Hybrid Data Scheduling Method for Industrial Wireless Sensor Networks Based on Age of Information
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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。Abstract: In Industrial Wireless Sensor Networks (IWSN), timely delivery of periodic control/sensing data flows and aperiodic event data flows is crucial to ensure production safety and efficiency. As a new metric of data freshness, Age of Information (AoI) can comprehensively measure the real-time performance of data delivery from the perspective of destination node. For industrial wireless sensor networks with hybrid periodic and aperiodic data, the data freshness metric of whole network is introduced. Considering that the freshness of periodic data exceeding the threshold may have a negative impact on industrial production, a joint optimization model is established, which minimizes the system average AoI and the probability of AoI overdue for periodic data, and then the optimization problem is formulated as a Markov Decision Process (MDP). Since the traditional optimal solution method based on relative value iteration is difficult to implement in large-scale networks result from dimensional disasters, Deep Reinforcement Learning (DRL) is used to reduce the state space dimension of the optimization problem. Moreover, the decision exploration mechanism is improved to speed up the learning speed, and a scheduling method of deep reinforcement learning based on Optimal Decision Exploration (DRL-ODE) is proposed. Simulation results show that the proposed method can improve the timeliness of network data delivery while reducing the probability of AoI overdue for periodic data effectively.
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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 
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