Volume 9 • Issue 2 • PP: 28–33 • 2024
Energy-Efficient Multi-Hop Clustering in WSN Using Intelligent Swarm-Based Algorithms
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© 2024 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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Efficient energy management in Wireless Sensor Networks (WSNs) is vital for extending network lifetime, particularly in applications requiring continuous monitoring in remote or challenging environments. This study proposes an energy-efficient multi-hop clustering approach for WSNs, utilizing intelligent swarm-based algorithms to optimize cluster formation and data routing. By applying Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) techniques, the proposed method dynamically selects optimal cluster heads and minimizes energy consumption during multi-hop data transmission. The algorithm was evaluated on simulated WSN scenarios with varying node densities, achieving an average energy savings of 28% compared to traditional clustering methods and a 35% increase in network lifetime. Additionally, the proposed approach improved packet delivery ratio and reduced latency by 20% and 15%, respectively. This swarm-based, energy-efficient clustering framework is well-suited for applications in environmental monitoring, smart agriculture, and industrial automation, where prolonged network operation is essential.
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