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Titlebook: Nature Inspired Computing for Wireless Sensor Networks; Debashis De,Amartya Mukherjee,Nilanjan Dey Book 2020 Springer Nature Singapore Pte

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發(fā)表于 2025-3-21 17:17:06 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Nature Inspired Computing for Wireless Sensor Networks
編輯Debashis De,Amartya Mukherjee,Nilanjan Dey
視頻videohttp://file.papertrans.cn/663/662002/662002.mp4
概述Discusses recent research trends in nature-inspired computing for wireless sensor networks.Presents applications to design, analysis and modeling – key areas in wireless sensors.Explores computational
叢書名稱Springer Tracts in Nature-Inspired Computing
圖書封面Titlebook: Nature Inspired Computing for Wireless Sensor Networks;  Debashis De,Amartya Mukherjee,Nilanjan Dey Book 2020 Springer Nature Singapore Pte
描述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 alg
出版日期Book 2020
關(guān)鍵詞Intelligent Sensor; Wireless Sensor Network; Ubiquitous Sensing; Nature Inspired Sensing; Cyber Physical
版次1
doihttps://doi.org/10.1007/978-981-15-2125-6
isbn_softcover978-981-15-2127-0
isbn_ebook978-981-15-2125-6Series ISSN 2524-552X Series E-ISSN 2524-5538
issn_series 2524-552X
copyrightSpringer Nature Singapore Pte Ltd. 2020
The information of publication is updating

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A GA-Based Fault-Aware Routing Algorithm for Wireless Sensor Networksto balance the load of CHs during data routing. The proposed algorithm has been extensively analyzed with some existing related algorithms and compared their performance in terms of different metrics like energy efficiency, number of alive nodes, and packet delivery ratio.
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A GA-Based Intelligent Traffic Management Technique for Wireless Body Area Sensor Networksciently by maximizing green signal of the network. The proposed method is compared with some existing techniques in terms of some features. The final comparison shows that the proposed method outperformed the existing methods.
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發(fā)表于 2025-3-22 14:49:57 | 只看該作者
Fault Diagnosis in Wireless Sensor Networks Using a Neural Network Constructed by Deep Learning Teched to identify and classify various types of faults in WSNs to avoid such kind of problems. However, the application of deep learning (DL) methods has sparked great interest in both the industry and academia in the last few years. In this chapter, neural network methods will be used in fault diagnos
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A Comprehensive Survey of Intelligent-Based Hierarchical Routing Protocols for Wireless Sensor Netwoer presents a comprehensive survey of the recently intelligent-based hierarchical routing protocols that are developed based on Particle Swarm Optimization, Ant Colony Optimization, Fuzzy Logic, Genetic Algorithm, and Artificial Immune Algorithm. These protocols will review in detail according to di
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發(fā)表于 2025-3-23 08:27:39 | 只看該作者
Qualitative Survey on Sensor Node Deployment, Load Balancing and Energy Utilization in Sensor Networeterministic, as well as heuristic-based algorithms incorporating optimization techniques to perform the node distribution has been developed and researched over the years. Researchers have also developed variegated models with bio-inspired algorithms like genetic algorithm, PSO algorithm, etc. to t
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