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Air-Ground Collaborative Edge Intelligence for Future Generation Networks
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AI
DOI：10.1109/MNET.008.2200287.png)
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Abstract
En 中文
The air-ground integrated mobile edge computing (MEC) is expected to fulfill the ever-growing resource demands of artificial intelligence (AI)-enabled applications in sixth-generation (6G) wireless networks, ranging from computer vision to natural language processing. Nevertheless, it is still challenging to offer high-quality AI services by fully exploring the advantages of terrestrial MEC networks and unmanned aerial vehicles (UAVs), especially as they have to share resources collaboratively. To meet this challenge, we propose a novel framework termed air-ground collaborative edge intelligence (EI), featuring the collaboration of terrestrial and aerial resources as a potential solution to enable persistent and ubiquitous Al services. By installing various modules on UAVs, three distinct air-ground collaboration schemes are considered and discussed, where these UAVs can provide communication, computation, and energy resources in different use cases. Next, we elaborate on two potential applications and some open research issues for the proposed airground collaborative EI framework. Specifically, we develop a novel machine learning model caching approach, where a popular deep neural network (DNN) model is cached on proper terrestrial edge devices and UAVs to relieve network congestion. Finally, we provide extensive simulation results to demonstrate that the proposed air-ground collaborative caching algorithm can improve inference efficiency dramatically.
Keywords:
Machine learning algorithms
Atmospheric modeling
Computational modeling
Wireless networks
Software algorithms
Collaboration
Machine learning
Journal
IF:
6.3
Papers:
2.6K
Citations:
1.1W
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