A novel anomaly detection method for multimodal WSN data flow via a dynamic graph neural network

Abstract

Anomaly detection is a critical technique that ensures the reliability of WSNs. However, most existing anomaly detectionmethods only consider the case of single modal data flow anomaly detection for each node or multiple modal time series data flowanomaly detection for a single node and do not consider the case of multiple nodes and multiple time series data flow simultaneously,and it limited the ability of anomaly detection. In this paper, a novel anomaly detection model is proposed for multimodal WSN data flows. First, the temporal features and modal correlation features extracted from each sensor node are fused into one vectorrepresentation, then it is further aggregated with the spatial features represented the spatial position relationship of the nodes; finally,the current time-series data of WSN nodes are predicted, and abnormal states are identified according to the fusion features. Thesimulation results obtained on a public dataset show that the proposed approach can significantly improve upon existing methods interms of robustness, and its F1 score reaches 0.90, which is 14.2% higher than that of the graph convolution network (GCN) with longshort-term memory (LSTM).