# Multi-Objective Edge Node Deployment Method Based on Improved Heuristic Algorithms for Smart Mines
## 3 System Model and Problem Description
### System Model
#### 3.1.2 Mobile Device Model  
In the case of the mine cart, the inspection trolley was used to simulate the transport task in the subsequent experimental section. The transport task involves the unmanned movement of mine vehicles along designated routes on mine roads, using sensory information collected from the environment. Throughout the process, these trucks interact with the platform, other vehicles, and surrounding equipment to perform tasks such as fault detection and warnings. However, the mobility of these vehicles introduces several challenges, including potential task transmission failures, disruption of service continuity, increased response delays, and compromised operational safety. To address these issues, edge nodes are strategically deployed in proximity to the equipment for task processing, ensuring both the safety and efficiency of transportation operations. As illustrated in Figure 2, the mining transportation scenario presents the critical challenge of rapidly responding to uncrewed transport vehicles, making it a focal point of research. This section, thus, examines the placement of mobile vehicle edge nodes within the context of edge computing systems specific to this scenario. Figure 1: Edge node placement system model for fixed equipment in a mining environment. To streamline the complexity of this problem, we make the following assumptions. (1) Each edge node can establish communication with the cloud center regardless of its location. (2) Edge nodes maintain a consistent communication latency when interacting with mobile devices. (3) Mobile devices operate in periodic cycles. (4) Mobile devices do not need to be co-located. The edge node placement model for mobile equipment within the mining production line shares several fundamental components with the model designed for fixed equipment. These common elements include mobile equipment, potential candidate locations for node placement, edge nodes, tasks, and other relevant factors critical to optimizing node deployment and ensuring efficient operation. When addressing the placement of edge nodes on mobile devices, this section no longer takes position constraints into account, as these devices are in motion with a broad range of movement. Consequently, any position along the trajectory can be deemed a potential candidate for node placement. In this scenario, the exact quantity of edge nodes remains uncertain, necessitating the selection of suitable algorithms to optimize the number of node deployments. The rest of the characteristics are similar to those in the fixed scene. This paper focuses on mobile devices with trajectories that remain nearly constant or exhibit minimal changes. In this paper, the group of mobile devices is represented as \(MD=\{md_{1},md_{2},...,md_{MN}\}\), where MN signifies the total number of mobile devices in the set. Furthermore, for each mobile device \(md_{i}\), its positional coordinates at time t are denoted as \(md_{i}^{t}=\{x_{i}^{t},y_{i}^{t}\}\). We use ME to denote the set of edge nodes, where \(ME=\{me_{1},me_{2},...,me_{MN}\}\), with MM representing the number of edge nodes. \(MFRE=\{mfre_{1},mfre_{2},...,mfre_{MM}\}\) signifies the CPU frequency of each edge node. The set of tasks is denoted as \(MT=\{mt_{1},mt_{2},...,mt_{MN}\}\). Each individual task request is specified as \(mt_{i}=\{mdini_{i},my_{i},mdout_{i}\}\), where \(mdini_{i}\) signifies the quantity of input data required by the task, \(my_{i}\) represents the task's load, and \(mdout_{i}\) denotes the amount of output data. In contrast to edge node deployments for fixed devices, the dynamic nature of mobile devices introduces additional challenges related to maintaining stable communication connectivity between multiple nodes and devices. Therefore, selecting the optimal location for edge node placement in such scenarios requires careful consideration, as any potential location within the environment may influence performance.