PlugSI: Plug-and-Play Test-Time Graph Adaptation for Spatial Interpolation

TL;DR

PlugSI is a flexible test-time framework that improves spatial interpolation in sensor networks by adapting to new graph structures and maintaining historical stability, leading to more accurate predictions.

Contribution

It introduces a plug-and-play test-time adaptation framework with novel modules for dynamic graph adaptation and stability, enhancing existing spatial interpolation methods.

Findings

Achieves up to 10.81% reduction in MAE.

Seamlessly integrates with existing SI methods.

Significantly improves test-time generalization.

Abstract

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).