## 4 Performance Evaluation and Analysis  
This section presents the performance assessment of the proposed CLCTS using definite metrics. The metrics include task completion time, completion ratio, TAE, processing time, and re-tasking overhead. These metrics are inferred from the existing [19, 20, 27, 28] references. From these, the methods PIOTS, H-GA-PSO, and TEG-ST in the order [19, 20, 28] are used as the proposed scheme for a comparative assessment. The number of edge nodes varies between 1 and 16, operating between 20 and 300 time slots. This information is picked from the considered dataset with a maximum of 256 tasks per split between three layered machines. Each machine is assigned two controllers that interface with the 16 edge nodes for information sharing. This information is cloud-dependent for sharing, controlling, and remote handling of the tasks assigned. The edge devices employed in smart industry layers are utilized for improving productivity and automation through graph learning. This analysis output is used to provide appropriate decisions for sequential task grouping and cross-layer control between the interconnected industrial layers in different time intervals. The two phases are operated under the proposed scheme to ensure monotonous accomplishments and re-tasking inside and outside the group by edge computing. The task acceptance error in prolonged task timing is addressed based on the task migration between the controllers. To enhance the production, maintenance, and outcome in smart industries, controller nodes with similar and dissimilar tasks are grouped concerning the tasking time. This analysis and network training is pursued to achieve better performance. Therefore, the first phase is processed to prevent TAE, and the second phase continuously trains the learning network based on task migration between the controllers. Hence, the industrial layer information is obtained to classify the similarity of completion and resource utilization to satisfy a lower task completion time (Figure 7). The proposed scheme reduces task completion time by 9.38% and 10.68% for the # of edge devices and the # of time slots in order. In this proposed cross-layer controller, tasking is performed based on migration and re-tasking information sharing between the IIoT layers to achieve a high completion ratio (Refer to Figure 8). The first and second phases are processed to improve automation and productivity in smart industries with fewer TAEs. The controller migration and re-tasking inside and outside the group are pursued between the controllers based on training the learning network through EC intelligence and graph-combined learning. The recurrent training of the learning network minimizes task prolonging time with the interconnected groups using edge nodes, and re-tasking/migration information sharing between the layers in the synchronized completion time leads to high productivity. Such controller nodes  
Figure 7: Assessment of task completion time. with similar tasks are grouped to achieve monotonous accomplishments between the IIoT layers. The function of task grouping and cross-layer controlling between the IIoT layers is pursued for improving task allocation, distribution, and similarity verification, thereby improving the completion ratio. Therefore, the productivity based on completion ratio is high compared to the other methods. For the # of edge nodes and the # of time slots, the completion ratio is improved by 13.19% and 13.27%, respectively. The learning network is trained to prevent task completion errors in edge-controlled IIoT. Based on the task grouping assignment and cross-layer controlling, is difficult to determine each interconnected layer and centralized processing unit in EC. The appropriate recommendation provided for operating the two phases through graph structures is to reduce TAE. The two phases are concurrently processed to achieve a high productivity rate in the smart industry. From the instance, the number of similar tasks grouped is the same as the monotonous accomplishments.