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.

这项研究引入了一种创新方法，旨在通过预测传感器数据来减少传感器网络的能耗，从而延长网络的运行寿命。我们的模型时间图传感器预测 (TG-SPRED) 基于不依赖于调度的方法来预测指定在每个时隙进入睡眠模式的传感器子集的读数。这种灵活性允许延长传感器的不活动时间，而不会影响数据的准确性。 TG-SPRED 通过识别和利用事件之间固有的时间和空间相关性，解决了基于事件的传感的复杂性（现有文献中在某种程度上忽视了这一领域）。它结合了门控循环单元和图卷积网络的优势，可以分析传感器网络图中的时间数据和空间关系，其中连接由传感器邻近度定义。对抗性训练机制，以采用 Wasserstein 距离进行性能测量的批评者网络为特色，进一步提高了预测的准确性。使用四个关键指标（F 分数、能耗、网络寿命和计算效率）对六种领先解决方案进行比较分析，展示了我们的方法在准确性和能源效率方面的卓越性能。