论文标题
通过AOI驱动的深钢筋学习在IoT网络中的自主维护
Autonomous Maintenance in IoT Networks via AoI-driven Deep Reinforcement Learning
论文作者
论文摘要
物联网（IoT）及其越来越多的部署设备和应用程序对网络维护程序提出了重大挑战。在这项工作中，我们将物联网网络中自主维护的问题制定为可观察到的马尔可夫决策过程。随后，我们利用深入的增强学习算法（DRL）来训练代理，以决定是否有井井有条，并且在前一种情况下，需要适当的维护类型。为了避免浪费物联网网络的稀缺资源，我们利用信息时代（AOI）指标作为训练智能代理的奖励信号。 AOI捕获了IOT传感器作为正常服务提供的一部分传输的感官数据的新鲜度。数值结果表明，AOI集成了有关系统的过去和现在状态的足够信息，将成功用于培训智能代理以自主维护网络。
Internet of Things (IoT) with its growing number of deployed devices and applications raises significant challenges for network maintenance procedures. In this work, we formulate a problem of autonomous maintenance in IoT networks as a Partially Observable Markov Decision Process. Subsequently, we utilize Deep Reinforcement Learning algorithms (DRL) to train agents that decide if a maintenance procedure is in order or not and, in the former case, the proper type of maintenance needed. To avoid wasting the scarce resources of IoT networks we utilize the Age of Information (AoI) metric as a reward signal for the training of the smart agents. AoI captures the freshness of the sensory data which are transmitted by the IoT sensors as part of their normal service provision. Numerical results indicate that AoI integrates enough information about the past and present states of the system to be successfully used in the training of smart agents for the autonomous maintenance of the network.
