De / Mukherjee / Kumar Das Nature Inspired Computing for Wireless Sensor Networks

1. Auflage 2020
ISBN: 978-981-15-2125-6
Verlag: Springer Nature Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 346 Seiten

Reihe: Springer Tracts in Nature-Inspired Computing

This book presents nature inspired computing applications for the wireless sensor network (WSN). Although the use of WSN is increasing rapidly, it has a number of limitations in the context of battery issue, distraction, low communication speed, and security. This means there is a need for innovative intelligent algorithms to address these issues.The book is divided into three sections and also includes an introductory chapter providing an overview of WSN and its various applications and algorithms as well as the associated challenges. Section 1 describes bio-inspired optimization algorithms, such as genetic algorithms (GA), artificial neural networks (ANN) and artificial immune systems (AIS) in the contexts of fault analysis and diagnosis, and traffic management. Section 2 highlights swarm optimization techniques, such as African buffalo optimization (ABO), particle swarm optimization (PSO), and modified swarm intelligence technique for solving the problems of routing, network parameters optimization, and energy estimation. Lastly, Section 3 explores multi-objective optimization techniques using GA, PSO, ANN, teaching-learning-based optimization (TLBO), and combinations of the algorithms presented. As such, the book provides efficient and optimal solutions for WSN problems based on nature-inspired algorithms.

Debashis De earned his M.Tech. from the University of Calcutta in 2002 and his Ph.D. (Engineering) from Jadavpur University in 2005. He is the Professor and Director in the Department of Computer Science and Engineering of the West Bengal University of Technology, India, and Adjunct Research Fellow at the University of Western Australia, Australia. He is a senior member of the IEEE, a life member of CSI, and a member of the International Union of Radio Science. He worked as R&D engineer for Telektronics and programmer at Cognizant Technology Solutions. He was awarded the prestigious Boyscast Fellowship by the Department of Science and Technology, Government of India, to work at the Heriot-Watt University, Scotland, UK. He received the Endeavour Fellowship Award during 2008-2009 by DEST Australia to work at the University of Western Australia. He received the Young Scientist Award both in 2005 at New Delhi and in 2011 at Istanbul, Turkey, from the International Union of Radio Science, Head Quarter, Belgium. His research interests include wireless sensor network, mobile cloud computing, green mobile networks, and nanodevice designing for mobile applications. He has published in more than 200 peer-reviewed international journals in IEEE, IET, Elsevier, Springer, World Scientific, Wiley, IETE, Taylor Francis and ASP, seventy international conference papers, and four researches monographs in Springer, CRC, NOVA, and ten textbooks published by Pearson education. Amartya Mukherjee is an Assistant Professor at Institute of Engineering & Management, Salt Lake, Kolkata, India. He holds M.Tech. in computer science and engineering from the National Institute of Technology, Durgapur, India. His primary research interest includes embedded application development, robotics, unmanned aircraft systems, Internet of things, intelligent sensor networks, and ad-hoc networks. He has various publications in the fields of robotics, embedded systems, and IoT in IEEE, Springer, World Scientific, CRC Press, IGI Global. His book 'Embedded Systems and Robotics with Open Source Tools' is one of the bestselling books in CRC Press (Taylor & Francis Group). Santosh Kumar Das received his Ph.D. degree in computer science and engineering from Indian Institute of Technology (ISM), Dhanbad, India, in 2018 and completed his M.Tech. degree in computer science and engineering from Maulana Abul Kalam Azad University of Technology (erstwhile WBUT), West Bengal, India, in 2013. He is currently working as an Assistant Professor at School of Computer Science and Engineering, National Institute of Science and Technology (Autonomous), Institute Park, Pallur Hills, Berhampur, Odisha, India, 761008. He is having more than eight years of teaching experience. He has authored/edited one book in Springer, and published more than 27 research articles. His research interests mainly focus on ad-hoc and sensor network, artificial intelligence, soft computing, and mathematical modelling. Nilanjan Dey is an Assistant Professor in the Department of Information Technology at Techno India College of Technology, Kolkata. He has completed his Ph.D. in 2015 from Jadavpur University. He is a Visiting Fellow of Wearables Computing Laboratory, Department of Biomedical Engineering University of Reading, UK, the Visiting Professor of College of Information and Engineering, Wenzhou Medical University, P.R. China, and Duy Tan University, Vietnam. He has held honorary position of Visiting Scientist at Global Biomedical Technologies Inc., CA, USA (2012-2015). He is the Editor-in-Chief of International Journal of Ambient Computing and Intelligence, IGI Global, the Series Co-Editor of Springer Tracts in Nature-Inspired Computing, Springer, Advances in Ubiquitous Sensing Applications for Healthcare (AUSAH), Elsevier, and the Series Editor of Intelligent Signal Processing and Data Analysis, CRC Press. He has authored/edited more than 40 books with Elsevier, Wiley, CRC, and Springer, and published more than 350 research articles. His main research interests include medical imaging, machine learning, bio-inspired computing, and data mining. He is a life member of Institute of Engineers (India). He is the Indian ambassador of International Federation for Information Processing (IFIP) - InterYIT (International Young ICT Professionals group).

Autoren/Hrsg.

De, Debashis

Mukherjee, Amartya

Kumar Das, Santosh

Dey, Nilanjan

Inhaltsverzeichnis

1;Preface;6
1.1;Objective of the Book;6
1.2;Organization of the Book;6
1.3;Part I: Bio-inspired Optimization (Chaps. 2–6);7
1.4;Part II: Swarm Optimization (Chaps. 7–9);8
1.5;Part III: Multi-objective Optimization (Chaps. 10–14);8
2;List of Reviewers;10
3;Contents;12
4;About the Editors;14
5;Bio-inspired Optimization;34
6;2 A GA-Based Fault-Aware Routing Algorithm for Wireless Sensor Networks;35
6.1;1 Introduction;35
6.2;2 Related Work;38
6.3;3 System Model and Terminologies;39
6.4;4 Proposed Algorithm;41
6.4.1;4.1 Information Sharing Phase;42
6.4.2;4.2 Network Setup Phase;42
6.4.3;4.3 Steady Phase;48
6.5;5 Simulation Results;48
6.5.1;5.1 Simulation Setup;48
6.5.2;5.2 Evaluation of Experimental Results;48
6.6;6 Conclusion;50
6.7;References;51
7;3 GA-Based Fault Diagnosis Technique for Enhancing Network Lifetime of Wireless Sensor Network;53
7.1;1 Introduction;53
7.1.1;1.1 Issues;53
7.1.2;1.2 Challenges;54
7.2;2 Fault, Errors and Failures;55
7.2.1;2.1 Types of Fault;56
7.2.2;2.2 Fault Diagnosis;56
7.3;3 Related Work;57
7.4;4 Proposed Method;58
7.5;5 Performance Evaluation;63
7.6;6 Conclusion;67
7.7;References;68
8;4 A GA-Based Intelligent Traffic Management Technique for Wireless Body Area Sensor Networks;71
8.1;1 Introduction;71
8.2;2 Literature Review;72
8.3;3 Proposed Method;75
8.4;4 Performance Evaluation;85
8.5;5 Conclusion;86
8.6;References;87
9;5 Fault Diagnosis in Wireless Sensor Networks Using a Neural Network Constructed by Deep Learning Technique;90
9.1;1 Introduction;90
9.2;2 Related Work;92
9.2.1;2.1 Statistical Test-Based Intermittent Fault Diagnosis;92
9.2.2;2.2 Soft Computing and Neural Network Approach for Fault Diagnosis;93
9.3;3 System Model;95
9.3.1;3.1 Assumptions;95
9.3.2;3.2 Sensor Network Model;96
9.3.3;3.3 Fault Model;96
9.3.4;3.4 Modelling of Sensor Data;97
9.4;4 Problem Formulation;98
9.5;5 Feature Selection;99
9.5.1;5.1 Mean;99
9.5.2;5.2 Standard Deviation (SD);100
9.5.3;5.3 Skewness and Kurtosis;100
9.5.4;5.4 Mean Absolute Deviation (MAD);101
9.5.5;5.5 Extracting the Features From Sensor Data—An Example;101
9.6;6 Neural Network with Deep Learning Algorithms For Intermittent Fault Detection of Sensor Nodes;103
9.6.1;6.1 Basic Neural Network Design;104
9.7;7 Results and Discussions;107
9.8;8 Conclusion;111
9.9;References;111
10;6 Immune Inspired Fault Diagnosis in Wireless Sensor Network;115
10.1;1 Introduction;115
10.1.1;1.1 Motivation;117
10.1.2;1.2 Contribution;118
10.2;2 Biological Immune System: An Overview;118
10.3;3 AIS Approaches for Fault Diagnosis in WSN;122
10.4;4 Applications of AIS Algorithms;123
10.5;5 Conclusion;126
10.6;References;126
11;Swarm Optimization;129
12;7 Intelligent Routing in Wireless Sensor Network Based on African Buffalo Optimization;130
12.1;1 Introduction;130
12.2;2 Related Work;132
12.3;3 Preliminary: African Buffalo Optimization;135
12.3.1;3.1 The Component View of the ABO;136
12.3.2;3.2 African Buffalo Optimization: The Algorithm;138
12.3.3;3.3 Merits of ABO Algorithm;138
12.3.4;3.4 Application of ABO Algorithm;139
12.4;4 Proposed Method;140
12.4.1;4.1 Problem Formulation;142
12.5;5 Performance Evaluation;144
12.5.1;5.1 Variation of Iterations;146
12.5.2;5.2 Unique Variation of Iterations;150
12.6;6 Conclusion;150
12.7;References;151
13;8 On the Development of Energy-Efficient Distributed Source Localization Algorithm in Wireless Sensor Networks Using Modified Swarm Intelligence;154
13.1;1 Introduction;154
13.2;2 Related Works;156
13.3;3 Maximum-Likelihood DOA Estimation of Narrow-Band Far-Field Signal;157
13.3.1;3.1 Formulation of ML-DOA Estimation Problem;158
13.4;4 Distributed DOA Estimation;159
13.4.1;4.1 Local Cost Function for DOA Estimation;160
13.4.2;4.2 Distributed DOA Estimation Using Local ML Functions;161
13.5;5 Diffusion Particle Swarm Optimization (DPSO);164
13.6;6 Diffusion PSO Algorithm for ML-DOA Estimation in Sensor Network;166
13.6.1;6.1 Performance Measure;167
13.6.2;6.2 Example;169
13.7;7 Clustering-Based Distributed DOA Estimation in Wireless Sensor Networks;174
13.7.1;7.1 Clustering-Based Distributed DOA Estimation;175
13.8;8 Conclusion;180
13.9;9 Future Direction;181
13.10;References;182
14;9 Quasi-oppositional Harmony Search Algorithm Approach for Ad Hoc and Sensor Networks;185
14.1;1 Introduction;185
14.2;2 Need of Optimization;187
14.2.1;2.1 Basic HSA;187
14.2.2;2.2 Improved HSA;190
14.2.3;2.3 Opposition-Based Learning;190
14.2.4;2.4 Quasi-Opposition-Based Learning: A Concept;192
14.3;3 Optimization Techniques Applied in WSN;199
14.4;4 Performance Evaluation;201
14.5;5 Conclusion;201
14.6;Appendix;203
14.6.1;Parameters of QOHS;203
14.7;References;203
15;Multi-objective Optimization;205
16;10 A Comprehensive Survey of Intelligent-Based Hierarchical Routing Protocols for Wireless Sensor Networks;206
16.1;1 Introduction;206
16.2;2 Taxonomy Metrics;209
16.2.1;2.1 WSN Types;209
16.2.2;2.2 Node Deployment;211
16.2.3;2.3 Control Manner;211
16.2.4;2.4 Network Architecture;212
16.2.5;2.5 Clustering Attributes;212
16.2.6;2.6 Protocol Operation;213
16.2.7;2.7 Path Establishment;213
16.2.8;2.8 Communication Paradigm;214
16.2.9;2.9 Radio Model;214
16.2.10;2.10 Protocol Objectives;215
16.2.11;2.11 Applications;215
16.3;3 Intelligent-Based Hierarchical Routing Protocols;216
16.3.1;3.1 Particle Swarm Optimization-Based Hierarchical Routing Protocols;216
16.3.2;3.2 Genetic Algorithm-Based Hierarchical Routing Protocols;236
16.3.3;3.3 Fuzzy Logic-Based Hierarchical Routing Protocols;242
16.3.4;3.4 Ant Colony Optimization-Based Hierarchical Routing Protocols;245
16.3.5;3.5 Artificial Immune Algorithm-Based Hierarchical Routing Protocols;247
16.4;4 Comparison and Discussion;250
16.5;5 Conclusion and Future Directions;262
16.6;References;263
17;11 Qualitative Survey on Sensor Node Deployment, Load Balancing and Energy Utilization in Sensor Network;267
17.1;1 Introduction;267
17.2;2 Overview of Sensor Node Deployment;268
17.2.1;2.1 IPP Based Approach for Ensuring Coverage;269
17.2.2;2.2 PSO Based Node Deployment;271
17.2.3;2.3 ACO in Node Deployment and Load Balancing;276
17.2.4;2.4 Honey Bee Optimization in Sensor Deployment;278
17.3;3 Load Balancing in Sensor Network;280
17.4;4 Conclusion;283
17.5;References;284
18;12 Bio-inspired Algorithm for Multi-objective Optimization in Wireless Sensor Network;286
18.1;1 Introduction;286
18.2;2 Review of Bio-Inspired Algorithms;291
18.2.1;2.1 Ant Colony Optimization (ACO);291
18.2.2;2.2 Artificial Bee Colony (ABC);291
18.2.3;2.3 Bat Algorithm (BA);292
18.2.4;2.4 Biogeography-Based Optimization (BBO);292
18.2.5;2.5 Cat Swarm Optimization (CSO);293
18.2.6;2.6 Cuckoo Search Algorithm;293
18.2.7;2.7 Chicken Swarm Optimization Algorithm (CSOA);293
18.2.8;2.8 Elephant Herding Optimization (EHO);293
18.2.9;2.9 Fish Swarm Optimization Algorithm (FSOA);294
18.2.10;2.10 Grey Wolf Optimization (GWO);294
18.2.11;2.11 Glowworm Swarm Optimization (GSO);294
18.2.12;2.12 Moth Flame Optimization (MFO) Algorithm;295
18.2.13;2.13 Particle Swarm Optimization (PSO);295
18.2.14;2.14 Whale Optimization Algorithm (WOA);296
18.3;3 Domains of Applications;296
18.4;4 Application of Bio-Inspired Algorithms in Different Areas of Wireless Sensor Network;298
18.5;5 Challenges and Key Issues of Bio-Inspired Computing;301
18.6;6 Bio-Inspired Computation and Its Future;301
18.7;7 Conclusion;303
18.8;References;303
19;13 TLBO Based Cluster-Head Selection for Multi-objective Optimization in Wireless Sensor Networks;309
19.1;1 Introduction;309
19.2;2 Literature Review;311
19.3;3 Preliminary: Teaching-Learning-Based Optimization (TLBO);315
19.3.1;3.1 Teacher Phase;315
19.3.2;3.2 Learner Phase;315
19.4;4 Proposed Method;316
19.4.1;4.1 Network Model;316
19.4.2;4.2 Parameter Formulation;317
19.4.3;4.3 TLBO Formulation;317
19.5;5 Conclusion;322
19.6;References;322
20;14 Nature-Inspired Algorithms for Reliable, Low-Latency Communication in Wireless Sensor Networks for Pervasive Healthcare Applications;326
20.1;1 Introduction;326
20.2;2 Literature Survey;328
20.3;3 Wireless Sensor Network Architecture;330
20.4;4 Routing Protocols for WSN in Healthcare;332
20.4.1;4.1 Deadline Classification;333
20.4.2;4.2 Architecture Design Objectives;333
20.5;5 Nature-Inspired Routing Protocols;334
20.5.1;5.1 Particle Swarm Optimization;335
20.5.2;5.2 Ant Colony Optimization;337
20.5.3;5.3 Artificial Immune System;339
20.5.4;5.4 Plant Biology-Inspired Framework for WSN;340
20.6;6 Conclusions;342
20.7;References;344