2571-1075ISSN (Online)
Volume 9 , Issue 2 , PP: 37-44, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Chandra Sekar P. 1 * , K. R. N. Aswini 2
Doi: https://doi.org/10.54216/IJBES.090205
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.
Energy Efficiency , Multi-Hop Clustering , Wireless Sensor Networks (WSN) , Swarm Intelligence , Particle Swarm Optimization (PSO) , Ant Colony Optimization (ACO) , Cluster Head Selection , Network Lifetime , Packet Delivery Ratio , Latency Reduction , Environmental Monitoring , Smart Agriculture
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