Wireless sensor networks : evolutionary algorithms for optimizing performance /
Damodar Reddy Edla, Mahesh Chowdary Kongara, Amruta Lipare, Venkatanareshbabu Kuppili, Kannadasan K.
- First edition.
- 1 online resource (xii, 134 pages) : illustrations (black and white)
"A Chapman & Hall book."
Introduction. Literature Survey. Load Balancing of Gateways using Shuffed Complex Evolution Algorithm. Novel Fitness Function for SCE Algorithm based Energy Efficiency in WSN. An Efficient Load Balancing of Gateways using Improved SFLA for WSNs. SCE-PSO based Clustering Technique for Load Balancing in WSN. PSO based Routing with Novel Fitness Function for Improving Lifetime of WSN. M-Curves Path Planning for Mobile Anchor Node and Localization of Sensor Nodes using DSA. Conclusion and Future Research. Bibliography. Index.
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 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.