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.

Meeting Name

5th ACM/IEEE Conference on Internet of Things Design and Implementation, IoTDI 2020


Computer Science

Research Center/Lab(s)

Center for High Performance Computing Research

International Standard Book Number (ISBN)


Document Type

Article - Conference proceedings

Document Version


File Type





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Publication Date

01 Apr 2020