AI-Based Energy-Aware Cluster Head Selection in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) consist of numerous sensor nodes deployed to monitor environmental conditions such as temperature, humidity, and pressure. One of the main challenges in WSNs is limited energy availability in sensor nodes. Efficient energy management is therefore essential to extend network lifetime and ensure reliable communication. This research analyses energy-efficient clustering protocols including LEACH, IMP-RES-EL, and EE-SEP. These protocols aim to improve cluster head selection by considering parameters such as residual energy, communication distance, and network conditions. MATLAB simulations are used to evaluate performance based on network lifetime, packet transmission, and energy consumption. The results demonstrate that improved clustering mechanisms can significantly enhance energy efficiency and prolong the operational lifetime of Wireless Sensor Networks.