TG-SPRED: Temporal Graph for Sensorial Data PREDiction
Article No.: 64, Pages 1 - 20
Abstract
This study introduces an innovative method aimed at reducing energy consumption in sensor networks by predicting sensor data, thereby extending the network’s 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—a domain that has been somewhat overlooked in existing literature—by 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. An adversarial training mechanism, featuring a critic network employing the Wasserstein distance for performance measurement, further refines the predictive accuracy. Comparative analysis against six leading solutions using four critical metrics—F-score, energy consumption, network lifetime, and computational efficiency—showcases our approach’s superior performance in both accuracy and energy efficiency.
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Index Terms
- TG-SPRED: Temporal Graph for Sensorial Data PREDiction
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Publication History
Published: 13 April 2024
Online AM: 28 February 2024
Accepted: 07 February 2024
Revised: 20 November 2023
Received: 16 July 2022
Published in TOSN Volume 20, Issue 3
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- Arab–German Young Academy of Sciences and Humanities (AGYA)
- German Federal Ministry of Education and Research (BMBF)
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