Air-Ground Collaborative Edge Intelligence for Future Generation Networks

Tang, J; Nie, J; Zhang, Y; Duan, Y; Xiong, Z; Niyato, D

Air-Ground Collaborative Edge Intelligence for Future Generation Networks

Tang, J Nie, J Zhang, Y Duan, Y

Xiong, Z Niyato, D

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Network, 2023, 37, (2), pp. 118-125
Issue Date:: 2023-03-01

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Field	Value	Language
dc.contributor.author	Tang, J
dc.contributor.author	Nie, J
dc.contributor.author	Zhang, Y
dc.contributor.author	Duan, Y https://orcid.org/0000-0003-1517-994X
dc.contributor.author	Xiong, Z
dc.contributor.author	Niyato, D
dc.date.accessioned	2024-04-15T22:09:10Z
dc.date.available	2024-04-15T22:09:10Z
dc.date.issued	2023-03-01
dc.identifier.citation	IEEE Network, 2023, 37, (2), pp. 118-125
dc.identifier.issn	0890-8044
dc.identifier.issn	1558-156X
dc.identifier.uri	http://hdl.handle.net/10453/177924
dc.description.abstract	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.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Network
dc.relation.isbasedon	10.1109/MNET.008.2200287
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 0906 Electrical and Electronic Engineering
dc.subject.classification	Networking & Telecommunications
dc.subject.classification	4006 Communications engineering
dc.subject.classification	4606 Distributed computing and systems software
dc.title	Air-Ground Collaborative Edge Intelligence for Future Generation Networks
dc.type	Journal Article
utslib.citation.volume	37
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2024-04-15T22:09:05Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	37
utslib.citation.issue	2

Abstract:

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.

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http://hdl.handle.net/10453/177924