Li Siqi, Li Weidong, Zheng Wanbo, Xia Yunni, Guo Kunyin, Peng Qinglan, Li Xu, Ren Jiaxin
Faculty of Science, Kunming University of Science and Technology, Kunming, 650500, China.
School of Mathematics and Statistics, Yunnan University, Kunming, 650500, China.
Sci Rep. 2025 May 9;15(1):16170. doi: 10.1038/s41598-025-00730-y.
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 heuristic rules is employed to generate an offloading decision population, while the lower layer utilizes the deep deterministic policy gradient (DDPG) algorithm to optimize the offloading strategy and the weighted coefficients for latency and energy consumption. To evaluate the effectiveness of the proposed method, we compare it with five baseline algorithms: the improved grey wolf optimizer metaheuristic algorithm, the traditional genetic algorithm, the binary offloading decision mechanism, the partial non-cooperative mechanism, and the fully local execution mechanism. Simulation results demonstrate that the proposed IGA-DDPG algorithm achieves significant improvements over these baseline methods. Specifically, under various experimental scenarios, IGA-DDPG reduces latency by an average of 24.5%, decreases energy consumption by 26.3%, and lowers overall system cost by 44.6%. Moreover, the algorithm consistently ensures a 100% task completion rate under different system configurations.
随着工业物联网的发展，越来越多的智能终端设备被部署到采矿作业中。然而，由于网络流量的激增和计算资源可用性的限制，这些终端设备在满足低传输延迟和低能耗等高性能要求方面面临挑战。为了解决这个问题，本文提出了一种将部分卸载与协作移动边缘计算（MEC）相结合的方法。这种方法利用设备到设备通信将计算任务划分为多个子任务，将其中一些子任务卸载到协作设备或MEC服务器上执行。这不仅减轻了MEC服务器的计算负担，还充分利用了终端设备的空闲计算资源，从而提高了资源利用效率。鉴于终端设备的计算能力有限，本文优化了终端设备与MEC服务器之间的卸载决策过程。通过引入延迟和能耗的加权系数，所提出的方法确保任务完成延迟不超过预定义的阈值，同时最小化整个系统成本。该问题被表述为一个多目标优化问题，使用两层交替优化框架进行求解。在上层，采用基于启发式规则的改进遗传算法（IGA）生成卸载决策种群，而在下层，利用深度确定性策略梯度（DDPG）算法优化卸载策略以及延迟和能耗的加权系数。为了评估所提出方法的有效性我们将其与五种基线算法进行比较：改进的灰狼优化元启发式算法、传统遗传算法、二进制卸载决策机制、部分非合作机制和完全本地执行机制。仿真结果表明，所提出的IGA-DDPG算法相对于这些基线方法取得了显著改进。具体而言，在各种实验场景下，IGA-DDPG平均将延迟降低了24.5%，将能耗降低了26.3%，并将整个系统成本降低了44.6%。此外，该算法在不同系统配置下始终确保100%的任务完成率。
