符号
调度(生产过程)
计算机科学
能源消耗
数学优化
资源配置
任务(项目管理)
移动边缘计算
数学
GSM演进的增强数据速率
算术
工程类
计算机网络
人工智能
电气工程
系统工程
作者
Mingxiong Zhao,Wentao Li,Lingyan Bao,Jia Luo,Zhenli He,Di Liu
出处
期刊:IEEE transactions on green communications and networking
[Institute of Electrical and Electronics Engineers]
日期:2021-12-01
卷期号:5 (4): 2174-2187
被引量:17
标识
DOI:10.1109/tgcn.2021.3095070
摘要
Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) has recently emerged to provide data processing and caching in the infrastructure-less areas. However, the limited battery capacity of UAV constrains its endurance time, and makes energy efficiency one of the top priorities in implementing UAV-enabled MEC architecture. In this backdrop, we aim to minimize the UAV’s energy consumption by jointly optimizing its trajectory and resource allocation, and task decision and bits scheduling of users considering fairness. The problem is formulated as a mix-integer nonlinear programming problem with strongly coupled variants, and further transformed into three more tractable subproblems: 1) trajectory optimization $\mathbf {P_{T}}$ ; 2) task decision and bits scheduling $\mathbf {P_{S}}$ ; and 3) resource allocation $\mathbf {P_{R}}$ . Then, we propose an iterative algorithm to deal with them in a sequence, and further design a penalty method-based algorithm to reduce computation complexity when the branch-and-bound (B&B) algorithm incurs a high complexity to solve $\mathbf {P_{S}}$ . Simulation results demonstrate that our proposed algorithm can efficiently reduce the energy consumption of UAV, and help save 17.7% – 54.6% and 78.9% – 91.9% energy compared with Equal Resource Allocation and Random Resource Allocation. Moreover, it reduces more than 88% running time and achieves relatively satisfactory performance compared with B&B.
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