Energy-Efficient Trajectory Optimization for Aerial Video Surveillance under QoS Constraints

Surveillance drones are unmanned aerial vehicles (UAVs) that are utilized to collect video recordings of targets. In this paper, we propose a novel design framework for aerial video surveillance in urban areas, where a cellular-connected UAV captures and transmits videos to the cellular network that services users. Fundamental challenges arise due to the limited onboard energy and quality of service (QoS) requirements over environment-dependent air-to-ground cellular links, where UAVs are usually served by the sidelobes of base stations (BSs). We aim to minimize the energy consumption of the UAV by jointly optimizing the mission completion time and UAV trajectory as well as transmission scheduling and association, subject to QoS constraints. The problem is formulated as a mixed-integer nonlinear programming (MINLP) problem by taking into account building blockage and BS antenna patterns. We first consider the average performance for uncertain local environments, and obtain an efficient sub-optimal solution by employing graph theory and convex optimization techniques. Next, we investigate the site-specific performance for specific urban local environments. By reformulating the problem as a Markov decision process (MDP), a deep reinforcement learning (DRL) algorithm is proposed by employing a dueling deep Q-network (DQN) neural network model with only local observations of sampled rate measurements. Simulation results show that the proposed solutions achieve significant performance gains over baseline schemes.

Index Terms-Unmanned aerial vehicle (UAV), video surveillance, quality of service (QoS), energy consumption, deep reinforcement learning (DRL).