Semi-Decentralized Prediction Method for Energy-Efficient Wireless Sensor Networks

Addressing  key  challenges   in   wireless   sensor   networks (WSNs), such as network lifetime and energy balance, this letter intro- duces the semi-decentralized prediction method (SDPM) for energy- efficient wireless sensor networks. This approach enhances energy efficiency by combining clustering principles with data prediction for smart cluster-head (CH) selection. SDPM facilitates the periodic election of an effective CH from among the cluster-nodes, who then pre- dicts data for the nodes within the cluster, thereby reducing transmission and conserving energy. Our findings demonstrate SDPM's significant impact on reducing energy consumption, promising for real-world WSNs to achieve longer network lifetime and better energy management.