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Computational Intelligent
Security in Wireless
Communications
Wireless network security research is multidisciplinary in nature, including data
analysis, economics, mathematics, forensics, information technology, and computer
science. This text covers cutting-edge research in computational intelligence systems
from diverse elds on the complex subject of wireless communications security.
It discusses important topics including computational intelligence in wireless
networks and communications, articial intelligence and wireless communications
security, security risk scenarios in communications, security/resilience metrics
and their measurements, data analytics of cybercrimes, modeling of wireless
communications security risks, advances in cyber threats and computer crimes,
adaptive and learning techniques for secure estimation and control, decision support
systems, fault tolerance and diagnosis, cloud forensics and information systems, and
intelligent information retrieval.
The book –
• Discusses computational algorithms for system modeling and optimization
from a security perspective.
• Focuses on error prediction and fault diagnosis through intelligent
information retrieval via wireless technologies.
• Explores a group of practical research problems where security experts can
help develop new data-driven methodologies.
• Covers application on articial intelligence and wireless communications
security risk perspectives.
The text is primarily written for senior undergraduate students, graduate students,
and researchers in the elds of electrical engineering, electronics and communication
engineering, and computer engineering.
The text comprehensively discusses a wide range of wireless communications
techniques with emerging computational intelligent trends, to help readers
understand the role of wireless technologies in applications touching various spheres
of human life with the help of hesitant fuzzy set-based computational modeling. It
will be a valuable resource for senior undergraduate students, graduate students, and
researchers in the elds of electrical engineering, electronics and communication
engineering, and computer engineering.
Wireless Communications and Networking Technologies:
Classications, Advancement and Applications
Series Editor:
D.K. Lobiyal, R.S. Rao and Vishal Jain
The series addresses different algorithms, architecture, standards and protocols,
tools and methodologies which could be benecial in implementing next generation
mobile network for the communication. Aimed at senior undergraduate students,
graduate students, academic researchers and professionals, the proposed series will
focus on the fundamentals and advances of wireless communication and networking,
and their such as mobile ad-hoc network (MANET), wireless sensor network (WSN),
wireless mess network (WMN), vehicular ad-hoc networks (VANET), vehicular
cloud network (VCN), vehicular sensor network (VSN) reliable cooperative network
(RCN), mobile opportunistic network (MON), delay tolerant networks (DTN), ying
ad-hoc network (FANET) and wireless body sensor network (WBSN).
Cloud Computing Enabled Big-Data Analytics in Wireless Ad-hoc Networks
Sanjoy Das, Ram Shringar Rao, Indrani Das, Vishal Jain and Nanhay Singh
Smart Cities
Concepts, Practices, and Applications
Krishna Kumar, Gaurav Saini, Duc Manh Nguyen, Narendra Kumar
and Rachna Shah
Wireless Communication
Advancements and Challenges
Prashant Ranjan, Ram Shringar Rao, Krishna Kumar and Pankaj Sharma
Wireless Communication with Articial Intelligence
Emerging Trends and Applications
Anuj Singal, Sandeep Kumar, Sajjan Singh and Ashish Kr. Luhach
Computational Intelligent Security in Wireless Communications
Suhel Ahmad Khan, Rajeev Kumar, Omprakash Kaiwartya, Mohammad Faisal
and Raees Ahmad Khan
For more information about this series, please visit: https://www .routledge .com /
Wire l ess %20 C om m unic at i o ns %2 0a nd %2 0 Netwo rk ing %20Tec hn o l o gies /b ook -
series /WCANT
Computational Intelligent
Security in Wireless
Communications
Edited by
Suhel Ahmad Khan, Rajeev Kumar,
Omprakash Kaiwartya, Mohammad Faisal,
and Raees Ahmad Khan
First edition published 2023
by CRC Press
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and by CRC Press
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© 2022 selection a nd editorial mat ter, [Suhel Ahmad Kh an, Rajeev Kumar, Ompr akash Kaiwar tya, Mohammad
Faisal, and Raees Ahmad Khan]; individual chapters, the contributors
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ISBN: 9781032081663 (hbk)
ISBN: 9781032347028 (pbk)
ISBN: 9781003323426 (ebk)
DOI: 10.1201/9781003323426
Typeset in Times
by Deanta Global Publishing Services, Chennai, India
v
Contents Contents
Contents
Preface......................................................................................................................vii
Editors ....................................................................................................................... ix
Acknowledgment ......................................................................................................xi
Contributor List ...................................................................................................... xiii
Chapter 1 An investigation on Cooperative Communication Techniques
in Mobile Ad-Hoc Networks ................................................................1
Prasannavenkatesan Theerthagiri
Chapter 2 IoE-Based Genetic Algorithms and Their Requisition ...................... 25
Neeraj Kumar Rathore, and Shubhangi Pande
Chapter 3 A Framework for Hybrid WBSN-VANET-based Health
Monitoring Systems ........................................................................... 51
Pawan Singh, Ram Shringar Raw, and Dac-Nhuong Le
Chapter 4 Managing IoT – Cloud-based Security: Needs and Importance ........ 63
Sarita Shukla, Vanshita Gupta, Abhishek Kumar Pandey,
Rajat Sharma, Yogesh Pal, Bineet Kumar Gupta, and Alka Agrawal
Chapter 5 Predictive Maintenance in Industry 4.0 ............................................. 79
Manoj Devare
Chapter 6 Fast and Efcient Lightweight Block Ciphers Involving
2d-Key Vectors for Resource-Poor Settings ...................................... 99
Shirisha Kakarla, Geeta Kakarla, D. Narsinga Rao, and
M. Raghavender Sharma
Chapter 7 Sentiment Analysis of Scraped Consumer Reviews (SASCR)
Using Parallel and Distributed Analytics Approaches on Big
Data in Cloud Environment.............................................................. 121
Mahboob Alam, Mohd. Amjad, and Mohd. Amjad
Chapter 8 The UAV-Assisted Wireless Ad hoc Network .................................. 131
Mohd Asim Sayeed, Raj Shree, and Mohd Waris Khan
vi Contents
Chapter 9 Integrating Cybernetics into Healthcare Systems:
Security Perspective ......................................................................... 161
Saquib Ali, Jalaluddin Khan, Jian Ping Li, Masood Ahmad,
Kanika Sharma, Amal Krishna Sarkar, Alka Agrawal, and
Ranjit Rajak
Chapter 10 Threats and Countermeasures in Digital Crime and
Cyberterrorism ................................................................................. 173
Mohit Kumar, Ram Shringar Raw, and Bharti Nagpal
Chapter 11 Cryptography Techniques for Information Security: A Review ...... 191
Ganesh Chandra, Satya Bhushan Verma, and Abhay Kumar Yadav
Chapter 12 A Critical Analysis of Cyber Threats and Their Global Impact ...... 201
Syed Adnan Afaq, Mohd. Shahid Husain, Almustapha Bello,
and Halima Sadia
Chapter 13 A Cybersecurity Perspective of Machine Learning Algorithms .........221
Adil Hussain Seh, Hagos Yirgaw, Masood Ahmad,
Mohd Faizan, Nitish Pathak, Majid Zaman, and Alka Agrawal
Chapter 14 Statistical Trend in Cyber Attacks and Security Measures ..............241
Shirisha Kakarla, Deekonda Narsinga Rao, Geeta Kakarla,
and Srilatha Gorla
Index ...................................................................................................................... 259
vii
Preface
The widespread use of wireless technology in our daily lives has resulted in the
increased demand for these devices. While the widespread use of wireless com-
munications systems provides undeniable benets to consumers, the communication
exchanges are vulnerable to adversarial assaults due to the open broadcast nature of
the wireless signals.
Wireless communications systems, unlike their wired equivalents, have major
security risks from the physical layer to the application layer, which makes them
less versatile than their wired counterparts. Security measures should be available to
the user in order to secure wireless communications from harmful attacks. Wireless
communications infrastructure and services require regular upgradation to man-
age the rapidly increasing demands to improve wireless communications security
to ght against cybercriminal activities, especially because more and more people
are using wireless networks (e.g., cellular networks and Wi-Fi) for online banking
and personal emails, owing to the widespread use of smartphones. Wireless com-
munications makes transmission of data more valuable than wired communication.
Wireless communications have more vulnerable, secure, passive eavesdropping for
data interception and active jamming. It needs authenticity, availability, condential-
ity, and integrity requirements. To ensure the requirements, we need to design the
wireless communications system to be secure and easy, to gain the users’ satisfaction.
Further, due to the rapid expansion of modern and developing information tech-
nology such as social media, articial intelligence, big data, Internet of Things (IoT),
and smart devices in the past several decades, cyber threats and computer crimes
have escalated in recent decades. Organizations due to actual and suspected cyber
threats correlated with such developments have slowed the implementation of big
data and the cloud. Secure communication will protect the cyber risks and it is an
emerging area of ongoing research, as there is generally no clear view of how to
model cyber risk and therefore how to price it. For companies, the value of cyber/
wireless communications protection is rising. Security of wireless communications
implies the ability to develop and assess a typology of cyber offenses and cyber
threats in order to address them.
Wireless communications security research is multidisciplinary in nature, includ-
ing researchers from data analysis, economics, mathematics, forensics, information
systems, information technology, and computer science. The proposed book delivers
an ideal platform to gather leading-edge work from diverse elds on the complex
subject of Computational Intelligence Security in Wireless Communications.
ix
Editors
Suhel Ahmad Khan is currently working as Assistant Professor in the Department
of Computer Science, Indira Gandhi National Tribal University (A Central
University), Amarkantak, Madhya Pradesh, India. He has 10 years of teaching
and research experience. His areas of interest are Software Engineering, Software
Security, Security Testing, Cyber Security, and Network Security. He has completed
one major research project with PI funded by UGC, New Delhi, India. He has pub-
lished numerous papers in international journals and conferences including IEEE,
Elsevier, IGI Global, Springer, etc. He is an active member of various professional
bodies such as IAENG, ISOC-USA, IACSIT, and UACEE.
Dr. Rajeev Kumar is currently working as Associate Professor in the Department of
Computer Science and Engineering, Babu Banarsi Das University, Lucknow, Uttar
Pradesh, India. He is a young and energetic researcher and has worked on two major
projects (with PI) funded by University Grants Commission, New Delhi, India and
Council of Science & Technology, Uttar Pradesh (CST-UP), India. He has more than
ve years of research and teaching experience. He has published numerous papers
in international journals and conferences including IEEE, Elsevier, IGI Global,
Springer, etc. His research interests are in the different areas of Security Engineering
and Computational Techniques.
Omprakash Kaiwartya is currently working at the School of Science and
Technology, Nottingham Trent University (NTU), Nottingham, UK, as Senior
Lecturer and Course Leader for MSc Engineering (Electronics, Cybernetics and
Communications). Previously, he was a research associate (equivalent to Senior
Lecturer) in the Department of Computer and Information Science at Northumbria
University, Newcastle, UK, where, he was involved in the gLINK, European
Union project. Prior to this, he was a post-doctoral fellow (equivalent to Lecturer)
in the Faculty of Computing, University of Technology (UTM), Johor, Malaysia.
Before moving to Malaysia, he completed his BSc in Computer Science from Guru
Ghansidas Central University, Bilaspur, Chhattisgarh, India, and combined MSc
and Ph.D. from the School of Computer and Systems Science, Jawaharlal Nehru
University (JNU), New Delhi, India. Overall, he has authored/co-authored over 100
international publications including journal articles, conference proceedings, book
chapters, and books. His research interest focuses on IoT-centric smart environment
for diverse domain areas including transport, healthcare, and industrial produc-
tion. His recent scientic contributions are in Internet of Connected Vehicles (IoV),
EMobility, Electronic Vehicles Charging Management (EV), Internet of Healthcare
Things (IoHT), Smart use case implementation of Sensor Networks, and Next
Generation Wireless Communication Technologies (6G and Beyond). Furthermore,
he is Fellow of Higher Education Academy (FHEA), IEEE Senior Member and
BCS Professional Member. He has served as a TPC member and reviewer in 100+
xEditors
international conferences and workshops including IEEE Globecom, IEEE ICC,
IEEE CCNC, IEEE ICNC, IEEE VTC, IEEE INFOCOM, ACM CoNEXT, ACM
MobiHoc, ACM SAC, and many more. Furthermore, he has been reviewing papers for
30+ international journals including IEEE Magazines on Wireless Communications,
Networks, Communications, IEEE Communications Letters, IEEE Sensors Letters,
IEEE Transactions on Industrial Informatics, Vehicular Technologies, Intelligent
Transportation Systems, Big Data, and Mobile Computing. Moreover, he has
been an editorial member of various special issues with top-ranked journals in
Communication Society and serving as Associate Editor of IET Intelligent Transport
Systems, IEEE Internet of Things Journal, Springer, EURASIP Journal on Wireless
Communication and Networking, MDPI Electronics, Ad-Hoc and Sensor Wireless
Networks, and KSII Transactions on Internet and Information Systems.
Mohammad Faisal is currently working as Associate Professor and Head of the
Department in the Department of Computer Application, Integral University,
Lucknow, Uttar Pradesh, India. He has more than 15 years of teaching and research
experience. His areas of interest are Software Engineering, Requirement Volatility,
Distributed Operating System, Cyber Security, and Mobile Computing. He has pub-
lished a book “Requirement Risk Management: A Practioner’s Approach” published
by Lambert Academic Publication, Germany, ISBN: 978-3-659-15494-2. He has
published quality research papers in journals and national and international confer-
ences of repute. He is contributing his knowledge and experience as a member of
the Editorial Board/Advisory committee and TPC in various international journals/
conferences of repute. He is an active member of various professional bodies such as
IAENG, CSTA, ISOC-USA, EASST, HPC, ISTE, IAENG, and UACEE.
Raees Ahmad Khan (Member, IEEE, ACM, CSI, etc.) is currently working as
Professor and Head of the Department in the Department of Information Technology,
Dean of School for Information Science and Technology, Babasaheb Bhimrao
Ambedka r University (A Central University), Vidya Vihar, Raebareli Road, Lucknow,
India. He has more than 20 years of teaching and research experience. He has pub-
lished more than 300 research publications with good impact factors in reputed inter-
national journals and conferences including IEEE, Springer, Elsevier, Inderscience,
Hindawi, IGI Global, etc. He has published a number of national and international
books (authored and edited) (including Chinese language). His research interests are
in the different areas of Security Engineering and Computational Techniques.
xi
Acknowledgment
The authors wish to express their sincere thanks to all those who participated in
developing and evaluating the work contained in this book. The authors are espe-
cially grateful to CRC publishing and their representatives for administering and
monitoring the process of the development of the manuscript, and for exercising such
care and expertise to see the work of this book through to publication.
We are also thankful to all who participated in the review process of the book’s
chapters. The authors would also like to thank Mr. Gauravjeet Singh Reen, Senior
Commissioning Editor-Engineering, for communicating, correcting errors, and
careful reading of the various materials during the process of developing the book.
Thanks also go to Dr. Ram Shringar Raw for his reading and administering the
material in this book and for communicating with various parties during the process
of nalizing the material covered by the book.
Dr. Suhel Ahmad Khan
Dr. Rajeev Kumar
Dr. Omprakash Kaiwartya
Dr. Mohammad Faisal
Prof. Raees Ahmad Khan
xiii
Contributor List
Syed Adnan Afaq
Integral University, Lucknow, Uttar
Pradesh, India
Alka Agrawal
Babasaheb Bhimrao Ambedkar
University, Lucknow, Uttar Pradesh,
India
Masood Ahmad
Babasaheb Bhimrao Ambedkar
University, Lucknow, Uttar Pradesh,
India
Mahboob Alam
Jamia Millia Islamia University,
NewDelhi, India
Saquib Ali
Department of BCA, Azad Degree
College, University of Lucknow,
Lucknow, Uttar Pradesh, India
Mohd. Amjad
Jamia Millia Islamia University,
NewDelhi, India
Ganesh Chandra
Goel Institute of Technology and
Management, Lucknow, Uttar
Pradesh, India
Manoj Devare
Amity University, Bhatan Pada,
Maharashtra, India
Mohd. Faizan
Babasaheb Bhimrao Ambedkar
University, Lucknow, Uttar Pradesh,
India
Srilatha Gorla
Ministry of Electronics and Information
Technology, Hyderabad, Telangana,
India
Bineet Kumar Gupta
Shri Ramswaroop Memorial University,
Barabanki, Uttar Pradesh, India
Vanshita Gupta
Kamla Nehru Institute of Technology,
Sultanpur, Uttar Pradesh, India
Mohd. Shahid Husain
University of Technology and Applied
Sciences, CAS-Ibri Campus, Oman
Geeta Kakarla
Department of CSE, Sreenidhi
Institute of Science and Technology,
Hyderabad, Telangana, India
Shirisha Kakarla
Department of CSE, Sreenidhi
Institute of Science and Technology,
Hyderabad, Telangana, India
Jalaluddin Khan
University of Electronic Science
and Technology of China,
Chengdu,China
Mohd. Waris Khan
Integral University, Lucknow, Uttar
Pradesh, India
Mohit Kumar
NSUT East Campus Formerly
Ambedkar Institute of Advanced
Communication Technologies and
Research, New Delhi, India
xiv Contributor List
Dac-Nhuong Le
Faculty of Information Technology,
Hai Phong University, Hai Phong,
Vietnam
Jian Ping Li
University of Electronic Science and
Technology of China, Chengdu,
China
Bharti Nagpal
NSUT East Campus Formerly
Ambedkar Institute of Advanced
Communication Technologies and
Research, New Delhi, India
Mohd. Naseem
Baba Ghulam Shah Badshah University,
Rajouri, Jammu and Kashmir, India
Yogesh Pal
Shri Ramswaroop Memorial University,
Barabanki, Uttar Pradesh, India
Shubhangi Pande
Shri G. S. Institute of Technology and
Science (SGSITS), Indore, Madhya
Pradesh, India
Abhishek Kumar Pandey
Department of Computer Science,
M.L. K. PG. College, Balrampur,
India
Nitish Pathak
Guru Gobind Singh Indraprastha
University, New Delhi, India
Ranjit Rajak
Dr. Harisingh Gour Central University,
Sagar, Madhya Pradesh, India
D. Narsinga Rao
Directorate of Economics and
Statistics (DES), Govt. of Telangana
State, India
Neeraj Kumar Rathore
Indira Gandhi National Tribal
University (A Central University),
Amarkantak, Madhya Pradesh, India
Ram Shringar Raw
NSUT East Campus Formerly
Ambedkar Institute of Advanced
Communication Technologies and
Research, New Delhi, India
Halima Sadia
Integral University, Lucknow, Uttar
Pradesh, India
Amal Krishna Sarkar
Babasaheb Bhimrao Ambedkar
University, Lucknow, Uttar Pradesh,
India
Mohd. Asim Sayeed
Babasaheb Bhimrao Ambedkar
University, Lucknow, Uttar Pradesh,
India
Adil Hussain Seh
Babasaheb Bhimrao Ambedkar
University, Lucknow, Uttar Pradesh,
India
Kanika Sharma
Mangalmay Institute of Engineering
and Technology, Greater Noida,
Uttar Pradesh, India
M. Raghavender Sharma
Osmania University, Hyderabad,
Telangana, India
xv Contributor List
Rajat Sharma
Shri Ramswaroop Memorial University,
Barabanki, Uttar Pradesh, India
Raj Shree
Babasaheb Bhimrao Ambedkar
University, Lucknow, Uttar Pradesh,
India
Sarita Shukla
Shri Ramswaroop Memorial University,
Barabanki, Uttar Pradesh, India
Pawan Singh
Indira Gandhi National Tribal
University (A Central University),
Amarkantak, Madhya Pradesh, India
Prasannavenkatesan Theerthagiri
GITAM University, Bengaluru,
Karnataka, India
Satya Bhushan Verma
Goel Institute of Technology and
Management, Lucknow, Uttar
Pradesh, India
Abhay Kumar Yadav
Shri Ramswaroop Memorial University,
Barabanki, Uttar Pradesh, India
Hagos Yirgaw
Adigrat University, Adigrat, Ethiopia
Majid Zaman
University of Kashmir, Srinagar,
Jammu and Kashmir, India
1
1An investigation
on Cooperative
Communication
Techniques in Mobile
Ad-Hoc Networks
Prasannavenkatesan Theerthagiri
1.1 INTRODUCTION
In mobile ad-hoc networks (MANETs), the terminals (nodes) such as mobile phones,
gaming devices, laptops, tablets, and PDA communicate through cooperation. The
wireless broadcasting mechanism is utilized for the communication of mobile nodes.
In recent days, as many applications utilize MANETs specically, cooperation is
an essential issue in this kind of application, including discovery, military battle-
eld, event monitoring systems, and more civilian applications. In cooperation, any
Computational Intelligent Security in Wireless Communications Cooperative Communication Techniques
CONTENTS
1.1 Introduction ......................................................................................................1
1.2 Cooperation Techniques ...................................................................................3
1.2.1 Crediting Mechanisms .......................................................................... 3
1.2.1.1 Incentive-based Approach .....................................................4
1.2.1.2 Reputation Schemes ...............................................................5
1.2.1.3 Hybrid system ........................................................................7
1.2.1.4 Trust-based schemes ..............................................................8
1.2.2 Acknowledgment-based Mechanisms ..................................................8
1.2.2.1 End-to-End ACK Method ...................................................... 8
1.2.2.2 TWO ACK Method ................................................................ 9
1.2.2.3 Cryptographic-based signature .............................................. 9
1.2.3 Punishment-based mechanism ........................................................... 10
1.2.3.1 Game-Theoretic Approach ................................................... 10
1.2.3.2 Non-cooperative Game-Theoretic Approach ....................... 12
1.3 Discussion and Evaluation of research ndings ............................................. 14
1.4 Conclusion ......................................................................................................20
References ................................................................................................................ 21
DOI: 10.1201/9781003323426-1
10.1201/9781003323426 -1
2 Computational Intelligent Security in Wireless Communications
information is processed, communicated, and forwarded by every node. The node
cooperation with the other nodes is randomly generated, and the routes are discov-
ered and used by MANET routing protocols [1]. As there is no centralized admin-
istration, cooperative communication plays a dynamic role in MANETs. The nodes
in the network act as a router and host to perform all of its network operations. The
MANET nodes are self-organizing and should cooperate well with the intermedi-
ate node to provide effective communication. In this concern, the neighbor nodes
play a vital role in forwarding the packets to reach the destination [2]. If the source
node’s packet needs to reach the destination, which is within the source node’s wire-
less communication (coverage) range, then the rate of successful data transmission
is reasonable. If the destination is beyond the coverage area, they need to reach
via the cooperative intermediate neighbor nodes intended to forward the packet to
the destination [3, 4]. Figure 1.1 shows the cooperation of nodes in the MANET.
Considering the example shown in Figure 1.1, node 1 needs to forward the packet to
the destination node 6; thus, it needs the well cooperating neighbor nodes. As shown
in Figure 1.1, nodes 4 and 5 are the well cooperating nodes so that the relaying pack-
ets can reach the destination node.
The best network performance can be achieved if the entire node in the network
involves relaying the packets. It cannot be achieved in the real world because of
MANET nodes’ nature, such as resource constraints and dynamic topology. The
behaviors of preserving their energy to survive in the network are called selshness,
and malicious activities can easily break this dynamic cooperation. The malicious
activities are generated by the malicious node to either break the actual operation
MANETs or disturb the whole MANET system. The selsh activity also shows
the behavior of a disturbing network, but in the way of non-cooperation [5]. This
kind of non-helping tendency in the forwarding of the packet is called selsh nodes.
Selshness is challenging to observe and qualify, unlike with the malicious nodes.
Causes for selshness are the heavy load in the network, which leads to dropping
any incoming packet beyond the specic limit, and depletion of unwanted energy is
FIGURE 1.1 Cooperation.
3Cooperative Communication Techniques
sustained. It seems to behave like malicious nodes instead of selshness [6]. These
selsh behaving nodes are detected and avoided or stimulated to cooperate with other
nodes in the network through cooperation mechanisms. One of the widely accepted
models for stimulating cooperation is the reputation-based model. In this model,
the reputation is collected directly from the node or indirectly from the neighbors’
collection. By observing and monitoring each node’s cooperation, the reputation is
evaluated. The direct information from the node is more trusted than the neighbor’s
indirect information [7].
1.2 COOPERATION TECHNIQUES
Many researchers have devoted their work to the development of several cooperative
techniques and have proposed many algorithms. These cooperative techniques are
commonly categorized into three major modules: crediting mechanisms, acknowl-
edgment-based mechanisms, and punishing mechanisms, based on the strategy
utilized to enable cooperativeness. Figure 1.2 shows the different classication of
cooperation techniques.
1.2.1 Crediting MeChanisMs
The credits are used on a node for its cooperativeness with other nodes. The
increase in the credits of a node shows that it helps in cooperatively forwarding
the packets. The non-cooperation decreases the credit of a node in the network.
A node earns the credits by forwarding the packets to others. Thus, it becomes
the motivation for another node to behave cooperatively. The nodes which are not
having credits will not participate in the cooperation process and not forward the
packets. The crediting mechanism is further classied into four methods: incen-
tive-based, reputation-based, hybrid-based, and trust-based approaches, as shown
in Figure 1.3.
FIGURE 1.2 Cooperation Techniques.
4 Computational Intelligent Security in Wireless Communications
1.2.1.1 Incentive-based Approach
Incentive-based methods are intended to motivate the nodes in the network to coop-
erate with all intermediate neighbor nodes. The incentives are given to the nodes in
the form of credits or awards, based on the observation and behaviors of the par-
ticular node. Many researchers analyzed the nodes’ cooperation based on the incen-
tive method, such as tamper-proof devices, central agent methods, ad-hoc Vickrey,
Clarke, and Groves (VCG), COMMIT Protocol, and Report-based pAyment
sChemE (RACE).
1.2.1.1.1 Ta m p e r- proof device
Buttyan and Hubaux (2001) had proposed a tamper-proof device for assigning the
credits. This device is installed on each node [8]. Credits are given to nodes based on
the node’s network services such as forwarding, sending packets. The authors also
dene two models as the Packet Purse Model (PPM) and the Packet Trade Model
(PTM). In the PPM, the source node is responsible for paying the credit to the nodes
based on their behavior along the path to the destination. The opposite method is
used on the PTM; here, the destination node pays the credit to the node. The author
introduces the credit count to avoid overloading of the packet by the source nodes.
Counter increases/decreases based on cooperation. This scheme has many draw-
backs as follows: rst, the tamper-proof device should be installed at each node,
which is impossible, and the device needs to be protected from external attack. It is
not produced in the real world. Only the central node achieves more credits than the
other nodes. Even for forwarding a node’s packets, it should have enough credits. It
affects the Packet Delivery Ratio (PDR), throughput of the network.
1.2.1.1.2 Cent ral Age nt
Zhong etal. (2003) proposed a credit-based scheme where the central agent is used
for paying the credits to a node [9]. The central agent veries the issuance of the
credit paid to a node and then conrms the credit based on the reports by each
forwarding node. The nodes have to keep track of their actions and then claim their
credit from the central agent. After receiving the claims, the agent gives credit to
FIGURE 1.3 Crediting Mechanisms.
5Cooperative Communication Techniques
the nodes; the agent gives credit to the participating nodes using the source node
and the cooperating node. Because of the central node, it reduces the burden of each
node credit assignment and tamper-proof device. However, it has the drawback of
a communication bottleneck between the nodes and the central agent. The authors
Anderegg et al. (2003) had introduced the ad-hoc VCG routing protocol; in this
protocol, every node should generally publish their available energy in the route dis-
covery process. The source node then chooses the best cost-effective energy path for
data packet forwarding and assigning the credit to those participating intermediating
nodes. It is an effective way of choosing a truthful energy-efcient route. However,
it is not necessary for all the nodes to give genuine energy values. There may be the
occurrence of collision between the nodes having similar energy values [10].
1.2.1.1.3 COMMIT Protocol
The COMMIT Protocol was proposed by Eldebenz et al. (2005). Rather than
depending on all intermediate participating nodes, the source node only involves in
the route discovery process. The source node should announce the maximum total
credits that it offers for data forwarding. On receiving this information, all of the
intermediate nodes agree or reject based on credits. When the offered credits are
found, the destination will send a path to the sender. Even though, it is easier to con-
trol the sender, for the nodes with tedious communication overhead, it is not easier to
achieve the offered credits [11]. A report-based payment scheme had been proposed
by the authors Mohmoud etal. (2003). They had proposed the lightweight payment-
reporting scheme. The credit reports are submitted to the central agent. The report
contains only less important information. When extra information is needed, the
central agent requests proof on the node. These proofs were stored only temporarily
because of the storage overhead concern [12].
1.2.1.2 Reputation Schemes
The reputation is another type of scheme in monitoring node cooperation to antici-
patory work with the intermediate neighbor nodes. In the reputation scheme, each
node should monitor, observe, and collect the other nodes’ behaviors and reports
in the network. This information was used to evaluate the reputation value of the
observed nodes. Based on these values, the node’s selshness level is determined. If
the reputation value is less than a certain verge value, it shows the particular node’s
selshness. This node needs to be avoided in the routing process. The important
issue in evaluating reputation values is based on the node behavior to determine the
passive/active or negative/positive acknowledgments. When the node’s behaviors are
examined, the reputation system takes further evaluation.
1.2.1.2.1 Trusting/voting system
In the reputation scheme, the trusting/voting technique is used by the monitoring
nodes to determine the other node’s cooperation or non-cooperation in the network
service, such as forwarding the packets and routing, and the opinion on the particu-
lar node is examined from the other nodes, to detect the selsh node. The watch-
dog mechanism had been proposed by Marti et al. (2000), the rating scheme for
6 Computational Intelligent Security in Wireless Communications
computing the reputation in which the monitoring node’s activities are involved [13].
The rate is assigned to the node by the information gathered from other nodes, and
the node, which has a low rating, is avoided from the routing path. This type of rat-
ing of the nodes is called the path rating. Here, the watchdog node listens, the next
neighbor hops transmission to know whether it helps in forwarding the relay or drops
the packets. When the packets are dropped by such a node, then the counter value
will be increased, that is, the misbehaving value for that particular node. When the
rate reaches a certain threshold value, the watchdog node makes it as the misbehav-
ing node, then the particular node will be excluded from the routing path established.
1.2.1.2.2 CONFIDENT Method
Authors Buchesser etal. (2002) had improved the path rating mechanism by a vot-
ing mechanism called Cooperation of Node: Fairness in Dynamic Ad-hoc Network
(CONFIDENT). When the neighbor node detects misbehavior in the network, this
information is forwarded to the reputation system. It gathers information from all
observing nodes and enough proof for such misbehavior. The modied Bayesian
approach gives less importance to the past observations. Here, assigning the rate to
different functions is based on their behavior in the network. If this rating exceeds the
threshold values, then the particular node is punished by not forwarding any packet
to it. In condent schemes, each node uses four components for detecting and isolat-
ing the selsh nodes. (1) The monitor is to listen to the nodes with deviating behavior
from the transmission in the network. Deviating node’s information (ALARM) was
alerted to the next component. (2) The Trust Manager analyzes these ALARM mes-
sages and decides whether to route with the node or not. (3) The Reputation System
provides the reputation value based on the behaviors in the network. (4) The Path
Manager nally avoids the nodes from the routing path, which are determined as the
malicious non-cooperative nodes. However, it requires periodic packet exchanges as
overhead [14].
1.2.1.2.3 CORE Metho d
In Collaborative Reputation Mechanism (CORE), Michiardi etal. (2002) had calcu-
lated the reputation values in three ways: the reputation value calculated by directly
observing node behavior is called subjective reputation. The reputation value calcu-
lated based on the information provided by other nodes is called indirect reputation.
The functional reputation is a combination of both. During the routing discovery
process (in the route request (RREQ), route reply (RREP)), the reputation values are
updated on each node’s reputation table. The nodes that did not relay the packet were
punished. It is important to the condent concept that the problem in condence is
false node voting. Here, it is conquered by restricting the negative rating dissemina-
tion in the network. The CORE only allows the good rating node [15]. Even when the
node has a false rating as acting good, it is actually a bad node.
1.2.1.2.4 SORI M etho d
On improving these two CONFIDENT, CORE, instead of globally broadcasting the
two-reputation information, is shared only on the intermediate neighbor nodes in
7Cooperative Communication Techniques
the Secure and Objective Reputation-based Incentive Scheme for Ad-hoc Network
(SORI) system. In SORI authors He etal. (2004) aim to reduce selshness by encour-
aging the nodes to participate in the relaying of packets [16]. The reputation values
are assigned to nodes by the neighbor nodes using the one-way hash chain-based
algorithm. In this way, the penalties are given to the selsh nodes refusing in the
packet relaying.
1.2.1.2.5 OCEAN Method
The Observation-based Cooperative Enforcement in Ad-hoc Networks (OCEAN)
was proposed by Bansal etal. (2003). In the OCEAN, the observation of the node’s
activities is not yet shared globally like in SORI. Here, each node maintains the rat-
ing information of the neighbor node [17]. Based on the cooperation of the nodes in
packet forwarding, the rating value can be increased or decreased. When the node’s
value is less than the threshold value, then such a node is listed in the misbehaving
node list. The services of such nodes are also avoided. The feature of this scheme
is that the nodes that are in the misbehaving list are inactive for some period. After
that, it allows the network to behave cooperatively, reducing the chance of false
detection. This scheme betters in all previous attempts at detecting cooperativeness
between the nodes in the network. Even though it was affected by false nodes, it will
be reported as misbehaving node by other nodes. The attacks are also possible for
those nodes in the misbehaving list. Guo etal. (2007) developed the Hybrid mecha-
nism to Enforce Nodes Cooperation (HEAD). The HEAD scheme uses the alerting
messages instead of broadcasting the faulty misbehavior lost in the route discovery
process [18]. S. Zhong etal. (2003) had developed a simple cheat-proof, credit-based
system (SPRITE) for effective communication [19]. In this strategy, cooperation
among the nodes is encouraged by assigning the reward in terms of credits called
the cheat-proof system. Tamper-proof hardware is not used in any node; instead, it
uses the receipts. The receipts from the forwarded or routed message are validated,
and the credits are assigned to each node in the system. The central credit clearance
service (CCCS) is to manage all the credits assigned to the nodes.
1.2.1.3 Hybrid system
The hybrid system utilizes the advantages of both credit and reputation schemes to
stimulate the MANET’s cooperation.
1.2.1. 3.1 ICAR US Metho d
Charilas etal. (2012) had presented the ICARUS: Hybrid incentive mechanism for the
cooperation stimulation scheme to control the credit exchanges between the partici-
pating nodes by utilizing the reputation schemes [20]. In the ICARUS, credit account
service (ICAS) is used by the central agent for assigning credits to the nodes to deter-
mine the selsh non-cooperative nodes. However, in ad-hoc networks, the reliance on
a central agent is difcult because of its dynamic nature. The account-based hierar-
chical Reputation Management (ARM) system uses the credit and reputation scheme
proposed by Shen etal. (2008) but does not use any central agent for the detection
[21]. In this scheme, each node maintains the credits for the peer nodes based on
8 Computational Intelligent Security in Wireless Communications
the reputation value. The nodes act as reputation managers for relaying the packets.
However, in heavy trafc, the relaying task on nodes could not be a good way.
1.2.1.4 Trust-based schemes
The trust-based mechanism uses trust features for determining node behaviors.
Based on the trust values, the good or bad trust on the particular node is to take.
Several researchers worked on computing and evaluating trust features and node’s
trustworthiness in determining cooperative nodes.
1.2.1.4.1 SDA Me t h o d
The authors Z. K. Chong etal. (2013) adopted the Separation of Detection Authority
(SDA) for detecting the selsh nodes in the network and improving the trustworthi-
ness of the nodes [22]. The improvement to the node behavior’s trustworthiness is
done by using three components, such as the reporting node, agent node, and central
authority. The reporting node nds out the misbehaving non-cooperative node and
generates reports to forward it to the central authority. The central authority investi-
gates the reports by using the agent nodes. The agent nodes are the neighbor moni-
toring nodes. Finally, all the agents submit the reports about the suspicious node to
the central authority. However, here, communication overload is tight between the
three entities and may degrade network performance.
1.2.1.4.2 TEAM Method
In the Trust-based Exclusion Access Control Mechanism, authors L. H. G. Ferraz
etal. (2014) used the two modules for computing node’s trustworthiness as local
and global. The nodes observe and gather the one-hop neighbor’s information from
the local module. Then, this collected behavioral information is forwarded to this
global module. The global module consists of nodes that evaluate the evidence on
such trusts by using the voting mechanisms [23]. Then the non-cooperative nodes are
dened in the access to the network. Here, the main advantage is the low overhead of
messages that have been used for detecting and excluding such misbehaving nodes.
However, the trust features and the friendship mechanisms need to be strengthened
in the modules.
1.2.1.4.3 Weighting Metho d
The authors Yu etal. (2009) had calculated the average of the observed node’s per-
formance to detect the node’s trustworthiness. The link quality information is also
measured to improve the accuracy of the trust value [24]. However, the routing anom-
alies, interference problems, need to be avoided in these trust-based techniques.
1.2.2 aCknowledgMent-based MeChanisMs
1.2.2.1 End-to-End ACK Method
End-to-End ACK is an acknowledgment-based mechanism proposed by Conti etal.
(2003) in which when the destination node receives the packet from the source node,
it has the responsibility to send back an ACK to the source node [25]. The source
9Cooperative Communication Techniques
node waits for some time to receive an ACK from the destination; if it does not
receive ACK within the time, it assumes that the packet did not reach the destination.
Here, authors use the reliability index for maintaining the performance and reli-
ability of packets. When the index exceeds threshold values, then the corresponding
route needs to be excluded. The reliability index is updated based on ACK from the
destination node to the source node. However, every node’s reliability index is vis-
ible to other nodes; it provides the attackers with chances to making an attack [26].
Refaei etal. (2005) had developed TCP acknowledgment, an ACK-based mecha-
nism, but the neighbor node’s activity was only used to measure the RI (Reliability
Index). The authors [27] used the same ACK scheme and maintained the RI for
neighbor nodes. When the source node receives ACK from the destination node, the
RI of their neighbor nodes increases. It decreases when the source node does not
receive ACK, or when some node retransmits the same packet.
1.2.2.2 TWO ACK Method
Instead of using many nodes in the network for end-to-end ACK, in TWO ACK
authors, Balakrishnan etal. (2005) used two hops for ACK. This scheme works by
sending ACK by a two-hop query from the sender node [28]. The two-hop away node
is required to send an ACK back to the sender node on receiving the packet. The RI
is calculated by the waiting time. The sender node waits for some time to receive the
ACK from the two-hop away node. The RI is increased or decreased in threshold
time values. When the RI is decreased to a low, the node and the link are avoided
from routing and are marked as misbehaving. However, there may be the occur-
rence of a potential false neighbor on the RI. It is not yet analyzed in this paper. It
is needed to gather the evidence on such occurrences from the neighbor node. Also,
the congestion causes misbehavior when the network trafc is high [29]. Figure 1.4
illustrates the working of the TWO ACK scheme.
1.2.2.3 Cryptographic-based signature
Authors H. Yang etal. (2002) adopted the cryptographic-based signature with the
Ad-hoc On-demand Distance Vector (AODV) to protect data forwarding and routing
FIGURE 1.4 Two ACK Scheme.
10 Computational Intelligent Security in Wireless Communications
in the network. This technique uses the cryptographically signed tokens to provide
such protection against misbehaviors [30]. This token has an expiry period, which
depends on the token holder’s behavior in the network. It means that the token holder
relays the packet as receive means, it will have a better expiry. It is also required
to renew the token before its expiration. The renewing is done by gathering the
k-number of different signals from its neighbor nodes. The neighbor node monitors
every other node for detecting the misbehavior about whether the data packet has
been dropped uncertainly. Based on such values, the nodes renew the request for the
token signature that is needed to be granted. It has a better solution in advance to the
watchdog. However, only depending on k-neighbor nodes for detecting misbehavior
causes the drawback. Even though the node is not misbehaving, the low k-valued
nodes are declared a suspicious node. The high k-value means that it has high con-
nectivity, which is not possible in MANETs.
1.2.3 PunishMent-based MeChanisM
In the punishment-based mechanism, the penalties are given to the node, which
behaves selshly. Moreover, they are isolated from the routing path and avoided in
any services because of the penalty. The punishing mechanism uses game-theoretic
approaches and non-cooperative game-theoretic approaches depicted in Figure 1.5.
1.2.3.1 Game-Theoretic Approach
1.2.3.1.1 DECADE Method
The selsh nodes, which will not participate in the cooperative forwarding of pack-
ets, are detected by using the Dynamic Source Routing (DSR) protocol in the distrib-
uted emergent cooperation through adaptive evolution in the MANET (DECADE)
FIGURE 1.5 Punishing Mechanisms.
11Cooperative Communication Techniques
technique proposed by M. Majia etal. (2012). In this method, the author uses this
scheme for each node to enable cooperation [31]. It uses the non-cooperative game
theory in which each node in the network is encouraged to maximize the successful
packet delivery, thus isolating the selsh node. By introducing the sociality param-
eter for each node in the network, the node’s interactions are also to be improved.
If any node forwards the packet to others, which they receive from neighbors, they
will be rewarded with “X.” However, cooperating intermediate node’s energy will
be reduced to the amount of “Y;” thus, “X-Y” reward will be given. Considering the
example as node “A” sends a packet to node “B;” here, the rewarding and punish-
ing the node are done by three scenarios. (1) If both nodes cooperatively transmit
the packet without any deviations, then the reward is “X-2Y.” (2) If any node does
not forward the packets cooperatively to others, then the reward is “X-Y.” (3) If both
nodes are unwilling to cooperate in forwarding the packets, punishment such as
“–Y” will be assigned to that node for that selshness. The DECADE uses the clas-
sical cellular algorithm; it is the feedback-based algorithm. It works based on the
intermediate node’s feedback about the packet, whether to forward or drop to detect
the cooperating nodes and isolate the selsh nodes. The sociality parameter was
included in the DECADE algorithm, which improves the general trust model’s per-
formance by encouraging the nodes to choose the best path by availing the wider
information from the intermediate nodes and providing the best performance when
the network environment changes, i.e., on mobility. However, in the DECADE mech-
anism, too many algorithms and parameters such as availability and sociality are
utilized to perform better on detecting and isolating the selsh nodes. It may degrade
the overall network performance, and the computations used are more complex. The
authors Niu etal. (2011) had proposed the approach, which uses three aspects for
cooperation stimulation among the nodes. (1) Using innite reported game concepts
to determine the optimal cooperativeness. (2) The worst behavior tit-for-tat strategy
for punishing the node cooperatively. (3) The realistic estimation mechanism for
node behavior monitoring, which is maybe imperfect. The monitoring results in pun-
ishing nodes that act selshly [32]. However, the monitoring results may be affected
by external factors like noise and interference.
1.2.3.1.2 Tit-for-tat (TFT) strategy:
The game-theoretical strategy called the tit-for-tat (TFT) strategy was used to detect
the selsh node by S. Ng etal. (2010). All nodes in the network will be cooperating
in the rst stage of the game. Based on the opponent’s behavior, further actions are
taken in the preceding stage. It works based on the forwarding game and repeated
game. The connectivity among the node, routing path, and network property for the
cooperation is also determined for detecting the node’s willingness to forward the
packets to others [33]. Huang etal. (2001) had proposed the resolving technique for
dropping of route request (RREQ) control packets, which are received during the
route discovery process. It is a monitoring approach utilized during the transmission
for detecting the selsh node. Authors utilized the concept that the number of RREQ
packets should be equal to the number of nodes in the network. When it is unequal,
it is assumed that there are some nodes acting as selsh nodes [34]. To reduce false
12 Computational Intelligent Security in Wireless Communications
detection, this gaming approach was used. However, it uses indirect communication
and complex distributed monitoring schemes for detecting the selsh nodes. Toledo
etal. (2007) had improved the non-cooperative behaviors by concentrating on layers
such as the network layer and the medium access control (MAC) layer. The selsh-
ness occurs in the network layer by refusing the route discovery process, delaying or
dropping the forwarding of packets by idle state nodes [35]. In the MAC layer, the
selshness can be monitored as the uneven channel access, false signal state.
1.2.3.2 Non-cooperative Game-Theoretic Approach
1.2.3.2.1 Eliminating Packet Droppers scheme
The authors Djenouri etal. (2009) had proposed the Eliminating Packet Droppers
scheme. It includes ve modules to detect and eliminate the misbehaving nodes,
such as monitors, detectors, isolators, investigators, and witnesses. These modules
have the responsibility to detect these misbehaving nodes [36]. The monitor controls
the relaying of data packets. The detector is responsible for detecting the misbe-
having node, based on the monitor module response. Isolator collaborates with the
witness module, which means it gets enough evidence before any node is isolated as
the misbehaving node. The investigator module investigates such suspected node’s
accusations for whether it has enough experience for the accusation. Finally, the wit-
ness module helps the isolator in isolating the misbehaving node. Isolating means
that the data packets cannot be forwarded to other nodes. Here, the randomized
two-hop acknowledgment algorithm is utilized for better performance. The random-
ized two-hop acknowledgment algorithm has less overhead compared to the two-hop
acknowledgment algorithm. However, using the randomized ACK, the possibility of
accurately detecting the misbehavior is not possible. The authors also made some
assumptions for detecting the misbehavior. The isolated nodes are permanently
excluded from the network, and they are not provided the option to rejoin the net-
work after some occurrences if it is falsely isolated.
1.2.3.2.2 Cross-layer Anomaly-based Intrusion Detection System (IDS)
The authors L.S. Casado et al. (2015) had developed the IDS system model for
detecting dropping attacks in the network. Here, authors concentrate on the mobil-
ity, collisions, and packet errors, which will deeply affect the communication
between the nodes in the network [37]. The RTS/CTS data-forwarding model
is intensely examined under the MANET conditions. The enhanced windowing
method is used to detect mobility and channel errors; this windowing helps to
detect packet dropping under these conditions. When the RTS is sent to a node,
which is not replied because of mobility, it is also categorized as the misbehaving
node, even though it uses temporal time-based windowing. By using the temporal
window, the node can be considered illegitimately as malicious, because it is not
able to detect the node’s mobility in a particular temporal windowing period. To
overcome this, the authors use event-based windowing instead of a time-based
one, thus avoiding the previously mentioned issues. However, using the time-based
windowing, each node exclusively needs to monitor and collect information of all
other nodes and carry out all features to determine the network’s misbehaving
13Cooperative Communication Techniques
actions. In addition, it is not feasible to trust each node’s monitoring and the imple-
mentation of the detection techniques.
1.2.3.2.3 FACES Method
The authors S. K. Dhurandher etal. (2011) had developed the Friend-based Ad-hoc
Routing using Challenges to Establish Security in MANET system (FACES) algo-
rithm to isolate the malicious nodes from the network. It uses the concept of sharing
the friend lists and sending the challenges to each node. The friend list is a list of
nodes having a friendship with other nodes obtained from the previous transmission
and it will be used for routing, instead of proving the list of trustful nodes to the
source node [38]. To improve the friendship with other nodes, the periodic process
called “Share Your Friend” is initiated into the network. By doing so, the friends for
each node are updated. The friends mean that the nodes that fully cooperate in the
relaying process of the network. The FACES works in four stages: in the “Challenge
Your neighbor,” stage nodes are challenged to provide the neighbor node’s authen-
tication details, and nodes which provide these details are listed in the “friend list”.
The “Question mark list” contains the nodes, which have not completed the rst
stage. The nodes in the question mark list are not used for the relaying packets. The
nodes, which did not perform well in the friend list, are also moved to the question
mark list. In “Rate Friends,” the nodes are rated based on their performance in the
network activities such as relaying packets, cooperation, etc. Based on the involve-
ment in the relaying process, the rating is given from 0 to 10. The authors have used
the DSR protocol for routing by checking whether the node is in the friend list and
not in the question mark list. Quality of the route is checked by every node, and the
source node is encrypted by the public key cryptography to protect against eaves-
dropping and man in the middle attack. However, maintaining too many lists such as
the question mark list, friend list, unauthorized list on each node may cause network
overhead. Moreover, it gradually decreases the network throughput on larger net-
works. In highly dynamic battery-constrained mobile nodes, preserving these lists
also consumes some battery power.
1.2.3.2.4 ERCRM Method
In the Exponential Reliability coefcient-based Reputation Mechanism for isolating
selsh nodes in MANET (ERCRM) method, the authors J. Sengathir etal. (2015)
estimate the energy measures on nodes and manipulate the reliability coefcient
for isolating selsh nodes from the routing path in the MANET [39]. By using the
exponential failure rate on nodes in networks through the moving average method, it
is highly utilized on the nodes for calculating the reliability coefcient. The moving
average method works by the most recent mobile nodes’ most recent past behavior in
relaying the packets to the neighbor nodes. The authors categorize the selsh nodes
into three types as, type I, II, and III. In type I, the selsh node participates in the
route discovery and maintenance process but does not participate and refers to relay
packets to other nodes. In type II, it participates neither in the route discovery and
maintenance process nor in relaying packets. In type III, the selsh nodes dynami-
cally change their behavior by forwarding the packets and dropping the packets. The
14 Computational Intelligent Security in Wireless Communications
protocol used for the routing process itself removes the type II selsh nodes. The
type III selsh nodes are removed from the network by using the estimated energy
metric. When this value falls below the threshold value, the mobile node is isolated
as the selsh node. By using both the energy metric and reliability coefcient values,
the type I, selsh nodes are isolated from the routing path in the network. However,
the estimated energy metric and reliability coefcient are computed for every node
in the network; it increases the network overhead gradually. Moreover, the nodes that
are not in the routing path and maintaining these values make the energy of nodes
decrease. The authors use the predened estimated energy metric of 0.45 Joules
and the reliability coefcient of 0.4 for determining the MANET’s selsh nodes. It
should be dynamically changed under different conditions for better results in isolat-
ing the selsh nodes.
1.2.3.2.5 Collaboration enforcement in MANET
Authors N. Jiang etal. (2007) developed the novel approach instead of using repu-
tation mechanisms. It has the scalability issues that cause bad accusations on the
neighbor nodes; it can lead to the misjudgment of determining malicious nodes [40].
To overcome these issues, the author introduced a one-hop neighbor observation
and rerouting the packets. Even though in this technique, each node needs to main-
tain the list of observations and actions of all nodes in the network. Moreover, the
DSR protocol maintains the list of route caches; it also adds the routing overhead to
the network. The Route Redirect (RRDIR) concept used for dynamic rerouting has
another route discovery process; it may also increase the throughput of the network
and the overall performance of the network. In addition, the number of the node used
for the simulation is 50; it is very small as the scalability concern. For the simulation
analysis, the selsh node’s percentage increases by up to 20% only. As the scalability
and robust concern, the nodes’ selshness should be evaluated up to 60%.
1.2.3.2.6 Best neighbor strategy
K. Komathy etal. (2007) used the best neighbor strategy to maintain scalability
and robustness among neighbor nodes while enforcing cooperation among selsh
nodes [41]. In this strategy, each node should play a packet forwarding to its neigh-
bors. Recording and updating these values are done in each round. Trust values are
obtained by this way of interaction with the individual neighbor nodes.
1.3 DISCUSSION AND EVALUATION OF RESEARCH FINDINGS
In this section, the discussion about the various categories of cooperation techniques
and their challenges in establishing cooperation are to be presented. The summary
and the comparison of different cooperative solutions for the MANET are tabu-
lated in Table 1.1. The cooperation in the wireless and Ad-hoc network faces several
issues, and those were considered in this summary. Moreover, it is not feasible to
assure the network similarity, and its stability and scalability create uneven con-
sequences for cooperative schemes. Mobile Ad-hoc networks contemporarily have
several constrictions such as mobile resources, storage capacity, battery, processor
15Cooperative Communication Techniques
TABLE 1.1
Summary and Comparison of the Cooperation Techniques
S. No. Authors Cooperation Strategy Cooperative Approach Type Key Concepts Features
1. L. Buttyan and J.
Hubaux [8]
Tamper-proof device Incentive-based
credit mechanism
Assigning credits based on
cooperation ability
Packet Purse Model and
Packet Trade Model were
adopted
2. S. Zhong etal. [19] Central agent Incentive-based crediting
mechanism
Paying credits by central
agents and it veries paid
nodes
Central agents reduce the
burden of other nodes in the
credit assignment
3. L. Anderegg and S.
Elderbenz [10]
Ad-hoc VCG Incentive-based crediting
mechanism
Nodes publish remaining
energy for routing
Best available energy path
discovered based on their
values
4. S. Elderbenz etal. [11] COMMIT Protocol Incentive-based crediting
mechanism
Source takes the
responsibility of credit
assignment
Source nodes reveal
maximum total credit to
participating nodes
5. M. Mohmoud and X.
S. Shen [12]
RACE Incentive-based crediting
mechanism
Central agents manage
reports from nodes
Central agents store the data
temporarily for future
evidence
6. S. Marti etal. [13] Watchdog scheme Reputation-based crediting
mechanism
A rating scheme for
reputation computation
Neighbor node activities were
monitored, and low rated
nodes avoided
7. S. Buchegger and J. Y.
Boudec [14]
CONFIDENT Reputation-based crediting
mechanism
Misbehaving nodes and their
reputations are collected
Lower reputation nodes from
other neighbor nodes were
identied
8. P. Michiardi and R.
Molva [15]
CORE Reputation-based crediting
mechanism
False reputation values were
restricted
Good rating nodes only
allowed for the routing
(Continued)
16 Computational Intelligent Security in Wireless Communications
TABLE 1.1 (CONTINUED)
Summary and Comparison of the Cooperation Techniques
S. No. Authors Cooperation Strategy Cooperative Approach Type Key Concepts Features
9. Q. He etal. [16] SORI Reputation-based crediting
mechanism
One-way hash chain
algorithm for packet relay
Reputation values shared only
on neighbor nodes
10. S. Bensal and M.
Baker [18]
OCEAN Reputation-based crediting
mechanism
Rating value, the
misbehaving list is used for
cooperation
Rating increased or decreased
based on node cooperation
and reduced false detection
11. J. Guo et al [22] HEAD Reputation-based crediting
mechanism
Enforces cooperation by the
alert-based route discovery
process
Alerting messages are used
for faulty nodes instead of
broadcasting
12. D. Charles et al [21] ICARUS Hybrid-based crediting
mechanism
Controls credits exchanged
between nodes
The central agent uses credit
control service for credit
assignment to nodes
13. H. Shen and Z. Li [31] ARM Hybrid-based crediting
mechanism
No central agents were used
for the for-credit
assignment
Each node maintains credits
for peer nodes based on the
reputation value of nodes
14. Z. K. Chong et al [23] SDA Trust-based crediting
mechanism
Reporting node, agent node,
the central authority for
trust analysis
Central authority investigates
the trust reports from all
agents for trustworthiness
15. L. H. G. Ferraz
etal. [24]
TEAM Trust-based crediting
mechanism
The local and global model
is used to calculate the trust
of node
The global model observes
and gathers the one-hop
neighbor values to evaluate
trust
16. M. Yu et al [20] Weighting Method Trust-based crediting
mechanism
Link quality and average
weights are calculated
Accurate trust values are
calculated by averages for
trusted node detection
(Continued)
17Cooperative Communication Techniques
TABLE 1.1 (CONTINUED)
Summary and Comparison of the Cooperation Techniques
S. No. Authors Cooperation Strategy Cooperative Approach Type Key Concepts Features
17. S. K. Ng and W. K. G.
Seah [33]
TFT Strategy Game theory-based
punishment mechanism
Forwarding game and
repeated game are adopted
The game theory denes the
routing path, network
topology for forwarding the
packets
18. B. Niu et al [34] Game Theory Game theory-based
punishment mechanism
Stimulation of nodes for the
cooperation among nodes
by game theory mechanism
The innite repeated game,
TFT worst behavior strategy,
and realistic estimation
mechanism are developed
for cooperation
19. L. Huang et al [35] Game Theory Game theory-based
punishment mechanism
Resolves the packet dropping
problems in the network
The concept of route request
packets should equal to the
number of nodes adopted
20. A. L. Toledo and X.
Wang [36]
Game Theory Game theory-based
punishment mechanism
Development of the network
layer and the Medium
Access Layer (MAC)
The MAC layer designed to
determine the
cooperativeness of nodes
using channel access, fake
signal state
21. M. Mejia et al [32] DECADE Non-cooperative Game-
Theoretic Approach
Sociality parameter and
encouraging the nodes for
cooperation
Rewarding and punishment
are given to nodes based on
packet delivery and
cooperation in three ways
(Continued)
18 Computational Intelligent Security in Wireless Communications
TABLE 1.1 (CONTINUED)
Summary and Comparison of the Cooperation Techniques
S. No. Authors Cooperation Strategy Cooperative Approach Type Key Concepts Features
22. M. Conti et al [26] End-to-End Acknowledgment based The receiver node and source
node are responsible for
acknowledgment (ACK)
The reliability index is
updated based on
acknowledgment and
cooperative nodes
determined
23. M. Refaei et al [28] TCP ACK Acknowledgment based Neighbor node activities
were utilized for the
reliability index
The neighbor nodes
determine the reliability of
nodes and neighbor works
by ACK of other nodes
24. K. Balakrishnan
etal. [29]
TWO ACK Acknowledgment based Two nodes were adopted for
the acknowledgment
instead of the end-to-end
acknowledgment process
Sending ACK by two hops
away from the sender node.
Corresponding two hops
required to send
acknowledgment
25. H. Yang et al [19] Cryptography-Signatures Acknowledgment based Cryptographic Token
renewal, K-neighbors for
detecting misbehaving
nodes
Cryptographically signed
tokens on nodes protect
nodes on cooperation
26. D. Djenouri and N.
Badache [37]
Eliminating packet Droppers Non-cooperative Game-
Theoretic Approach
Monitor, detector, isolator,
witness, investigator,
randomized ACK modules
adopted
Based on the evidence, the
misbehaving nodes are
isolated by using each stage
of modules
(Continued)
19Cooperative Communication Techniques
TABLE 1.1 (CONTINUED)
Summary and Comparison of the Cooperation Techniques
S. No. Authors Cooperation Strategy Cooperative Approach Type Key Concepts Features
27. L.S. Casaclo et al [38] Anomaly-based IDS Non-cooperative Game-
Theoretic Approach
To detect the packing
dropping attack IDS system
model developed and RTS/
CTS model adopted
Mobility and collision packet
error, the channel error
event-based windowing
method is adapted to detect
misbehaviors
28. S. Zhong et al [41] SPRITE Credit based Credit proof system, receipts
of nodes, assigning reward
in terms of credits
Receipts of nodes are
validated and credits
assigned. CCCS encourages
cooperation
29. S. K. Dhurandher
etal. [39]
FACES Non-cooperative Game-
Theoretic Approach
Friend list, Sharing friend
list, share your friend,
Challenging the neighbor
nodes
Misbehaving nodes are listed
in the Question mark list. A
node in this list was not used
for relaying
30. J. Sengathir and R.
Manoharan [40]
ERCRM Non-cooperative Game-
Theoretic Approach
Estimated energy node,
Reliability coefcient (RC),
Exponential failure rate
Based on the energy of nodes,
the RC calculated and selsh
nodes are isolated in routing
31. N. Jiang et al [25] SRACEM Non-cooperative Game-
Theoretic Approach
Scalability issues, one-hop
neighbor observation, DSR
protocol
Each node maintains the
observation of all other
nodes. It nds selsh nodes
32. K. Komathy and P.
Narayana samy [42]
Neighbor Strategy Non-cooperative Game
theory-based
Trust values are updated for
each neighbor node for
cooperation
Scalability, stability, and
robustness of nodes are
evaluated
20 Computational Intelligent Security in Wireless Communications
power, limited bandwidth, network connections, and latency, which make the coop-
eration strategy deployment for the mobile environments as a challenging task. Most
signicantly, the cooperation mechanisms aim to solve such problems. The coopera-
tion systems necessitate solving the network latency issues and low bandwidth for
network efciency and effectiveness. In addition, issues in synchronization, security,
and trustfulness require extra add-ons to the systems.
Generally, the cooperation systems typically undertake the selsh node discov-
ery to determine the superlative node to cooperate in the network and to accom-
plish the nest routing path. Nonetheless, this one could be toughest in the presence
of unpredictable non-cooperative nodes. The cooperation strategy should also be
capable of incorporating the node heterogeneity. Thus, cooperation schemes need to
adjust their operating environments and control node communication to attain the
best cooperation. For mobile environments, fault-tolerance could be often required
to enhance the fault/disconnections in communication. Consequently, identifying
the cause of disconnection, recovering the original message, and retransmitting the
original is most essential. For the effective and efcient establishment and organiza-
tion of cooperation mechanisms, all the aspects mentioned above are the key issues
and should be considered. Moreover, the best cooperation-based approaches have an
optimal QoS delivery and quality in the network performance.
In this paper, the major classications of the cooperative schemes in MANETs
are discussed. As mentioned previously, it is generally divided into three types of
approaches as credit-based approaches, acknowledgment-based mechanisms, and pun-
ishment-based approaches. Based on the study performed in this paper, the reputation
mechanisms have further concerns compared to acknowledgment- and credit-based
methods. There is more ease of possibility of communication with the non-coopera-
tive nodes to gain more reputation in reputation schemes. The efcient node reputa-
tion computation through the network is also another challenging issue. Likewise, the
dependence on the wireless broadcast approach is another weakness in these schemes.
Better reliability is achieved in the credit-based approaches to these issues.
However, the dependence of tamper-proof hardware and node cheating behaviors
should be avoided for this scheme, and it may add more complexity to the cooperative
system. Most of the credit-based systems attempted to avoid tamper-proof hardware
even though it has many difculties for the security mechanisms. The effective coop-
eration-based approaches should aim to provide better efciency and performance for
the mobile devices in terms of device battery, storage, and network. In many network
architectures, the cooperation between the applications is much needed, supported by
mobile devices. Generally, in industry services, the agents must cooperate and share
information over mobile devices and healthcare services. In contrast, mobile health
(m-health) applications share medical information with patients and physicians [42].
Likewise, in healthcare scenarios, the cooperation among applications is more chal-
lenging, which requires a comprehensive study of these approaches.
1.4 CONCLUSION
The cooperation between nodes is much needed for communication establishment
in dynamically unstable network infrastructures, and many solutions (cooperation
21Cooperative Communication Techniques
approaches) have been proposed to address the issues and challenges. The mobile
nodes should cooperate with each other for relaying data and accomplishing all net-
working functions. In this paper, the elaborated deep literature analysis from various
research developments and their limitations and features of the network is per-
formed. In addition, the cooperation stimulation strategies such as cooperative game
theory-based approaches, and non-cooperative game theory-based approaches, pun-
ishment mechanisms, incentive-based methods, and hybrid methods are focused on
the MANETs. The challenges, merits, weaknesses of approaches, and their signi-
cance in cooperation establishment are also included in this paper. Furthermore, the
open issues in the construction and design of cooperative solutions for MANETs are
also surveyed.
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An investigation on Cooperative Communication Techniques in Mobile
Ad-Hoc Networks
X. Wang , J. Li , and S. Member , “Improving the network lifetime of MANETs through
cooperative MAC protocol design,” IEEE transactions on parallel and distributed systems vol.
26, no. 4, pp. 1010–1020, 2015.
B. Karaoglu , W. Heinzelman , and S. Member , “Cooperative load balancing and dynamic
channel allocation for cluster-based mobile ad hoc networks,” IEEE transactions on mobile
computing vol. 14, no. 5, pp. 951–963, 2015.
T. Prasannavenkatesan , “FUCEM: Futuristic cooperation evaluation model using Markov
process for evaluating node reliability and link stability in mobile ad hoc network”, Wireless
Networks, Springer, (Article in Press), 2020, IF: 2.405, ISSN: 1572-8196, DOI:
https://doi.org/10.1007/s11276-020-02326-y.
P. Theerthagiri , “COFEE: Context-aware futuristic energy estimation model for sensor nodes
using Markov model and auto-regression”, International Journal of Communication System, p.
e4248, 2019. IF: 1.278, ISSN: 1099-1131, DOI: https://doi.org/10.1002/dac.4248.
T. Prasannavenkatesan , K. Udhayakumar , and R. Ramkumar “Security attacks and detection
techniques for MANET,” Discovery Journal, vol. 15, no. 42, pp. 89–93, Ghaziabad, March 2014.
J. N. Al-karaki, and A. E. Kamal , “Stimulating node cooperation in mobile ad hoc networks,”
Wireless Personal Communications, vol. 44, pp. 1–15.
N. Samian , Z. Ahmad , W. K. G. Seah , and A. Abdullah , “Cooperation stimulation
mechanisms for wireless multihop networks: A survey,” Journal of Network and Computer
Applications, vol. 54, pp. 88–106, 2015.
L. Buttyan , and J. P. Hubaux , “Nuglets: A virtual currency to stimulate cooperation in self-
organized mobile ad hoc networks,” Technical Report DSC/2001/001. Swiss Federal Institute of
Technology, Lausanne, Switzerland; 2001.
B. M. C. Silva , J. Rodrigues , N. Kumar , and G. Han , “Cooperative strategies for challenged
networks and applications: A survey,” IEEE Systems Journal, vol. 11, pp. 1–12, 2015.
L. Anderegg , S. Eidenbenz , “Ad-hoc-VCG: A truthful and cost-efficient routing protocol for
mobile Ad-hoc networks with selfish agents,” in: Proceedings of the 9th International
Conference on Mobile Computing and Networking (MobiCom). San Diego, CA; 14–19
September 2003. pp. 245–259.
S. Eidenbenz , G. Resta , P. Santi , “COMMIT: a sender-centric truthful and energy-efficient
routing protocol for Ad-hoc networks with selfish nodes,” in Proceedings of 19th IEEE
International, Parallel and Distributed Processing Symposium (IPDPS). Denver, Colorado, USA;
3–8 April 2005. pp. 239–249, 2005.
M. Mahmoud , X. Shen , “A secure payment scheme with low communication and processing
overhead for multihop wireless networks,” IEEE Trans Parallel Distrib Syst, vol. 24, no. 2, pp.
209–224, 2013.
S. Marti , T. J. Giuli , K. Lai , M. Baker , “Mitigating routing misbehavior in mobile ad-hoc
networks,” In: Proceedings of the 6th Annual International Conference on Mobile Computing
and Networking. Boston, MA; August 2000, pp. 255–265, 2000.
S. Buchegger , J. Y. L. Boudec , “Performance analysis of the CONFIDANT protocol,” in
Proceedings of the 3rd ACM International Symposium on Mobile Ad-Hoc Networking and
Computing (MOBIHOC). Lausanne, Switzerland; 9–11 June 2002, pp. 226–236, 2002.
P. Michiardi , R. Molva , “CORE: A collaborative reputation mechanism to enforce node
cooperation in mobile Ad-hoc networks,” in Proceedings of the 6th Joint Working Conference on
Communications and Multimedia Security. Netherlands; 26–27 September 2002, pp. 107–112,
2002.
Q. He , D. Wu , P. Khosla , “SORI: A secure and objective reputation-based incentive scheme
for Ad-hoc networks,” in Proceedings of the IEEE Wireless Communications and Networking
Conference (WCNC). NewOrleans, LA; 21–25 March 2004, pp. 825–830, 2004.
S. Bansal , M. Baker , “Observation-based cooperation enforcement in ad hoc networks,”
Technical Report cs.NI/0307012. Computer Science Department, Stanford University, USA;
2003.
J. Guo , H. Liu , J. Dong , X. Yang , “HEAD: A hybrid mechanism to enforce node cooperation in
mobile Ad-hoc networks,” Tsinghua Science and Technology, vol. 12, no. 1, pp. 202–207, 2007.
S. Zhong , Y. R. Yang , J. Chen . “Sprite: A simple, cheat proof, a credit-based system for
mobile Ad-hoc networks,” in: Proceedings of the 22nd IEEE International Conference on
Information Communications (INFOCOM). San Francisco; 1–3 April 2003, pp. 1987–1997,
2003.
D. E. Charilas , K. D. Georgilakis , A. D. Panagopoulos , “ICARUS: hybrid inCentive mechanism
for cooperation stimulation in ad-hoc networks,” Ad-hoc Networks, vol. 10, no.6, pp. 976–989,
2012.
H. Shen , Z. Li , “ARM: an account-based hierarchical reputation management system for
wireless ad-hoc networks,” in 28th International Conference on Distributed Computing Systems
Workshops (ICDCS'08). Beijing, China; 17–20 June 2008, pp. 370–375.
Z. K. Chong , S. W. Tan , B. M. Goi , B. C. K. Ng , “Outwitting smart selfish nodes in wireless
mesh networks”, International Journal of Communication System, vol. 26 no. 9, 2013, ISSN:
1163–1175.
L. H. G. Ferraz , P. B. Velloso , O. C. M. B. Duarte , “An accurate and precise malicious node
exclusion mechanism for Ad-hoc networks”, Ad-hoc Networks, vol. 19, pp. 142–155, 2014.
M. Yu , M. Zhou , W. Su . “A secure routing protocol against Byzantine attacks for manets in
adversarial environments,” IEEE Trans Veh Technol, vol. 58, no. 1, pp. 449–460, 2009.
M. Conti , E. Gregori , G. Maselli , “Towards reliable forwarding for ad-hoc networks,” in:
Proceedings of the Personal Wireless Communications (PWC). Venice, Italy; 23–25 September
2003, pp. 790–804.
T. Prasannavenkatesan , R. Raja , P. Ganeshkumar , “PDA-misbehaving node detection &
prevention for MANETs,” in IEEE Explore and Proceedings of International Conference on
Communication and Signal Processing (ICCSP). Melmaruvathur, pp. 1808–1812, April 2014.
M. T. Refaei , V. Srivastava , L. Da Silva , M. Eltoweissy , “A reputation-based mechanism for
isolating selfish nodes in ad-hoc networks,” in Proceedings of the Second Annual International
Conference on Mobile and Ubiquitous Systems: Networking and Services (MobiQuitous).
SanDiego, CA; 17–21 July 2005, pp. 3–11, 2005.
K. Balakrishnan , J. Deng , P. K. Varshney , “TWOACK: Preventing selfishness in mobile ad hoc
networks,” in Proceedings of the IEEE Wireless Communications and Networking Conference
(WCNC). New Orleans, LA; 13–17 March 2005, pp. 2137–2142, 2005.
T. Prasannavenkatesan , P. Rajakumar , A. Pitchaikkannu , “An effective intrusion detection
system for MANETs,” in Proceedings on International Journal of Computer Applications (IJCA)
and ICACEA. Ghaziabad, vol. 3, pp. 29–34, March 2014.
H. Yang , J. Shu , X. Meng , S. Lu , “SCAN: Self-organized network-layer security in mobile ad
hoc networks." IEEE Journal on Selected Areas in Communications vol. 24, no. 2, 261-273,
2006.
M. Mejia , N. Pena , J. L. Munoz , O. Esparza , and M. Alzate , “Ad hoc networks DECADE:
Distributed emergent cooperation through adaptive evolution in mobile ad hoc networks,” Ad
Hoc Networks, vol. 10, no. 7, pp. 1379–1398, 2012.
S. K. Ng , W. K. G. Seah , “Game-theoretic approach for improving cooperation in wireless
multihop networks,” IEEE Transactions on Systems, Man, and Cybernetics, Part B, 40(3),
559–574.
B. Niu , H. V. Zhao , J. Hai , “A cooperation stimulation strategy in wireless multicast networks,”
IEEE Transactions on Signal Processing, vol. 59, no. 5, pp. 2355–2369, 2011.
L. Huang , L. Li , L. Liu , H. Zhang , L. Tang , “Stimulating cooperation in route discovery of ad
hoc networks,” in Proceedings of the 3rd ACM Workshop on QoS and Security for Wireless and
Mobile Networks (Q2SWinet'07). Chania, Crete Island, Greece; 22–26 October 2007, pp.
39–46, 2007.
A. L. Toledo , X. Wang , “Robust detection of selfish misbehavior in wireless networks,” IEEE
Journal on Selected Areas in Communications, vol. 25, no. 6, pp. 1124–1134, 2007.
D. Djenouri and N. Badache , “Ad hoc networks on eliminating packet droppers in MANET: A
modular solution,” Ad Hoc Networks, vol. 7, no. 6, pp. 1243–1258, 2009.
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Springer Publication-New-York (USA), ISSN print 0929-6212
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publication-London (England (UK), ISSN: 0177-0667 (print version).
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grid environment”, in: World Congress on Information and Communication Technology (WICT),
Mumbai, pp. 77–82, Dec. 2011. IEEE proceedings paper, ISBN -978-1-4673-0127-5.
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using variable threshold value” in International Conference IEEE-ISPC, pp. 1–6, Solan, India,
26–28 Sept. 2013. Paper Presented & Published, ISBN- 978-1-4673-6188-0.
N. Rathore , I. Chana , “Job migration with fault tolerance and QoS scheduling using hash table
functionality in social grid computing”, Journal of Intelligent & Fuzzy Systems (Q-3), vol. 27, no.
6, pp. 2821–2833, June 2014. IOS Press publication-Netherland, ISSN print 1064-1246, IF-
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N. Rathore , I. Chana , “Job migration policies for grid environment”, Wireless Personal
Communication (Q-3), vol. 89, no. 1, pp. 241–269, July 2016. Springer Publication-New-York
(USA), ISSN print 0929-6212, IF- 2.313.
N.K. Rathore , I. Chana , “Report on hierarchal load balancing technique in grid environment”,
Journal on Information Technology (JIT), vol. 2, no. 4, pp. 21–35, Sept.–Nov. 2013. ISSN Print:
2277-5110, ISSN Online: 2277-5250, IF= 2.235.
N.K. Rathore , I. Chana , “Checkpointing algorithm in Alchemi.NET”, in: Annual Conference of
Vijnana Parishad of India and National Symposium Recent Development in Applied Math-
ematics & Information Technology, JUET, Guna, MP, Dec. 2009. Abstract Published.
N. Rathore , “Performance of hybrid load balancing algorithm in distributed web server system”,
Wireless Personal Communication (Q-3), vol. 101, no. 3, pp. 1233–1246, 2018. Springer
Publication-New-York (USA), ISSN print 0929-6212, IF -2.313.
N. Rathore , “Dynamic threshold based load balancing algorithms”, Wireless Personal
Communication (Q-3), vol. 91, no. 1, pp. 151–185, Nov 2016. Springer Publication-New-York
(USA), ISSN print 0929–6212, ISSN online 1572-834X, IF -2.313.
N.K. Rathore , “Ethical hacking & security against cyber crime”, Journal on Information
Technology (JIT), vol. 5, no. 1, pp. 7–11, 2016. December 2015–February 2016. ISSN Print:
2277-5110, ISSN Online: 2277-5250, IF= 2.235.
N.K. Rathore , I. Chana , “Comparative analysis of checkpointing”, IT Enabled Practices and
Emerging Management Paradigm, pp. 321–327, 2008.
N.K. Rathore , “Efficient agent-based priority scheduling and load balancing using fuzzy logic in
grid computing”, Journal on Computer Science (JCOM), vol. 3, no. 3, pp. 11–22, Sept.–Nov.
2015. ISSN Print: 2347-2227, ISSN online: 2347-6141, IF= 0.750.
N.K. Rathore , “Map reduce architecture for grid”, Journal on Software Engineering (JSE), vol.
10, no. 1, pp. 21–30, July–Sept. 2015. ISSN Print: 0973-5151, ISSN Online: 2230-7168, IF=
3.765.
N.K. Rathore , “Faults in grid”, International Journal of Software and Computer Science
Engineering, ManTech Publication, vol. 1, no. 1, pp. 1–19, 2016.
N.K. Rathore , A. Sharma , Efficient Dynamic Distributed Load Balancing Technique: A Smart
Tool & Technology to Balance the Load Among the Network, LAP LAMBERT Academic
Publishing, 19 Oct. 2015. Project ID: 127478, ISBN no-978-3-659-78288-6.
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Young Scientist Congress, vol. 55, Solan, India, 2014.
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Conference on Education & Research (ConFR10), Third CSI National conference, Jaypee
University of Engg. & Tech., Guna, 2010.
N.K. Rathore , “Efficient load balancing algorithm in grid”, 30th MP Young Scientist Congress,
vol. 56, Bhopal, MP, 2015.
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Conference of Gwalior Academy of Mathematical Science, Jaypee University of Engg. & Tech.,
Guna, 2012.
R.I. Doewes , A.A.A. Ahmed , A. Bhagat , R. Nair , P.K. Donepudi , S. Goon , V. Jain , N.K.
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Patent Application No: 2021101792, Australian Official Journal of Patents, vol. 35, no. 17, 2021.
N.K. Rathore , R. Chohan , An Enhancement of Gridsim Architecture with Load Balancing.
Scholar's Press, 23 Oct. 2016. ISBN: 978-3-639-76989-0, Project id: 4900.
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N.K. Rathore , P. Singh , An Efficient Load Balancing Algorithm in Distributed Networks.
Lambert Academic Publication House (LBA), 2016.
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i`manager Journal of Computer Science (JCS), vol. 5, no. 2, pp. 23–33, 2017.
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(JCC), vol. 2, no. 4, pp. 29–40, 2015.
N.K. Rathore , An Efficient Dynamic & Decentralized Load Balancing Technique for Grid.
Scholars' Press, 2018. Project id: 6621.
N. Jain , N. Rathore , A. Mishra , “An efficient image forgery detection using improved relevance
vector machine”, Interciencia Journal, vol. 42, no. 11, pp. 95–120, 2017.
N.K. Rathore , H. Singh , “Analysis of grid simulators architecture”, Journal on Mobile
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N.K. Rathore , I. Chana , PIMR ThirdNational IT conference, IT Enabled Practices and
Emerging Management Paradigm book and category is Communication Technologies and
Security Issues, pp. 32–35, Topic No/Name-46, Prestige Management And Research, Indore,
2008.
F. Khan , N.K. Rathore , “Internet of things: A review article”, i-manager's Journal on Cloud
Computing, vol. 5, no. 1, pp. 20–25, 2018.
N. Jain , N.K. Rathore , A. Mishra , “An efficient image forgery detection using biorthogonal
wavelet transform and singular value decomposition”, in: 5th International Conference on
Advance Research Applied Science, Environment, Agriculture & Entrepreneurship Development
(ARABSEED), Bhopal organized & sponsored by Jan Parishad, JMBVSS & International
Council of people at Bhopal, pp. 274–281, held on 04-06 December 2017, 2017. ISBN No-978-
93-5267-869-3
N.K. Rathore , N.K. Jain , P.K. Shukla , U.S. Rawat , R. Dubey , “Image forgery detection using
singular value decomposition with some attacks”, National Academy Science Letters, vol. 44,
no. 4, pp. 331–338, 2021.
D. Pandey , U. Rawat , N.K. Rathore , K. Pandey , P.K. Shukla , “Distributed biomedical
scheme for controlled recovery of medical encrypted images'', IRBM, 2020. Innovation
andResearch in BioMedical Engineering (Q-3), Elsevier IF=1.09, ISSN no- 1959-0318, Issue-
43, pp. 151160, May 2022. https://doi.org/10.1016/j.irbm.2020.07.003
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Technology, vol. 8, no. 1, pp. 32–38, 2010.
N.K. Rathore , “Rupendra tandekar and Alok Gour \Hotel Management" in Scholar's Press,
Mauritius, Project id: 12332, ISBN: 978-613-8-95576-4, 2021
N. Rathore , D. Pandey , R.I. Doewes , A. Bhatt , “A novel security technique based on
controlled pixel based encryption of image blocks for sharing a secret image”, Wireless
Personal Communication (Q-3), pp. 191207, 18-June 2021, DOI: 10.1007/s11277-021-08630-
w, ISSN print 0929-6212, IF-2.313.
N.K. Rathore , V. Jaiswal , V. Sharma , S. Varma , A Hybrid Methodology for Flower Images
Segmentation & Recognition with extended Deep-Convolution Neural Network. CNN, 2021.
P. Laxkar , N.K. Rathore , “Load balancing algorithm in distributed network”, Solid State
Technology, vol. 63, no. 2s, pp. 6633–6645, 2020. ISSN: 0038–111X, SCOPUS Indexed,
IF=0.05.
N.K. Rathore , Load balancing algorithm in distributed network”, in: Janparishad 6th
International Conference on Science & Environmental, Sustainability for a peaceful Society
(SESPS-2018)in association with international cities of Peace (USA), SusTranCon (USA),
International Council of people(India), Global Network for Sustainable Development, Center for
Global Nonkilling (USA) and JMBVSSat Bhopal (M.P.) India, Conference ID-SESPS 2018:06,
ISBN No-978-93-5321-737-2, pp. 02–03, held on 19-21 January 2019
N.K. Rathore , J. Rathore , “Efficient checkpoint algorithm for distributed system”, International
Journal of Engineering and Computer Science (IJECS), vol. 1, no. 2, pp. 59–66, 2019. E-ISSN:
2663-3590, P-ISSN: 2663-3582.
N.K. Rathore , An Efficient Load Balancing Technique for Grid. Scholar's Press, Mauritius,
2018. ISBN: 978-3-330-65134-0, Project id: 6621.
N.K. Rathore , F. Khan , “Survey of IoT”, International Journal of Computer Science and Soft
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