Power Aware Data Mining based Intelligent Cluster Head Selection Framework for Wireless Sensor Networks - Lalita, Lalita; Katara, Mayuree; Zaib, Naheeda; Kumari, Ankita; Gupta, Anshul; Verma, Aruna; Verma, Harsh (Journal of Neonatal Surgery, 2025)
Efficient energy utilization is a critical challenge in Wireless Sensor Networks (WSNs), where the dynamic nature of ad hoc communication often leads to rapid energy depletion, particularly at Cluster Heads (CHs). Traditional CH selection algorithms frequently result in unbalanced energy consumption, compromising network stability and lifespan. This paper proposes a Power-Aware Data Mining-based Intelligent Cluster Head Selection Framework that leverages an enhanced K-means clustering algorithm to optimize CH selection. The framework intelligently incorporates multiple key parameters residual energy, node density, signal strength, and distance to the base station to ensure balanced CH rotation and prevent premature energy exhaustion. Extensive simulation studies benchmark the proposed approach against established protocols such as LEACH and HEED. Results demonstrate significant improvements in residual energy conservation, packet delivery ratio, throughput, and node longevity, confirming the framework’s effectiveness in enhancing overall network performance and sustainability.