Dual Graph Learning for Multivariate Time Series Anomaly Detection in IoUT

In Internet of Underwater Things (IoUT) systems, the unpredictable underwater environment and unstable transmission conditions often lead to anomalies in the generated sensory signals. While numerous anomaly detection methods have emerged, many fail to effectively address the challenges posed by underwater settings, particularly the temporal dependencies and inter-signal relationships for accurate anomaly detection. To overcome these limitations, we propose a novel reconstruction-based framework incorporating a dual graph learning strategy tailored for underwater scenarios. Specifically, our method simultaneously learns intra-signal graph structures to capture temporal patterns and inter-signal graph structures to model dependencies between sensor signals. To enhance the fusion of these graph branches, we introduce a parametric matrix fusion module that dynamically optimizes the influence of each graph through training. Furthermore, a multi-head attention-based decoder is employed to efficiently process the fused features and improve decoding performance. We validated our method through extensive experiments on real-world datasets and simulate different error rates under varying transmission conditions, and the results demonstrate that our approach maintains high anomaly detection accuracy even in the presence of higher bit error rates underwater settings.