标题： 基于深度强化学习的无人机辅助物联网网络新鲜数据收集 显示英文标题

标题： Deep Reinforcement Learning for Fresh Data Collection in UAV-assisted IoT Networks

摘要： 由于灵活性和低运营成本，将无人飞行器（UAV）调度以从分布式传感器收集信息被认为是物联网（IoT）中一种有前景的解决方案，尤其是在时间关键的应用中。 如何保持信息的新鲜度是一个具有挑战性的问题。 在本文中，我们研究了无人机辅助物联网网络中的新鲜数据收集问题。 特别是，无人机在保持非负剩余能量的同时，飞向传感器在给定时间内收集状态更新数据包。 我们建立了一个马尔可夫决策过程（MDP）来找到无人机的最佳飞行轨迹和传感器的传输调度，以最小化信息年龄（AoI）的加权和。 进一步提出了一种基于深度强化学习（DRL）的无人机辅助数据收集算法，以克服维度灾难。 大量仿真结果表明，与其它基线算法相比，所提出的基于DRL的算法可以显著降低AoI的加权和。 显示英文摘要

摘要： Due to the flexibility and low operational cost, dispatching unmanned aerial vehicles (UAVs) to collect information from distributed sensors is expected to be a promising solution in Internet of Things (IoT), especially for time-critical applications. How to maintain the information freshness is a challenging issue. In this paper, we investigate the fresh data collection problem in UAV-assisted IoT networks. Particularly, the UAV flies towards the sensors to collect status update packets within a given duration while maintaining a non-negative residual energy. We formulate a Markov Decision Process (MDP) to find the optimal flight trajectory of the UAV and transmission scheduling of the sensors that minimizes the weighted sum of the age of information (AoI). A UAV-assisted data collection algorithm based on deep reinforcement learning (DRL) is further proposed to overcome the curse of dimensionality. Extensive simulation results demonstrate that the proposed DRL-based algorithm can significantly reduce the weighted sum of the AoI compared to other baseline algorithms.