An Anomaly Node Detection Method for Wireless Sensor Networks 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. 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 sma...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/10/3033 |
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| author | Ziheng Wang Miao Ye Jin Cheng Cheng Zhu Yong Wang |
| author_facet | Ziheng Wang Miao Ye Jin Cheng Cheng Zhu Yong Wang |
| author_sort | Ziheng Wang |
| collection | DOAJ |
| description | 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%. |
| format | Article |
| id | doaj-art-09eded9907aa4a37b47a0736763e97b4 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-09eded9907aa4a37b47a0736763e97b42025-08-20T02:33:55ZengMDPI AGSensors1424-82202025-05-012510303310.3390/s25103033An Anomaly Node Detection Method for Wireless Sensor Networks Based on Deep Metric Learning with Fusion of Spatial–Temporal FeaturesZiheng Wang0Miao Ye1Jin Cheng2Cheng Zhu3Yong Wang4School of Information and Communication, Guilin University of Electronic Technology, Guilin 541000, ChinaSchool of Information and Communication, Guilin University of Electronic Technology, Guilin 541000, ChinaSchool of Information and Communication, Guilin University of Electronic Technology, Guilin 541000, ChinaSchool of Information and Communication, Guilin University of Electronic Technology, Guilin 541000, ChinaSchool of Computer and Information Security, Guilin University of Electronic Technology, Guilin 541000, ChinaWireless 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%.https://www.mdpi.com/1424-8220/25/10/3033wireless sensor networksanomaly detectiongraph neural networkmetric learning |
| spellingShingle | Ziheng Wang Miao Ye Jin Cheng Cheng Zhu Yong Wang An Anomaly Node Detection Method for Wireless Sensor Networks Based on Deep Metric Learning with Fusion of Spatial–Temporal Features Sensors wireless sensor networks anomaly detection graph neural network metric learning |
| title | An Anomaly Node Detection Method for Wireless Sensor Networks Based on Deep Metric Learning with Fusion of Spatial–Temporal Features |
| title_full | An Anomaly Node Detection Method for Wireless Sensor Networks Based on Deep Metric Learning with Fusion of Spatial–Temporal Features |
| title_fullStr | An Anomaly Node Detection Method for Wireless Sensor Networks Based on Deep Metric Learning with Fusion of Spatial–Temporal Features |
| title_full_unstemmed | An Anomaly Node Detection Method for Wireless Sensor Networks Based on Deep Metric Learning with Fusion of Spatial–Temporal Features |
| title_short | An Anomaly Node Detection Method for Wireless Sensor Networks Based on Deep Metric Learning with Fusion of Spatial–Temporal Features |
| title_sort | anomaly node detection method for wireless sensor networks based on deep metric learning with fusion of spatial temporal features |
| topic | wireless sensor networks anomaly detection graph neural network metric learning |
| url | https://www.mdpi.com/1424-8220/25/10/3033 |
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