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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 proceedingConference contributionpeer-review

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 languageEnglish
Title of host publication34th International Conference on Information Networking, ICOIN 2020
PublisherIEEE Computer Society
Pages10-12
Number of pages3
ISBN (Electronic)9781728141985
DOIs
StatePublished - Jan 2020
Event34th International Conference on Information Networking, ICOIN 2020 - Barcelona, Spain
Duration: 7 Jan 202010 Jan 2020

Publication series

NameInternational Conference on Information Networking
Volume2020-January
ISSN (Print)1976-7684

Conference

Conference34th International Conference on Information Networking, ICOIN 2020
Country/TerritorySpain
CityBarcelona
Period7/01/2010/01/20

Keywords

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

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