Authors
- Guanjie Cheng Zhejiang University
- Boyi Li Northeastern University
- Peihan Wu Zhejiang University
- Feiyi Chen Zhejiang University
- Xinkui Zhao Zhejiang University
- Mengying Zhu Zhejiang University
- Shuiguang Deng Zhejiang University
DOI:
https://doi.org/10.1609/aaai.v40i1.36973Abstract
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.
How to Cite
Cheng, G., Li, B., Wu, P., Chen, F., Zhao, X., Zhu, M., & Deng, S. (2026). DyC-STG: Dynamic Causal Spatio-Temporal Graph Network for Real-time Data Credibility Analysis in IoT. Proceedings of the AAAI Conference on Artificial Intelligence, 40(1), 137–146. https://doi.org/10.1609/aaai.v40i1.36973
Issue
Section
AAAI Technical Track on Application Domains I