论文标题
最大程度地减少具有深度Q学习的雾计算支持车辆网络的信息年龄
Minimizing Age-of-Information for Fog Computing-supported Vehicular Networks with Deep Q-learning
论文作者
论文摘要
连接的车辆网络是下一代云/雾化自动驾驶车辆的关键推动力之一。大多数连接的车辆应用都需要频繁的状态更新，并且信息年龄（AOI）是一个更相关的指标，可以评估车辆与云/雾服务器之间的无线链接的性能。本文介绍了一种新型的积极主动和数据驱动的方法，以优化驾驶路线，主要目的是保证AOI的信心。特别是，我们报告了一项关于通过商业LTE网络连接到云/雾服务器的多车校园航天飞机系统三个月测量的研究。我们在连接的车辆中建立了AOI的经验模型，并研究了主要因素对AOI性能的影响。我们还提出了一个基于Q的Q学习NetWrok（DQN）算法，以确定以最大化置信度的最高置信度的最佳驾驶途径。数值结果表明，所提出的方法可能会导致对支持各种服务的AOI信心的显着提高。
Connected vehicular network is one of the key enablers for next generation cloud/fog-supported autonomous driving vehicles. Most connected vehicular applications require frequent status updates and Age of Information (AoI) is a more relevant metric to evaluate the performance of wireless links between vehicles and cloud/fog servers. This paper introduces a novel proactive and data-driven approach to optimize the driving route with a main objective of guaranteeing the confidence of AoI. In particular, we report a study on three month measurements of a multi-vehicle campus shuttle system connected to cloud/fog servers via a commercial LTE network. We establish empirical models for AoI in connected vehicles and investigate the impact of major factors on the performance of AoI. We also propose a Deep Q-Learning Netwrok (DQN)-based algorithm to decide the optimal driving route for each connected vehicle with maximized confidence level. Numerical results show that the proposed approach can lead to a significant improvement on the AoI confidence for various types of services supported.
