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基于信息年龄的工业无线传感器网络混合数据调度方法

王恒 余蕾 谢鑫

王恒, 余蕾, 谢鑫. 基于信息年龄的工业无线传感器网络混合数据调度方法[J]. 电子与信息学报, 2023, 45(3): 1065-1073. doi: 10.11999/JEIT220088
引用本文: 王恒, 余蕾, 谢鑫. 基于信息年龄的工业无线传感器网络混合数据调度方法[J]. 电子与信息学报, 2023, 45(3): 1065-1073. doi: 10.11999/JEIT220088
WANG Heng, YU Lei, XIE Xin. Hybrid Data Scheduling Method for Industrial Wireless Sensor Networks Based on Age of Information[J]. Journal of Electronics & Information Technology, 2023, 45(3): 1065-1073. doi: 10.11999/JEIT220088
Citation: WANG Heng, YU Lei, XIE Xin. Hybrid Data Scheduling Method for Industrial Wireless Sensor Networks Based on Age of Information[J]. Journal of Electronics & Information Technology, 2023, 45(3): 1065-1073. doi: 10.11999/JEIT220088

基于信息年龄的工业无线传感器网络混合数据调度方法

doi: 10.11999/JEIT220088
基金项目: 国家自然科学基金(61972061),重庆市自然科学基金杰出青年基金(cstc2019jcyjjqX0012),重庆基础研究与前沿探索项目(cstc2021ycjh-bgzxm0017)
详细信息
    作者简介:

    王恒:男,教授,博士生导师,研究方向为工业物联网、时钟同步、实时调度等

    余蕾:男,硕士生,研究方向为无线网络调度

    谢鑫:男,博士生,研究方向为无线网络调度

    通讯作者:

    王恒 wangheng@cqupt.edu.cn

  • 中图分类号: TN929.5

Hybrid Data Scheduling Method for Industrial Wireless Sensor Networks Based on Age of Information

Funds: The National Natural Science Foundation of China (61972061), The Natural Science Foundation of Chongqing, for Distinguished Young Scholars (cstc2019jcyjjqX0012), The Fundamental Research and Frontier Exploration of Chongqing (cstc2021ycjh-bgzxm0017)
  • 摘要: 在工业无线传感器网络(IWSN)中,实时交付工业现场的周期性控制/传感数据流与非周期性事件数据流,是保障生产安全高效运行的关键。信息年龄(AoI)作为一种新兴的数据新鲜度衡量指标,能够从目标节点角度全面地度量网络数据交付的实时性。针对周期性和非周期性数据混合的工业无线传感器网络,该文在引入网络数据整体新鲜度指标的同时,考虑到周期性数据新鲜度在超过阈值后可能会对工业生产造成负面影响,建立了最小化系统平均AoI和周期性数据AoI逾期概率的联合优化模型,并将优化问题表述为马尔可夫决策过程(MDP)进行求解。由于传统基于相对值迭代的最优求解方法在大规模网络中因为维度灾难难以实施,因此采用深度强化学习(DRL)降低优化问题的状态空间维度,并改进决策探索机制以加快学习速度,提出了基于优化决策探索的深度强化学习(DRL-ODE)调度方法。仿真结果表明,所提方法能够提高网络数据交付的实时性,并有效减少周期性数据的AoI逾期概率。
  • 图  1  混合更新的工业无线传感器网络示意图

    图  2  DRL调度方法训练示意图

    图  3  不同网络规模下各方法性能对比

    图  4  不同传输成功率下系统平均AoI对比

    图  5  优化决策探索性能对比

    图  6  不同$\alpha $的结果对比

    算法1 DRL-ODE调度方法训练算法流程
     (1) 初始化:网络参数${\mathbf{w}}$和${{\mathbf{w}}_N}$以及回放记忆单元
     (2) for k = 0,1,···,K do
     (3)    生成一个0和1之间的随机数b
     (4)    if $b < \mu $ then
     (5)      根据式(12)计算每个源节点的期望下降值${e_m}$并生
            成优化动作空间$O\left( k \right)$;
     (6)      从$O\left( k \right)$中随机选取决策$d\left( k \right)$;
     (7)    else then
     (8)      选择$\min V\left( {s\left( k \right),d\left( k \right)|{\mathbf{w}}} \right)$对应的决策$d\left( k \right)$;
     (9)    end
     (10)   当前值网络执行决策$d\left( k \right)$并与当前系统状态为$s\left( k \right)$的
            IWSN环境交互;
     (11)   获取IWSN环境反馈的下一状态$s\left( {k + 1} \right)$和惩罚$c\left( k \right)$;
     (12)   将当前经验集合$\left( {s\left( k \right),d\left( k \right),c\left( k \right),s\left( {k + 1} \right)} \right)$存入回放
            记忆单元;
     (13)   从回放记忆单元中随机选取经验集合并根据式(11)计算
            损失函数$ L\left( {\mathbf{w}} \right) $;
     (14)   根据式(12)利用梯度下降法更新参数向量${\mathbf{w}}$;
     (15)   每迭代N次将当前值网络参数拷贝至目标值网络;
     (16)   end for
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-01-19
  • 修回日期:  2022-04-21
  • 网络出版日期:  2022-04-26
  • 刊出日期:  2023-03-10

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