Paper Info
Title
A Novel Data Collection Framework for Telemetry and Anomaly Detection in Industrial IoT Systems
Abstract
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.
Year
DOI
Venue
2020
10.1109/IoTDI49375.2020.00032
2020 IEEE/ACM Fifth International Conference on Internet-of-Things Design and Implementation (IoTDI)
Keywords
DocType
ISBN
telemetry,heterogeneous sensors,industrial assembly plant,anomaly detection algorithm,anomaly detector,industrial system,novel data collection framework,industrial IoT systems,cyber-physical systems,sensor-actuator enabled devices,physical worlds,human worlds,stringent time requirements,industrial applications,IoT data,industrial IoT architectural framework,data offloading
Conference
978-1-7281-6603-2
Citations 
PageRank 
References 
1
0.35
9
Authors
3
Name
Order
Citations
PageRank
Fabrizio De Vita164.36
Dario Bruneo236237.34
Sajal K. Das311.70