Collaborative Service Provisioning for UAV-Assisted Mobile Edge Computing  

作　　者：Yuben QU Zhenhua WEI Zhen QIN Tao WU Jinghao MA Haipeng DAI Chao DONG 

机构地区：[1]The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space,Ministry of Industry and Information Technology,Nanjing 21106,China [2]Colllege of Electronic and Information Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China [3]Xi'an Research Institute of High Technology,Xi'an 710025,China [4]Department of Information and Communication,Noncommissioned Officer Academy of PAP,Hangzhou 310000,China [5]National University of Defense Technology,Hefei 230009,China [6]Department of Computer Science and Technology,Nanjing University,Nanjing 210023,China

出　　处：《Chinese Journal of Electronics》2024年第6期1504-1514,共11页电子学报（英文版）

基　　金：supported by the Primary Research&Developement Plan of Jiangsu Province(Grant No.BE2021013-4);the National Natural Science Foundation of China(Grant Nos.61931011,62072303,61872178,62272223,62171465,62002377,and 62072424);the Hong Kong Scholars Program(Grant No.2021-101)。

摘　　要：Unmanned aerial vehicle(UAV)-assisted mobile edge computing(MEC),as a way of coping with delaysensitive and computing-intensive tasks,is considered to be a key technology to solving the challenges of terrestrial MEC networks.In this work,we study the problem of collaborative service provisioning(CSP)for UAV-assisted MEC.Specifically,taking into account the task latency and other resource constraints,this paper investigates how to minimize the total energy consumption of all terrestrial user equipments,by jointly optimizing computing resource allocation,task offloading,UAV trajectory,and service placement.The CSP problem is a non-convex mixed integer nonlinear programming problem,owing to the complex coupling of mixed integral variables and non-convexity of CSP.To address the CSP problem,this paper proposes an alternating optimization-based solution with the convergence guarantee as follows.We iteratively deal with the joint service placement and task offloading subproblem,and UAV movement trajectory subproblem,by branch and bound and successive convex approximation,respectively,while the closed form of the optimal computation resource allocation can be efficiently obtained.Extensive simulations validate the effectiveness of the proposed algorithm compared to three baselines.

关 键 词：Service provisioning Unmanned aerial vehicle(UAV) Mobile edge computing(MEC) 

分 类 号：V279[航空宇航科学与技术—飞行器设计] TN929.5[电子电信—通信与信息系统]