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Netw."],"published-print":{"date-parts":[[2024,5,31]]},"abstract":"<jats:p>This study introduces an innovative method aimed at reducing energy consumption in sensor networks by predicting sensor data, thereby extending the network\u2019s operational lifespan. Our model, Temporal Graph Sensor Prediction (TG-SPRED), predicts readings for a subset of sensors designated to enter sleep mode in each time slot, based on a non-scheduling-dependent approach. This flexibility allows for extended sensor inactivity periods without compromising data accuracy. TG-SPRED addresses the complexities of event-based sensing\u2014a domain that has been somewhat overlooked in existing literature\u2014by recognizing and leveraging the inherent temporal and spatial correlations among events. It combines the strengths of Gated Recurrent Units and Graph Convolutional Networks to analyze temporal data and spatial relationships within the sensor network graph, where connections are defined by sensor proximities. 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