table16.3.
16.3.2 Visualization forHostel Blocks
Here, we observe that the data collected through the sensors is at its maximum dur-
ing the nighttime between 10pm and 6am when nearly every student has returned
to the hostel post in time. The density from 6am to 10 pm is around 0.5 as the
Fig. 16.2 Human footfall in an academic block
Table 16.2 Day calculations 1
Days 6am–10am 10am–2pm 2pm–6pm 6pm–10pm 10pm–6am
Monday 0.64 0.7 0.51 0.32 0.0012
Tuesday 0.73 0.57 0.42 0.21 0.001
Wednesday 0.82 0.35 0.56 0.41 0.0015
Thursday 0.58 0.64 0.55 0.35 0.001
Friday 0.61 0.52 0.61 0.18 0.003
R. Rajasekaran et al.

247
classes start from 8am. The 10am–2pm slot also sees a rise in density as most of
the students return to the hostel during lunch hours. Overall, the heat map for human
footfall at hostels can be plotted as follows in Fig.16.6:
Here, a line graph shown in Fig.16.7 and the bar plots provide a better insight to
the data collected by the PIR sensors. The overall human footfall through these
visualizations can prove to be extremely benecial in the process of group activity
detection, analysis, and recognition as shown in Fig.16.8.
16.4 Summary
Thus, through this chapter, we have tried to devise the HMB algorithm, which is an
algorithm based on heat maps, for the purpose of detecting and identifying group
activities, in order to avoid loss of life and property in case of a mishap. The use of
PIR sensors which are exceptional devices for detecting presence of humans, with
the help of a minor form-factor and strong-featured design, is the best choice for
Fig. 16.3 Heat map
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248
Fig. 16.4 Bar graphs and the line graphs
Fig. 16.5 Foot path comparison
R. Rajasekaran et al.

249
achieving cost-efcient surveillance. We, therefore, propose to generate the heat
map, for the purpose of demonstrating the group activities, and then consequently
implement the SF method for recognition of crowd activity. Based on the results, we
can effectively avoid any mishap based on the density and activity detection of the
people and hence can save many precious lives.
Table 16.3 Day calculations 2
Days 6am–10am 10am–2pm 2pm–6pm 6pm–10pm 10pm–6am
Monday 0.49 0.38 0.33 0.69 0.9988
Tuesday 0.51 0.41 0.38 0.78 0.999
Wednesday 0.52 0.68 0.36 0.66 0.9985
Thursday 0.48 0.54 0.32 0.68 0.999
Friday 0.46 0.56 0.37 0.82 0.997
Fig. 16.6 Human foot path
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250
Fig. 16.7 Human foot path densities
Fig.16.8 Overall analysis
R. Rajasekaran et al.

251
References
1. Chu, H., Lin, W., Wu, J., Zhou, X., Chen, Y., & Li, H.A new heat-map-based algorithm for
human group activity recognition. ACM.
2. Amresh, A., Femiani, J., Faireld, J., & Faireld, A. (2013, July). UAV sensor operator train-
ing enhancement through heat map analysis. In Information Visualisation (IV), 2013 17th
international conference. IEEE.
3. Ihaddadene, N., & Djeraba, C. (2008). Real-time crowd motion analysis. In 2008. ICPR 2008.
19th international conference on pattern recognition (pp.1–4). IEEE.
4. Anandakumar, H., & Umamaheswari, K. (Mar. 2017). Supervised machine learning techniques
in cognitive radio networks during cooperative spectrum handovers. Cluster Computing, 20(2),
1505–1515.
5. Anandakumar, H., & Umamaheswari, K. (Oct. 2018). A bio-inspired swarm intelligence tech-
nique for social aware cognitive radio handovers. Computers & Electrical Engineering, 71,
925–937. https://doi.org/10.1016/j.compeleceng.2017.09.016.
6. Arulmurugan, R., & Anandakumar, H. (2018). Early detection of lung Cancer using
wavelet feature descriptor and feed forward Back propagation neural networks classi-
er. Lecture Notes in Computational Vision and Biomechanics, 103–110. https://doi.
org/10.1007/978-3-319-71767-8_9.
7. Haldorai, A. R., & Murugan, S. Social aware cognitive radio networks. In Social net-
work analytics for contemporary business organizations (pp. 188–202). https://doi.
org/10.4018/978-1-5225-5097-6.ch010.
8. Arulmurugan, R., & Anandakumar, H. (2018). Region-based seed point cell segmentation and
detection for biomedical image analysis. International Journal of Biomedical Engineering and
Technology, 27(4), 273.
9. Suganya, M., & Anandakumar, H.. (2013, December). Handover based spectrum alloca-
tion in cognitive radio networks. In 2013 International Conference on Green Computing,
Communication and Conservation of Energy (ICGCE). https://doi.org/10.1109/
icgce.2013.6823431. https://doi.org/10.4018/978-1-5225-5246-8.ch012
10. Huacón, C.F., & Pelegrin, L. (2018). SURV: A system for massive urban data visualization. In
URTC 2017. IEEE MIT.
11. Gami, H. (2017). Movement direction and distance classication using asingle PIR sensor. In
IEEE sensors letters. IEEE.
12. Sharawi, L.I., & Sammour, G. Utilization of data visualization for knowledge discovery in
modern logistic service companies. In SENSET 2017. IEEE.
16 Heat Maps forHuman Group Activity inAcademic Blocks

253© Springer Nature Switzerland AG 2020
A. Haldorai et al. (eds.), Business Intelligence for Enterprise Internet of Things,
EAI/Springer Innovations in Communication and Computing,
https://doi.org/10.1007/978-3-030-44407-5_17
Chapter 17
Emphasizing onSpace Complexity
inEnterprise Social Networks
fortheInvestigation ofLink Prediction
Using Hybrid Approach
J.GowriThangam andA.Sankar
17.1 Introduction
Social Network is a structure comprised of a lot of actors. It provides a set of meth-
ods for analyzing the structure of whole social entities as well as a variety of theo-
ries explaining the patterns are pragmatic in these structures. By using those
structures, local and global patterns and visible entities can be identied and the
network dynamics can be examined.
Social Network Analysis (SNA) [1] is based on the assumption of the importance
of relationships among interacting units. In recent decades, online social networks
[2] have turned out to be progressively signicant assets for individual interaction,
information process and social impact dispersion. Understanding and modeling the
mechanisms by which these networks evolve becomes a fundamental issue and
dynamic research area. Therefore, the conviction of SNA has pulled in signicant
intrigue and interest from social network community. Quite a bit of this intrigue can
be credited to the engaging focal point of social network investigation on connec-
tions among social elements and ramications of these connections.
It has become very signicant in today’s scenario because of the accomplishment
of online person to person communication. This makes use of network theory [3] to
analyze social networks. SNA views social relationships in terms of network theory,
consisting of nodes, representing individual actors within the network, and ties or
edges that represent relationships between the individuals, such as friendship, kin-
ship, and organizations. These networks are often depicted in a social network dia-
gram or sociograph, where nodes are represented as points and ties are represented
as lines.
J. G. Thangam (*) · A. Sankar
Department of Computer Applications, PSG College of Technology,
Coimbatore, Tamil Nadu, India

254
Link Prediction [4] is a noteworthy issue in social network. It is the problem of
predicting the existence of link between two entities. Since the social network is
vast, it is very difcult to predict the link at the earliest. So there is real urge to con-
sider about the efciency of the algorithm in terms of time and space. This tech-
nique is to reduce the dimensions so that link can be rapidly predicted.
This chapter is organized as follows: the related works is presented in next sec-
tion, while the preliminaries, the problem description, and proposed work are pre-
sented in Sects. 17.3, 17.4, and 17.5. In Sect. 17.6, an experimental study is
conducted on real data sets. The social network centrality measures are described in
Sect. 17.7. The complexity analysis is represented in Sect. 17.8. The evaluation
metrics are analyzed in Sect. 17.9. Finally, Sect. 17.10 is conclusion and the
future work.
17.2 Related Works
A Hybrid Active Learning Link Prediction (HALLP) [5] has used machine learning
techniques for binary classication of links. D.Sharma [6] has made a test examina-
tion of the connection forecast procedures. Michael Fire etal. [7] have used topo-
logical feature (friend’s measure) for predicting the links. A novel strategy has been
proposed by Naveen Gupta etal. [8], for identifying the missing links by consider-
ing the uncommon neighbors. Manu Kurakar etal. [9] have identied complex dis-
eases based on the sequence similarity in protein networks.
A link prediction model has been proposed by Wal etal. [10], for identifying the
links that might appear and disappear in future. A friend recommendation has been
suggested by traversing all paths of limited length by Alexis etal. [11]. A Social
Attribute Network (SAN) approach proposed by Neil Zhengiang Gong etal. [12],
have considered the performance of supervised and unsupervised algorithms. A
novel approach proposed by Aditya Krishna Menon etal. [13] has addressed the
class imbalance problem by optimizing the ranking loss. Daniel et al. [14] have
proposed a tensor-based method for predicting the future links temporally.
Jichang Zhao etal. [15] evaluated the performance of link prediction based on
local information by using sampling techniques. Ryan N.Lichtenwalter [16] pro-
posed a machine learning algorithm with class imbalance. An algorithm for param-
eter reduction of soft binary relation was proposed and investigated by Guangh Yu
[17]. Xiuqin Ma [18] proposed a parameter reduction algorithm of soft set for deci-
sion making.
17.3 Preliminaries
The notion of a social network and the methods of SNA have attracted considerable
interest and curiosity from the social and behavioral science community in recent
decades. It is used to quantify the relationships among social entities and on the pat-
J. G. Thangam and A. Sankar

255
terns and implications of these relationships. Milgram’s small-world phenomenon
is revealed in social network. It states that the average path length between any two
nodes should be shorter. The social network is represented as a graph.
17.3.1 Graph
A graph G is dened as G=(V, E), where V is a set of vertices and E is a set of
edges. The notion of vertices and nodes, links and edges is used interchangeably in
the literature. The adjacent matrix is used for representing any graph. The adjacency
matrix of a graph G with n vertices is an n∗n binary matrix given by A =[aij]
dened asaij= 1 if the ith and jth vertices are adjacent, that is, there is an edge con-
necting the ith and jth vertices, and aij= 0, otherwise, that is, if there is no edge con-
necting the ith and jth vertices.
The graph shown in Fig.17.1 is a simple undirected graph with set of vertices as
{A, B, C, D, E, F} and set of edges as {AB, AC, BF, BD, BC, CE}. An edge from a
node A to node B is called a path. It is of length 1 and known to be length-1 path. In
a similar manner, the path from A node D through node B is of length 2 which is
referred as length-2 path.
17.3.2 Link Prediction Problem
Link Prediction Problem [19, 20] is identifying a potential or possible link among
vertices or nodes in a network. It is dened as given a snapshot of a social network,
where new interactions among its members that are likely to occur in the near future
can be inferred. Predicting such links is not an easy task. Many similarity index-
based link prediction methods have been based on nature of computing them, as
local structure, global structure, and quasi local structure. Friendlink algorithm is
based on Quasi-local structure measure. It uses path of length more than 2.
A B
C
E
F
D
Fig. 17.1 Graph
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256
17.4 Problem Description
The link prediction is an issue for foreseeing whether the link will exist in future or
not. The similarity score between the nodes is calculated for predicting the social
links. Since the social network data set is enormous, there is a need for dimensional-
ity reduction, without affecting the consistency of the data set. Soft set theory is
used for dimensionality reduction. The reduced dimension is used for predicting the
future links. The similarity score between the nodes are calculated for the reduced
dimension. Based on this similarity score, ranking of all other nodes has to be done
with respect to a particular node. The future link is more probable to occur, if the
similarity score between the nodes is higher.
17.4.1 Soft Set
Molodtsov [21] soft set theory is an overview of fuzzy set theory [22] that deals with
uncertainty in a nonparametric manner. Let U be an initial universe and E be the set
of possible parameters. The power set of U is denoted by P(U). The Soft set is
dened as follows:
Denition A pair (F, E) is called a soft set overU, where F is a mapping of E into
P(U), that is,
FE PU,→
()
(17.1)
In a soft set theory, the reduction of parameter is a vital problem. It deals with the
elimination of frequent occurring parameters in order to take an optimal decision
making, without affecting the novelty of the data set, so that the number of param-
eters is reduced to simply the calculations. Hence the soft set is used to discover the
similar links between the nodes. These similar links are removed from data set that
yields in reducing the dimension. Thereby the space as well time complexities will
have a signicant improvement.
17.4.2 Friendlink Algorithm
Friendlink algorithm [23] nds the similarities between nodes in a sociograph using
the adjacency matrix. Rubin’s algorithm [24] is used to nd the paths of lengthl.
Denition The similarity score sim(ux, uy) between two nodes ux and uy is dened
as counting of paths of varying length l from ux touy is as given below:
J. G. Thangam and A. Sankar

257
sim,
paths
uu k
nm
xy
k
luu
k
m
k
xy
()
=−∗
−
()
==
∑∏
22
1
1
,
(17.2)
where n is the number of vertices in a graph, l is the maximum length of a path from
ux and uy. 1
1k− is an attenuation factor that weighs paths according to their length.
pathsuu
k
xy
, is the number of all length-l paths from ux to uy.
m
k
nm
=
∏−
()
2
is the number
of all possible length-l paths from ux to uy. k and m denote current iteration. For
example, if the two nodes are similar, the similarity score is expected to be close to
1. On the other hand, if the two nodes are not similar, the similarity score is expected
to be close to 0.
The Friendlink algorithm comprises of two functionalities, one is for computing
the path of length-2 between two nonexistent nodes and other is for computing the
similarities between them. Then the similarity scores will be ranked. Finally the
node with the highest similarity score will be declared as the node which is more
probable to be communicated in future. Hence nodes can be recommended to the
target node according to the similarity scores.
17.5 Proposed Work
Link prediction is a pivotal problem in SNA in order to be acquainted with associa-
tions between nodes in any social communities. The proposed approach is explained
in Fig.17.2.
The Social Network data set is obtained as edge list for a particular network. The
edge list is then converted into an adjacency representation. The soft set is applied
and the preprocessed data is obtained. The Soft set and Friendlink-based Link
Prediction algorithm (SFLP) uses the Friendlink algorithm for the path length of 2
and 3. The similarity score is calculated for the original data set using the length-2
path and length-3 path. Ultimately, the similarity score of a node against all other
nodes are computed and similarity scores are ranked. The node with higher similar-
ity score is the node to occur in future which is predicted. The pseudo code for
ranking the nodes is provided in Algorithm 1.
17 Emphasizing onSpace Complexity inEnterprise Social Networks…

258
The algorithm is rst organized to reduce the dimensions of the dataset. The reduced
data set is used for nding the similarity score between the nodes. Then all the
similarity scores are ranked, and the node with the maximum similarity score value
will be the node to be linked in future. The similarity scores obtained using SFLP is
compared with the similarity score of Friendlink Link Prediction (FLP) algorithm.
Those results are analyzed against the social network centrality measures.
17.6 Experimental Setup andResults
The experiments are conducted on a 2.50-GHz Intel Dual core PC with 4GB RAM
running Microsoft 7 ultimate. The SFLP and FLP algorithms are implemented using
MATLAB. An examination is made to assess the exhibition of the proposed method.
To evaluate the efciency of the algorithm, it is examined using real world data
set. The data set is loaded from UCI Network Data Repository. The Zachary’s
Karate Club data set is a social network of friendships of a karate club at a US uni-
versity. Each node represents a member of the club and each edge represents a tie
between two members of the club. It represents the presence (1) or absence (0) of
ties among the members of the club. Hence, the work is carried out to predict the
indirect ties that exist between the members of the club in future. From this, the
friendships of the karate club in future will be predicted.
Algorithm 1: Soft Set and Friendlink-Based Link Prediction (SFLP)
Algorithm
Input: Adjacency Matrix.
Output: Ranked prediction list
1. Reduce the dimension using soft set.
For each nodenodei.
Find all the links such that aij> 0
Removeaij if the link is redundant
End For
2. Compute similarity score sim(nodei, nodej) using Friendlink algorithm.
For each nodenodei
If aij!=1
Find the similarity scores with all other nodes.
End if
End For
3. Rank the nodes.
J. G. Thangam and A. Sankar

259
For nding the strong friendship between the nodes, the similarity score between
two nodes before dimensionality reduction (FLP) is calculated using Friendlink
algorithm. It considers all indirect links of length-2 and length-3 path. Out of all the
nodes, some nodes are extracted and are shown in the Table17.1.
In this table the zero represents that there is no link between the nodes with the
path length of length-2 path and length-3 path and a nonzero value represents that
the nodes are having a probability of some value to get linked in future. For exam-
ple, the node1is having indirect link with many other nodes like node24 and node26.
But it is found that node1 is having highest similarity score with node26 when com-
pared to all other remaining nodes. It is concluded that node1 will have higher prob-
ability to link with node26 in future. The graphical representation for the values of
Social Network dataset
Similarity Score Calculation using Friendlink algorithm
Link Prediction Result
Ranking
Dimension Reduction
using Soft set
Pre-processed
Dataset
Reduced Dimension
Preprocessing
Fig. 17.2 The proposed approach
17 Emphasizing onSpace Complexity inEnterprise Social Networks…

260
node1 against all other remaining nodes is shown in Fig.17.3a. Similarly, the node15
is having links with other nodes such as node9 and node32, and it is found that node9
is having higher similarity score. Therefore node15 is having higher probability to be
linked with node9. The graphical representation for the values of node15 against all
other remaining nodes is shown in Fig.17.3b.
Since the data set is immense, the dimensionality reduction technique is applied
for extracting the friendship without affecting the members in the karate club. The
Table 17.1 Similarity scores of Friendlink Link Prediction
Nodes 9 24 26 32
1 0 0.000504 0.031754 0
2 0.03125 0 0 0
3 0 0.063004 0.000504 0.03125
40000
50000
60000
70000
80000
9 0 0.03125 0.001008 0.063004
10 0.032258 0.001512 0.000504 0.031754
11 0 0 0 0
12 0 0 0 0
13 0 0 0 0
14 0.032258 0.001512 0.000504 0.031754
15 0.064012 0.03125 0.001008 0.063004
16 0.064012 0.03125 0.001008 0.063004
17 0 0 0 0
18 0 0 0 0
19 0.064012 0.03125 0.001008 0.063004
20 0.032258 0.001512 0.000504 0.031754
21 0.064012 0.03125 0.001008 0.063004
22 0 0 0 0
23 0.064012 0.03125 0.001008 0.063004
24 0.031754 0 0 0
25 0.032258 0.0625 0 0.031754
26 0 0.000504 0 0
27 0 0.03125 0 0
28 0.032258 0.001512 0.000504 0.031754
29 0.032258 0 0.031754 0.031754
30 0.064012 0.03125 0.001008 0
31 0.064012 0.03125 0.001008 0.063004
32 0.064012 0.03125 0.001008 0.063004
33 0.032258 0.001512 0.000504 0.031754
34 0 0 0 0
J. G. Thangam and A. Sankar

261
similarity score after applying Soft set-based FLP (SFLP) algorithm is calculated
and is shown in Table17.2.
It also considers all indirect links of length-2 and length-3 path. For example,
with reduced dimension, the node1is having indirect link with many other nodes
such as node26 and node30. But it is found that node1 is having highest similarity
score with node26 with respect to all other remaining nodes even after the dimen-
sionality reduction technique is employed. So, it is concluded that the node1will
have a chance to be linked with node26. The graphical representation for the values
of node1 against all other remaining nodes is shown in Fig.17.4a. Similarly, the
node15 is having links with other nodes such as node9 and node26, and it is found that
node9 is having higher similarity score. Therefore node15 is having higher probabil-
ity to be linked with node9. Hence it is observed that the node that is having an
indirect link of length-2 and length-3 path length will be similar even after the
a) Similarity Score performance
0.035
0.03
0.025
0.02
0.015
0.01
0.005
0
01020
Node
30 40
Similarity Score
b) Similarity Score performance
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0
01020
Node
30 40
Similarity Score
Fig. 17.3 Similarity scores of Friendlink Link Prediction
17 Emphasizing onSpace Complexity inEnterprise Social Networks…

262
dimensionality reduction. The graphical representation for the values of node15
against all other remaining nodes is shown in Fig.17.4b.
So, it is observed that the node that will occur in future is same for both the algo-
rithms. Finally the consistency of the data set is preserved. The running time for
executing the FLP algorithm is 0.1525s and SFLP is 0.0147s. It is found that the
approach with dimensionality reduction technique works signicantly better.
Table 17.2 Similarity scores of Soft set-based Friendlink Link Prediction
Nodes 9 26 30 31
1 0 0.094254 0.09375 0.001008
2 0.000504 0.0625 0 0
3 0 0 0.064012 0.032258
4 0.001008 0.03125 0.03125 0.001008
5 0 0.000504 0 0
6 0 0.001512 0 0
7 0 0.001008 0 0
80000
9 0.03125 0.032762 0.03125 0
10 0.03125 0.001008 0.001512 0.031754
11 0 0 0 0
12 0 0 0 0
13 0 0 0 0
14 0 0.001008 0.001512 0.031754
15 0.063508 0.032762 0.001008 0.03125
16 0.03125 0.032762 0.001008 0.063508
17 0 0 0 0
18 0 0 0 0
19 0.03125 0.032762 0.001008 0.063508
20 0.03125 0.001008 0.001512 0.031754
21 0.03125 0.032762 0.001008 0.063508
22 0 0 0 0
23 0.03125 0.032762 0.001008 0.063508
24 0 0 0 0.031754
25 0.03125 0 0.001512 0.031754
26 0 0 0 0
27 0 0 0 0
28 0.03125 0.001008 0.001512 0.031754
29 0.03125 0.001008 0.001512 0.031754
30 0.03125 0.032762 0.001008 0.063508
31 0.03125 0.032762 0.001008 0.063508
32 0.03125 0.032762 0.001008 0.063508
33 0.03125 0.001008 0.001512 0.031754
34 0 0 0 0
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263
17.7 Centrality Measures
In SNA, the graph theory is exceptionally essential to discover the “central” actors.
The graph theoretic thoughts measure an individual actor’s prominence quality
depending on centrality and prestige. An actor is said to be prominent if the ties of
the actor make the actor particularly noticeable to the other actors in the network.
Centrality is a measure of the importance of a node in a network. A prestigious actor
is characterized as one who is the object of broad ties, thus concentrating exclu-
sively on the actor as a recipient. Obviously, prestige is a more rened idea than
centrality and can’t generally be estimated. So, the centrality measures are consid-
ered to be signicant. The commonly used centrality measures are degree, close-
ness, betweenness, eigenvector centrality, and clustering coefcient.
403020
Node
010
b) Similarity Score performance
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0
Similarity Score
4030
20
Node
010
a) Similarity Score performance
0.1
0.08
0.06
0.04
0.02
0
Similarity Score
Fig. 17.4 Similarity scores of Soft set Friendlink Link Prediction
17 Emphasizing onSpace Complexity inEnterprise Social Networks…

264
17.7.1 Degree Centrality
The degree centrality can be of, in-degree centrality, and out-degree centrality. The
in-degree centrality is dened as an actor who receives many ties, and considered as
prominent. The out-degree centrality is dened as an actor who disperses the infor-
mation quickly to many others in a network and is categorized as inuential. The
degree centrality index [25] is given by
Cn dn
Di i
()
=
()
(17.3)
where CD is the degree centrality for theithnode denoted by ni, and d(ni) is the
degree of the node ni. This measure focuses on the most visible actors in the network.
17.7.2 Closeness Centrality
The closeness centrality is a measure of the degree to which an individual is close
to all other individuals in a network. The closeness centrality is computed by using
Cn dnn
Ci
j
g
ij
()
=
()





=
−
∑
1
1
,
(17.4)
where CC is the closeness centrality for the ithnode denoted by ni, d(ni, nj) is the
length of any shortest path from ni to nj,
j
g
ij
dnn
=
∑
()
1
, is the total distance from all
other nodes to node i. This measure depends on the whole of the geodesic distances
from one another to all others. However, it is ambiguous for complicated graphs.
17.7.3 Betweenness Centrality
The betweenness centrality [26] is a measure of the extent to which a node is con-
nected to other nodes that are not connected to each other. The betweenness central-
ity index is computed by using
Cn gn g
Bi
jk
jk
ij
k
()
=
()
<
∑
/
(17.5)
where CB is the betweenness centrality for the ithnode denoted by ni,gjk(ni) is the
number of geodesics linking two nodes j and kthat contain the node i. This measure
J. G. Thangam and A. Sankar

265
estimates the degree of gatekeeping for every pair of actors in the network, concen-
trating on how much gatekeeping the subsequent actor accomplishes for the rst.
17.8 Eigenvector Centrality
The eigenvector centrality is used to quantify the relative importance of a node in a
network. The eigenvector centrality is calculated using
Cn
ax
Ei
n
i
ij j
()
=∗
=
∑
1
1
λ
(17.6)
where CE is the eigenvector centrality for the ithnode denoted by ni, λ is a constant,
aij is equal to 1 if the ith and jth vertices are adjacent, that is, there is an edge connect-
ing the ith and jth vertices.aij is equal to 0 otherwise, that is, if there is no edge con-
necting the ith and jth vertices.
17.8.1 Clustering Coefcient
The clustering coefcient is the measure of the degree to which a node in a network
tends to cluster together. It is also known as local clustering coefcient. The cluster-
ing coefcient is calculated using
Cu ni
Co
()
=numberofpairs of neighborsconnectedbyedges
numberofp
aairs of neighbors
(17.7)
where CuCo is the clustering coefcient for the ithnode denoted by ni. This measure
has become popular in Nature journal by Watts and Strogatz [27]. The centrality
measures are computed using the open source tools for the original data set and is
tabulated in Table17.3.
It is observed that node34 and node1 are having high degree centrality and is con-
sidered as the prominent as well the most inuential node in the Zachary’s Karate
Club data set. The node1 has high betweenness centrality which means that through
node1 only most of the nodes are connected to other nodes in a network. The node1
will be the gatekeeper for every pair of nodes in a network. The degree centrality of
node1 is same as that of the node that is having high betweenness centrality. So, the
degree centrality is directly proportional to the betweenness centrality.
The closeness centrality for the nodes such as node15 and node23 is higher, thereby
indicating that those nodes are very close to all other nodes in a network. The node34
is having higher eigenvector value which shows that the node is relatively important
17 Emphasizing onSpace Complexity inEnterprise Social Networks…

266
with respect to other nodes in a network. The clustering coefcient for some of the
nodes like node15, node8,node19 that have higher values shows the property of one’s
friends are also friends of each other. Also, degree centrality is inversely propor-
tional to the closeness centrality. It is concluded that nodes having low degree and
betweenness centrality can be removed. The density of the graph G is the ratio of
Table 17.4 Topological properties of the real data set for Friendlink Link Prediction
UE EWD TE SL V GD
78 0 78 0 34 0.069518
Table 17.3 Centrality measure calculation of Friendlink Link Prediction
Vertex
Degree
Centrality
Betweenness
Centrality
Closeness
Centrality
Eigenvector
Centrality
Clustering
Coefcient
1 8 0.852 2.200 0.221 0.143
2 5 0.042 2.467 0.165 0.300
3 8 0.423 2.033 0.277 0.143
4 5 0.030 2.600 0.143 0.350
5 1 0.000 3.933 0.007 0.000
6 2 0.000 3.900 0.008 0.500
7 4 0.523 2.967 0.041 0.083
8 4 0.000 2.633 0.134 0.500
9 5 0.249 2.033 0.263 0.250
10 1 0.000 2.800 0.076 0.000
13 2 0.000 3.133 0.060 0.500
14 1 0.000 2.800 0.076 0.000
15 2 0.000 2.600 0.144 0.500
16 2 0.000 2.600 0.144 0.500
17 2 0.000 3.900 0.008 0.500
19 2 0.000 2.600 0.144 0.500
20 1 0.000 2.800 0.076 0.000
21 2 0.000 2.600 0.144 0.500
23 2 0.000 2.600 0.144 0.500
24 4 0.191 2.600 0.128 0.083
25 4 0.048 2.300 0.156 0.250
26 2 0.000 2.867 0.067 0.500
27 1 0.000 3.433 0.028 0.000
28 4 0.113 2.167 0.169 0.083
29 3 0.017 2.300 0.162 0.167
30 4 0.201 2.467 0.170 0.167
31 4 0.073 2.233 0.215 0.250
32 6 0.421 1.967 0.245 0.133
33 12 0.708 1.867 0.412 0.091
34 16 1.000 1.833 0.456 0.054
J. G. Thangam and A. Sankar

267
edges in G to the maximum number of edges. The graph density is computed for the
original data set and shown in Table 17.4. The topological properties are Unique
Edges (UE), Edges With Duplicates (EWD), Total Edges (TE), Self-Loops (SL),
Vertices (V), and Graph Density (GD).
The centrality measures are computed for the data set for which the soft set the-
ory is applied and nally the reduced dimension data set is obtained. The centrality
measure is tabulated in Table17.5.
Table 17.5 Centrality measure calculation of Soft set Friendlink Link Prediction
Vertex
Degree
Centrality
Betweenness
Centrality
Closeness
Centrality
Eigenvector
Centrality
Clustering
Coefcient
1 16 1.000 1.758 0.356 0.075
2 9 0.123 2.061 0.266 0.167
3 10 0.328 1.788 0.317 0.122
4 6 0.027 2.152 0.211 0.333
5 3 0.001 2.636 0.076 0.333
6 4 0.069 2.606 0.079 0.250
7 4 0.069 2.606 0.079 0.250
8 4 0.000 2.273 0.171 0.500
9 5 0.128 1.939 0.227 0.250
10 2 0.002 2.303 0.103 0.000
11 3 0.001 2.636 0.076 0.333
12 1 0.000 2.727 0.053 0.000
13 2 0.000 2.697 0.084 0.500
14 5 0.105 1.939 0.226 0.300
15 2 0.000 2.697 0.101 0.500
16 2 0.000 2.697 0.101 0.500
17 2 0.000 3.515 0.024 0.500
18 2 0.000 2.667 0.092 0.500
19 2 0.000 2.697 0.101 0.500
20 3 0.074 2.000 0.148 0.167
21 2 0.000 2.697 0.101 0.500
22 2 0.000 2.667 0.092 0.500
23 2 0.000 2.697 0.101 0.500
24 5 0.040 2.545 0.150 0.200
25 3 0.005 2.667 0.057 0.167
26 3 0.009 2.667 0.059 0.167
27 2 0.000 2.758 0.076 0.500
28 4 0.051 2.182 0.133 0.083
29 3 0.004 2.212 0.131 0.167
30 4 0.007 2.606 0.135 0.333
31 4 0.033 2.182 0.175 0.250
32 6 0.316 1.848 0.191 0.100
33 12 0.332 1.939 0.309 0.098
34 17 0.695 1.818 0.374 0.055
17 Emphasizing onSpace Complexity inEnterprise Social Networks…

268
The degree for node34 and node1 is higher, so the original data set node34 and
node1 are prominent and inuential nodes. The betweenness centrality for the node1
is higher which is same as that of the centrality measures obtained for the original
data set. The remaining metrics are also the same. Hence, the centrality measures
for both original data set and reduced dimension are compared. It is agreed that the
node having low degree and betweenness centrality is removed without affecting
the consistency of the data set, so it improves the memory space utilization. The
graph density is computed and shown in Table17.6.
It is observed that the graph density is compressed for the reduced dimension.
17.9 Theoretical Analysis
The proposed work comprises of three tasks, the rst task is to construct a reduced
data set and the time complexity for constructing isΟ(n+e), where nis the nodes
and e is the number of edges, alwaysn <e. The second task is to nd similarity
score using Friendlink algorithm and the time complexity isΟ(n∗a), where n is the
number of nodes and a is the average degree of the network. So, the time complexity
of these two tasks would be Ο(max(n+e, n∗a)), since the average degree is less
than the number of edges, the time complexity would beΟ(n+e). The third task is
to rank all the similarity scores and the time complexity isΟ(nlogn). Hence, the
total time complexity of the proposed algorithm would beΟ(max(n+ e, nlogn)),
since the number of nodes is comparatively less than the number of edges, the time
complexity would beΟ(nlog n) and the space complexity would be Ο(n1∗a1),
where a1 is the average node degree and n1 is number of nodes after dimensionality
reduction which is strictly less than the number of nodes present in the original data
set. The proposed technique signicantly improves the performance of link predic-
tion compared to Katz [28] index, since it needs the inverse matrix representation
for further computation. For implementation, the adjacent list representation is used
for storing the adjacent nodes in a graph.
17.10 Empirical Analysis
Most standard evaluation metric is Mean Average Precision (MAP), which provides
a single-gure quality measure. It is dened by
Table 17.6 Topological properties of the real data set for Soft set Friendlink Link Prediction
UE EWD TE SL V GD
60 0 60 0 34 0.064516
J. G. Thangam and A. Sankar

269
MAPAveP n
n
i
=
()
=
∑
1
(17.8)
where n is the total number of nodes, i denotes the iteration,AveP(n) is the average
precision value. It has been shown to have especially good discrimination and sta-
bility among the various metrics. So, the link prediction accuracy is evaluated using
MAP for both algorithms. The MAP value for SFLP is 0.38853 and FLP is 0.07865.
The empirical analysis shows that MAP value is higher for SFLP than FLP
algorithm.
17.11 Conclusion andFuture Work
Social networks play a signicant role in all domains, in which the data set is very
large. This chapter concentrates on soft set theory that has been applied for dimen-
sionality reduction. The Friendlink algorithm is used for nding the similarity score
of a node with other nodes. Both the Soft set theory and Friendlink algorithm are
applied for the real data set in order to predict the node that will occur in future.
Then the consistency of the data is veried against the various social network met-
rics. The experimental results show that the approach works better in terms of space.
Further improvement might be predicting the links that can be applied to various
domains like community detection, name disambiguation, and health care.
References
1. Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications (Vol.
8). Cambridge: Cambridge University Press.
2. Song, Han Hee, Tae Won Cho, Vacha Dave, Yin Zhang, and Lili Qiu. (2009) Scalable proxim-
ity estimation and link prediction in online social networks. In Proceedings of the 9th ACM
SIGCOMM conference on Internet measurement conference, ACM (pp.322–335).
3. Aldous, J. M., & Wilson, R.J.. (2003) Graphs and applications: An introductory approach
(Vol. 1). Springer.
4. Liben-Nowell, D., & Kleinberg, J. (2007). The link-prediction problem for social networks.
Journal of the Association for Information Science and Technology, 58(7), 1019–1031.
5. Chen, K.-J., Han, J., & Li, Y. (2014). HALLP: A hybrid active learning approach to link predic-
tion task. JCP, 9(3), 551–556.
6. Sharma, D., Sharma, U., & Khatr, S.K. (2014). An experimental comparison of the link pre-
diction techniques in social networks. International Journal of Modeling and Optimization,
4(1), 21.
7. Michael, F., Tenenboim-Chekina, L., Puzis, R., Lesser, O., Rokach, L., & Elovici, Y. (2013).
Computationally efcient link prediction in a variety of social networks. ACM Transactions on
Intelligent Systems and Technology (TIST), 5(1), 10.
8. Gupta, N., & Singh, A.. (2014). A novel strategy for link prediction in social networks. In
Proceedings of the 2014 CoNEXT on Student Workshop, ACM (pp.12–14).
17 Emphasizing onSpace Complexity inEnterprise Social Networks…

270
9. Kurakar, M., & Izudheen, S. (2014). Link prediction in protein-protein networks. Survey:
International Journal of Computer Trends & Technology, 1(9), 164–168.
10. Almansoori, Wadhah, Shang Gao, Tamer M.Jarada, Reda Alhajj, & Jon Rokne. (2011) Link
prediction and classication in social networks and its application in healthcare. In Information
Reuse and Integration (IRI), 2011 IEEE International Conference on, IEEE, pp.12–14.
11. Papadimitriou, A., Symeonidis, P., & Manolopoulos, Y. (2012). Fast and accurate link predic-
tion in social networking systems. Journal of Systems and Software, 85(9), 2119–2132.
12. Gong, N. Z., Talwalkar, A., Mackey, L., Huang, L., Shin, E.C. R., Stefanov, E., & Song, D.
(2012). Jointly predicting links and inferring attributes using a social-attribute network (san).
ACM, 1–9.
13. Menon, A.K., & Elkan, C. (2011). Link prediction via matrix factorization. In Joint European
conference on machine learning and knowledge discovery in databases (pp.437–452). Berlin
Heidelberg: Springer.
14. Dunlavy, D.M., Kolda, T.G., & Acar, E. (2011). Temporal link prediction using matrix and
tensor factorizations. ACM Transactions on Knowledge Discovery from Data (TKDD), 5(2),
10.
15. Zhao, J., Xu, F., Dong, L., Liang, X., & Xu, K. (2012). Performance of local information-based
link prediction: A sampling perspective. Journal of Physics A Mathematical and Theoretical,
45(34), 345001.
16. Lichtenwalter, R.N., Lussier, J.T., & Chawla, N.V. (2010). New perspectives and meth-
ods in link prediction. Proceedings of the 16th ACM SIGKDD international conference on
Knowledge discovery and data mining ACM, 243–252.
17. Yu, G. (2015). On the parameter reduction of soft binary relations. Annals of Fuzzy Athematics
and Informatics, 1–10.
18. Ma, X., & Qin, H. (2014). Application of a new efcient Normal parameter reduction algo-
rithm of soft sets in online shopping. World Academy of Science, Engineering and Technology,
International Journal of Computer, Electrical, Automation, Control and Information
Engineering, 8(7), 1174–1176.
19. Liben-Nowell, D., & Kleinberg, J. The link-prediction problem for social networks. Journal
of the American Society for Information Science and Technology, 58(7), 1019–1031.
20. Yu, C., Zhao, X., Lu, A., & Lin, X. (2017). Similarity-based link prediction in social networks:
A path and node combined approach. Journal of Information Science, 43(5), 683–695.
21. Molodtsov, D. (1999). Soft set theory—First results. Computers & Mathematics with
Applications, 37(4–5), 19–31.
22. Zimmermann, H.-J. (2011). Fuzzy set theory—And its applications. Springer.
23. Papadimitriou, A., Symeonidis, P., & Manolopoulos, Y. (2011). Friendlink: link prediction in
social networks via bounded local path traversal. In Computational Aspects of Social Networks
(CASoN) (pp.66–71). 2011 International Conference on, IEEE.
24. Rubin, F.R. A.N. K. (1978). Enumerating all simple paths in a graph. IEEE Transactions on
Circuits and Systems, 25(8), 641–642.
25. Baagyere, E. Y., Qin, Z., Xiong, H., & Zhiguang, Q. (2016). The structural properties of
online social networks and their application areas. IAENG International Journal of Computer
Science, 43(2).
26. Freeman, L. C. (1977) A set of measures of centrality based on: (1977): 35- betweenness.
Sociometry 41.
27. Watts, D.J., & Strogatz, S.H. (1998). Collective dynamics of ‘small-world’ networks. Nature,
393(6684), 440.
28. Katz, L. (1953). A new status index derived from sociometric analysis. Psychometrika, 18(1),
39–43.
J. G. Thangam and A. Sankar

271© Springer Nature Switzerland AG 2020
A. Haldorai etal. (eds.), Business Intelligence for Enterprise Internet of Things,
EAI/Springer Innovations in Communication and Computing,
https://doi.org/10.1007/978-3-030-44407-5_18
Chapter 18
Overview onDeep Neural Networks:
Architecture, Application andRising
Analysis Trends
V. NiranjaniandN.SaravanaSelvam
18.1 Introduction
Alongside Big Data [1], the subject of deep education has become a dominant sub-
ject for the industry and research elds for the growth of different intelligent sys-
tems, Cloud Computing [2] and the Internet of Things (IoT) [3]. The ability to
estimate and reduce comprehensive, complicated data sets into extremely precise,
transformative output has demonstrated an important potential [4, 5]. Conversely,
deep neural networks (DNN) architectures may be applied to all kinds of informa-
tion—whether numerical, visual, texts, audio, or a certain mixture—as opposed to
complicated hard-coded programs for one inexible job only. In addition, sophisti-
cated, often open-source and widely accessible advanced, deep learning platforms
become increasingly accessible. Moreover, signicant businesses such as Amazon,
Flipkart, Microsoft, Apple, and others are investing strongly in profound learning
technology.
Supervised classication tasks have outstripped human capacities in elds like
manuscript and picture recognition, one in each of the key elds for deep learning
applications [6]. Furthermore, unattended data set learning without specic labels
have shown the possibility of extraction by means of the clustering and statistical
analysis of unpredicted assessment and business value. Most potentially attractive
yet is strengthened learning, providing feedback from a linked setting that offers the
opportunity for deep learning without human oversight. The eld of robotics and
computer view was widely used for this sort of profound learning.
V. Niranjani (*)
Sri Eshwar College of Engineering, Coimbatore, India
N. S. Selvam
PSR Engineering College, Sivakasi, India

272
As IoT and smart-world systems are steadily growing, powered by CPS(cyber-
physical system) technologies in which networking in all devices and capable of
communicated sensitive information and physical items monitor, bigger and bigger
datasets are accessible for profound learning which can have a material effect on our
everyday life [6, 7–9]. For example, smart phones have provided the ability to create
rich video, audio, and text data through integrated GPS modules from various media
apps and integrated sensors, as well as massively locating population movement.
All such apps are clearly generated, alone, or together, by unprecedented Big Data.
Intelligent cities are designed to allow resource management in almost all elds like
communications, electricity, transportation, emergency, and other utilities to build-
ing. Intelligent transport systems will interconnect auto-drive cars and infrastruc-
ture networks to revolutionize day-to-day mass transit, eliminate accidents, and
facilitate the storage of the secondary grid.
18.2 Overview ofDeep Neural Networks
Machine learning (ML) includes a wide range of algorithms, which cannot all be
categorized as deep learning. Specic algorithms like Bayesian algorithms are
restricted in implementation and in the capacity to learn massive complicated infor-
mation representations, including statistical processes such as linear-regression or
decision-making trees. The articial intelligence eld is primarily when machines
can perform functions that typically require human intelligence [10]. It includes
machine learning, where machines can obtain abilities through experience and
without participation from human beings [11]. Deep learning is a component of
machine learning that is used to learn from big quantities of information by articial
neural networks, human brain-inspired algorithms. Similar to how we learn from
experience, each time the deep learning algorithm tweaks a job a bit in order to
enhance the result. We are talking about ‘profound learning’ because neural net-
works have different (profound) levels that allow learning to take place[12].
The quantity of information we produce each day—currently estimated at 2.6
quintillion octets—is astonishing and is the resource that enables profound learning.
Since profound learning algorithms require a ton of information to draw on, this rise
in information development is one reason why in the latest years profound learner
skills have grown. In relation to further development of information and the prolif-
eration of articial intelligence (AI), deep learning algorithms prot from today’s
greater computing power. AI as the service has offered smaller organizations access,
especially the AI algorithms needed for deep learning without a big one, to articial
intelligence technology shown in Fig.18.1.
V. Niranjani and N. S. Selvam

273
18.2.1 Deep-Neural Network Architecture
Three signicant layers are input layer, hidden layers, and output layers for deep-
learning architectures. The number of layers concealed determines the architectural
depth. Various nonlinear features can be learned depending on the sort of concealed
layers used. Equations (18.1) and (18.2) are straightforward ANN patterns. In Eq.
(18.1) the nonlinearity observed within the information (concealed layer) is where
the activation function determines the nonlinearity features of the model. The same
applies to Eq. (18.2) (Output layer) which translates the feature of nonlinearity into
a forecast. An easy model for deep learning has several hidden layers.
AC ActZ
AC
11
1
=+
()
(18.1)
ˆ
Y
=+
ZA
CC
21 2 (18.2)
18.2.2 Activation Function
The hidden layer activation function helps to map the nonlinearity connection
between input and output. In concealed layers, most used activation functions are
sigmoid and hyperbolic (Tanh). Specic activation functions are not applicable. For
datasets, various activation functions must be assessed. In this document, we use the
rectied linear activation function (also known as “relu”) (see Eq. (18.3)), since the
techniques of the gradation optimization facilitate model training [13].
Fig. 18.1 Diagram of ML vs. DL
18 Overview on Deep Neural Networks: Architecture, Application and Rising…

274
fx
x
xx
()
=
<
≥
{
,
,
00
0 (18.3)
18.2.3 Hidden Layer
Equation (18.1) is a straightforward concealed layer. A number of parameters are
used to dene the complexity of each hidden layer. The number of parameters that
are the number of concealed units and the regularization parameter in L2 are gov-
erned by hyper parameters. The number of hidden parameters in each layer (i.e., the
weights) and the regularization of L2 reduce the extent of the parameters in order to
avoid over tting [13]. To ensure a good model t, tuning hyper parameters is
essential.
18.2.4 Outer Layer
Typically, the output comprises inactivation of an identity (also known as linear
activation) for regression issues. Activation of identity allows for adverse projec-
tions. As the demand for cooling and heating energy is non-negative reaction values
with a minimum “zero,” identication and activation for this implementation is not
appropriate. The linear activation function is used rather than rectied in Fig.18.2.
Fig. 18.2 Diagram of deep learning architecture
V. Niranjani and N. S. Selvam

275
18.3 Deep-Neural Network Architecture
18.3.1 Supervised Learning
Supervised learning (SL) is named because of the need for consistent marking of the
studied data and for tracking or classifying the study results as accurate or incorrect.
Supervised learning(SL) is, in particular, used as a mechanism of prediction where
some of the data are learned (also known as training-set), a second part to validate a
model which are already trained (cross-validation), and the remaining data are used
to the predict the accuracy and effectiveness. While accuracy is a signicant mea-
sure, the ability of a trained model to generalize to new information is used through
other statistically signicant processes, such as precision, recall, and F1 scores.
Classication and regression are the two main tasks for monitored learning.
18.3.2 Unsupervised Learning
In unsupervised learning, datasets are not marked in any manner that identies a
right or improper outcome, given as input for machine learning. Rather, the out-
come may reach a larger required objective, the capacity to nd something easy to
understand by humans or a complicated use of a statistical function to obtain the
required value may be assessed. For example, the clustering algorithm may cluster-
data or fugitive groups, but it cannot be simple to tell if the clustering is actually
correct without an appropriate visual representation. Similarly, the density estimate
provides only for an estimate to be used for compression or a decrease in dimension
that is either relevant or irrelevant to a dataset or to encode information effectively.
However, it may still be necessary to nd the extraction ability of compressed
images accurately to establish the adequacy of implementation.
18.3.3 Reinforcement Learning
Reinforcement Learning is an intermediate between monitored and uncontrolled
learning, although information is not specically labeled, there is a reward for the
performance in each action. In particular, the architecture of this learning is
enhanced learning that interacts directly with the setting, thus providing a particular
reward to a changing setting. In order to enlarge the benet to all the state transi-
tions, the enhanced education scheme aims to learn the best measures to be taken at
every state. This can be done for an endless moment or can be implemented in meet-
ings to maximize the results of each session through a perception-action-learning
loop. Furthermore, the feedback can either be provided straight from the setting or,
18 Overview on Deep Neural Networks: Architecture, Application and Rising…

276
as a consequence of some calculation or function, by way of numerical counter in
an interactive setting shown Table18.1.
The search policy and approximation function can be divided between the two
main means of enhancement. Policy search can be performed using gradient-based
(back propagation) or gradient-free (evolutionary) techniques to search for an ideal
policy directly. Value function is a technique through which an evaluation of the
expected return of a particular state and the selection of an ideal strategy to select an
action to maximize the desired value are required for each state action.
18.4 Applications ofDeep Neural Networks
Here we explore the primary applications of deep learning. An important body of
job has continuously evolved over the past few years towards the implementation of
deep learning. In particular, the main advances were in applying deep neural net-
works to analyze the multimedia data including image, video, audio, and NLP that
resulted in signicant state-of-the-art leaps for all systems. In fact, ML is primarily
worried about information tting, whose main uses are discrimination, prediction,
and optimization. In addition, the progress made in Big Data and the Cloud
Computing sector has created a chance for machine learning to ourish, which
enables the information collection, dissemination, and computational model perfor-
mances. The presence of the information and the nature of their potential immedi-
ately required more precise, widespread, and effective processes of learning.
18.4.1 Fraud Detection
The nancial and banking industry is an area that benets from deep learning and is
affected by the job of detecting fraud with digital cash transactions. Fraud detection
is performed on the basis of identication of patterns and credit scores for client
Table 18.1 Different categorization of deep learning
Supervised learning Unsupervised learning Reinforcement learning
Denition Both predictors and
predictions exist for
the training set.
Training package has
only data set predictors.
In every assignment they can
produce cutting-edge outcomes.
Algorithm SVM, Linear
regression and logistic
regression, Naive
Bayes
Dimensionality
reduction, K-Means,
Clustering
Q-Learning, SARSA, DQN
Uses Forecasting Pre-process the data,
pre-train supervised
learning algorithms.
Warehouses, Inventory
management, delivery
management, Power system,
Financial systems.
V. Niranjani and N. S. Selvam

277
operations, anomalous conduct, and outlines. Auto-encoders are created for credit-
card- fraud in Tensor ow and Keras that save billions of USD of nancial institution
recapture and insurance price. Machine learning methods and neural networks are
used for fraud detection. Mainly machine learning is used to highlight cases of fraud
which require human deliberation; deep learning is intended to minimize such
efforts.
18.4.2 Diagnostics Medical
Highly affected by advances in image analysis, the fast improvements in deep learn-
ing have greatly beneted medical diagnostics. Considerable research has been per-
formed to improve the detection of pictures of MRI, tumors, CT scans, illnesses,
and other abnormalities. Furthermore, the devices of IoT for medical application are
providing independent patient surveillance and to extract helpful medical popula-
tion information.
18.4.3 Self-Driving Car
Deep learning gives life to self-employed movement. One million set of information
are supplied in a scheme for model building, machine training, and ensuring that
outcomes are tested in secure setting. Autonomous car developers are concerned
with handling scenarios that are unprecedented. Typical to deep learning algorithms,
a periodic cycle of testing and execution ensures secure driving and increasing
exposure to millions of situations. Dash cams, geo-mapping, and sensor data help to
generate brief and advanced models for navigating through trafc, identifying paths,
signage, pedestrian-only routings, etc.
18.5 Summary
Deep learning is a technology that continues to evolve and has been implemented to
great effect in a multitude of applications and domains. Deep learning methods are
practical for us to x a lot of issues. Although full-scale implementation of DL tech-
nology in industry is ongoing, calculated steps must be taken to ensure proper appli-
cation of deep learning, as the subversion of deep learning models can lead to
signicant loss of nancial value, condence, or even extreme existence. We ana-
lyzed in detail deep learning architectures based on learning mechanisms (super-
vised, unsupervised, and reinforced) and target output models. In addition, in deep
learning science, we have thoroughly investigated the state of the art. Such areas
include the processing of multimedia (text, audio, and video), automated systems,
18 Overview on Deep Neural Networks: Architecture, Application and Rising…

278
medical diagnostics, nancial applications, and security analysis. We hope that this
work offers a useful reference for both scientists and computer science profession-
als in considering deep learning methods, and application, and causes interest in
elds that urgently need further consideration.
References
1. Liang, F., Yu, W., An, D., Yang, Q., Fu, X., & Zhao, W. (2018). A survey on big data market:
Pricing, trading and protection. IEEE Access, 6, 15132–15154.
2. Huang, H. H., & Liu, H. (2014, October). Big data machine learning and graph analytics:
Current state and future challenges. In Proceedings of IEEE international conference on Big
Data (Big Data) (pp.16–17).
3. Yu, W., etal. (2017). A survey on the edge computing for the internet of things. IEEE Access,
6, 6900–6919.
4. Chen, X.W., & Lin, X. (2014). Big data deep learning: Challenges and perspectives. IEEE
Access, 2, 514–525.
5. Nguyen, N.D., Nguyen, T., & Nahavandi, S. (2017). System design perspective for human-
level agents using deep reinforcement learning: A survey. IEEE Access, 5, 27091–27102.
6. Nielsen, M.. (2017). Neural networks and deep learning. [Online]. Available: http://neuralnet-
worksanddeeplearning.com/.
7. Xu, H., Lin, J., & Yu, W. (2017). Smart transportation systems: Architecture, enabling tech-
nologies, and open issues (pp.23–49). Singapore: Springer.
8. Wu, J., & Zhao, W. (2016, November). Design and realization of internet: From net of things
to internet of things. ACM Transactions on Cyber-Physical Systems, 1(1):1–2.
9. Ekedebe, N., Lu, C., & Yu, W. (2015, June). Towards experimental evaluation of intelligent
transportation system safety and trafc efciency. In Proceedings of International Conference
on Communications (ICC) (pp.3757–3762).
10. Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016, October). Edge computing: Vision and
challenges. IEEE Internet of Things Journal, 3(5): 637–646.
11. Arulkumaran, K., Deisenroth, M. P., Brundage, M., & Bharath, A. A. (2017, November).
Deep reinforcement learning: A brief survey. IEEE Signal Processing Magazine, 34, no. 6,
pp.26–38.
12. Fadlullah, Z.M. etal. (2017). State-of-the-art deep learning: Evolving machine intelligence
toward tomorrow’s intelligent network trafc control systems. IEEE Communications Surveys
and Tutorials, 19(4): 2432–2455, 4th Quart.
13. Goodfellow, I., Bengio, Y., Courville, A. (2016). Deep Learn. Google Scholar
V. Niranjani and N. S. Selvam

279© Springer Nature Switzerland AG 2020
A. Haldorai etal. (eds.), Business Intelligence for Enterprise Internet of Things,
EAI/Springer Innovations in Communication and Computing,
https://doi.org/10.1007/978-3-030-44407-5
A
Access-level attacks, 84
AdaBoost algorithm, 106
Adaptive FPSO (AFPSO), 179–180
Advanced machine learning algorithms
activities, 118
application
architecture diagram, 108
articial intelligence, 109
articial neural network algorithm, 106
automobile insurance agency, 106
big data, 109
business enterprise, 107
classication and prediction
models, 106
classication model, 109
cloud-based architecture, 110
cluster-based negative binomial
regression method, 106
clustering algorithms, 106
data analytics, 103
dataset, 104, 105
decision making and planning, 110
decision support system, 109
development, 104
domains, 105, 107
E-IoT, 108
healthcare insurance agencies, 106
hidden Markov method, 109
integrated system model, 109
Internet, 110
Internet of Things, 110, 111
IoMT, 110
IoT sensors, 105, 107
irrigation eld, 109
multi-class model, 108
negative binomial regression
models, 106
network communication data, 109
network trafc analysis, 108
non-linear pattern, 106
physical sensor devices, 107
state-of-the-art techniques, 105
stress behavior, 109
supervised learning, 103
time-series data, 108
unsupervised learning, 103
web services, 109
BPM, 101
business processes, 100, 101
embedding computing devices, 99
enhancing customer value, 117
enterprise forecasting, 101–103
enterprise IoT (E-IoT), 99, 100
enterprise supply chain, 115, 116
ERP software, 100
heterogenous business data, 100
human intervention, 99
inter-connection, 99
real-time stream analytics, 100
sensors and actuators, 99
technologies, 99
Advanced message queuing protocol
(AMQP), 85
Adversary location attack, 84
Agent-based technologies, 82
ANN controller, 217
Ant colony optimization (ACO) algorithm
ABC algorithm, 174
aim, 174
Index

280
Ant colony optimization (ACO) algorithm (cont.)
AS (see Ant system (AS))
bin-packing algorithm, 174
inclusive behavior, 174
path forming, 174
stagnation, 175
Ant system (AS)
complexity, 175
elitist, 177
extended versions, 175
max-min, 177
rank-based, 177
travelling algorithm steps, 175
travelling salesman, 175
Apache Spark cluster, 109
Application layer, 237
Application layer threats, 39
Apriori algorithm, 144
Arduino server, 235
Articial intelligence, 153
Articial intelligence eld, 272
Articial neural network algorithm, 106
Articial neural networks (ANN), 40, 117,
143, 189, 214
Association rule mining, 143, 144
Associative memory algorithms, 157, 158
Asymmetric travelling salesman problem
(ATSP), 175
Attribute-based access control (ABAC), 44
AutoFOCUS31, 9
B
Backpropagation neural network
(BPNN), 189–193
Bare bones particle swarm optimization
(BBPSO), 179
Base stations, 29
Bat algorithm, 173
Battery class, 18
Bayesian algorithms, 272
Bayesian cognitive learning methods, 156, 157
Bayesian network, 109
Big data, 9
business use cases
customer experience, 152
new product establishment, 152
predictions mechanical failures, 152
security breaches, 152
challenges, 158
cloud computing, 151
components, 139
data processing, 158
denition, 151
dynamic networks, 166
healthcare, 140
huge data handling, 153
innovative approach, 139
IoT research
advancement, 164
communication protocols, 164
devices, 165
information gathering, 164
infrastructure, 165
issues, 165
low-power-consumption devices, 165
outdoor-computing facility, 164
smart objects, 165
standardized protocols, 165
trafc management, 164
web services, 165
machine learning, 154
management, 140
opportunistic data distribution, 162–163
Big Data methods, 74
Binarization, 187
Biomarkers (BM), 145
Bolster vector relapse, 147
Buffer-overow attacks, 39
Business domain model, 59
Business forecast, 101
Business process management (BPM), 27, 124
Business process modeling (BPM), 101
C
Capability-based access control (CAC), 44
Cardiovascular autonomic neuropathy
(CAN), 147
Centrality measures
betweenness centrality, 264
closeness centrality, 264
clustering coefcient, 265–268
degree centrality, 264
eigenvector, 265
graph theory, 263
Chaotic PSO (CPSO), 179, 180
Charlson Comorbidity Index score, 146
Cloud-based services, 89
Cloud computing (CC), 28, 47, 48, 271
advantage, 5
data centers, 160
decision-making process, 6
industrial operation, 5
internet services, 173
IoT devices, 160
Index

281
mapping tasks, 172
memory and storage units, 170
service frameworks, 169
tool, 23
trafc engineering, 171
VM placement, 169, 170
Cloudlets, 160
Cloud-to-cloud, 130
Clustering, 144
CoAP protocols, 12
Cognitive computing
process design, 155
tools, 155
unstructured information, 153
Cognitive learning (CL)
denition, 153
learning technologies, 153
machine learning algorithms, 154
role, 153
usage, 153
Cognitive-machine learning
algorithms
articial intelligence, 155
associative memory, 157, 158
Bayesian learning methods, 156, 157
HCA-GA, 157
ordered version space search, 156
PSO, 156
decision-making, 155
implementation, 155
intelligent personal assistants, 155
principles, 155
Community brokers, 163
Community detection methodology, 163
Community-related algorithms, 163
Constrained application protocol (CoAP), 7,
37, 45, 85
Contagion effect, 4
ContentPlace method, 164
Context-aware access control (CWAC), 44
Context-aware applications, 160
Context-aware computing, 160, 161
Context ltering, 160
Continuous-and discrete-time systems, 204
Contract decoupling, 124
Conventional algorithms, 170
Covariance matrix adaptation evolution
(CMA-ES), 198
D
Data-centric networks, 35
Data distribution services (DSS), 85
Data ow diagram (DFD), 90
Data integrity, 88
Daubechies wavelet function, 189
DCSim, 23
Decentralized decision-making process, 4
Decision trees (DT), 62, 117, 143
Dedicated short-range communication
(DSRC), 29
Deep learning (DL), 33, 153
Deep neural network (DNN), 45
activation function, 273
applications
deep learning, 276
diagnostics medical, 277
fraud detection, 276
self-driving car, 277
architecture, 273
Big Data, 272
data set learning, 271
development, 272
hidden layer, 274
industry and research elds, 271
intelligent transport systems, 272
IoT and smart-world systems, 272
kinds of information, 271
linear-regression/decision-making
trees, 272
outer layer, 274
quantity, 272
reinforcement learning, 275, 276
robotics and computer view, 271
SL, 275
smart phones, 272
supervised classication tasks, 271
unsupervised learning, 275
Deep reinforcement learning, 42, 43
Delta operator, 204, 210
Denial of service (DoS), 84
Denoising, 188
Device management, 133
Device-to-cloud, 130
Device-to-device (D2D), 29, 130
Device-to-gateway, 130
Diabetes mellitus (DM)
anti-diabetic medications, 141
Big Data analytics, 141
BM, 145
components, 144
denition, 140
descriptive/predictive analytics, 141
diagnosis, 144
endocrine issue, 140
Hadoop MapReduce, 141
Index

282
Diabetes mellitus (DM) (cont.)
KDD, 142
pathophysiologies, 140
prevention and management, 140
types, 140
Diabetes Preprocessing Research Activity
(DiScRi), 147
Diabetic fundus image recuperation
(DFIR), 147
Diabetic nephropathy, 146
Diabetic retinopathy (DR), 147
Digital communication system, 229
Distributed denial of service (DDoS), 33
Distributed DoS (DDoS), 47, 48
Distributed Frank-Wolfe (dFW) algorithm, 45
DM prediction
high-dimensional datasets, 146
hyperglycemia, 146–147
LDA–MWSVM, 145
Domain-based architectures, 84
Dyna-Q model, 45
E
Ease of development, 125
Ease of testing, 125
Eclipse framework, 9
Econometric model, 103
Edge processing, 159, 160
EdgeBroker class, 17
EdgeCloudSim, 11
EdgeDataCenter Class, 16
Edgedevice, 16
EdgeDevice class, 17
EdgeLet class, 17
Edgelet model, 16
Electromagnetic (EM), 145
Elitist ant system, 177
Elliptic curve cryptography (ECC), 88, 89
Elliptic curve integrated encryption scheme
(ECIES), 90
Embedded frameworks, 28
Emulation, 227
Energy exchange, 226
Energy management, 171
Enterprise architecture (EA)
application, 124, 125
basic features, 126
big data technology, 126
business model
“as-a-service” enterprise model, 130
clients, 130
subscription-centered framework, 130
types, 130–132
challenges, 124, 125
characteristics, 123, 124
client-centric enterprise, 126
cloud data technology, 126
cloud platforms, 125
data streaming, 128
device layer, 129
devices and machines, 124
efciency, 126
functioning capacity, 129
gateways, 128
heterogeneous device, 129
industrial architecture, 123
industrial missions and visions, 123
Internet, 126
internet-based communication, 125
key attributes, 126, 127
legacy networking devices, 127
low-power and legacy devices, 128
middleware, 129
monetization models
application development, 133
device management, 133
enable exible, 132
license and entitlement management,
132, 133
product-based monetizing
approach, 132
service-based approach, 132
software upgrades, 133
network device, 129
networking and hardware vendors, 129
opportunity, 124
organizational processing, 125
procedures, 125
process, 129
processes and services, 123
sector, 124, 125
secure communication, 129
sensors, 128
smart cold chain model, 134, 135
smart security, 135, 136
standard-centered wireless, 127
technology architectures, 124
tiers, 127
traditional business, 124
transfer control instructions, 125
trends, 124, 125
waste management, 134
wired network paradigms, 127
Enterprise IoT (E-IoT)
agent-based technologies, 82
applications, 81, 83
CCTVs, 82
Index

283
customized/generic services, 81
data points and data, 82
devices, 82
electronic revenue, 82
elements of security, 87, 88
emerging technology, 81
growth, 83
hardware components, 82
human threats, 82
lightweight secure measures, 88–90
natural threats, 82
power management applications, 83
security and privacy, 83
security practices, 94
security threats and attacks
access-level attacks, 84
adversary location attack, 84
data, 83
devices, 83
host-based attack, 84
information-level attacks, 83
layered architecture and associated
attacks, 84–86
phases, 86, 87
sensor technology, 81
sensors, 82
use cases, 82
user’s security and privacy, 83
Enterprise resource planning (ERP)
software, 100
Enterprise supply chain, 115, 116
Euclidean distances, 198
Extensible access control mark-up language
(XACML), 45
Extensible message and presence protocol
(XMPP), 85
Extreme learning machine (ELM) algorithms, 46
F
Fault tolerance, 125
Fitness element, 197
Fitness function, 197
Flower pollination algorithm (FAdFPA)
convergence curve, 206, 209
tness function, 206
hybrid technique, 205
metaheuristic algorithms, 206
optimization, 204
PRBS input signal, 206
p-values, 206, 209
user-dened parameters, 205
Fog computing (FC), 3, 28, 159
Friendlink algorithm, 255–257, 259, 268
Functional-centered selection method, 36
Fuzzy C-means (FCM) techniques, 46
G
Gartner Press, 81
Genetic algorithms (GA), 143
actual-parameter, 195
adaptability, 195
CMA-ES, 198
constrained optimization, 199
DAG tasks, 199
elements, 196
G3 system, 198
objective value, 199, 200
operator’s role, 197
parameters, 199, 200
practical optimization issues, 195
SPX, 198
structure, 196
task scheduling, 197, 199
techniques, 197
UNDX, 198
GRAD algorithms, 244
GreenCloud, 11, 23
Grid-connected PV system, 221
Group activity recognition
HMM, 241
multifarious algorithms, 241
targeted advertising, 241
GWOCFA algorithm, 204
H
Hadoop MapReduce, 141
Hammerstein and Wiener model identication
Delta domain, 204
FAdFPA (see Flower pollination algorithm
(FAdFPA))
metaheuristic method approaches, 203, 204
polynomial nonlinearity, 204
Hammerstein and Wiener model
parameters, 210
Handwriting identications, 187
Handwritten character recognition (HCR),
186, 187
Hardware components, 243
Hardware-in-the-loop simulation, 227
Heat map (HM)
analysis, 250
characteristics, 244
day calculations, 244, 246, 249
density of people, academic building, 245
foot path comparison, 248
Index

284
Heat map (HM) (cont.)
GRAD algorithms, 244
HMB algorithm, 244
human activity recognition, 243
human foot path
densities, 250
hostels, 247, 249
human footfall, academic block, 246
PGTB algorithm, 244
result, 245
SF method, 243–245
standard surface classes, 243
WF-SVM algorithms, 244
Heat sources, 243
Heterogeneity, 5, 29, 49
Heterogeneous business applications, 94
Heterogeneous network, 113
Hidden Markov models (HMM), 241
Hill climbing-GA (HCA-GA), 157
HMB algorithm, 244
Host-based attack, 84
Human crowd, 242
Hybrid Active Learning Link Prediction
(HALLP), 254
Hyperglycemia
dataset, 146
diabetic nephropathy, 146
insulin resistance, 146
morbidity and mortality, 146
negative effects, 146
random forest, 146
Hypertext Transfer Protocol (HTTP), 7
Hypoglycemia, 147
I
IEEE 13-node test feeder system, 214
DC-link capacitors, 215
photovoltaic module, 214
point of common coupling, 215
RL lter, 215
transformer and load, 215
iFogSim, 11
Industrial architecture, 123
Industrial IoT (IIoT), 159
Inexpensive and less-energy information
transfer, 30
Information-level attacks, 83
Infrastructure as a service (IaaS), 169
Instance-based learning (IBL), 143
Intellectual property, 86
Intelligent learning, 153
Intelligent systems, 271
Intelligent urban environments, 30
Inter-connection, 29
Internal domain operability, 125
International Telecommunication Union
(ITU-T), 81
Internet of Energy (IoE), 225
Internet of Intelligent Things (IoIT), 70
Internet of Manufacturing Things (IoMT), 110
Internet of Things (IoT), 271
application engineers and developers, 8
application layer threats, 39
applications, 28, 233
architecture, 7, 8
Big Data technology, 1, 114
challenges, 10, 11
authentication and identication, 73
connectivity, 74
consumer awareness, 77
data capturing capabilities, 75
data security, 76
delivering value, 77
handling unstructured data, 74
integration, 74
intelligent analytics, 75, 76
interoperability and compatibility, 73
privacy issues, 76
commercialization and technological
advancements, 28
communication competencies, 35
communication protocol, 13, 14
contributions, 11
cross-sensing organization procedures, 27
data-centric networks, 35
data collection, 113
data evaluation systems, 6
data heterogeneity, 28
data security issues, 112
data sensing, 27
developers, 27
devices, 28
distributed and interlinked network, 28
diversities, 27
domains, 28
edge computing, 7, 11, 12
factors, 9
heterogeneous network, 113
ICT, 1
industrial application
centralized processing, 6
cloud computing, 5
IoTs and human, 6
Pi and UDOO boards, 6
system dynamics, 5
transformative implication, 6
WSNs and RFIDs Networks, 5
Index

285
integration, 113
intelligent devices, 28
intelligent predictive analytics, 114
intelligent sensors, 27
IP-and TCP-centered information
transfer, 35
lack of awareness, 114
lot of services and applications, 35
ML, 28
modelling ecosystems, 24
modern manufacturing, 2–4
multiple-layer threats, 39, 40
network connection, 112, 113
networking layer threats, 38
network simulators, 22–24
organizational automations, 1
organizational procedure, 27
physical threats, 35–37
privacy and security concerns, 34
PV systems (see Photovoltaic (PV) system)
reliability theory, 2–4
remote evaluation of models, 233
resource-based, 35
SDNs, 35
services and applications, 28
survey scope and research contributions, 34
surveys, 34
technological vendors, 1
transportation layer threats, 38
Internet of Vehicles (IoV) network, 90
Internet Technology (IT), 2
Inverse sign test (IST), 145
Inverter circuit, 219
Inverter controller
ANN, 217, 218
monitoring system, 218
PI, 217
IoT-based inverter controller systems, 222
IoT-based solar power plant, 237
IoTs battery drainage
architectural computing, 14, 15
computation and event processing, 19
description, 14
sim edge (see IoTs Sim edge model)
sim edge architecture, 15
IoTs ecosystem, 10
IoTs sim edge architecture, 15
IoTs Sim edge model
architecture, 15
classes
battery class, 18
EdgeBroker class, 17
EdgeDataCenter Class, 16
EdgeDevice class, 17
EdgeLet class, 17
MEL class, 17
mobility class, 18
policies class, 18
user interface class, 18
cloudsim, 15
design and implementation, 15, 16
healthcare sectors, 20, 21
RSU controllers, 21, 22
smart building systems, 21
IoTSim-Edge simulators, 11
J
Java Context Awareness Framework
(JCAF), 160
K
k-clique algorithm, 164
k-nearest neighbors (k-NN), 143
Knowledge discovery in databases
(KDD), 142
Korea Transportation Safety Authority, 106
L
LabVIEW software, 229
Large-scale deployment, 29
League Championship Algorithm (LCA), 173
Levelized energy costs (LCoE), 227–229
Levenberg-Marquardt algorithm, 218
Lightweight implementation, 125
Linear Discriminant Analysis (LDA), 146
Linear regression, 41
Local computing, 159
M
Machine learning (ML), 28, 32, 33, 272
access control, 44
advantages, 71
AI, 70
architectural layers, 44
articial intelligence industry, 56
asset tracking, 69
authentication and access, 44
authentication processes, 45
automatic fulllment, 69, 70
business activities, 58
business domain model, 59
compliance monitoring, 67
consumer lifetime valuation, 57
consumers, 58
Index

286
Machine learning (ML) (cont.)
data-driven approach, 154
DDoS, 47, 48
decision-aided system, 44
decision tree, 62
deep reinforcement learning, 42, 43
denition, 142, 153
domains, 57
DoS, 47, 48
economics, 66
features, 154
frequency channels, 45
improved accuracy rate, 71
improved customer satisfaction, 72
increased operational efciency, 72
industrial applications, 63, 65
industry, 154
information transformation, 57
Internet, 55
IoT data, 58
IoT networking system, 43
life cycle, 154
lot of applications, 44
malicious programs, 49–51
methods, 56
mitigation and detection, 46
naïve Bayes (NB), 43
new innovative IoT emerging business
models, 65
objectives, 154
predictive analysis and maintenance, 71
preventative maintenance, 67, 68
regression, 43
remote diagnostics, 68
segments, 44
sellers, 58
semi-supervised learning, 42
shaping experiences to individuals, 65
SL, 60, 61
supervised learning algorithms, 43
supervised machine learning, 40
SVMs, 43
tasks, 142
unsupervised learning, 62, 63
unsupervised machine learning, 41
Wi-Fi signals, 45
Makespan, 199, 201
Man-in-the-middle (MITM), 84
Maturity onset diabetes of the young
(MODY), 140
Maximum power point (MPP), 213–214
Maximum power point tracking (MPPT),
214, 216
Mean Average Precision (MAP), 268
MEL class, 17
Message queuing telemetry transport protocol
(MQTTP), 85
Metaheuristic algorithms, 203
Metaheuristic techniques, 173
Micro-grid
feature, 213
IEEE 13-node, 214
inverter circuit, 215
inverter controller, 217–219
modes, 216
MPPT, 216
power system, 213
types, 213
Microsoft Security Development Lifecycle
(SDL), 91
Minimum and maximum ant system, 177
ML-centered methods, 48
MLP network models, 193
MobiAd, 162
Mobile edge computing (MEC), 160
Morlet Wavelet Support Vector Machine
(MWSVM), 146
MPPT controller, 216
Multi-class model, 108
Multifarious algorithms, 241
Multilayer perceptron neural network
human brain, 190
performance analysis
accuracy, 191
sensitivity, 191
specicity, 192
Multiple linear regression algorithm, 109
Multiple-layer threats, 39, 40
N
Nash equilibrium (NE), 45, 50
National Environment Agency (NEA), 134
Nature-inspired algorithms, 172
Network-operated intelligent bots, 160
Network simulators, 22–24
Network virtualization, 159
Neural network (NN), 116, 187, 214
Next-generation enterprise
Big Data, 5
decentralized decision-making process, 4
dynamics and at structures, 4
heterogeneity, 5
Non-conventional algorithms, 170
Non-parametric Wilcoxon rank-sum analysis, 206
Nonproliferative (NPDR), 147
Novel performance models, 9
NP-hard issues, 172
Index

287
O
OMNeT++ system, 23
OneMax function, 200
Open authorization (OAuth), 45
Open-source platform, 151
Open Web Application Security Project
(OWASP), 39, 50, 93
Opportunistic data distribution
challenges, 162
interaction pattern, 162
mobile devices, 162
short-range communication, 162
Opportunistic networks (ONs)
data distribution, 162
community brokers, 163
community detection, 164
community-related algorithms, 163
ContentPlace method, 164
gossiping, 163
socio-aware overlay algorithm, 163
subscribe/unsubscribe approach, 163
DTNs, 162
smart city application, 163
smart city sensors, 163
topology, 162
Optical character recognition (OCR)
applications, 185
digital image format, 185
Optimization algorithms, 170, 171
Organization-based access control (OAC), 44
P
Particle swarm optimization (PSO), 156
AFPSO, 180
applications, 180
BBPSO, 179
bird ocking, 177
cognitive element, 179
comparison, 180
CPSO, 179
particles movement, 178
pseudo-code, 178, 179
QPSO, 179
TVAC, 180
Particle swarm optimization method, 106–107
Performance Management Work Tool (PMWT), 9
Perturbation period (Tp), 218
PGTB algorithm, 244
Phase-locked loop (PLL) algorithm, 217
Photovoltaic (PV) system
application layer, 237
architecture, 235, 236
Arduino server, 235
components, 235
DC electricity, 234
environmental conditions, 235
installation, 233
IoT, 233, 234
layout, 234
models, 234
parameters, 235
power generation potential, 233
power harvester, 234
PV arrays, 234
remote monitoring and control layer, 235
remote server data, 236
sensing layer, 236
solar power plant, 237
system layer, 236
Photovoltaic system, 214
Physical threats, 35–37
PIR sensors, 242, 243, 247
Platform as a service (PaaS), 169
Policies classes, 18
Policy-based access control (PBAC), 44
Polynomial regression, 41
Power quality, 221
Power-use-centered selection method, 36
Predictive analytics, 148
Predictive markers
assessment, 145
EM and IST, 145
Priority-aware VM allocation (PAVA), 173
Product-as-a-service (PaaS), 132
Product-based monetizing approach, 132
Proliferative (PDR), 147
Public key infrastructure (PKI), 89
PV arrays, 234
PV generators, 234
Python, 242
Q
Quality of services (QoS), 12, 37
Quantum-behaved Particle Swarm
Optimization–Simplex method
(QPSO), 179
Quasi-Newton method, 198
R
Radial basis function (RBF), 189–193
Radio-frequency identication devices
ML, 32, 33
network features, 29–31
privacy issue, 31
privacy resolutions, 31, 32
Index

288
Random candidate selection (RCS), 189–193
Random neural network (RNN), 48
Rank-based ant system, 177
Raspberry Pi 3, 93
Recurrent neural network (RNN), 116
Reinforcement learning (RL), 42, 144, 275, 276
Reliability theory, 2–4
Remote monitoring and control layer, 235
Renewable energy sources (RESs), 213
Resource management, 171
Resource utilization, 159
Role-based access control (RBAC), 44
Rubin’s algorithm, 256
S
SamBot, 109
Sanskrit character recognition
ANN, 186
classication
BPNN with RBF, 190
multilayer perceptron neural network, 190
RCS with BPNN, 189, 190
handwritten, 186
HCR, 186
NNs, 186
OCR, 186
process
feature extraction, 189
image acquisition, 187
pre-processing, 187, 188
segmentation, 188
stages, 187
traditional classiers, 192
Scalability, 124
Segmentation, 188
Semi-supervised learning, 42, 147
Sensing layer, 236
Sensing/perception layer, 85
Sensitivity, 191
SensorLogic, 132, 133
Sensors, 82
Service-based approach, 132
Simplex crossover (SPX), 198
Simulators, 11
Smart building systems, 21
Smart city, 158
Smart cold chain model, 134, 135
Smart grid, 226
Smart networking, 30
Smart remote monitoring system
IoT, 237
PV system (see Photovoltaic (PV) system)
web-based technique, 237, 238
Smart sensors, 159
Smart Urban Visualization system
(SURV), 242
Smart wearables, 159
Social Network Analysis (SNA)
dataset, 258
efciency, 254, 258
elements and ramications, 253
empirical analysis, 268
experiments, 258
Friendlink link prediction, 260
graph, 255
link prediction model, 254
Link Prediction Problem, 255
MATLAB, 258
mechanisms, 253
network theory, 253
nodes, 259
problem description
Friendlink algorithm, 256, 257
social links, 256
soft set theory, 256
proposed approach, 259
sampling techniques, 254
SFLP, 261, 262
social entities, 253
supervised and unsupervised
algorithms, 254
theoretical analysis, 268
Socio-aware overlay algorithm, 163
Soft set and Friendlink-based Link Prediction
algorithm (SFLP), 257, 261, 262
Soft set theory, 254, 256, 257, 267, 269
Software architecture, 9
Software database, 243
Software as a service (SaaS), 130, 169
Software-dened networks (SDNs), 28, 35
Solar photovoltaic system, 233
Solar power plant, 236
Stagnation, 175
Standard communication protocols, 166
STRIDE framework methodology, 91, 92
Subscribe/unsubscribe approach, 163
Supervised learning (SL), 60, 61, 142,
143, 275
Supervised machine learning, 40
Supervised machine learning
algorithms, 103
Support vector machine (SVM), 116, 141, 143
Supported vector regression (SVR), 41
Surface tting (SF) method, 243
Surveillance cameras, 243
System Dynamics (SD), 3
System layer, 236
Index

289
T
Tableau, 242
Targeted advertising, 241
Target-of-evaluation (ToE), 90
Temporal motion data, 241
Thermal diffusion method, 243, 244
Threat modeling (TM)
Kali Linux-based ethical hacking,
92–94
lifecycle, 90
Microsoft SDL, 91
SDL, 90
STRIDE framework methodology,
91, 92
TMT, 92
Threat modeling tool (TMT), 92
Time-series models, 103
Traditional classiers, 192
Traditional network, 162
Transmission control protocol
(TCP), 85
Transportation layer threats, 38
Travelling salesman problem
(TSP), 175
U
UCI Network Data Repository, 258
Ultra-reliable and low-latency communication
(URLLC), 30
Unimodal normal distribution crossover
(UNDX), 198
Unsupervised learning, 62, 63, 143, 275
Unsupervised machine learning, 41
Unsupervised machine learning
algorithms, 103–104
Usage control-based access control
(UCAC), 44
User datagram protocol (UDP), 85
User interface classes, 18
User-managed access (UMA), 45
V
Vehicle-to-grid (V2G), 226
Verication of success (VoS), 90
Virtual machine (VM) placement
aim, 172
cloud computing, 172, 173
migration, 173
nature-inspired algorithms, 172
objects, 172
optimization, 173
VM migration, 173
Virtual machine (VM) scheduling, 169
Virtualization, 169
W
Waste management, 134
Web-service-related middleware
ESCAPE, 160
WF-SVM algorithms, 244
Wide area networks (WANs), 160
Wind energy conversion system (WECS)
cybersecurity, 226
wind ow, 227
WT, 227
Wind energy system, 225
Wind technology, 225
Wind turbine (WT), 227
Wind turbine emulator
generators, 227
hardware-in-the-loop simulation, 227
IoT-based, 229, 230
line diagram, 227, 228
motor buck duties, 229
quality and reliability, 227
Wireless multimedia system (WMS), 50
Z
Zachary’s Karate Club data set, 258
Zhang-Suen thinning algorithm, 188
Index