With the rapid advancement of IoT and edge computing, sensor networks have become indispensable, driving the need for large-scale sensor deployment. However, the high deployment cost hinders their scalability. To tackle the issues, Spatial Interpolation (SI) introduces virtual sensors to infer readings from observed sensors, leveraging graph structure. However, current graph-based SI methods rely on pre-trained models, lack adaptation to larger and unseen graphs at test-time, and overlook test data utilization. To address these issues, we propose PlugSI, a plug-and-play framework that refines test-time graph through two key innovations. First, we design an Unknown Topology Adapter (UTA) that adapts to the new graph structure of each small-batch at test-time, enhancing the generalization of SI pre-trained models. Second, we introduce a Temporal Balance Adapter (TBA) that maintains a stable historical consensus to guide UTA adaptation and prevent drifting caused by noise in the current batch. Empirically, extensive experiments demonstrate PlugSI can be seamlessly integrated into existing graph-based SI methods and provide significant improvement (e.g., a 10.81% reduction in MAE).

翻译：随着物联网与边缘计算的快速发展，传感器网络已成为不可或缺的基础设施，推动着大规模传感器部署的需求。然而，高昂的部署成本制约了其可扩展性。为解决这一问题，空间插值（SI）通过引入虚拟传感器，利用图结构从已观测传感器中推断读数。然而，当前基于图的SI方法依赖于预训练模型，缺乏在测试时对更大规模及未见图的适应能力，且忽视了测试数据的利用。针对这些不足，我们提出PlugSI——一个即插即用的框架，通过两项关键创新优化测试时图处理。首先，我们设计了未知拓扑适配器（UTA），能够在测试时自适应每个小批次的新图结构，从而提升SI预训练模型的泛化能力。其次，我们引入了时序平衡适配器（TBA），通过维持稳定的历史共识来引导UTA的自适应过程，防止当前批次中的噪声导致模型漂移。实验表明，PlugSI能够无缝集成到现有基于图的SI方法中，并带来显著性能提升（例如，平均绝对误差降低10.81%）。