Age of Information (AoI) measures the freshness of the information at a remote location. AoI reflects the time that is elapsed since the generation of the packet by a transmitter. In this paper, we consider a remote monitoring problem (e.g., remote factory) in which a number of sensor nodes are transmitting time sensitive measurements to a remote monitoring site. We consider minimizing a metric that strikes a trade-off between minimizing the sum of the expected AoI of all sensors and minimizing an Ultra Reliable Low Latency Communication (URLLC) term. The URLLC term minimization is represented by ensuring that the probability the AoI of each sensor exceeds a predefined threshold should be at its minimum. Moreover, we assume that sensors require different threshold values and generate different packet sizes. Motivated by the success of machine learning in solving large networking problems at low complexity, we develop a low complexity reinforcement learning based algorithm to solve the proposed formulation. We trained our algorithm using the state-of-the-art actor-critic algorithm over a set of public bandwidth traces. Simulation results show that the proposed algorithm outperforms the considered baselines in terms of minimizing the expected AoI and the threshold violation of each sensor.
翻译:信息年龄( AoI) 衡量远程地点信息的新鲜度。 AoI 表示的是发报机生成信息包所花的时间。 在本文中,我们考虑到一个远程监测问题(例如远程工厂),一些传感器节点将时间敏感度测量传送到远程监测站。我们考虑尽量减少一个衡量标准,在尽可能减少所有传感器预期的AoI和尽量减少超可靠低纬度通信(URLLC)的总量之间作出权衡。URLLC 术语的最小化表现是确保每个传感器的AoI 超过预定阈值的概率。此外,我们假设传感器需要不同的阈值并产生不同的包尺寸。由于机器成功地以低复杂性解决大型联网问题,我们开发了一个基于低复杂度强化学习的算法,以解决拟议的配方。我们用一套公共带宽跟踪的状态、最先进的演算法来培训我们的算法。 模拟结果显示,提议的算法超越了每个传感器的临界值。