@Article{cmc.2026.078282,
AUTHOR = {Miao Ye, Ziheng Wang, Qiuxiang Jiang, Xingsi Xue, Wenxi Liu, Yu Ning, Cheng Zhu},
TITLE = {A Method for Detecting Spatio-Temporal Correlation Anomalies of WSN Nodes Based on Topological Information Enhancement and Time-Frequency Feature Extraction},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/cmc/online/detail/27017},
ISSN = {1546-2226},
ABSTRACT = {In recent years, anomaly detection in Wireless Sensor Networks (WSNs) has been widely studied using Graph Neural Networks and Transformer-based methods. However, in multi-node and multi-modal data scenarios, these approaches still face challenges such as insufficient extraction of spatiotemporal correlation features, limited modeling capabilities when relying solely on either time-domain or frequency-domain information, and high computational overhead. To address these issues, this work aims to develop an anomaly detection model that balances detection performance with computational efficiency, enabling effective identification of complex anomaly patterns. Specifically, we propose a time–frequency feature extraction method with topological information enhancement, topology-enhanced multi-modal spatio-temporal anomaly detection (TE-MSTAD). Building upon the Receptance Weighted Key Value (RWKV) model with linear complexity, a cross-modal feature extraction module is introduced to strengthen the modeling of multi-modal correlations. Meanwhile, adaptive adjacency matrices are constructed by integrating time–frequency features and combining outputs from different Graph Neural Networks, thereby enhancing topological information. Furthermore, a dual-branch structure is designed to jointly model time-domain and frequency-domain features, improving the extraction of complex anomaly characteristics. Experiments on both publicly available datasets and real-world collected data demonstrate that the proposed method achieves F1-scores of 92.52% and 93.28%, respectively, outperforming existing methods in detection performance and generalization capability.},
DOI = {10.32604/cmc.2026.078282}
}