Research Repository

Joint Trajectory-Task-Cache Optimization in UAV-Enabled Mobile Edge Networks for Cyber-Physical System

Mei, Haibo and Wang, Kezhi and Zhou, Dongdai and Yang, Kun (2019) 'Joint Trajectory-Task-Cache Optimization in UAV-Enabled Mobile Edge Networks for Cyber-Physical System.' IEEE Access, 7. pp. 156476-156488. ISSN 2169-3536

08883173.pdf - Published Version

Download (4MB) | Preview


This paper studies an unmanned aerial vehicle (UAV)-enabled mobile edge network for Cyber-Physical System (CPS), where UAV with fixed-wing or rotary-wing is dispatched to provide communication and mobile edge computing (MEC) services to ground terminals (GTs). To minimize the energy consumption so as to extend the endurance of the UAV, we intend to jointly optimize its 3D trajectory and the task-cache strategies among GTs to save the energies spent on flight propulsion and GT tasks. Such joint trajectory-task-cache problem is difficult to be optimally solved, as it is non-convex and involves multiple constraints. To tackle this problem, we reformulate the optimizing of task offloading and cache into two tractable linear program (LP) problems, and the optimizing of UAV trajectory into three convex Quadratically Constrained Quadratically Program (QCQP) problems on horizontal trajectory, vertical trajectory and flight time of the UAV respectively. Then a block coordinate descent algorithm is proposed to iteratively solve the formed sub-problems through a successive convex optimization (SCO) process. A high-quality sub-optimal solution to the joint problem then will be obtained, after the algorithm converging to a prescribed accuracy. The numerical results show the proposed solution significantly outperforms the baseline solution.

Item Type: Article
Uncontrolled Keywords: Task analysis; Trajectory; Unmanned aerial vehicles; Three-dimensional displays; Wireless communication; Energy consumption; Servers; Unmanned aerial vehicle; Internet of Thing; mobile edge computing; 3D trajectory design; cache deployment
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 02 Mar 2020 14:20
Last Modified: 23 Sep 2022 19:36

Actions (login required)

View Item View Item