Open Access

ARTICLE

Energy-Efficient Clustering Using Optimization with Locust Game Theory

by P. Kavitha Rani¹, Hee-Kwon Chae², Yunyoung Nam^2,*, Mohamed Abouhawwash^3,4

1 Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, 641008, India
2 Department of ICT Convergence, Soonchunhyang University, Asan, 31538, Korea
3 Department of Mathematics, Faculty of Science, Mansoura University, Mansoura, 35516, Egypt
4 Department of Computational Mathematics, Science, and Engineering (CMSE), Michigan State University, East Lansing, MI, 48824, USA

* Corresponding Author: Yunyoung Nam. Email:

Intelligent Automation & Soft Computing 2023, 36(3), 2591-2605. https://doi.org/10.32604/iasc.2023.033697

Received 24 June 2022; Accepted 14 October 2022; Issue published 15 March 2023

Abstract

Wireless sensor networks (WSNs) are made up of several sensors located in a specific area and powered by a finite amount of energy to gather environmental data. WSNs use sensor nodes (SNs) to collect and transmit data. However, the power supplied by the sensor network is restricted. Thus, SNs must store energy as often as to extend the lifespan of the network. In the proposed study, effective clustering and longer network lifetimes are achieved using multi-swarm optimization (MSO) and game theory based on locust search (LS-II). In this research, MSO is used to improve the optimum routing, while the LS-II approach is employed to specify the number of cluster heads (CHs) and select the best ones. After the CHs are identified, the other sensor components are allocated to the closest CHs to them. A game theory-based energy-efficient clustering approach is applied to WSNs. Here each SN is considered a player in the game. The SN can implement beneficial methods for itself depending on the length of the idle listening time in the active phase and then determine to choose whether or not to rest. The proposed multi-swarm with energy-efficient game theory on locust search (MSGE-LS) efficiently selects CHs, minimizes energy consumption, and improves the lifetime of networks. The findings of this study indicate that the proposed MSGE-LS is an effective method because its result proves that it increases the number of clusters, average energy consumption, lifespan extension, reduction in average packet loss, and end-to-end delay.

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