Energy-efficient knapsack algorithm for intelligent cluster head selection in IoT enabled wireless sensor networks

Abstract

The demand for wireless sensor networks (WSN) has grown rapidly with the development of the internet of things (IoT), which requires sensors that are both energy-efficient and scalable to support continuous data collection and real-time monitoring applications. The main challenge is limited battery life in network nodes, which necessitates effective energy management strategies to prolong network lifespan. This paper introduces an energyefficient knapsack algorithm (EEKA) for smart cluster head (CH) selection in IoT WSNs, aiming to optimize energy use while enhancing network stability and data transmission efficiency. The approach features a CH selection strategy based on residual energy, ensuring an even distribution of energy among sensor nodes. The incorporation of the knapsack optimization technique enhances resource allocation, thereby minimizing energy consumption and maximizing transmission reliability. Simulation results using NS2.34/2.35 show remarkable improvement in performance metrics compared to existing techniques: EEKA extends the network lifetime by 16% whereas throughput is enhanced by 17% with reduced latency by 14% under efficient data distribution. Moreover, adaptive CH selection strategy extends coverage by another 20% for wider and effective monitoring. All these results therefore confirm that EEKA has successfully focused on improving energy efficiency, stability, and scalability regarding IoT-driven WSNs to make it a practical solution for real-world applications like smart cities, environmental observation, and industrial automation.