Cross-Layer Controller Tasking Scheme Using Deep Graph Learning for Edge-Controlled Industrial Internet of Things (IIoT)

Abdullah Mohammed Alharthi (), Fahad S. Altuwaijri, Mohammed Alsaadi, Mourad Elloumi and Ali A. M. Al-Kubati
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Abdullah Mohammed Alharthi: College of Computing and Information Technology, University of Bisha, Bisha 61922, Saudi Arabia
Fahad S. Altuwaijri: Department of Information Technology, College of Computer, Qassim University, Buraydah 52571, Saudi Arabia
Mohammed Alsaadi: College of Engineering and Physical Sciences, Computer Science University of New Hampshire, Durham, NH 03824, USA
Mourad Elloumi: College of Computing and Information Technology, University of Bisha, Bisha 61922, Saudi Arabia
Ali A. M. Al-Kubati: Applied College, University of Bisha, Bisha 61922, Saudi Arabia

Future Internet, 2025, vol. 17, issue 8, 1-18

Abstract: Edge computing (EC) plays a critical role in advancing the next-generation Industrial Internet of Things (IIoT) by enhancing production, maintenance, and operational outcomes across heterogeneous network boundaries. This study builds upon EC intelligence and integrates graph-based learning to propose a Cross-Layer Controller Tasking Scheme (CLCTS). The scheme operates through two primary phases: task grouping assignment and cross-layer control. In the first phase, controller nodes executing similar tasks are grouped based on task timing to achieve monotonic and synchronized completions. The second phase governs controller re-tasking both within and across these groups. Graph structures connect the groups to facilitate concurrent tasking and completion. A learning model is trained on inverse outcomes from the first phase to mitigate task acceptance errors (TAEs), while the second phase focuses on task migration learning to reduce task prolongation. Edge nodes interlink the groups and synchronize tasking, migration, and re-tasking operations across IIoT layers within unified completion periods. Departing from simulation-based approaches, this study presents a fully implemented framework that combines learning-driven scheduling with coordinated cross-layer control. The proposed CLCTS achieves an 8.67% reduction in overhead, a 7.36% decrease in task processing time, and a 17.41% reduction in TAEs while enhancing the completion ratio by 13.19% under maximum edge node deployment.

Keywords: cross-layer; edge computing; graph learning; IIoT; task migration (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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