Optimized Energy-Efficient Knapsack Algorithm for Intelligent Cluster Head Selection in Wireless Sensor Networks
Abdul Aleem, Rajesh Thumma · Engineering Technology & Applied Science Research · 2025
Wireless Sensor Networks (WSNs) are vital for data collection, monitoring and environmental analysis. This study presents a new energy balancing method that uses a Cluster Head (CH) selection policy based on the residual energy state of nodes, involving uniform distribution of energy consumption, with the aim to increase network lifespan and performance. Calculations are performed with the Knapsack method, which considers energy constraints and optimizes resource allocation. Performance tests with NS2.34/2.35 show significant improvements. Important findings are the extended network longevity, with the proposed solution increasing network lifetime by 16%, increased data usage by 17%, reduced latency by 14%, improved coverage by widening the monitored locations by 20%. These findings show that the proposed energy-balancing algorithm can be used to increase the lifetime and performance of WSNs. This work contributes to the ongoing effort to improve WSN performance and sustainability, particularly in circumstances when energy efficiency is essential.