Fast and Reliable Offloading via Deep Reinforcement Learning for Mobile Edge Video Computing

Soohyun Park; Yeongeun Kang; Yafei Tian; Joongheon Kim

doi:10.1109/ICOIN48656.2020.9016591

Fast and Reliable Offloading via Deep Reinforcement Learning for Mobile Edge Video Computing

Soohyun Park
, Yeongeun Kang
, Yafei Tian
, Joongheon Kim^*

^*Corresponding author for this work

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

7 Citations (Scopus)

Abstract

In this paper, we propose an adaptive video streaming method which is inspired by deep reinforcement learning in mobile edge computing systems for autonomous driving applications. In fast moving autonomous driving applications, it is challenge to design fast and reliable video streaming (those are obtained by vision-based autonomous vehicles) task offloading. This paper handles this issue inspired by deep Q-network (DQN) which is one of the most well-known deep reinforcement learning algorithms.

Original language	English
Title of host publication	34th International Conference on Information Networking, ICOIN 2020
Publisher	IEEE Computer Society
Pages	10-12
Number of pages	3
ISBN (Electronic)	9781728141985
DOIs	https://doi.org/10.1109/ICOIN48656.2020.9016591
Publication status	Published - 2020 Jan
Event	34th International Conference on Information Networking, ICOIN 2020 - Barcelona, Spain Duration: 2020 Jan 7 → 2020 Jan 10

Publication series

Name	International Conference on Information Networking
Volume	2020-January
ISSN (Print)	1976-7684

Conference

Conference	34th International Conference on Information Networking, ICOIN 2020
Country/Territory	Spain
City	Barcelona
Period	20/1/7 → 20/1/10

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

Autonomous driving
deep Q-network
mobile edge computing
offloading
reinforcement learning

ASJC Scopus subject areas

Computer Networks and Communications
Information Systems

Access to Document

10.1109/ICOIN48656.2020.9016591

Cite this

Park, S., Kang, Y., Tian, Y., & Kim, J. (2020). Fast and Reliable Offloading via Deep Reinforcement Learning for Mobile Edge Video Computing. In 34th International Conference on Information Networking, ICOIN 2020 (pp. 10-12). Article 9016591 (International Conference on Information Networking; Vol. 2020-January). IEEE Computer Society. https://doi.org/10.1109/ICOIN48656.2020.9016591

Fast and Reliable Offloading via Deep Reinforcement Learning for Mobile Edge Video Computing. / Park, Soohyun; Kang, Yeongeun; Tian, Yafei et al.
34th International Conference on Information Networking, ICOIN 2020. IEEE Computer Society, 2020. p. 10-12 9016591 (International Conference on Information Networking; Vol. 2020-January).

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

Park, S, Kang, Y, Tian, Y & Kim, J 2020, Fast and Reliable Offloading via Deep Reinforcement Learning for Mobile Edge Video Computing. in 34th International Conference on Information Networking, ICOIN 2020., 9016591, International Conference on Information Networking, vol. 2020-January, IEEE Computer Society, pp. 10-12, 34th International Conference on Information Networking, ICOIN 2020, Barcelona, Spain, 20/1/7. https://doi.org/10.1109/ICOIN48656.2020.9016591

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abstract = "In this paper, we propose an adaptive video streaming method which is inspired by deep reinforcement learning in mobile edge computing systems for autonomous driving applications. In fast moving autonomous driving applications, it is challenge to design fast and reliable video streaming (those are obtained by vision-based autonomous vehicles) task offloading. This paper handles this issue inspired by deep Q-network (DQN) which is one of the most well-known deep reinforcement learning algorithms.",

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author = "Soohyun Park and Yeongeun Kang and Yafei Tian and Joongheon Kim",

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AB - In this paper, we propose an adaptive video streaming method which is inspired by deep reinforcement learning in mobile edge computing systems for autonomous driving applications. In fast moving autonomous driving applications, it is challenge to design fast and reliable video streaming (those are obtained by vision-based autonomous vehicles) task offloading. This paper handles this issue inspired by deep Q-network (DQN) which is one of the most well-known deep reinforcement learning algorithms.

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Fast and Reliable Offloading via Deep Reinforcement Learning for Mobile Edge Video Computing

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this