DarkFarseer: Robust Spatio-temporal Kriging under Graph Sparsity and Noise

TL;DR

DarkFarseer is a new spatio-temporal kriging framework that enhances virtual sensor inference by improving graph representations and denoising noisy connections, leading to better performance in sensor network applications.

Contribution

It introduces three novel modules: style transfer-enhanced virtual node representation, contrastive learning for virtual-region association, and a similarity-based denoising strategy for noisy graphs.

Findings

DarkFarseer outperforms existing ISK methods in experiments.

The proposed modules improve virtual sensor inference accuracy.

The framework effectively handles graph sparsity and noise.

Abstract

With the rapid growth of the Internet of Things and Cyber-Physical Systems, widespread sensor deployment has become essential. However, the high costs of building sensor networks limit their scale and coverage, making fine-grained deployment challenging. Inductive Spatio-Temporal Kriging (ISK) addresses this issue by introducing virtual sensors. Based on graph neural networks (GNNs) extracting the relationships between physical and virtual sensors, ISK can infer the measurements of virtual sensors from physical sensors. However, current ISK methods rely on conventional message-passing mechanisms and network architectures, without effectively extracting spatio-temporal features of physical sensors and focusing on representing virtual sensors. Additionally, existing graph construction methods face issues of sparse and noisy connections, destroying ISK performance. To address these issues, we propose DarkFarseer, a novel ISK framework with three key components. First, we propose the Neighbor Hidden Style Enhancement module with a style transfer strategy to enhance the representation of virtual nodes in a temporal-then-spatial manner to better extract the spatial relationships between physical and virtual nodes. Second, we propose Virtual-Component Contrastive Learning, which aims to enrich the node representation by establishing the association between the patterns of virtual nodes and the regional patterns within graph components. Lastly, we design a Similarity-Based Graph Denoising Strategy, which reduces the connectivity strength of noisy connections around virtual nodes and their neighbors based on their temporal information and regional spatial patterns. Extensive experiments demonstrate that DarkFarseer significantly outperforms existing ISK methods.