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

[graphicalabstract] Addressing key challenges in Wireless Sensor Networks (WSNs) such as network lifetime, and energy balance, this paper introduces 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 predicts 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.