Nature Inspired Computing for Wireless Sensor Networks (Springer Tracts in Nature-Inspired Computing)

✍ Scribed by Debashis De (editor), Amartya Mukherjee (editor), Santosh Kumar Das (editor), Nilanjan Dey (editor)

• Publisher: Springer
• Year: 2020
• Tongue: English
• Leaves: 346
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Preface
Objective of the Book
Organization of the Book
Part I: Bio-inspired Optimization (Chaps. 2–6)
Part II: Swarm Optimization (Chaps. 7–9)
Part III: Multi-objective Optimization (Chaps. 10–14)
List of Reviewers
Contents
About the Editors
Bio-inspired Optimization
2 A GA-Based Fault-Aware Routing Algorithm for Wireless Sensor Networks
1 Introduction
2 Related Work
3 System Model and Terminologies
4 Proposed Algorithm
4.1 Information Sharing Phase
4.2 Network Setup Phase
4.3 Steady Phase
5 Simulation Results
5.1 Simulation Setup
5.2 Evaluation of Experimental Results
6 Conclusion
References
3 GA-Based Fault Diagnosis Technique for Enhancing Network Lifetime of Wireless Sensor Network
1 Introduction
1.1 Issues
1.2 Challenges
2 Fault, Errors and Failures
2.1 Types of Fault
2.2 Fault Diagnosis
3 Related Work
4 Proposed Method
5 Performance Evaluation
6 Conclusion
References
4 A GA-Based Intelligent Traffic Management Technique for Wireless Body Area Sensor Networks
1 Introduction
2 Literature Review
3 Proposed Method
4 Performance Evaluation
5 Conclusion
References
5 Fault Diagnosis in Wireless Sensor Networks Using a Neural Network Constructed by Deep Learning Technique
1 Introduction
2 Related Work
2.1 Statistical Test-Based Intermittent Fault Diagnosis
2.2 Soft Computing and Neural Network Approach for Fault Diagnosis
3 System Model
3.1 Assumptions
3.2 Sensor Network Model
3.3 Fault Model
3.4 Modelling of Sensor Data
4 Problem Formulation
5 Feature Selection
5.1 Mean
5.2 Standard Deviation (SD)
5.3 Skewness and Kurtosis
5.4 Mean Absolute Deviation (MAD)
5.5 Extracting the Features From Sensor Data—An Example
6 Neural Network with Deep Learning Algorithms For Intermittent Fault Detection of Sensor Nodes
6.1 Basic Neural Network Design
7 Results and Discussions
8 Conclusion
References
6 Immune Inspired Fault Diagnosis in Wireless Sensor Network
1 Introduction
1.1 Motivation
1.2 Contribution
2 Biological Immune System: An Overview
3 AIS Approaches for Fault Diagnosis in WSN
4 Applications of AIS Algorithms
5 Conclusion
References
Swarm Optimization
7 Intelligent Routing in Wireless Sensor Network Based on African Buffalo Optimization
1 Introduction
2 Related Work
3 Preliminary: African Buffalo Optimization
3.1 The Component View of the ABO
3.2 African Buffalo Optimization: The Algorithm
3.3 Merits of ABO Algorithm
3.4 Application of ABO Algorithm
4 Proposed Method
4.1 Problem Formulation
5 Performance Evaluation
5.1 Variation of Iterations
5.2 Unique Variation of Iterations
6 Conclusion
References
8 On the Development of Energy-Efficient Distributed Source Localization Algorithm in Wireless Sensor Networks Using Modified Swarm Intelligence
1 Introduction
2 Related Works
3 Maximum-Likelihood DOA Estimation of Narrow-Band Far-Field Signal
3.1 Formulation of ML-DOA Estimation Problem
4 Distributed DOA Estimation
4.1 Local Cost Function for DOA Estimation
4.2 Distributed DOA Estimation Using Local ML Functions
5 Diffusion Particle Swarm Optimization (DPSO)
6 Diffusion PSO Algorithm for ML-DOA Estimation in Sensor Network
6.1 Performance Measure
6.2 Example
7 Clustering-Based Distributed DOA Estimation in Wireless Sensor Networks
7.1 Clustering-Based Distributed DOA Estimation
8 Conclusion
9 Future Direction
References
9 Quasi-oppositional Harmony Search Algorithm Approach for Ad Hoc and Sensor Networks
1 Introduction
2 Need of Optimization
2.1 Basic HSA
2.2 Improved HSA
2.3 Opposition-Based Learning
2.4 Quasi-Opposition-Based Learning: A Concept
3 Optimization Techniques Applied in WSN
4 Performance Evaluation
5 Conclusion
Appendix
Parameters of QOHS
References
Multi-objective Optimization
10 A Comprehensive Survey of Intelligent-Based Hierarchical Routing Protocols for Wireless Sensor Networks
1 Introduction
2 Taxonomy Metrics
2.1 WSN Types
2.2 Node Deployment
2.3 Control Manner
2.4 Network Architecture
2.5 Clustering Attributes
2.6 Protocol Operation
2.7 Path Establishment
2.8 Communication Paradigm
2.9 Radio Model
2.10 Protocol Objectives
2.11 Applications
3 Intelligent-Based Hierarchical Routing Protocols
3.1 Particle Swarm Optimization-Based Hierarchical Routing Protocols
3.2 Genetic Algorithm-Based Hierarchical Routing Protocols
3.3 Fuzzy Logic-Based Hierarchical Routing Protocols
3.4 Ant Colony Optimization-Based Hierarchical Routing Protocols
3.5 Artificial Immune Algorithm-Based Hierarchical Routing Protocols
4 Comparison and Discussion
5 Conclusion and Future Directions
References
11 Qualitative Survey on Sensor Node Deployment, Load Balancing and Energy Utilization in Sensor Network
1 Introduction
2 Overview of Sensor Node Deployment
2.1 IPP Based Approach for Ensuring Coverage
2.2 PSO Based Node Deployment
2.3 ACO in Node Deployment and Load Balancing
2.4 Honey Bee Optimization in Sensor Deployment
3 Load Balancing in Sensor Network
4 Conclusion
References
12 Bio-inspired Algorithm for Multi-objective Optimization in Wireless Sensor Network
1 Introduction
2 Review of Bio-Inspired Algorithms
2.1 Ant Colony Optimization (ACO)
2.2 Artificial Bee Colony (ABC)
2.3 Bat Algorithm (BA)
2.4 Biogeography-Based Optimization (BBO)
2.5 Cat Swarm Optimization (CSO)
2.6 Cuckoo Search Algorithm
2.7 Chicken Swarm Optimization Algorithm (CSOA)
2.8 Elephant Herding Optimization (EHO)
2.9 Fish Swarm Optimization Algorithm (FSOA)
2.10 Grey Wolf Optimization (GWO)
2.11 Glowworm Swarm Optimization (GSO)
2.12 Moth Flame Optimization (MFO) Algorithm
2.13 Particle Swarm Optimization (PSO)
2.14 Whale Optimization Algorithm (WOA)
3 Domains of Applications
4 Application of Bio-Inspired Algorithms in Different Areas of Wireless Sensor Network
5 Challenges and Key Issues of Bio-Inspired Computing
6 Bio-Inspired Computation and Its Future
7 Conclusion
References
13 TLBO Based Cluster-Head Selection for Multi-objective Optimization in Wireless Sensor Networks
1 Introduction
2 Literature Review
3 Preliminary: Teaching-Learning-Based Optimization (TLBO)
3.1 Teacher Phase
3.2 Learner Phase
4 Proposed Method
4.1 Network Model
4.2 Parameter Formulation
4.3 TLBO Formulation
5 Conclusion
References
14 Nature-Inspired Algorithms for Reliable, Low-Latency Communication in Wireless Sensor Networks for Pervasive Healthcare Applications
1 Introduction
2 Literature Survey
3 Wireless Sensor Network Architecture
4 Routing Protocols for WSN in Healthcare
4.1 Deadline Classification
4.2 Architecture Design Objectives
5 Nature-Inspired Routing Protocols
5.1 Particle Swarm Optimization
5.2 Ant Colony Optimization
5.3 Artificial Immune System
5.4 Plant Biology-Inspired Framework for WSN
6 Conclusions
References