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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
Attention for Chapter 12: Bio-inspired Algorithm for Multi-objective Optimization in Wireless Sensor Network
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Chapter title
Bio-inspired Algorithm for Multi-objective Optimization in Wireless Sensor Network
Chapter number 12
Book title
Nature Inspired Computing for Wireless Sensor Networks
Published by
Springer, Singapore, January 2020
DOI 10.1007/978-981-15-2125-6_12
Book ISBNs
978-9-81-152124-9, 978-9-81-152125-6
Authors

Anindita Raychaudhuri, Debashis De, Raychaudhuri, Anindita, De, Debashis

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 22%
Unspecified 1 11%
Lecturer 1 11%
Other 1 11%
Student > Master 1 11%
Other 1 11%
Unknown 2 22%
Readers by discipline Count As %
Computer Science 4 44%
Engineering 2 22%
Unspecified 1 11%
Unknown 2 22%