Constrained Multiobjective Optimization for UAV-Assisted Mobile Edge Computing in Smart Agriculture: Minimizing Delay and Energy Consumption

被引:3
作者
Li, Kangshun [1 ,2 ]
Xie, Shumin [2 ]
Zhu, Tianjin [2 ]
Wang, Hui [3 ]
机构
[1] Guangdong Univ Sci & Technology, Coll Comp, Dongguan 523808, Peoples R China
[2] South China Agr Univ, Coll Math & Informat, Guangzhou 510642, Peoples R China
[3] Shenzhen Inst Informat Technol, Sch Software, Shenzhen 518172, Peoples R China
来源
IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING | 2024年 / 9卷 / 06期
基金
中国国家自然科学基金;
关键词
Task analysis; Autonomous aerial vehicles; Optimization; Resource management; Statistics; Sociology; Green computing; Evolutionary algorithm; mobile edge computing; multiobjective optimization; unmanned aerial vehicles; RESOURCE-ALLOCATION; ALGORITHM; TECHNOLOGIES; INTERNET;
D O I
10.1109/TSUSC.2024.3401003
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
With the development of technology, unmanned aerial vehicles (UAVs) and Internet of Things devices are widely used in smart agriculture, resulting in significant energy consumption. In this paper, the optimization problem for UAV-assisted mobile computing in smart agriculture is modeled as a constrained multiobjective optimization problem. By jointly optimizing the deployment position of UAVs, the offloading location of the tasks, the transmit power of the devices, and the resource allocation of the UAVs, two optimization objectives (total delay and energy consumption) are minimized simultaneously. In view of the complex constraints, a constrained multiobjective algorithm named JO-DPTS is proposed. The algorithm adopts dual-population and two-stage approach to improve population convergence and diversity. The simulation results substantiate that JO-DPTS exhibits superior performance compared to the other three state-of-the-art constrained multiobjective evolutionary algorithms.
引用
收藏
页码:948 / 957
页数:10
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