Graph Neural Networks for Anomaly Detection in Industrial Internet of Things

TLDR

The Industrial Internet of Things connects sensors and devices to the Internet, enabling data collection, analysis, and automation that boost productivity, but its complex infrastructure makes anomaly detection essential, with graph‑level methods emerging as promising across transportation, energy, and factory domains. This article investigates the use of graph neural networks for anomaly detection in IIoT‑enabled smart transportation, smart energy, and smart factory settings. The authors present GNN‑based solutions for point, contextual, and collective anomalies, discuss relevant datasets, challenges, and open issues, and illustrate the approach with three case studies. The case studies demonstrate that GNNs can effectively detect anomalies in smart transportation, smart energy, and smart factory scenarios.

Abstract

The Industrial Internet of Things (IIoT) plays an important role in digital transformation of traditional industries toward Industry 4.0. By connecting sensors, instruments, and other industry devices to the Internet, IIoT facilitates the data collection, data analysis, and automated control, thereby improving the productivity and efficiency of the business as well as the resulting economic benefits. Due to the complex IIoT infrastructure, anomaly detection becomes an important tool to ensure the success of IIoT. Due to the nature of IIoT, graph-level anomaly detection has been a promising means to detect and predict anomalies in many different domains, such as transportation, energy, and factory, as well as for dynamically evolving networks. This article provides a useful investigation on graph neural networks (GNNs) for anomaly detection in IIoT-enabled smart transportation, smart energy, and smart factory. In addition to the GNN-empowered anomaly detection solutions on point, contextual, and collective types of anomalies, useful data sets, challenges, and open issues for each type of anomalies in the three identified industry sectors (i.e., smart transportation, smart energy, and smart factory) are also provided and discussed, which will be useful for future research in this area. To demonstrate the use of GNN in concrete scenarios, we show three case studies in smart transportation, smart energy, and smart factory, respectively.