An Anomaly Node Detection Method for Wireless Sensor Networks Based on Deep Metric Learning with Fusion of Spatial-Temporal Features.

Journal: Sensors (Basel, Switzerland)
Published Date: May 12, 2025

Abstract

Wireless sensor networks (WSNs) use distributed nodes for tasks such as environmental monitoring and surveillance. The 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 a 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 the existing approaches by 7%.

Authors

  • Ziheng Wang
  • Miao Ye
    Electrocardiogram Department, Sir Run Run Shaw Hospital, Affiliated with the Zhejiang University School of Medicine, HangZhou, 310016, Zhejiang, China.
  • Jin Cheng
    School of Medical Technology, Qiqihar Medical University, Qiqihar, 161006, Heilongjiang, China.
  • Cheng Zhu
    Translational Sciences, Sanofi US, Framingham, MA, 01701, USA. Cheng.Zhu@sanofi.com.
  • Yong Wang
    State Key Laboratory of Chemical Biology and Drug Discovery, Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University Hunghom Kowloon Hong Kong P. R. China kwok-yin.wong@polyu.edu.hk.

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