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

Reem Alkanhel1, Kalaiselvi Chinnathambi2, C. Thilagavathi3, Mohamed Abouhawwash4,5, Mona A. Al duailij6, Manal Abdullah Alohali7, Doaa Sami Khafaga6,*

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: email

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

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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APA Style
Alkanhel, R., Chinnathambi, K., Thilagavathi, C., Abouhawwash, M., duailij, M.A.A. et al. (2023). An energy-efficient multi-swarm optimization in wireless sensor networks. Intelligent Automation & Soft Computing, 36(2), 1571-1583. https://doi.org/10.32604/iasc.2023.033430
Vancouver Style
Alkanhel R, Chinnathambi K, Thilagavathi C, Abouhawwash M, duailij MAA, Alohali MA, et al. An energy-efficient multi-swarm optimization in wireless sensor networks. Intell Automat Soft Comput . 2023;36(2):1571-1583 https://doi.org/10.32604/iasc.2023.033430
IEEE Style
R. Alkanhel et al., "An Energy-Efficient Multi-swarm Optimization in Wireless Sensor Networks," Intell. Automat. Soft Comput. , vol. 36, no. 2, pp. 1571-1583. 2023. https://doi.org/10.32604/iasc.2023.033430



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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