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An energy-aware clustering algorithm for wireless sensor networks: GA-based approach

An energy-aware clustering algorithm for wireless sensor networks: GA-based approach Energy conservation is the predominant requirement of wireless sensor networks. Clustering is a technique which helps in achieving the goal of energy efficiency and scalability. Several clustering approaches using genetic algorithm (GA) as an optimisation tool are proposed in the literature. Most of these clustering approaches lead to multi-objective optimisation. In this paper, we propose a GA-based clustering algorithm (GACA) which considers major factors responsible for effective clustering. The proposed approach has been compared with existing approaches for the best fit and optimal fit case. Simulation results show that the proposed GACA approach is more energy efficient than existing approaches and optimal fit results are better than the best fit results. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Autonomous and Adaptive Communications Systems Inderscience Publishers

An energy-aware clustering algorithm for wireless sensor networks: GA-based approach

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd
ISSN
1754-8632
eISSN
1754-8640
DOI
10.1504/IJAACS.2018.093696
Publisher site
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Abstract

Energy conservation is the predominant requirement of wireless sensor networks. Clustering is a technique which helps in achieving the goal of energy efficiency and scalability. Several clustering approaches using genetic algorithm (GA) as an optimisation tool are proposed in the literature. Most of these clustering approaches lead to multi-objective optimisation. In this paper, we propose a GA-based clustering algorithm (GACA) which considers major factors responsible for effective clustering. The proposed approach has been compared with existing approaches for the best fit and optimal fit case. Simulation results show that the proposed GACA approach is more energy efficient than existing approaches and optimal fit results are better than the best fit results.

Journal

International Journal of Autonomous and Adaptive Communications Systems – Inderscience Publishers

Published: Jan 1, 2018

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