Joint Task Offloading Scheduling and Resource Allocation in Air-Ground Cooperation UAV-Enabled Mobile Edge Computing

被引:0
|
作者
Kuang, Zhufang [1 ]
Pan, Yihui [1 ]
Yang, Fan [1 ]
Zhang, Yan [2 ]
机构
[1] Cent South Univ Forestry & Technol, Sch Comp & Informat Engn, Changsha 410004, Peoples R China
[2] Univ Oslo, Dept Informat, N-0316 Oslo, Norway
基金
中国国家自然科学基金;
关键词
Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC); offloading scheduling; resource allocation; minimize energy; convex optimization; MEC; NETWORKS; OPTIMIZATION;
D O I
10.1109/TVT.2023.3334143
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Unmanned Aerial Vehicles (UAVs) are heavily used in disaster or emergency scenarios. In this paper, we investigate the joint problem of task offloading, task scheduling, transmission power and computing resources allocation, and optimization of UAV deployment location for UAV-enabled Mobile Edge Computing (MEC), considering and highlighting the order of execution and transmission of different tasks. The corresponding optimization problem, which is a non-convex mixed-integer optimization problem, is formulated. In oder to solve this problem, the formulated problem is decomposed into three Sub-Problems (SP), and an iterative method based on Block Coordinate Descent (BCD) is proposed. Given the UAV location and resource allocation, the 1-st SP (SP1) of task offloading scheduling optimization is solved by greedy strategy optimization methods. Given the task offloading decision, task scheduling order, and resource allocation, the 2-nd SP (SP2) of optimizing the deployment location of UAV is solved by Successive Convex Approximation (SCA) optimization methods. Given the task offloading decision, task scheduling order, and UAV location, the 3-rd SP (SP3) of transmission and computing resources allocating is solved by convex optimization methods. Simulation results show that our proposed method can significantly reduce energy consumption compared to the benchmark schemes.
引用
收藏
页码:5796 / 5807
页数:12
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