## 5 Conclusions  
This study presents the development and implementation of a Cross-Layer Controller Tasking Scheme (CLCTS) aimed at enhancing task completion in IIoT environments supported by edge computing. The scheme addresses the challenges of prolonged processing time and persistent task acceptance errors arising from asynchronous operations. Task assignment, processing, and re-tasking are orchestrated by controllers embedded within each IIoT layer, functioning both independently and in coordination via edge nodes. These nodes facilitate the exchange of task-related and completion-specific data among layer controllers, thereby increasing the likelihood of controller migration. This migration is guided by a deep graph learning paradigm that underpins the scheme's dual-phase operational model. The learning framework is trained to promote the grouping of similar tasks in order to minimize processing delays and enable timely migration in cases of asynchronous tasking. Inverse training is employed to reduce the incidence of task offloading failures and acceptance errors. Consequently, consecutive time slots are defined with dynamic task groupings and pre-assigned controller readiness, informed by prior decisions derived from graph-based learning. The approach is iterated until optimal task completion is achieved, thereby enhancing overall IIoT productivity. This work presents an architectural design that integrates graph-based controller migration, temporal task grouping, and learning-based scheduling. The implementation is validated through performance comparisons with existing techniques, which demonstrate measurable improvements in task handling and system coordination.  
Though the intelligence of the brim network is reliable in assisting IIoT in cross-layer control, the shuffled edge connectivity requires a pause time before device allocation. This pause time was found to increase with the increasing edge devices. It is quite obvious that for a large-scale IIoT, the layer count is high, and therefore, large densities of edge nodes are required. For such a scenario, to reduce the pause time, a completion-time-primitive allocation with precise training is intended. In that case, the network operations are to be extended to accommodate time-specific metrics for better productivity decisions.  
Conceptualization, A.M.A. and F.S.A.; methodology, A.M.A. and M.E.; software, M.E. and M.A.; validation, A.M.A., M.E. and A.A.M.A.-K.; formal analysis, F.S.A., M.E. and A.A.M.A.-K.; investigation, A.M.A., M.E. and A.A.M.A.-K.; resources, M.E.; data curation, M.A.; writing--original draft preparation, A.M.A., F.S.A., M.E. and A.A.M.A.-K.; writing--review and editing, A.M.A., F.S.A., M.A., M.E. and A.A.M.A.-K.; visualization, M.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 11: Assessment of re-tasking overhead.  
Data Availability Statement: A scheduling optimization dataset has been used, which is publicly available at ([[https://zenodo.org/records/4106746](https://zenodo.org/records/4106746)]([https://zenodo.org/records/4106746](https://zenodo.org/records/4106746)), accessed on 15 December 2024).  
The authors declare that there are no conflicts of interest.