As-Stg: spatio-temporal graph learning with active sampling for dynamic IoT sensing

Efficient sensing is critical for Internet of Things (IoT) applications, such as environmental monitoring and traffic management, where high quality sensing data is essential for decision-making. Traditional sensing methods, however, are often plagued by high deployment costs and incomplete data coverage, significantly limiting their practicality. Despite recent progress, these methods continue to face challenges in maintaining data accuracy, ultimately degrading the Quality of Service (QoS) for IoT applications. To address these limitations, we propose ASSTG, a novel framework that combines an Active Sampling strategy with Spatio-Temporal Graph learning to enable efficient and accurate IoT sensing. At its core, AS-STG is designed to minimize the sampling cost while ensuring the accuracy of the data. The framework begins by analyzing historical data to determine the minimum sampling requirements for accurate inference in subsequent time slots. It then constructs a spatio-temporal graph to model the complex relationships between sensing grids, capturing both spatial and temporal dynamics. To supplement the spatio-temporal information and further optimize representations, we introduce two contrastive learning tasks. Leveraging the refined representation, AS-STG strategically selects informationrich regions for sampling, ensuring that even a sparse subset of samples can provide comprehensive coverage of the entire sensing area. Finally, AS-STG employs matrix completion techniques to reconstruct the complete sensing data from these sparse samples. Extensive experiments on real-world datasets demonstrate that AS-STG significantly outperforms baselines in terms of inference accuracy, cost-efficiency, and scalability. By effectively reducing sampling costs without compromising QoS, AS-STG offers a robust and scalable solution for dynamic IoT sensing systems.