DyC-STG: Dynamic Causal Spatio-Temporal Graph Network for Real-time Data Credibility Analysis in IoT

2025-09-08

AI Summary

Assessing the credibility of massive spatiotemporal data streams in IoT applications—such as smart homes—remains challenging, as existing static spatiotemporal graph models fail to capture dynamic physical state evolution and event-driven dynamics, and often conflate spurious correlations with genuine causal relationships, leading to insufficient robustness in human-centric environments. Method: We propose the Dynamic Causal Spatiotemporal Graph Network (DC-STGN), which innovatively integrates an event-driven dynamic graph construction mechanism with a time-priority-aware causal inference module to enable real-time responsiveness to environmental changes and causal-aware representation learning. Contribution/Results: Evaluated on two newly released real-world datasets, DC-STGN achieves an F1-score of 0.930—outperforming the best baseline by 1.4 percentage points—and establishes a novel, interpretable, and highly robust paradigm for trustworthy spatiotemporal data analysis.

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

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

The wide spreading of Internet of Things (IoT) sensors generates vast spatio-temporal data streams, but ensuring data credibility is a critical yet unsolved challenge for applications like smart homes. While spatio-temporal graph (STG) models are a leading paradigm for such data, they often fall short in dynamic, human-centric environments due to two fundamental limitations: (1) their reliance on static graph topologies, which fail to capture physical, event-driven dynamics, and (2) their tendency to confuse spurious correlations with true causality, undermining robustness in human-centric environments. To address these gaps, we propose the Dynamic Causal Spatio-Temporal Graph Network (DyC-STG), a novel framework designed for real-time data credibility analysis in IoT. Our framework features two synergistic contributions: an event-driven dynamic graph module that adapts the graph topology in real-time to reflect physical state changes, and a causal reasoning module to distill causally-aware representations by strictly enforcing temporal precedence. To facilitate the research in this domain we release two new real-world datasets. Comprehensive experiments show that DyC-STG establishes a new state-of-the-art, outperforming the strongest baselines by 1.4 percentage points and achieving an F1-Score of up to 0.930.

Problem

• Ensuring real-time data credibility in dynamic IoT environments
• Overcoming static graph limitations in spatio-temporal data analysis
• Distinguishing true causality from spurious correlations in IoT data

Innovation

• Event-driven dynamic graph module adapts topology
• Causal reasoning module enforces temporal precedence
• Real-time data credibility analysis in IoT