Unmanned Aerial Vehicles (UAVs) have been emerging as an effective solution for IoT data collection networks thanks to their outstanding flexibility, mobility, and low operation costs. However, due to the limited energy and uncertainty from the data collection process, speed control is one of the most important factors to optimize the energy usage efficiency and performance for UAV collectors. This work aims to develop a novel autonomous speed control approach to address this issue. To that end, we first formulate the dynamic speed control task of a UAV as a Markov decision process taking into account its energy status and location. In this way, the Q-learning algorithm can be adopted to obtain the optimal speed control policy for the UAV. To further improve the system performance, we develop an highly-effective deep dueling double Q-learning algorithm utilizing outstanding features of the deep neural networks as well as advanced dueling architecture to quickly stabilize the learning process and obtain the optimal policy. Through simulation results, we show that our proposed solution can achieve up to 40% greater performance compared with other conventional methods. Importantly, the simulation results also reveal significant impacts of UAV's energy and charging time on the system performance.

中文翻译：

---

快或慢：无人机辅助物联网数据收集网络的自主速度控制方法

由于其出色的灵活性、移动性和低运营成本，无人驾驶飞行器 (UAV) 已成为物联网数据收集网络的有效解决方案。然而，由于数据采集过程的能量有限和不确定性，速度控制是优化无人机采集器能量使用效率和性能的最重要因素之一。这项工作旨在开发一种新颖的自主速度控制方法来解决这个问题。为此，我们首先将无人机的动态速度控制任务制定为考虑其能量状态和位置的马尔可夫决策过程。这样就可以采用Q-learning算法来获得无人机的最优速度控制策略。为了进一步提高系统性能，我们开发了一种高效的深度决斗双 Q 学习算法，利用深度神经网络的突出特点以及先进的决斗架构来快速稳定学习过程并获得最佳策略。通过仿真结果，我们表明，与其他传统方法相比，我们提出的解决方案可以实现高达 40% 的性能提升。重要的是，仿真结果还揭示了无人机的能量和充电时间对系统性能的显着影响。