Joint Optimization on Trajectory, Altitude, Velocity, and Link Scheduling for Minimum Mission Time in UAV-Aided Data Collection

• Published in: IEEE internet of things journal 2020-02, Vol.7 (2), p.1464-1475
• Main Authors: Li, Jiaxun, Zhao, Haitao, Wang, Haijun, Gu, Fanglin, Wei, Jibo, Yin, Hao, Ren, Baoquan
• Format: Article
• Language: English
• Subjects: Algorithms; Altitude; Completion time; Computer simulation; Data collection; Data links; Data models; Internet of Things; Line of sight communication; Minimization; Nonlinear programming; Optimization; Optimization algorithms; Routing; Scheduling; time minimization; Trajectory; Trajectory optimization; Unmanned aerial vehicles; unmanned aerial vehicles (UAVs); Velocity; wireless sensor networks (WSNs)
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2019.2955732

Due to the flexibility in 3-D space and high probability of line-of-sight (LoS) in air-to-ground communications, unmanned aerial vehicles (UAVs) have been considered as means to support energy-efficient data collection. However, in emergency applications, the mission completion time should be main concerns. In this article, we propose a UAV-aided data collection design to gather data from a number of ground users (GUs). The objective is to optimize the UAV's trajectory, altitude, velocity, and data links with GUs to minimize the total mission time. However, the difficulty lies in that the formulated time minimization problem has mutual effect with trajectory variables. To tackle this issue, we first transform the original problem equivalently to the trajectory length problem and then decompose the problem into three subproblems: 1) altitude optimization; 2) trajectory optimization; and 3) velocity and link scheduling optimization. In the altitude optimization, the aim is to maximize the transmission region of GUs which can benefit trajectory designing; then, in the trajectory optimization, we propose a segment-based trajectory optimization algorithm (STOA) to avoid repeat travel; besides, we also propose a group-based trajectory optimization algorithm (GTOA) in large-scale high-density GU deployment to relieve massive computation introduced by STOA. Then, the velocity and link scheduling optimization is modeled as a mixed-integer nonlinear programming (MINLP) and block coordinate descent (BCD) is employed to solve it. Simulations show that both STOA and GTOA achieve shorter trajectory compared with the existing algorithm and GTOA has less computational complexity; besides, the proposed time minimization design is valid by comparing to the benchmark scheme.