國立虎尾科技大學 |

Nature Inspired Computing for Wireless Sensor Networks

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Nature Inspired Computing for Wireless Sensor Networks/ edited by Debashis De, Amartya Mukherjee, Santosh Kumar Das, Nilanjan Dey.
other author:	Dey, Nilanjan.
Description:	XIV, 322 p.online resource. :
Contained By:	Springer Nature eBook
Subject:	Computational Intelligence. -
Online resource:	https://doi.org/10.1007/978-981-15-2125-6
ISBN:	9789811521256

Nature Inspired Computing for Wireless Sensor Networks
Nature Inspired Computing for Wireless Sensor Networks[electronic resource] /edited by Debashis De, Amartya Mukherjee, Santosh Kumar Das, Nilanjan Dey. - 1st ed. 2020. - XIV, 322 p.online resource. - Springer Tracts in Nature-Inspired Computing,2524-552X. - Springer Tracts in Nature-Inspired Computing,.

Wireless Sensor Network: Applications, Challenges and Algorithms -- Section 1: Bio-Inspired Optimization -- A GA based Fault-Aware Routing Algorithm for Wireless Sensor Networks -- GA based Fault Diagnosis Technique for Enhancing Network Lifetime of Wireless Sensor Network -- A GA based Intelligent Traffic Management Technique for Wireless Body Area Sensor Networks -- Fault Diagnosis in Wireless Sensor Networks using a Neural Network Constructed by Deep Learning Technique -- Section 2: Swarm Optimization -- Intelligent Routing in Wireless Sensor Network based on African Buffalo Optimization -- Robust Estimation of Feedback System’s Parameter in Wireless Sensor Network using Distributed Particle Swarm Optimization -- On the Development of Energy Efficient Distributed Source Localization Algorithm in Wireless Sensor Networks using Modified Swarm Intelligence -- Swarm Intelligence Approach for Ad-Hoc & Sensor Networks -- Section 3: Multi-Objective Optimization -- A Comparensive Survey of Intelligent-based Hierarchical Routing Protocols for Wireless Sensor Networks -- A Qualitative Survey on Sensor Node Deployment, Load Balancing & Energy Utilization in Sensor Network -- Bio-Inspired Algorithm for Multi-Objective Optimization in Wireless Sensor Network -- TLBO based Multi-objective Optimization System in Wireless Sensor Networks -- Nature Inspired Algorithms for Reliable, Low-Latency Communication in Wireless Sensor Networks for Pervasive Healthcare Applications.

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.

ISBN: 9789811521256

Standard No.: 10.1007/978-981-15-2125-6doiSubjects--Topical Terms:

768837
Computational Intelligence.

LC Class. No.: TK1-9971

Dewey Class. No.: 621.382

Nature Inspired Computing for Wireless Sensor Networks
LDR:04239nam a22004095i 4500 001 1020737
003 DE-He213
005 20200701090339.0
007 cr nn 008mamaa
008 210318s2020 si | s |||| 0|eng d
020 $a 9789811521256 $9 978-981-15-2125-6
024 7 $a 10.1007/978-981-15-2125-6 $2 doi
035 $a 978-981-15-2125-6
050 4 $a TK1-9971
072 7 $a TJK $2 bicssc
072 7 $a TEC041000 $2 bisacsh
072 7 $a TJK $2 thema
082 0 4 $a 621.382 $2 23
245 1 0 $a Nature Inspired Computing for Wireless Sensor Networks $h [electronic resource] / $c edited by Debashis De, Amartya Mukherjee, Santosh Kumar Das, Nilanjan Dey.
250 $a 1st ed. 2020.
264 1 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2020.
300 $a XIV, 322 p. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Springer Tracts in Nature-Inspired Computing, $x 2524-552X
505 0 $a Wireless Sensor Network: Applications, Challenges and Algorithms -- Section 1: Bio-Inspired Optimization -- A GA based Fault-Aware Routing Algorithm for Wireless Sensor Networks -- GA based Fault Diagnosis Technique for Enhancing Network Lifetime of Wireless Sensor Network -- A GA based Intelligent Traffic Management Technique for Wireless Body Area Sensor Networks -- Fault Diagnosis in Wireless Sensor Networks using a Neural Network Constructed by Deep Learning Technique -- Section 2: Swarm Optimization -- Intelligent Routing in Wireless Sensor Network based on African Buffalo Optimization -- Robust Estimation of Feedback System’s Parameter in Wireless Sensor Network using Distributed Particle Swarm Optimization -- On the Development of Energy Efficient Distributed Source Localization Algorithm in Wireless Sensor Networks using Modified Swarm Intelligence -- Swarm Intelligence Approach for Ad-Hoc & Sensor Networks -- Section 3: Multi-Objective Optimization -- A Comparensive Survey of Intelligent-based Hierarchical Routing Protocols for Wireless Sensor Networks -- A Qualitative Survey on Sensor Node Deployment, Load Balancing & Energy Utilization in Sensor Network -- Bio-Inspired Algorithm for Multi-Objective Optimization in Wireless Sensor Network -- TLBO based Multi-objective Optimization System in Wireless Sensor Networks -- Nature Inspired Algorithms for Reliable, Low-Latency Communication in Wireless Sensor Networks for Pervasive Healthcare Applications.
520 $a 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.
650 2 4 $a Computational Intelligence. $3 768837
650 2 4 $a Wireless and Mobile Communication. $3 1207058
650 1 4 $a Communications Engineering, Networks. $3 669809
650 0 $a Computational intelligence. $3 568984
650 0 $a Mobile communication systems. $3 562917
650 0 $a Wireless communication systems. $3 562740
650 0 $a Electrical engineering. $3 596380
700 1 $a Dey, Nilanjan. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1110380
700 1 $a Kumar Das, Santosh. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1316274
700 1 $a Mukherjee, Amartya. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1087004
700 1 $a De, Debashis. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 768694
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9789811521249
776 0 8 $i Printed edition: $z 9789811521263
776 0 8 $i Printed edition: $z 9789811521270
830 0 $a Springer Tracts in Nature-Inspired Computing, $x 2524-552X $3 1313864
856 4 0 $u https://doi.org/10.1007/978-981-15-2125-6
912 $a ZDB-2-ENG
912 $a ZDB-2-SXE
950 $a Engineering (SpringerNature-11647)
950 $a Engineering (R0) (SpringerNature-43712)