# Multi-Objective Edge Node Deployment Method Based on Improved Heuristic Algorithms for Smart Mines
## 6 Results and Discussion
### Analysis of the Results of the Mobile Device Edge Node Placement Algorithm  
In mobile device scenarios, it is important to note that the determination of candidate location points for nodes becomes a challenging task. As a result, the range of potential edge node deployments remains uncertain. In our laboratory environment, all inspection vehicles are equipped with identical functions and generate similar task requests. Therefore, a single type of edge node is deployed, and the primary factor influencing overall deployment cost is the number of nodes. In this section, we specifically examine the impact of varying the number of edge nodes on the overall task delay. To evaluate the performance of ISCSO, we use random distribution and mean distribution algorithms [37] as benchmark methods. Additionally, we compare ISCSO with other algorithms, such as SCSO and PSO [38], in terms of task completion time and deployment cost. Currently, the laboratory has a total of 20 mobile cards. To simulate small-scale, medium-scale, and large-scale mining environments, we conducted three sets of experiments, with 5, 10, and 20 trolleys placed in each set, respectively. Considering the limited coverage of edge nodes, a minimum of three nodes must be deployed to ensure task completion. As the number of mobile devices increases, the number of nodes deployed remains fewer than the number of devices. Consequently, we deployed [3; 5; 3; 9], and [3; 18] nodes across the three experimental sets. Observing Figures 12-14, it becomes evident that the total delay of all algorithms increases with the growing number of mobile devices. This is due to the increased task volume generated by the higher number of devices, leading to a corresponding rise in total task completion delay. Figure 12: Performance comparison of mobile device edge node deployment algorithms (number of mobile devices is five). (**a**) Total task delay. (**b**) Node deployment location. The red dots represent the node deployment location, and the yellow area represents the node’s coverage. (**c**) Algorithm convergence curve. Figure 12 illustrates the relationship between task completion delay and the number of nodes, with the number of cars set at 5. As shown in Figure 12a, the overall task delay decreases as the number of nodes increases. As the number of edge nodes increases, additional computational resources are provided, leading to a higher number of tasks being processed. Consequently, the overall delay decreases with the increase in the number of nodes. After analysis and comparison, the ISCSO algorithm demonstrates a delay reduction of up to 45%, outperforming the other four algorithms in minimizing delay. This improvement is attributed to the proposed algorithm's consideration of device mobility and the correlation between tasks and locations, which leads to a more effective node matching model. Furthermore, the enhanced spiral search and Levy flight strategies in the ISCSO algorithm expand the exploration space, enhance the algorithm's global search capability, and facilitate the identification of the optimal solution in a shorter time. Figure 12b represents the deployment positions and coverage ranges of the nodes. Figure 12c shows that as the number of iterations increases, the device coverage stabilizes at 67%, and the algorithm converges after 200 iterations. Figure 13 depicts the relationship between task completion delay and the number of nodes with a fleet size of 10 cars. As shown in Figure 13a, the total task delay decreases as the number of nodes increases. After analysis and comparison, the ISCSO algorithm demonstrates a reduction in total delay by up to 13%, outperforming the other four algorithms in minimizing delay. In Figure 13b, the deployment location and coverage of the nodes are illustrated. In contrast, Figure 13c demonstrates thatas the number of iterations increases, the device coverage stabilizes at 90%, and the algorithm converges after 300 iterations.