GNN-Based Energy-Efficient Anomaly Detection for IoT Multivariate Time-Series Data

Anomaly detection is an important topic in the Internet of Things (IoT). Recently, some anomaly detection methods based on graph neural networks (GNNs) have gained much attention. However, such methods require a large amount of sensory data for inference, which leads to high energy consumption for data transmission and can hardly be applied in IoT scenarios. This paper proposes a subgraph-based anomaly detection strategy as an energy-efficient anomaly detection method. To accomplish this task, we use graph structure generation to divide subgraphs by feature similarity and reduce energy consumption for data transmission. To validate the effectiveness of our mechanism, we use real-world IoT multivariate time-series data for modelling. The results show that our scheme is more energy efficient and has higher precision compared to other methods in anomaly detection.