计算机科学
斯塔克伯格竞赛
服务器
移动边缘计算
计算卸载
计算
水准点(测量)
纳什均衡
GSM演进的增强数据速率
博弈论
边缘计算
分布式计算
计算机网络
数学优化
算法
人工智能
微观经济学
经济
地理
数理经济学
数学
大地测量学
作者
Huan Zhou,Zhenning Wang,Geyong Min,Haijun Zhang
出处
期刊:IEEE Internet of Things Journal
[Institute of Electrical and Electronics Engineers]
日期:2023-04-15
卷期号:10 (8): 6622-6633
被引量:16
标识
DOI:10.1109/jiot.2022.3197155
摘要
Unmanned aerial vehicles (UAVs) are considered as a promising method to provide additional computation capability and wide coverage for mobile users (MUs), especially when MUs are not within the communication range of the infrastructure. In this article, a UAV-aided mobile-edge computing (MEC) network, including one UAV-MEC server, one BS-MEC server, and several MUs, is investigated for computation offloading, in which the edge service provider (ESP) manages two kinds of servers. It is considered that MUs have a large number of computation tasks to conduct, while the ESP has idle computational resources. MUs can choose to offload their tasks to the ESP to reduce their pressure and cost, and the ESP can make a profit by selling computational resources. The interaction among the ESP and MUs is modeled as a Stackelberg game, and both the ESP and MUs want to maximize their utility. The proposed game is analyzed by using the backward induction method, and it is proved that a unique Nash equilibrium can be achieved in the game. Then, a gradient-based dynamic iterative search algorithm (GDISA) is proposed to get the approximate optimal solution. Finally, the effectiveness of GDISA is verified by extensive simulations, and the results show that GDISA performs better than other benchmark methods under different scenarios.
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