Completion Time and Energy Optimization in the UAV-Enabled Mobile-Edge Computing System

• Published in: IEEE internet of things journal Vol. 7; no. 8; pp. 7808 - 7822
• Main Authors: Zhan, Cheng, Hu, Han, Sui, Xiufeng, Liu, Zhi, Niyato, Dusit
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.08.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Approximation; Cloud computing; Completion time; Computation offloading; Computer simulation; Decoupling; Discretization; Edge computing; Energy budget; Energy conservation; Energy consumption; Internet of Things; Internet of Things (IoT); Lower bounds; Mathematical analysis; Minimization; Mobile computing; mobile-edge computing (MEC); Optimization; Optimization algorithms; Resource allocation; Resource management; Task analysis; Tradeoffs; unmanned aerial vehicle (UAV); Unmanned aerial vehicles
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2020.2993260

Completion time and energy consumption of the unmanned aerial vehicle (UAV) are two important design aspects in UAV-enabled applications. In this article, we consider a UAV-enabled mobile-edge computing (MEC) system for Internet-of-Things (IoT) computation offloading with limited or no common cloud/edge infrastructure. We study the joint design of computation offloading and resource allocation, as well as UAV trajectory for minimization of energy consumption and completion time of the UAV, subject to the IoT devices' task and energy budget constraints. We first consider the UAV energy minimization problem without predetermined completion time, a discretized nonconvex equivalent problem is obtained by using the path discretization technique. An efficient alternating optimization algorithm for the discretized problem is proposed by decoupling it into two subproblems and addressing the two subproblems with successive convex approximation (SCA)-based algorithms iteratively. Subsequently, we focus on the completion time minimization problem, which is nonconvex and challenging to solve. By using the same path discretization approximation model to reformulate problem, a similar alternating optimization algorithm is proposed. Furthermore, we study the Pareto-optimal solution that balances the tradeoff between the UAV energy and completion time. The simulation results are provided to corroborate this article and show that the proposed designs outperform the baseline schemes. Our results unveil the tradeoff between completion time and energy consumption of the UAV for the MEC system, and the proposed solution can provide the performance close to the lower bound.