# Multi-Objective Edge Node Deployment Method Based on Improved Heuristic Algorithms for Smart Mines
## 2 Related Work  
The rational and efficient deployment of edge nodes is essential for establishing a low-latency edge computing platform in mining environments. The extensive geographical distribution of end devices and the limited resources of edge nodes significantly influence the decisions regarding the number and location of node placements. In recent years, numerous scholars have explored various strategies for edge node placement. Numerous scholars have explored methodologies for deploying edge nodes in fixed equipment scenarios. [PERSON] et al. [11] proposed a static node placement framework that incorporates various node placement and optimization strategies based on network sensing capabilities. However, this study primarily focused on general suggestions for edge node deployment without providing specific solutions tailored to different application scenarios.Abdenacer et al. [12] proposed a marine predator algorithm to improve network connectivity and device coverage. [PERSON] et al. [13] investigated the deployment challenges of edge nodes in smart cities, focusing on scenario-specific constraints related to load balancing and delay minimization. They proposed an enhanced nondominated sorting genetic algorithm (NSGA-II) that integrates an elitist strategy to optimize the selection process in subsequent iterations. Experimental results demonstrated a significant reduction of 40.6% in system overhead, highlighting the algorithm's effectiveness in optimizing edge node placement. However, it is important to note that while the aforementioned studies consider service type as a significant factor in node deployment, they do not account for the physical characteristics of the devices being serviced in their deployment models. [PERSON] et al. [14] proposed a deployment strategy utilizing a distributed genetic algorithm to optimize system performance and network costs, addressing the challenges posed by resource constraints and the wide geographical distribution of devices. [PERSON] et al. [15] focused on the task offloading aspect in edge server deployment, using a simulated annealing algorithm to determine optimal node locations. Their approach aimed to balance the load among edge nodes and improve user experience. However, it is important to note that previous studies did not fully consider the comprehensive cost implications related to the number of deployed edge nodes. [PERSON] et al. [16] introduced an enhanced clustering algorithm that accounts for variations in device functionality and layout, as well as disparities in computational and storage capacities across different edge nodes. The optimization of edge node deployment aims to balance network latency with the costs associated with computational resource deployment. The results demonstrate the algorithm's superiority over traditional methods. [PERSON] et al. [17] examined the issue of fault-tolerant node deployment, considering real-world scenarios with node failures. They framed the problem as a multi-objective challenge focused on achieving load balance and minimizing deployment costs. To solve this, they employed a genetic algorithm with heuristic enhancements. The results demonstrate the method's effectiveness in reducing both latency and deployment costs compared to other algorithms. Nevertheless, prior research has not thoroughly explored the specific requirements of edge node deployment within mining environments, making the proposed techniques less suitable for smart mining applications. In edge computing, certain devices are in constant motion, resulting in frequent changes to their location. This mobility increases service latency and creates challengesfor efficient task execution. Therefore, it is crucial to consider the effects of device mobility when determining the optimal placement of edge nodes. [PERSON] et al. [18] developed virtual nodes within a pay-per-use framework and utilized deep learning techniques to predict service requests and placement cost parameters.