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An Efficient Clustering Algorithm for Enhancing the Lifetime and Energy Efficiency of Wireless Sensor Networks

Peng Zhou1,2, Wei Chen1, Bingyu Cao1,*

1 School of Information Science and Engineering, Xinjiang College of Science & Technology, Korla, 841000, China
2 School of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, China

* Corresponding Author: Bingyu Cao. Email: email

Computers, Materials & Continua 2025, 84(3), 5337-5360. https://doi.org/10.32604/cmc.2025.065561

Abstract

Wireless Sensor Networks (WSNs), as a crucial component of the Internet of Things (IoT), are widely used in environmental monitoring, industrial control, and security surveillance. However, WSNs still face challenges such as inaccurate node clustering, low energy efficiency, and shortened network lifespan in practical deployments, which significantly limit their large-scale application. To address these issues, this paper proposes an Adaptive Chaotic Ant Colony Optimization algorithm (AC-ACO), aiming to optimize the energy utilization and system lifespan of WSNs. AC-ACO combines the path-planning capability of Ant Colony Optimization (ACO) with the dynamic characteristics of chaotic mapping and introduces an adaptive mechanism to enhance the algorithm’s flexibility and adaptability. By dynamically adjusting the pheromone evaporation factor and heuristic weights, efficient node clustering is achieved. Additionally, a chaotic mapping initialization strategy is employed to enhance population diversity and avoid premature convergence. To validate the algorithm’s performance, this paper compares AC-ACO with clustering methods such as Low-Energy Adaptive Clustering Hierarchy (LEACH), ACO, Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). Simulation results demonstrate that AC-ACO outperforms the compared algorithms in key metrics such as energy consumption optimization, network lifetime extension, and communication delay reduction, providing an efficient solution for improving energy efficiency and ensuring long-term stable operation of wireless sensor networks.

Keywords

Internet of Things; wireless sensor networks; ant colony optimization; clustering algorithm; energy efficiency

Cite This Article

APA Style
Zhou, P., Chen, W., Cao, B. (2025). An Efficient Clustering Algorithm for Enhancing the Lifetime and Energy Efficiency of Wireless Sensor Networks. Computers, Materials & Continua, 84(3), 5337–5360. https://doi.org/10.32604/cmc.2025.065561
Vancouver Style
Zhou P, Chen W, Cao B. An Efficient Clustering Algorithm for Enhancing the Lifetime and Energy Efficiency of Wireless Sensor Networks. Comput Mater Contin. 2025;84(3):5337–5360. https://doi.org/10.32604/cmc.2025.065561
IEEE Style
P. Zhou, W. Chen, and B. Cao, “An Efficient Clustering Algorithm for Enhancing the Lifetime and Energy Efficiency of Wireless Sensor Networks,” Comput. Mater. Contin., vol. 84, no. 3, pp. 5337–5360, 2025. https://doi.org/10.32604/cmc.2025.065561



cc Copyright © 2025 The Author(s). Published by Tech Science Press.
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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