Multi-Objective Edge Node Deployment Method Based on Improved Heuristic Algorithms for Smart Mines

As the number of intelligent devices in mining environments increases, the transmission time for large datasets, including equipment status and environmental parameters, also rises. This increase leads to longer response times for service requests, making it difficult to meet the equipment’s real-time requirements. Edge computing effectively addresses the demands for low latency and high performance. However, the deployment of edge nodes can negatively affect overall service performance due to resource limitations and node heterogeneity. In this paper, we propose two node deployment strategies: an improved genetic algorithm (IBGA) for fixed device scenarios and an improved sand cat swarm optimization algorithm (ISCSO) for mobile device scenarios, both accounting for the mobility characteristics of the devices. Additionally, we developed a simulation platform based on a production line system and an intelligent patrol vehicle to evaluate the proposed method’s effectiveness. The experimental results show that the IBGA and ISCSO algorithms effectively reduce task delay and deployment cost. Both deployment methods outperform the benchmark algorithms and offer better service quality assurance.