Open Access

ARTICLE

Design of Evolutionary Algorithm Based Energy Efficient Clustering Approach for Vehicular Adhoc Networks

V. Dinesh¹, S. Srinivasan², Gyanendra Prasad Joshi³, Woong Cho^4,*

1 Department of Electronics and Communication Engineering, Kongu Engineering College, Perundurai, 638060, India
2 Institute of Biomedical Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, 602105, Chennai, India
3 Department of Computer Science and Engineering, Sejong University, Seoul, 05006, Korea
4 Department of Software Convergence, Daegu Catholic University, Gyeongsan, 38430, Korea

* Corresponding Author: Woong Cho. Email:

Computer Systems Science and Engineering 2023, 46(1), 687-699. https://doi.org/10.32604/csse.2023.035459

Received 22 August 2022; Accepted 28 October 2022; Issue published 20 January 2023

Abstract

In a vehicular ad hoc network (VANET), a massive quantity of data needs to be transmitted on a large scale in shorter time durations. At the same time, vehicles exhibit high velocity, leading to more vehicle disconnections. Both of these characteristics result in unreliable data communication in VANET. A vehicle clustering algorithm clusters the vehicles in groups employed in VANET to enhance network scalability and connection reliability. Clustering is considered one of the possible solutions for attaining effectual interaction in VANETs. But one such difficulty was reducing the cluster number under increasing transmitting nodes. This article introduces an Evolutionary Hide Objects Game Optimization based Distance Aware Clustering (EHOGO-DAC) Scheme for VANET. The major intention of the EHOGO-DAC technique is to portion the VANET into distinct sets of clusters by grouping vehicles. In addition, the DHOGO-EAC technique is mainly based on the HOGO algorithm, which is stimulated by old games, and the searching agent tries to identify hidden objects in a given space. The DHOGO-EAC technique derives a fitness function for the clustering process, including the total number of clusters and Euclidean distance. The experimental assessment of the DHOGO-EAC technique was carried out under distinct aspects. The comparison outcome stated the enhanced outcomes of the DHOGO-EAC technique compared to recent approaches.

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Keywords

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