Opportunistic Task Offloading in UAV-assisted Mobile Edge Computing: A Deep Reinforcement Learning Approach

Taewon Song

doi:10.1109/ICTC58733.2023.10392829

Opportunistic Task Offloading in UAV-assisted Mobile Edge Computing: A Deep Reinforcement Learning Approach

Taewon Song

Department of Electronics & Electrical Engineering

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

3 Scopus citations

Abstract

Mobile edge computing (MEC) aims to extend cloud services to the network edge to reduce network traffic and latency for 5G mobile networks. Unmanned aerial vehicles (UAVs) are being used as assisted edge clouds for large-scale sparsely-distributed user equipment, due to their flexible deployment, wide coverage, and reliable wireless communication. In this paper, we propose a deep Q learning-based opportunistic task offloading algorithm for UAV-assisted mobile edge computing. To this end, we formulate a Markov decision process (MDP) model in which the UAV can choose whether to offload tasks to the cloud server or process them on the local MEC server. Extensive simulations show that our task offloading algorithm outperforms both offload-only and local-only algorithms, ensuring satisfactory service quality for 5G services.

Original language	English
Title of host publication	ICTC 2023 - 14th International Conference on Information and Communication Technology Convergence
Subtitle of host publication	Exploring the Frontiers of ICT Innovation
Publisher	IEEE Computer Society
Pages	881-884
Number of pages	4
ISBN (Electronic)	9798350313277
DOIs	https://doi.org/10.1109/ICTC58733.2023.10392829
State	Published - 2023
Event	14th International Conference on Information and Communication Technology Convergence, ICTC 2023 - Jeju Island, Korea, Republic of Duration: 11 Oct 2023 → 13 Oct 2023

Publication series

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

Conference

Conference	14th International Conference on Information and Communication Technology Convergence, ICTC 2023
Country/Territory	Korea, Republic of
City	Jeju Island
Period	11/10/23 → 13/10/23

Keywords

5G mobile networks
deep reinforcement learning
DQN
mobile edge computing
task offloading

Access to Document

10.1109/ICTC58733.2023.10392829

Cite this

Song, T. (2023). Opportunistic Task Offloading in UAV-assisted Mobile Edge Computing: A Deep Reinforcement Learning Approach. In ICTC 2023 - 14th International Conference on Information and Communication Technology Convergence: Exploring the Frontiers of ICT Innovation (pp. 881-884). (International Conference on ICT Convergence). IEEE Computer Society. https://doi.org/10.1109/ICTC58733.2023.10392829

@inproceedings{f79b336dd8514e38896edc37d44b16c2,

title = "Opportunistic Task Offloading in UAV-assisted Mobile Edge Computing: A Deep Reinforcement Learning Approach",

abstract = "Mobile edge computing (MEC) aims to extend cloud services to the network edge to reduce network traffic and latency for 5G mobile networks. Unmanned aerial vehicles (UAVs) are being used as assisted edge clouds for large-scale sparsely-distributed user equipment, due to their flexible deployment, wide coverage, and reliable wireless communication. In this paper, we propose a deep Q learning-based opportunistic task offloading algorithm for UAV-assisted mobile edge computing. To this end, we formulate a Markov decision process (MDP) model in which the UAV can choose whether to offload tasks to the cloud server or process them on the local MEC server. Extensive simulations show that our task offloading algorithm outperforms both offload-only and local-only algorithms, ensuring satisfactory service quality for 5G services.",

keywords = "5G mobile networks, deep reinforcement learning, DQN, mobile edge computing, task offloading",

author = "Taewon Song",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 14th International Conference on Information and Communication Technology Convergence, ICTC 2023 ; Conference date: 11-10-2023 Through 13-10-2023",

year = "2023",

doi = "10.1109/ICTC58733.2023.10392829",

language = "English",

series = "International Conference on ICT Convergence",

publisher = "IEEE Computer Society",

pages = "881--884",

booktitle = "ICTC 2023 - 14th International Conference on Information and Communication Technology Convergence",

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}

Song, T 2023, Opportunistic Task Offloading in UAV-assisted Mobile Edge Computing: A Deep Reinforcement Learning Approach. in ICTC 2023 - 14th International Conference on Information and Communication Technology Convergence: Exploring the Frontiers of ICT Innovation. International Conference on ICT Convergence, IEEE Computer Society, pp. 881-884, 14th International Conference on Information and Communication Technology Convergence, ICTC 2023, Jeju Island, Korea, Republic of, 11/10/23. https://doi.org/10.1109/ICTC58733.2023.10392829

Opportunistic Task Offloading in UAV-assisted Mobile Edge Computing: A Deep Reinforcement Learning Approach. / Song, Taewon.
ICTC 2023 - 14th International Conference on Information and Communication Technology Convergence: Exploring the Frontiers of ICT Innovation. IEEE Computer Society, 2023. p. 881-884 (International Conference on ICT Convergence).

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

TY - GEN

T1 - Opportunistic Task Offloading in UAV-assisted Mobile Edge Computing

T2 - 14th International Conference on Information and Communication Technology Convergence, ICTC 2023

AU - Song, Taewon

PY - 2023

Y1 - 2023

N2 - Mobile edge computing (MEC) aims to extend cloud services to the network edge to reduce network traffic and latency for 5G mobile networks. Unmanned aerial vehicles (UAVs) are being used as assisted edge clouds for large-scale sparsely-distributed user equipment, due to their flexible deployment, wide coverage, and reliable wireless communication. In this paper, we propose a deep Q learning-based opportunistic task offloading algorithm for UAV-assisted mobile edge computing. To this end, we formulate a Markov decision process (MDP) model in which the UAV can choose whether to offload tasks to the cloud server or process them on the local MEC server. Extensive simulations show that our task offloading algorithm outperforms both offload-only and local-only algorithms, ensuring satisfactory service quality for 5G services.

AB - Mobile edge computing (MEC) aims to extend cloud services to the network edge to reduce network traffic and latency for 5G mobile networks. Unmanned aerial vehicles (UAVs) are being used as assisted edge clouds for large-scale sparsely-distributed user equipment, due to their flexible deployment, wide coverage, and reliable wireless communication. In this paper, we propose a deep Q learning-based opportunistic task offloading algorithm for UAV-assisted mobile edge computing. To this end, we formulate a Markov decision process (MDP) model in which the UAV can choose whether to offload tasks to the cloud server or process them on the local MEC server. Extensive simulations show that our task offloading algorithm outperforms both offload-only and local-only algorithms, ensuring satisfactory service quality for 5G services.

KW - 5G mobile networks

KW - deep reinforcement learning

KW - DQN

KW - mobile edge computing

KW - task offloading

UR - https://www.scopus.com/pages/publications/85184592971

U2 - 10.1109/ICTC58733.2023.10392829

DO - 10.1109/ICTC58733.2023.10392829

M3 - Conference contribution

AN - SCOPUS:85184592971

T3 - International Conference on ICT Convergence

SP - 881

EP - 884

BT - ICTC 2023 - 14th International Conference on Information and Communication Technology Convergence

PB - IEEE Computer Society

Y2 - 11 October 2023 through 13 October 2023

ER -

Song T. Opportunistic Task Offloading in UAV-assisted Mobile Edge Computing: A Deep Reinforcement Learning Approach. In ICTC 2023 - 14th International Conference on Information and Communication Technology Convergence: Exploring the Frontiers of ICT Innovation. IEEE Computer Society. 2023. p. 881-884. (International Conference on ICT Convergence). doi: 10.1109/ICTC58733.2023.10392829

Opportunistic Task Offloading in UAV-assisted Mobile Edge Computing: A Deep Reinforcement Learning Approach

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