Graph Neural Networks for Anomaly Detection in Industrial Internet of Things

Abstract

The Industrial Internet of Things (IIoT) plays an important role in digital transformation of traditional industries towards 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 paper provides a useful investigation on graph neural networks (GNN) for anomaly detection in IIoTenabled 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 datasets, 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.

Graph Neural Networks in IoT: A Survey

“…Then they developed a context graph convolution block and a soft clustering graph convolution block to capture both local and global spatial dependencies between parking lots. Similar to this work, authors in the following studies [30,128,207,264,284,321] also created graph representations from geospatial sensors. They used the located cameras and satellite as nodes to create graph structures to perform traffic network prediction.…”

“…For example, in a multi-modal sensor network (e.g., lighting, environment), each sensor can be represented as a node in a graph, and their latent interconnections need to be learned by using data-driven approaches. Established works [19,25,30,40,42,56,58,71,160,161,175,201,207,208,239,258,284,286,290,318,320,321,334] illustrated the performance of applied GNNs in smart city applications that involved IoT sensor interconnections. Table 3 summarizes the sensor infrastructures, GNN models, and learning targets in the collected works.…”

“…IoT applications, such as anomaly detection [56,284], remote sensing [160], and traffic prediction [318], require huge amount of sensor data to achieve diverse goals. In complex IoT sensing, different sensor infrastructures play important roles to capture various data representations from different angles.…”

Editorial: Deep Learning for Big Data Analytics

“…Deep learning is currently a promising technology on data analysis in a number of key application areas, such as speech recognition, computer vision, and natural language processing. It is coming to play an important role in coping with the challenges of big data and providing effective big data analytics solutions [1][2][3][4][5]. This special issue is devoted to the most recent developments and research outcomes addressing the related theoretical and practical aspects on deep learning for big data analytics, and it also aims to provide worldwide researchers and practitioners an ideal platform to innovate new solutions targeting at the corresponding key challenges.…”

Editorial: Big data technologies and applications

“…Big data analytics has received numerous attentions in many areas [1][2][3][4][5]. This special issue contains 19 papers accepted by the 9th EAI International Conference on Big Data Technologies and Applications (BDTA-2018), which was held in Exeter, United Kingdom on 4-5 September 2018.…”