Cluster head selection optimization in wireless sensor network via genetic-based evolutionary algorithm

Vincent Chung and Hamzarul Alif Hamzah and Norah Tuah and Kit, Guan Lim and Min, Keng Tan and Kenneth Tze Kin Teo (2020) Cluster head selection optimization in wireless sensor network via genetic-based evolutionary algorithm. ICTACT Journal on Communication Technology, 11 (4). pp. 2301-2309. ISSN 2229-6948

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URL: http://ictactjournals.in/ArticleDetails.aspx?id=50...

Abstract

Wireless sensor network (WSN) is an embedded system comprises of spatially distributed sensor nodes where an energy-efficient mechanism is needed to prolong the network lifetime. Existing approaches for this optimization problem have several drawbacks, including non-adaptive network configuration that may cause premature death of sensor nodes. Genetic-based evolutionary algorithms such as Genetic Algorithm (GA) and Differential Evolution (DE) have been popularly used to optimize cluster head selection in WSN to improve energy efficiency for the extension of network lifetime. Therefore, the performances of GA and DE are evaluated through comparative analysis to determine their efficiency in cluster head selection optimization. Simulation results show that GA outperforms DE with higher round number for first node dies (FND) but lower round number for last node dies (LND) in terms of network lifetime. Besides, GA also leads to a network with lower number of transmission failures than DE. On the other hand, fitness convergence of GA is slower but it has higher fitness value of population.

Item Type:	Article
Keyword:	Brent’s method , Optimal power allocation , Ant colony optimization , Secrecy rate
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Department:	FACULTY > Faculty of Engineering
Depositing User:	DG MASNIAH AHMAD -
Date Deposited:	14 Jun 2021 13:57
Last Modified:	14 Jun 2021 13:57
URI:	https://eprints.ums.edu.my/id/eprint/27189

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