A Survey on Deep Reinforcement Learning-driven Task Offloading in Aerial Access Networks

Tri Hai Nguyen; Laihyuk Park

doi:10.1109/ICTC55196.2022.9952687

A Survey on Deep Reinforcement Learning-driven Task Offloading in Aerial Access Networks

Tri Hai Nguyen
, Laihyuk Park

Dept. of Computer Science and Engineering

Seoul National University of Science and Technology (SNUST)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

21 Scopus citations

Abstract

Internet of Things computation offloading is a challenging problem, particularly in distant places where mobile edge computing (MEC) or cloud infrastructure is absent. Fortunately, aerial access networks (AANs), which include unmanned aerial vehicles and satellite communications, are employed as effective aerial platforms to deliver ubiquitous and reliable access. Furthermore, deep reinforcement learning (DRL) is a viable method to boost the efficiency of edge network resource management in achieving energy-efficient, low-delay MEC services. This paper investigates recent advances in DRL-based task offloading strategies in the MEC-based AANs. Research challenges and directions are also discussed.

Original language	English
Title of host publication	ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence
Subtitle of host publication	Accelerating Digital Transformation with ICT Innovation
Publisher	IEEE Computer Society
Pages	822-827
Number of pages	6
ISBN (Electronic)	9781665499392
DOIs	https://doi.org/10.1109/ICTC55196.2022.9952687
State	Published - 2022
Event	13th International Conference on Information and Communication Technology Convergence, ICTC 2022 - Jeju Island, Korea, Republic of Duration: 19 Oct 2022 → 21 Oct 2022

Publication series

Name	International Conference on ICT Convergence
Volume	2022-October
ISSN (Print)	2162-1233
ISSN (Electronic)	2162-1241

Conference

Conference	13th International Conference on Information and Communication Technology Convergence, ICTC 2022
Country/Territory	Korea, Republic of
City	Jeju Island
Period	19/10/22 → 21/10/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Aerial access network
computation offloading
deep reinforcement learning
mobile edge computing
satellite
unmanned aerial vehicles

Access to Document

10.1109/ICTC55196.2022.9952687

Cite this

Nguyen, T. H., & Park, L. (2022). A Survey on Deep Reinforcement Learning-driven Task Offloading in Aerial Access Networks. In ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence: Accelerating Digital Transformation with ICT Innovation (pp. 822-827). (International Conference on ICT Convergence; Vol. 2022-October). IEEE Computer Society. https://doi.org/10.1109/ICTC55196.2022.9952687

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title = "A Survey on Deep Reinforcement Learning-driven Task Offloading in Aerial Access Networks",

abstract = "Internet of Things computation offloading is a challenging problem, particularly in distant places where mobile edge computing (MEC) or cloud infrastructure is absent. Fortunately, aerial access networks (AANs), which include unmanned aerial vehicles and satellite communications, are employed as effective aerial platforms to deliver ubiquitous and reliable access. Furthermore, deep reinforcement learning (DRL) is a viable method to boost the efficiency of edge network resource management in achieving energy-efficient, low-delay MEC services. This paper investigates recent advances in DRL-based task offloading strategies in the MEC-based AANs. Research challenges and directions are also discussed.",

keywords = "Aerial access network, computation offloading, deep reinforcement learning, mobile edge computing, satellite, unmanned aerial vehicles",

author = "Nguyen, \{Tri Hai\} and Laihyuk Park",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 13th International Conference on Information and Communication Technology Convergence, ICTC 2022 ; Conference date: 19-10-2022 Through 21-10-2022",

year = "2022",

doi = "10.1109/ICTC55196.2022.9952687",

language = "English",

series = "International Conference on ICT Convergence",

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Nguyen, TH & Park, L 2022, A Survey on Deep Reinforcement Learning-driven Task Offloading in Aerial Access Networks. in ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence: Accelerating Digital Transformation with ICT Innovation. International Conference on ICT Convergence, vol. 2022-October, IEEE Computer Society, pp. 822-827, 13th International Conference on Information and Communication Technology Convergence, ICTC 2022, Jeju Island, Korea, Republic of, 19/10/22. https://doi.org/10.1109/ICTC55196.2022.9952687

A Survey on Deep Reinforcement Learning-driven Task Offloading in Aerial Access Networks. / Nguyen, Tri Hai; Park, Laihyuk.
ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence: Accelerating Digital Transformation with ICT Innovation. IEEE Computer Society, 2022. p. 822-827 (International Conference on ICT Convergence; Vol. 2022-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Park, Laihyuk

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N2 - Internet of Things computation offloading is a challenging problem, particularly in distant places where mobile edge computing (MEC) or cloud infrastructure is absent. Fortunately, aerial access networks (AANs), which include unmanned aerial vehicles and satellite communications, are employed as effective aerial platforms to deliver ubiquitous and reliable access. Furthermore, deep reinforcement learning (DRL) is a viable method to boost the efficiency of edge network resource management in achieving energy-efficient, low-delay MEC services. This paper investigates recent advances in DRL-based task offloading strategies in the MEC-based AANs. Research challenges and directions are also discussed.

AB - Internet of Things computation offloading is a challenging problem, particularly in distant places where mobile edge computing (MEC) or cloud infrastructure is absent. Fortunately, aerial access networks (AANs), which include unmanned aerial vehicles and satellite communications, are employed as effective aerial platforms to deliver ubiquitous and reliable access. Furthermore, deep reinforcement learning (DRL) is a viable method to boost the efficiency of edge network resource management in achieving energy-efficient, low-delay MEC services. This paper investigates recent advances in DRL-based task offloading strategies in the MEC-based AANs. Research challenges and directions are also discussed.

KW - Aerial access network

KW - computation offloading

KW - deep reinforcement learning

KW - mobile edge computing

KW - satellite

KW - unmanned aerial vehicles

UR - https://www.scopus.com/pages/publications/85143251915

U2 - 10.1109/ICTC55196.2022.9952687

DO - 10.1109/ICTC55196.2022.9952687

M3 - Conference contribution

AN - SCOPUS:85143251915

T3 - International Conference on ICT Convergence

SP - 822

EP - 827

BT - ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence

PB - IEEE Computer Society

T2 - 13th International Conference on Information and Communication Technology Convergence, ICTC 2022

Y2 - 19 October 2022 through 21 October 2022

ER -

Nguyen TH, Park L. A Survey on Deep Reinforcement Learning-driven Task Offloading in Aerial Access Networks. In ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence: Accelerating Digital Transformation with ICT Innovation. IEEE Computer Society. 2022. p. 822-827. (International Conference on ICT Convergence). doi: 10.1109/ICTC55196.2022.9952687

A Survey on Deep Reinforcement Learning-driven Task Offloading in Aerial Access Networks

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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