Joint Offloading and Trajectory Design for UAV-Enabled Mobile Edge Computing Systems

• Published in: IEEE internet of things journal Vol. 6; no. 2; pp. 1879 - 1892
• Main Authors: Hu, Qiyu, Cai, Yunlong, Yu, Guanding, Qin, Zhijin, Zhao, Minjian, Li, Geoffrey Ye
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.04.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Automotive parts; Computational modeling; Computer simulation; Computing costs; Delay; Delays; Edge computing; Energy consumption; Internet of Things; low-delay control; Maneuverability; Mobile computing; mobile edge computing (MEC); Optimization; Servers; Task analysis; Task scheduling; Trajectory; Trajectory optimization; unmanned aerial vehicle (UAV) communications; Unmanned aerial vehicles; Wireless communication systems; Wireless communications
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2018.2878876

Unmanned aerial vehicles (UAVs) have been considered in wireless communication systems to provide high-quality services for their low cost and high maneuverability. This paper addresses a UAV-aided mobile edge computing system, where a number of ground users are served by a moving UAV equipped with computing resources. Each user has computing tasks to complete, which can be separated into two parts: one portion is offloaded to the UAV and the remaining part is implemented locally. The UAV moves around above the ground users and provides computing service in an orthogonal multiple access manner over time. For each time period, we aim to minimize the sum of the maximum delay among all the users in each time slot by jointly optimizing the UAV trajectory, the ratio of offloading tasks, and the user scheduling variables, subject to the discrete binary constraints, the energy consumption constraints, and the UAV trajectory constraints. This problem has highly nonconvex objective function and constraints. Therefore, we equivalently convert it into a better tractable form based on introducing the auxiliary variables, and then propose a novel penalty dual decomposition-based algorithm to handle the resulting problem. Furthermore, we develop a simplified <inline-formula> <tex-math notation="LaTeX">{l}_{0} </tex-math></inline-formula>-norm algorithm with much reduced complexity. Besides, we also extend our algorithm to minimize the average delay. Simulation results illustrate that the proposed algorithms significantly outperform the benchmarks.