UAVs as an Intelligent Service: Boosting Edge Intelligence for Air-Ground Integrated Networks

被引:46
|
作者
Dong, Chao [1 ]
Shen, Yun [2 ]
Qu, Yuben [3 ]
Wang, Kun [4 ]
Zheng, Jianchao [5 ]
Wu, Qihui [1 ]
Wu, Fan [6 ]
机构
[1] Nanjing Univ Aeronaut & Astronaut, Coll Elect & Informat Engn, Nanjing, Peoples R China
[2] Nanjing Univ Aeronaut & Astronaut, Elect Commun Engn, Nanjing, Peoples R China
[3] Shanghai Jiao Tong Univ, Shanghai, Peoples R China
[4] Univ Calif Los Angeles, Los Angeles, CA 90024 USA
[5] Natl Innovat Inst Def Technol, Changsha, Peoples R China
[6] Shanghai Jiao Tong Univ, Dept Comp & Engn, Shanghai, Peoples R China
来源
IEEE NETWORK | 2021年 / 35卷 / 04期
基金
国家重点研发计划; 中国国家自然科学基金;
关键词
CHALLENGES; 5G;
D O I
10.1109/MNET.011.2000651
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
The air-ground integrated network is a key component of future sixth generation (6G) networks to support seamless and near-instant super-connectivity. There is a pressing need to intelligently provision various services in 6G networks, which is challenging. To meet this need, in this article, we propose a novel architecture called UaalS, that is, unmanned aerial vehicles (UAVs) as a intelligent service for the air-ground integrated network, featuring the UAV as a key enabler to boost edge intelligence with the help of machine learning (ML) techniques. We envision that the proposed UaalS architecture could intelligently provision wireless communication service, edge computing service, and edge caching service by a network of UAVs, making full use of UAVs' flexible deployment and diverse ML techniques. We also conduct a case study where UAVs participate in the model training of distributed ML among multiple terrestrial users, whose result shows that the model training is efficient with low energy consumption of UAVs. Finally, we discuss the challenges and some open research issues in UaaIS.
引用
收藏
页码:167 / 175
页数:9
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