Leveraging Support Vector Machines for Optimizing Cluster Head Selection and Energy Management in Large-Scale Wireless Sensor Networks - Susee, SK; Venkatesh, S; Kumar, M. Senthil; Chidhambararajan, B (International Journal of Computer Sciences and Engineering, 2025)
Wireless Sensor Networks have been employed in several applications such as industries, smart agriculture and Disaster Management. But they often lack longevity in their effectiveness by limited energy resources. A new Support Vector Machine based cluster head (CH) selection approach is proposed to achieve the aim of energy efficiency of the wireless sensor networks in this paper. SVM, with its excellent classification capability, is used to select the CHs intelligently using different factors such as residual energy, node degree and distance to the BS. Once trained on past network data, the support vector machine model can accurately identify the best attainable node for inefficient Coverage, which results in a balanced distribution of energy and extended lifetime of the overall network. Simulation results reveal that the SVM-based CH selection algorithm leads to a much lower global energy usage in comparison with LEACH and HEED clustering protocols. Results show improvements in network stability, decreased energy exhaustion rates, and increased reliability of data transfers. SVM has thus been found to be the best of the aforementioned classifiers and this trial shows the potential of machine learning methods for the enhancement of WSN performance and sets the stage for future work in sensor networks that save energy.