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Nature Inspired Computing for Wireless Sensor Networks

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Cover of 'Nature Inspired Computing for Wireless Sensor Networks'

Table of Contents

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    Book Overview
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    Chapter 1 Wireless Sensor Network: Applications, Challenges, and Algorithms
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    Chapter 2 A GA-Based Fault-Aware Routing Algorithm for Wireless Sensor Networks
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    Chapter 3 GA-Based Fault Diagnosis Technique for Enhancing Network Lifetime of Wireless Sensor Network
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    Chapter 4 A GA-Based Intelligent Traffic Management Technique for Wireless Body Area Sensor Networks
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    Chapter 5 Fault Diagnosis in Wireless Sensor Networks Using a Neural Network Constructed by Deep Learning Technique
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    Chapter 6 Immune Inspired Fault Diagnosis in Wireless Sensor Network
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    Chapter 7 Intelligent Routing in Wireless Sensor Network Based on African Buffalo Optimization
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    Chapter 8 On the Development of Energy-Efficient Distributed Source Localization Algorithm in Wireless Sensor Networks Using Modified Swarm Intelligence
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    Chapter 9 Quasi-oppositional Harmony Search Algorithm Approach for Ad Hoc and Sensor Networks
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    Chapter 10 A Comprehensive Survey of Intelligent-Based Hierarchical Routing Protocols for Wireless Sensor Networks
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    Chapter 11 Qualitative Survey on Sensor Node Deployment, Load Balancing and Energy Utilization in Sensor Network
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    Chapter 12 Bio-inspired Algorithm for Multi-objective Optimization in Wireless Sensor Network
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    Chapter 13 TLBO Based Cluster-Head Selection for Multi-objective Optimization in Wireless Sensor Networks
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    Chapter 14 Nature-Inspired Algorithms for Reliable, Low-Latency Communication in Wireless Sensor Networks for Pervasive Healthcare Applications
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Title
Nature Inspired Computing for Wireless Sensor Networks
Published by
Springer Singapore, April 2020
DOI 10.1007/978-981-15-2125-6
ISBNs
978-9-81-152124-9, 978-9-81-152125-6
Editors

De, Debashis, Mukherjee, Amartya, Kumar Das, Santosh, Dey, Nilanjan

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