Semi-Decentralized Prediction Method for Energy-Efficient Wireless Sensor Networks
Imourane Abdoulaye, Cécile Belleudy, Laurent Rodriguez, Benoît Miramond
Abstract
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.