Wireless Sensor Networks: Evolutionary Algorithms for Optimizing Performance


Wireless Sensor Networks: Evolutionary Algorithms for Optimizing Performance Hardcover – 24 Nov. 2020
By 作者:Damodar Reddy Edla (Author), Mahesh Chowdary Kongara (Author), Amruta Lipare (Author), Venkatanareshbabu Kuppili (Author)
Hardcover : 152 pages
ISBN-10 : 0367342413
ISBN-13 : 9780367342418
Product Dimensions : 15.6 x 23.39 cm
Publisher Finelybook 出版社 : Chapman and Hall/CRC (24 Nov. 2020)
Language 语言: : English
The Book Description robot was collected from Amazon and arranged by Finelybook
Wireless Sensor Networks: Evolutionary Algorithms for Optimizing Performance provides an integrative overview of bio-inspired algorithms and their applications in the area of Wireless Sensor Networks (WSN). Along with the usage of the WSN, the number of risks and challenges occurs while deploying any WSN. Therefore, to defeat these challenges some of the bio-inspired algorithms are applied and discussed in this book.
Discussion includes a broad, integrated perspective on various challenges and issues in WSN and also impact of bio-inspired algorithms on the lifetime of the WSN. It creates interdisciplinary theory, concepts, definitions, models and findings involved in WSN and Bio-inspired algorithms making it an essential guide and reference. It includes various WSN examples making the book accessible to a broader interdisciplinary readership.
The book offers comprehensive coverage of the most essential topics, including:

Evolutionary algorithms
Swarm intelligence
Hybrid algorithms
Energy efficiency in WSN
Load balancing of gateways
Localization
Clustering and routing
Designing fitness functions according to the issues in WSN.
The book explains about practices of shuffled complex evolution algorithm, shuffled frog leaping algorithm, particle swarm optimization and dolphin swarm optimization to defeat various challenges in WSN. The author elucidates how we must transform our thinking, illuminating the benefits and opportunities offered By 作者:bio-inspired approaches to innovation and learning in the area of WSN. This book serves as a reference book for scientific investigators who shows an interest in evolutionary computation and swarm intelligence as well as issues and challenges in WSN.


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