Evolutionary Computing Approaches for Clustering and Routing in Wireless Sensor Networks

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Author(s): Pratyay Kuila (NIT Sikkim, India)and Prasanta K. Jana (Indian School of Mines, India)
Copyright: 2016
Pages: 21
Source title: Handbook of Research on Natural Computing for Optimization Problems
Source Author(s)/Editor(s): Jyotsna Kumar Mandal (University of Kalyani, India), Somnath Mukhopadhyay (Calcutta Business School, India)and Tandra Pal (National Institute of Technology Durgapur, India)
DOI: 10.4018/978-1-5225-0058-2.ch011

Keywords: Artificial Intelligence / Biologically-Inspired Computing / Information Science Reference / Medicine & Healthcare

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Abstract

With proliferation of Computational Intelligence (CI), evolutionary algorithms have drawn enormous attention among researchers. Such algorithms have been studied to solve many optimization problems. Clustering and routing are two well known optimization problems which are well researched in the field of Wireless Sensor Networks (WSNs). These problems are NP-hard. Therefore, many researchers have applied meta-heuristic approaches to develop various evolutionary algorithms to solve them. In this chapter, the authors rigorously study and present various evolutionary algorithms that include Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution, etc. and show how these algorithms are applied to solve clustering and routing problems in WSNs. The chapter starts with an introduction of WSNs along with clustering and routing problems in WSNs accompanied by a discussion why these problems are solved by evolutionary algorithms. The authors then give an overview of various evolutionary approaches that are applied to solve clustering and routing problems. Various evolutionary algorithms are then presented towards the solution of these problems. A comparison table is also made by highlighting strengths and weaknesses of the algorithms. Finally, the authors present new directions of future research in this domain.

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