  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.
