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Anomaly detection is widely used to distinguish system anomalies by analyzingthe temporal and spatial features of wireless sensor network (WSN) datastreams; it is one of critical technique that ensures the reliability of WSNs.Currently, graph neural networks (GNNs) have become popular state-of-the-artmethods for conducting anomaly detection on WSN data streams. However, theexisting anomaly detection methods based on GNNs do not consider the temporaland spatial features of WSN data streams simultaneously, such as multi-node,multi-modal and multi-time features, seriously impacting their effectiveness.In this paper, a novel anomaly detection model is proposed for multimodal WSNdata flows, where three GNNs are used to separately extract the temporalfeatures of WSN data flows, the correlation features between different modesand the spatial features between sensor node positions. Specifically, first,the temporal features and modal correlation features extracted from each sensornode are fused into one vector representation, which is further aggregated withthe spatial features, i.e., the spatial position relationships of the nodes;finally, the current time-series data of WSN nodes are predicted, and abnormalstates are identified according to the fusion features. The simulation resultsobtained on a public dataset show that the proposed approach is able tosignificantly improve upon the existing methods in terms of its robustness, andits F1 score reaches 0.90, which is 14.2% higher than that of the graphconvolution network (GCN) with long short-term memory (LSTM).

A Novel Anomaly Detection Method for Multimodal WSN Data Flow via a Dynamic Graph Neural Network