A Novel Data Collection Framework for Telemetry and Anomaly Detection in Industrial Iot Systems
The advent of IoTs has catalyzed the development of a variety of cyber-physical systems in which hundreds of sensor-actuator enabled devices (including industrial IoTs) cooperatively interact with the physical and human worlds. However, due to the large volume and heterogeneity of data generated by such systems and the stringent time requirements of industrial applications, the design of efficient frameworks to store, monitor and analyze the IoT data is quite challenging. This paper proposes an industrial IoT architectural framework that allows data offloading between the cloud and the edge. Specifically, we use this framework for telemetry of a set of heterogeneous sensors attached to a scale replica of an industrial assembly plant. We also design an anomaly detection algorithm that exploits deep learning techniques to assess the working conditions of the plant. Experimental results show that the proposed anomaly detector is able to detect 99% of the anomalies occurred in the industrial system demonstrating the feasibility of our approach.
F. De Vita et al., "A Novel Data Collection Framework for Telemetry and Anomaly Detection in Industrial Iot Systems," Proceedings - 5th ACM/IEEE Conference on Internet of Things Design and Implementation, IoTDI 2020, pp. 245-251, Apr 2020.
The definitive version is available at https://doi.org/10.1109/IoTDI49375.2020.00032
5th ACM/IEEE Conference on Internet of Things Design and Implementation, IoTDI 2020
Center for High Performance Computing Research
International Standard Book Number (ISBN)
Article - Conference proceedings
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01 Apr 2020