# 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
Figure 14 illustrates the relationship between task completion delay and the number of nodes when the number of cars is set to 20. Figure 14a demonstrates that 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 23%, outperforming  
Figure 14: Performance comparison of mobile device edge node deployment algorithms (number of mobile devices is 20). (**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 13: Performance comparison of mobile device edge node deployment algorithms (number of mobile devices is 10). (**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. the other four algorithms in minimizing delay. Figure 14b illustrates the deployment location and coverage of the nodes. In contrast, Figure 14c reveals that as the number of iterations increases, the device coverage stabilizes at 99%, and the algorithm converges after 150 iterations. The time complexity of the ISCSO algorithm is influenced by factors such as population size, dimensionality, and the computational complexity of the fitness function. In this study, we analyze the time complexity of fitness evaluations and location update operations. Assuming that the number of sand cats is ME and the dimension D, the complexity of the fitness function is f(D). The time complexity of the whole iterative process is \(O(MAXGEN*ME*(D+f(D)))\). Even in environments with significant changes or the presence of obstacles, the ISCSO algorithm demonstrates strong robustness and consistently identifies optimized paths. The proposed scheme demonstrates that the rational deployment of edge nodes can enhance the real-time monitoring capabilities of devices, optimize data processing, and improve decision-making processes. Firstly, by reducing task response times and optimizing data flow, the system can detect equipment failures or abnormal conditions more rapidly, thereby providing early warnings and reducing both downtime and maintenance costs. Secondly, it reduces the dependence on central servers, enhances the speed and accuracy of data processing, enables real-time decision-making at the edge nodes, and minimizes the demand for network bandwidth. Finally, by deploying IBGA and ISCSO algorithms to optimize the deployment location of the edge nodes, it not only improves the efficiency of task processing, but also makes more accurate decisions based on real-time data, and the intelligent decision-making process effectively improves the flexibility and responsiveness of mine management. The approach presented in this paper has demonstrated promising results. However, the limitations of the experimental simulation conditions, coupled with the variability across different mining environments, may lead to deviations in the algorithm's performance. For Assumption 1, it is important to note that not all tasks necessarily require service by edge nodes. Mandating that every task be handled by at least one edge node could result in resource over-allocation or inefficiency. For Assumption 2, some edge nodes may serve purposes such as data aggregation or collaboration with other nodes, rather than directly providing computational resources. Requiring each edge node to directly serve devices could lead to unnecessary node deployment, overlooking the potential roles these nodes may play in network orchestration or data processing. For Assumption 3, in real-world scenarios, the response time of an edge node can be influenced by factors such as network latency, device load, and other external conditions.