# Multi-Objective Edge Node Deployment Method Based on Improved Heuristic Algorithms for Smart Mines
## 1 Introduction  
The mining environment is inherently complex, marked by high temperatures, humidity, distinct geological conditions, confined spaces, and numerous obstacles. These conditions significantly heighten the risks of hazards such as collapses, fires, and explosions, posing considerable challenges to the safety of both workers and equipment [1]. Given these factors, implementing a comprehensive monitoring system for the mining environment is essential. However, the uneven terrain in mining areas induces significant vibrations in the sensing equipment, which compromises the accuracy of the collected data. The accumulation of sand and dust also increases the risk of equipment failure. In addition, the high-temperature and humid conditions necessitate careful consideration of the temperature resistance of the devices. The mountainous topography may further weaken signal strength. Consequently, the application of multi-sensor fusion technology becomes critical, requiring the deployment of multiple sensors to achieve comprehensive, multi-attribute detection of mechanical equipment within mining environments. Mining operations inherently produce time-sensitive data that require immediate processing to ensure the safety of personnel and assets. However, the need for real-time processing demands significant computational power and storage capacity, which often surpass the capabilities of traditional computing systems [2]. To address these challenges, we utilize the vast computing and storage resources provided by cloud computing to handle large datasets. However, this approach faces challenges due to the substantial distance between the sensors and the cloud server, along with constraints in network bandwidth. The large volume of data transmitted to the cloud leads to network congestion and significant transmission delays, making it insufficient for fulfilling the real-time requirements of latency-sensitive tasks. In response to these challenges, edge computing has emerged as a complementary paradigm to cloud computing, enabling data processing closer to the source. In mining operations, edge nodes, equipped with essential computing, storage, and network communication capabilities, are strategically deployed near the equipment to ensure timely responsiveness to real-time tasks [3; 4]. Despite the clear advantages of edge computing in managing large datasets, the deployment of critical edge nodes presents persistent challenges. Several factors contribute to this complexity. First, there is a significant divergence in the hardware architectures and software configurations across different sensing devices. Second, the network interactions are highly heterogeneous, involving not only device-to-device communication but also interactions between devices, edge nodes, and sensors [5; 6]. Third, edge nodes from different manufacturers exhibit disparities in computing power, storage capacities, and communication capabilities [7]. Therefore, the efficient deployment of edge nodes at the network edge is essential for implementing edge computing in mining operations. This approach is also critical for ensuring service quality via the edge computing platform [8]. The mechanical equipment in the mine environment is divided into fixed equipment and mobile equipment [9]. Fixed equipment primarily includes large, immovable devices such as conveyors, stationary crushers, and vibrating screens. The operational states of these devices are complex and variable, requiring the deployment of various sensors to collect multi-faceted data. This imposes periodic computational loads on the processing nodes. Additionally, structural and layout differences among devices necessitate different sensor types to meet specific requirements. The available locations for edge node deployment also vary, and the flexibility of deployment schemes remains limited. Mobile equipment primarily comprises devices that necessitate mobility during mining operations.