论文标题
在无人机辅助物联网网络中收集新鲜数据的深度加固学习
Deep Reinforcement Learning for Fresh Data Collection in UAV-assisted IoT Networks
论文作者
论文摘要
由于灵活性和低运营成本，因此派遣无人机（UAV）从分布式传感器中收集信息将成为物联网（IoT）的有前途的解决方案（IOT），尤其是对于时间关键的应用程序。如何维护新鲜信息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的物联网网络中的新鲜数据收集问题。尤其是，无人机朝着传感器飞行，以在给定持续时间内收集状态更新数据包，同时保持非负剩余能量。我们制定了马尔可夫决策过程（MDP），以找到无人机的最佳飞行轨迹和传感器的传输计划，以最大程度地减少信息时代的加权总和（AOI）。进一步提出了基于深入增强学习（DRL）的无人机辅助数据收集算法，以克服维度的诅咒。广泛的仿真结果表明，与其他基线算法相比，所提出的基于DRL的算法可以显着减少AOI的加权总和。
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.
