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能量收集辅助的矿山物联网智能计算卸载方法

闵明慧 张鹏 朱浩鹏 程志鹏 马帅 李世银 肖亮 彭国军

闵明慧, 张鹏, 朱浩鹏, 程志鹏, 马帅, 李世银, 肖亮, 彭国军. 能量收集辅助的矿山物联网智能计算卸载方法[J]. 电子与信息学报, 2023, 45(10): 3547-3557. doi: 10.11999/JEIT220973
引用本文: 闵明慧, 张鹏, 朱浩鹏, 程志鹏, 马帅, 李世银, 肖亮, 彭国军. 能量收集辅助的矿山物联网智能计算卸载方法[J]. 电子与信息学报, 2023, 45(10): 3547-3557. doi: 10.11999/JEIT220973
MIN Minghui, ZHANG Peng, ZHU Haopeng, CHENG Zhipeng, MA Shuai, LI Shiyin, XIAO Liang, PENG Guojun. Energy Harvesting Assisted Intelligent Computation Offloading Method for the IoT in Mining[J]. Journal of Electronics & Information Technology, 2023, 45(10): 3547-3557. doi: 10.11999/JEIT220973
Citation: MIN Minghui, ZHANG Peng, ZHU Haopeng, CHENG Zhipeng, MA Shuai, LI Shiyin, XIAO Liang, PENG Guojun. Energy Harvesting Assisted Intelligent Computation Offloading Method for the IoT in Mining[J]. Journal of Electronics & Information Technology, 2023, 45(10): 3547-3557. doi: 10.11999/JEIT220973

能量收集辅助的矿山物联网智能计算卸载方法

doi: 10.11999/JEIT220973
基金项目: 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)
详细信息
    作者简介:

    闵明慧:女,讲师,研究方向为无线通信、网络安全、隐私保护等

    张鹏:男,博士生,研究方向为无线通信、边缘计算

    朱浩鹏:男,硕士生,研究方向为无线通信、隐私保护

    程志鹏:男,博士生,研究方向为无线通信、边缘计算

    马帅:男,副教授,研究方向为语义通信、可见光通信等

    李世银:男,教授,研究方向为煤矿信息化、移动目标定位等

    肖亮:女,教授,研究方向为无线通信、网络安全、强化学习等

    彭国军:男,教授,研究方向为网络安全、恶意代码、可信软件等

    通讯作者:

    李世银 lishiyin@cumt.edu.cn

  • 中图分类号: TN929.5

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

Funds: The National Natural Science Foundation of China(62101557, 61971366), China Postdoctoral Science Foundation (2022M713378), Fundamental Research Fundations for the Central Universities (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。
  • 图  1  矿山物联网中的MEC架构

    图  2  基于DDPG的计算卸载机制(DDRLOM)框架

    图  3  不同智能计算卸载机制的收敛性能

    图  4  能量收集产能对计算任务失败率的影响

    图  5  边缘服务器计算能力对计算时延的影响

    图  6  计算卸载的平均性能与计算任务量之间的关系

    算法1 基于Q-learning的计算卸载机制
     初始化系统参数$\alpha $, $\gamma $, $B_1^0, \cdots ,B_M^0$ ,${g^{(1)}}$, ${b^0}$;设置学习迭代次数
     1: For $k = 1,2,3, \cdots $do
     2:   预测能量收集的产能${g^{(k)}}$,观察得到${b^{(k)}}$和$B_1^{(k - 1)}, \cdots ,B_M^{(k - 1)}$
     3:   得到状态${{\boldsymbol{s}}^{(k)} } = [B_1^{(k - 1)}, \cdots ,B_M^{(k - 1)},{g^{(k)} },{b^{(k)} }]$
     4:   基于$\varepsilon {\text{ - greedy}}$选择计算卸载策略${{\boldsymbol{a}}^{(k)} } = [{i^{(k)} },{x^{(k)} }]$,并卸载任务量$ {R^{(k)}}{x^{(k)}} $至边缘服务器$i$
     5:   与环境交互,得到电量变化${b^{(k + 1)}}{\text{ = }}\max \{ {b^{(k)}} - {E^{(k)}} + {g^{(k)}},0\} $,计算评估${E^{(k)}}$, ${T^{(k)}}$和${I}({b^{(k + 1)} } = 0)$
     6:   通过式(7)获得效益${U^{(k)}}$,通过式(8)更新Q值$Q({{\boldsymbol{s}}^{(k)} },{{\boldsymbol{a}}^{(k)} })$
     7: End
    下载: 导出CSV
    算法2 基于DDPG的计算卸载机制
     初始化学习参数$\alpha $,$\lambda $,${\xi _1}$,${\xi _2}$,$Z$,$\kappa $; OU噪声$\mathcal{N}$的参数;设置学习迭代次数
     1: 观测得到${g^{(k)}}$, ${b^{(k)}}$, $B_1^{(k - 1)}, \cdots ,B_M^{(k - 1)}$,组成状态$ {s^{(k)}} = [B_1^{(k - 1)}, \cdots ,B_M^{(k - 1)},{g^{(k)}},{b^{(k)}}] $
     2: For $k = 1,2,3, \cdots $ do
     3:   基于状态$ {s^{(k)}} $输出确定性动作,加入噪声$\mathcal{N}$后,产生卸载策略${{\boldsymbol{a}}^{(k)} } = \mu ({s^{(k)} }|{\xi _2}^{(k)}) + \mathcal{N}$
     4:   卸载任务量$ {R^{(k)}}{x^{(k)}} $至边缘服务器$i$,评估得到${s^{(k + 1)}}$和${U^{(k)}}$
     5:   将$({{\boldsymbol{s}}^{(k)} },{{\boldsymbol{a}}^{(k)} },{U^{(k)} },{s^{(k + 1)} })$存入经验池
     6:   从经验池中抽取$Z$组学习经验$({s_h},{a_h},{U_h},{s_{h + 1}}),h \in [1,Z]$,更新网络参数
     7:   由式(10)和式(11)更新Critic网络参数${\xi _1}$,由式(12)更新Actor网络参数${\xi _2}$
     8:   由式(13)对Target网络参数${\xi _1}^\prime ,{\xi _2}^\prime $进行软更新
     9:   ${{\boldsymbol{s}}^{(k)} }{\text{ = } }{{\boldsymbol{s}}^{(k + 1)} }$
     10: End for
    下载: 导出CSV

    表  1  DDRLOM机制的超参数设置

    参数数值
    Actor/Critic学习率0.0001/0.002
    折扣因子0.9
    OU噪声均值/标准差/均值回归速度5/0.015/40
    batch-size64
    激活函数Leaky ReLU
    卷积网络隐藏层数/隐藏的单元数2层/32,16
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-07-21
  • 修回日期:  2023-03-31
  • 网络出版日期:  2023-04-04
  • 刊出日期:  2023-10-31

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