Multi-user joint task offloading and resource allocation based on mobile edge computing in mining scenarios

作者：Si‐Qi Li, Weidong Li, Wanbo Zheng, Yunni Xia, Kunyin Guo, Qinglan Peng, Xu Li, Jiaxin Ren · 发表于：Scientific Reports · 年份：2025 · DOI：10.1038/s41598-025-00730-y · 被引用次数：16 · 研究领域：IoT and Edge/Fog Computing、Age of Information Optimization、IoT Networks and Protocols

With the development of the industrial internet of things, an increasing number of intelligent terminal devices are being deployed in mining operations. However, due to the surge in network traffic and the limited availability of computational resources, these terminal devices face challenges in meeting high-performance requirements such as low transmission latency and low energy consumption. To address this issue, this paper proposes a method that combines partial offloading with collaborative mobile edge computing (MEC). This approach leverages device-to-device communication to partition computational tasks into multiple subtasks, offloading some of them to collaborative devices or MEC servers for execution. This not only alleviates the computational burden on MEC servers but also makes full use of the idle computing resources of terminal devices, thereby enhancing resource utilization efficiency. Given the limited computational capacity of terminal devices, this paper optimizes the offloading decision-making process between terminal devices and MEC servers. By introducing weighted coefficients for latency and energy consumption, the proposed method ensures that task completion latency does not exceed a predefined threshold while minimizing the overall system cost. The problem is formulated as a multi-objective optimization problem, which is solved using a two-layer alternating optimization framework. In the upper layer, an improved genetic algorithm (IGA) based on heuristi...