能量收集辅助的矿山物联网智能计算卸载方法  

Energy Harvesting Assisted Intelligent Computation Offloading Method for the IoT in Mining

作　　者：闵明慧 张鹏 朱浩鹏 程志鹏 马帅 李世银 肖亮[3] 彭国军[2] MIN Minghui;ZHANG Peng;ZHU Haopeng;CHENG Zhipeng;MA Shuai;LI Shiyin;XIAO Liang;PENG Guojun(School of Information and Control Engineering,China University of Mining and Technology,Xuzhou 221116,China;Key Laboratory of Aerospace Information Security and Trusted Computing,Ministry of Education,Wuhan University,Wuhan 430072,China;School of Informatics,Xiamen University,Xiamen 361005,China)

机构地区：[1]中国矿业大学信息与控制工程学院,徐州221116 [2]武汉大学空天信息安全与可信计算教育部重点实验室,武汉430072 [3]厦门大学信息学院,厦门361005

出　　处：《电子与信息学报》2023年第10期3547-3557,共11页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(62101557,61971366);中国博士后科学基金(2022M713378);中央高校基本科研业务费专项资金(2042022kf0021)。

摘　　要：针对计算、能量和内存资源受限的矿山物联网设备和大量时延敏感型计算任务需求的智慧矿山场景,该文提出一种能量收集(EH)辅助的矿山物联网智能计算卸载方法。通过采用移动边缘计算(MEC)技术协助矿山物联网设备任务计算,同时利用能量收集技术为能量受限的矿山物联网设备供电。基于Q-learning的智能计算卸载机制实现在不可精确获取矿井系统模型的情况下动态探索最优计算卸载策略。此外,为处理复杂矿井环境下的维度灾难问题并减小策略离散化导致的离散化误差,该文还提出一种基于深度确定性策略梯度(DDPG)的计算卸载机制来进一步提高井下任务计算卸载性能。理论分析与仿真结果表明所提机制可降低系统的能量损耗、计算时延和任务处理失败率,有助于保障矿山物联网的安全和高效生产。This paper proposes an Energy Harvesting(EH)-assisted mining IoT intelligent computation offloading method for the mine IoT device with limited computing,energy,and memory resources and smart mining scenario with a large number of latency-sensitive computation tasks.Mobile Edge Computing(MEC)technology is used to assist task computing of mine IoT devices,and EH technology is investigated to power energy-constrained mine IoT devices.The intelligent computation offloading mechanism based on Q-learning can dynamically explore and optimize computation offloading policy under the condition of an unknown precise mine system model.In addition,a computation offloading mechanism based on Deep Deterministic Policy Gradient(DDPG)is proposed.The curse of dimensionality in complex mining scenarios is resolved,the discretization error caused by policy discretization is reduced,and the computation offloading performance is further improved.Theoretical analysis and simulation results verify that the proposed mechanism can reduce energy consumption,computing delays,and task failure rate.This helps ensure safety and improve the production efficiency of IoT in mining.

关 键 词：矿山物联网 移动边缘计算 能量收集 Q-LEARNING 深度确定性策略梯度(DDPG) 

分 类 号：TN929.5[电子电信—通信与信息系统]