An Anomaly Node Detection Method for WSN Based on Deep Metric Learning with Fusion of Spatial-Temporal Features

Wireless Sensor Networks (WSNs) use distributed nodes for tasks such as environmental monitoring and surveillance. Existing anomaly detection methods fail to fully capture correlations in multi-node, multi-modal time series data, limiting their effectiveness. Additionally, they struggle with small sample scenarios, because they do not effectively map features to classes. To address these challenges, this paper presents an anomaly detection approach that integrates deep learning with metric learning. A framework incorporating a Graph Attention Network (GAT) and Transformer is developed to capture spatial and temporal features. A novel distance measurement module improves similarity learning by considering both intra-class and inter-class relationships. Joint metric-classification training improves model accuracy and generalization. Experiments conducted on public datasets demonstrate that the proposed approach achieves an F1 score of 0.89, outperforming existing approaches by 7%.