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Nature inspired computing for wirele...

De, Debashis.

Nature inspired computing for wireless sensor networks

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Nature inspired computing for wireless sensor networksedited by Debashis De ... [et al.].
其他作者:	De, Debashis.
出版者:	Singapore :Springer Singapore :2020.
面頁冊數:	xii, 341 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Wireless sensor networks.
電子資源:	https://doi.org/10.1007/978-981-15-2125-6
ISBN:	9789811521256$q(electronic bk.)

Nature inspired computing for wireless sensor networks
Nature inspired computing for wireless sensor networks[electronic resource] /edited by Debashis De ... [et al.]. - Singapore :Springer Singapore :2020. - xii, 341 p. :ill., digital ;24 cm. - 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$q(electronic bk.)

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

376151
Wireless sensor networks.

LC Class. No.: TK7872.D48 / N388 2020

Dewey Class. No.: 006.25

Nature inspired computing for wireless sensor networks
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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.
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