Joint Status Sampling and Updating for Minimizing Age of Information in the Internet of Things
Joint Status Sampling and Updating for Minimizing Age of Information in the Internet of Things
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DOI：
10.1109/tcomm.2019.2931538
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发表时间：
2019-11-01
影响因子：
8.3
通讯作者：
Saad, Walid
中科院分区：
文献类型：
--
作者：
Zhou, Bo;Saad, Walid
时间关键型物联网（IoT）应用的有效运行需要实时报告底层物理过程的最新状态信息。本文考虑了一种实时物联网监控系统，其中物联网设备以采样代价对物理过程进行采样，并以更新代价将状态包发送到给定的目的地。该联合状态采样和更新过程被设计为在每个设备处的平均能量成本约束下最小化目的地节点处的信息的平均年龄（AoI）。该随机问题被表示为无限时间平均成本约束的马尔可夫决策过程（CMDP），并使用拉格朗日方法将其转化为无约束的马尔可夫决策过程（MDP）。对于单个IoT设备的情况，CMDP的最优策略被示出为用于无约束MDP的两个确定性策略的随机混合，其为阈值类型。这揭示了目的地的平均AoI与采样和更新成本之间的基本权衡。然后，结构感知的优化算法，以获得最佳的CMDP的政策，提出和无线信道动态的影响进行了研究，同时表明，具有较大的平均信道增益和散射较小的信道可以实现更好的AoI性能。针对多个物联网设备的情况，提出了一种低复杂度的半分布式次优策略，在目标端进行更新控制，在每个物联网设备处进行采样控制.然后，开发一种在线学习算法来获得该策略，该策略可以在每个物联网设备上实现，并且只需要本地知识和来自目的地的少量信令。所提出的学习算法几乎必然收敛到次优策略。仿真结果显示了单个物联网设备情况下最优策略的结构特性;并显示了多个物联网设备的建议策略优于零等待基线策略，平均AoI降低高达33%。
The effective operation of time-critical Internet of things (IoT) applications requires real-time reporting of fresh status information of underlying physical processes. In this paper, a real-time IoT monitoring system is considered, in which the IoT devices sample a physical process with a sampling cost and send the status packet to a given destination with an updating cost. This joint status sampling and updating process is designed to minimize the average age of information (AoI) at the destination node under an average energy cost constraint at each device. This stochastic problem is formulated as an infinite horizon average cost constrained Markov decision process (CMDP) and transformed into an unconstrained Markov decision process (MDP) using a Lagrangian method. For the single IoT device case, the optimal policy for the CMDP is shown to be a randomized mixture of two deterministic policies for the unconstrained MDP, which is of threshold type. This reveals a fundamental tradeoff between the average AoI at the destination and the sampling and updating costs. Then, a structure-aware optimal algorithm to obtain the optimal policy of the CMDP is proposed and the impact of the wireless channel dynamics is studied while demonstrating that channels having a larger mean channel gain and less scattering can achieve better AoI performance. For the case of multiple IoT devices, a low-complexity semi-distributed suboptimal policy is proposed with the updating control at the destination and the sampling control at each IoT device. Then, an online learning algorithm is developed to obtain this policy, which can be implemented at each IoT device and requires only the local knowledge and small signaling from the destination. The proposed learning algorithm is shown to converge almost surely to the suboptimal policy. Simulation results show the structural properties of the optimal policy for the single IoT device case; and show that the proposed policy for multiple IoT devices outperforms a zero-wait baseline policy, with average AoI reductions reaching up to 33%.