Data Mining-Based Smart Cluster Head Selection (SCHS) Approach for Energy Efficiency in Wireless Sensor Networks

Abstract

In Wireless Sensor Networks (WSNs), efficient energy management is crucial for extending network lifespan, particularly given the dynamic mobility and communication demands of ad hoc mobile devices. Traditional Cluster Head (CH) selection methods, which organize nodes into clusters for data routing and management, often suffer from biases that lead to uneven energy depletion. CHs tend to exhaust their energy rapidly due to excessive workload, resulting in network instability. To address this issue, this paper presents an enhanced CH selection approach based on the K-means algorithm, ensuring a more balanced energy distribution across all nodes. The proposed method considers multiple critical factors, including residual energy, node density, distance to the base station, and signal strength, to make informed CH selections. By integrating these parameters, the algorithm promotes equitable CH rotations, preventing premature energy depletion and enhancing network sustainability. Extensive simulations evaluate the proposed approach against conventional CH selection protocols such as LEACH (Low-Energy Adaptive Clustering Hierarchy) and HEED (Hybrid Energy-Efficient Distributed). Performance analysis based on residual energy, packet delivery ratio, throughput, and the number of live and dead nodes demonstrates that the proposed K-means-based approach significantly improves energy efficiency and overall network performance.