Journal
IEEE INTERNET OF THINGS JOURNAL
Volume 9, Issue 12, Pages 9214-9231Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2021.3094295
Keywords
Industrial Internet of Things; Anomaly detection; Industries; Smart manufacturing; Smart transportation; Sensors; Intelligent sensors; Anomaly detection; graph neural networks (GNNs); Industrial Internet of Things (IIoT); industry 4; 0
Categories
Funding
- Engineering and Physical Sciences Research Council (EPSRC) [EP/R030863/1]
- Macao Science and Technology Development Fund through Macao Funding Scheme for Key Research and Development Projects [0025/2019/AKP]
- Zhejiang Lab [2019KE0AB03]
- National Natural Science Foundation of China (NSFC) [52071312]
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The Industrial Internet of Things (IIoT) is crucial for the digital transformation of traditional industries towards Industry 4.0. Graph neural networks (GNNs) have shown promise in anomaly detection in IIoT-enabled smart transportation, smart energy, and smart factory. This article provides valuable insights and case studies in utilizing GNNs for anomaly detection in IIoT.
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.
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