A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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-tekijät tai -toimittajat

Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Alvi, Unaiza; Malik, Asad Waqar; Rahman, Anis Ur; Khattak, Muazzam A. Khan; Khan, Samee U.

Lehti tai sarja: IEEE Transactions on Industrial Informatics

ISSN: 1551-3203

eISSN: 1941-0050

Julkaisuvuosi: 2024

Ilmestymispäivä: 29.03.2023

Volyymi: 20

Lehden numero: 1

Artikkelin sivunumerot: 390-398

Kustantaja: Institute of Electrical and Electronics Engineers (IEEE)

Julkaisumaa: Yhdysvallat (USA)

Julkaisun kieli: englanti

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

Julkaisun avoin saatavuus: Ei avoin

Julkaisukanavan avoin saatavuus: 

Tiivistelmä

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%.

YSO-asiasanat: reunalaskenta; esineiden internet; laitteet; langattomat verkot; verkonhallinta

Vapaat asiasanat: anomalous detection; device network; edge computing; trusted task offloading

Liittyvät organisaatiot

OKM-raportointi: Kyllä

Raportointivuosi: 2024

Alustava JUFO-taso: 3