Optimizing energy efficiency in wireless sensor networks with integration of Calinski-Harabasz index in K-means clusterings

Abstract

The optimization of energy consumption and the assurance of efficient data transmission are critical factors in enhancing the longevity and performance of wireless sensor networks (WSNs). This study introduces an advanced clustering technique aimed at prolonging the network's lifespan while facilitating reliable data delivery. By integrating the Calinski-Harabasz index into the traditional K-Means clustering approach, the methodology evaluates the quality of clusters and determines the optimal number of clusters, which leads to better node organization within the network. Moreover, the selection of routing pathways from cluster heads to the base station is strategically optimized to conserve energy. Simulation results demonstrate that this novel dual enhancement technique surpasses traditional K-Means in multiple areas, including power consumption, network reliability, and successful data delivery. Consequently, the suggested advancements in cluster formation and routing substantially enhance the performance of energy-limited wireless sensor networks, boosting their robustness and reliability in practical applications.