Joint Computation and Communication Design for UAV-Assisted Mobile Edge Computing in IoT

• Published in: IEEE transactions on industrial informatics Vol. 16; no. 8; pp. 5505 - 5516
• Main Authors: Zhang, Tiankui, Xu, Yu, Loo, Jonathan, Yang, Dingcheng, Xiao, Lin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.08.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Edge computing; Energy conservation; Energy consumption; Internet of Things; Internet of Things (IoT); Minimization; Mobile computing; mobile edge computing (MEC); Optimization; Relaying; resource allocation; Task analysis; Trajectory; trajectory optimization; unmanned aerial vehicle (UAV) communication; Unmanned aerial vehicles
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2019.2948406

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is a prominent concept, where a UAV equipped with an MEC server is deployed to serve a number of terminal devices (TDs) of Internet of Things in a finite period. In this article, each TD has a certain latency-critical computation task in each time slot to complete. Three computation strategies can be available to each TD. First, each TD can operate local computing by itself. Second, each TD can partially offload task bits to the UAV for computing. Third, each TD can choose to offload task bits to access point via UAV relaying. We propose a new optimization problem formulation that aims to minimize the total energy consumption including communication-related energy, computation-related energy and UAV's flight energy by optimizing the bits allocation, time slot scheduling, and power allocation as well as UAV trajectory design. As the formulated problem is nonconvex and difficult to find the optimal solution, we propose to solve the problem by two parts, and obtain the near optimal solution by the Lagrangian duality method and successive convex approximation technique, respectively. By analysis, the proposed algorithm can be guaranteed to converge within a dozen of iterations. Finally, numerical results are given to validate the proposed algorithm, which is verified to be efficient and superior to the other benchmark cases.