DarkFarseer: Inductive Spatio-temporal Kriging via Hidden Style Enhancement and Sparsity-Noise Mitigation

2025-01-06

AI Summary

Existing Inductive Spatio-Temporal Kriging (ISK) methods suffer from insufficient spatio-temporal information exploitation, inaccurate node representation, and degradation in prediction accuracy and robustness due to noisy graph structures in virtual sensor modeling. To address these issues, this paper proposes an inductive graph neural network–based spatio-temporal kriging method. Its core contributions are: (1) a neighborhood-aware implicit style enhancement module to improve spatio-temporal representation of virtual sensors; (2) a virtual component contrastive learning mechanism to enforce cross-sensor semantic consistency; and (3) a similarity-driven dynamic graph denoising strategy to enhance graph structural reliability. The method integrates style transfer, contrastive learning, and spatio-temporal graph neural networks, supporting dynamic graph evolution. Extensive experiments on multiple real-world sensor datasets demonstrate that our method achieves 12.7%–23.4% higher inference accuracy than state-of-the-art ISK approaches, with significantly improved noise resilience and generalization capability.

Technology Category

• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures

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.

Problem

• Inductive Spatio-Temporal Kriging
• Virtual Sensor Representation
• Sensor Connectivity

Innovation

• Style Transfer
• Contrastive Learning
• Noise Reduction