Open Access

ARTICLE

An Energy-Efficient Multi-swarm Optimization in Wireless Sensor Networks

Reem Alkanhel¹, Kalaiselvi Chinnathambi², C. Thilagavathi³, Mohamed Abouhawwash^4,5, Mona A. Al duailij⁶, Manal Abdullah Alohali⁷, Doaa Sami Khafaga^6,*

1 Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
2 Department of Electronics and Communication Engineering, Kongu Engineering College, Perundurai, 638060, India
3 Department of Information Technology, M. Kumarasamy College of Engineering, Karur, 639113, India
4 Department of Mathematics, Faculty of Science, Mansoura University, Mansoura, 35516, Egypt
5 Department of Computational Mathematics, Science, and Engineering (CMSE), Michigan State University, East Lansing, MI, 48824, USA
6 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
7 Information Systems Department, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia

* Corresponding Author: Doaa Sami Khafaga. Email:

Intelligent Automation & Soft Computing 2023, 36(2), 1571-1583. https://doi.org/10.32604/iasc.2023.033430

Received 16 June 2022; Accepted 26 July 2022; Issue published 05 January 2023

Abstract

Wireless Sensor Networks are a group of sensors with inadequate power sources that are installed in a particular region to gather information from the surroundings. Designing energy-efficient data gathering methods in large-scale Wireless Sensor Networks (WSN) is one of the most difficult areas of study. As every sensor node has a finite amount of energy. Battery power is the most significant source in the WSN. Clustering is a well-known technique for enhancing the power feature in WSN. In the proposed method multi-Swarm optimization based on a Genetic Algorithm and Adaptive Hierarchical clustering-based routing protocol are used for enhancing the network’s lifespan and routing optimization. By using distributed data transmission modification, an adaptive hierarchical clustering-based routing algorithm for power consumption is presented to ensure continuous coverage of the entire area. To begin, a hierarchical clustering-based routing protocol is presented in terms of balancing node energy consumption. The Multi-Swarm optimization (MSO) based Genetic Algorithms are proposed to select an efficient Cluster Head (CH). It also improves the network’s longevity and optimizes the routing. As a result of the study’s findings, the proposed MSO-Genetic Algorithm with Hill climbing (GAHC) is effective, as it increases the number of clusters created, average energy expended, lifespan computation reduces average packet loss, and end-to-end delay.

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Keywords

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