UAV-assisted cooperative offloading energy efficiency system for mobile edge computing  

作　　者：Xue-Yong Yu Wen-Jin Niu Ye Zhu Hong-Bo Zhu 

机构地区：[1]Jiangsu Key Laboratory of Wireless Communications,Nanjing University of Posts and Telecommunications Nanjing,210003,China [2]Engineering Research Centerof Health Service System Based on Ubiquitous Wireless Networks,Ministry of Education,Nanjng Universityof Posts and Telecommunications Nanjing,210003,China

出　　处：《Digital Communications and Networks》2024年第1期16-24,共9页数字通信与网络（英文版）

基　　金：supported by the Jiangsu Provincial Key Research and Development Program(No.BE2020084-4);the National Natural Science Foundation of China(No.92067201);the National Natural Science Foundation of China(61871446);the Open Research Fund of Jiangsu Key Laboratory of Wireless Communications(710020017002);the Natural Science Foundation of Nanjing University of Posts and telecommunications(NY220047).

摘　　要：Reliable communication and intensive computing power cannot be provided effectively by temporary hot spots in disaster areas and complex terrain ground infrastructure.Mitigating this has greatly developed the application and integration of UAV and Mobile Edge Computing(MEC)to the Internet of Things(loT).However,problems such as multi-user and huge data flow in large areas,which contradict the reality that a single UAV is constrained by limited computing power,still exist.Due to allowing UAV collaboration to accomplish complex tasks,cooperative task offloading between multiple UAVs must meet the interdependence of tasks and realize parallel processing,which reduces the computing power consumption and endurance pressure of terminals.Considering the computing requirements of the user terminal,delay constraint of a computing task,energy constraint,and safe distance of UAV,we constructed a UAV-Assisted cooperative offloading energy efficiency system for mobile edge computing to minimize user terminal energy consumption.However,the resulting optimization problem is originally nonconvex and thus,difficult to solve optimally.To tackle this problem,we developed an energy efficiency optimization algorithm using Block Coordinate Descent(BCD)that decomposes the problem into three convex subproblems.Furthermore,we jointly optimized the number of local computing tasks,number of computing offloaded tasks,trajectories of UAV,and offloading matching relationship between multi-UAVs and multiuser terminals.Simulation results show that the proposed approach is suitable for different channel conditions and significantly saves the user terminal energy consumption compared with other benchmark schemes.

关 键 词：Computation offloading Internet of things(IoT) Mobile edge computing(MEC) Block coordinate descent(BCD) 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]