A1 Journal article (refereed)
Predicting Device Anomalous Condition in a Collaborated Industrial Environment (2024)

Alvi, U., Malik, A. W., Rahman, A. U., Khattak, M. A. K., & Khan, S. U. (2024). Predicting Device Anomalous Condition in a Collaborated Industrial Environment. IEEE Transactions on Industrial Informatics, 20(1), 390-398. https://doi.org/10.1109/TII.2023.3262815

JYU authors or editors

Publication details

All authors or editors: Alvi, Unaiza; Malik, Asad Waqar; Rahman, Anis Ur; Khattak, Muazzam A. Khan; Khan, Samee U.

Journal or series: IEEE Transactions on Industrial Informatics

ISSN: 1551-3203

eISSN: 1941-0050

Publication year: 2024

Publication date: 29/03/2023

Volume: 20

Issue number: 1

Pages range: 390-398

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1109/TII.2023.3262815

Publication open access: Not open

Publication channel open access: 

Abstract

The industrial environment augments resource-constrained devices to bring services closer to autonomous devices. However, over time, these devices get overburdened due to computational workload, which results in degraded network performance. Therefore, the devices are programmed to share resources with nearby devices. However, owing to real-time collaboration, there is the possibility that the device moves to an undefined state and starts behaving maliciously. This can impact the entire collaborative environment laid to meet the industrial product deadline. In this article, we propose an industrial simulation framework that enables the resource-sharing environment and identifies the undefined device behavior. Furthermore, our detection scheme is based on an intelligent model trained on device behavior through the machine-in-a-loop mechanism and deployed at network intersections, i.e., edge nodes. The proposed technique improves the efficiency of the collaborative network by 30%.

Keywords: edge computing; Internet of things; devices; wireless networks; network management (information technology)

Free keywords: anomalous detection; device network; edge computing; trusted task offloading

Contributing organizations

Ministry reporting: Yes

Reporting Year: 2024

Preliminary JUFO rating: 3