Deep Learning Empowered Task Offloading for Mobile Edge Computing in Urban Informatics

Journal article


Zhang, K, Zhu, Y, Leng, S, He, Y, Maharjan, S and Zhang, Y (2019). Deep Learning Empowered Task Offloading for Mobile Edge Computing in Urban Informatics. IEEE Internet of Things Journal. 6 (5), pp. 7635-7647.
AuthorsZhang, K, Zhu, Y, Leng, S, He, Y, Maharjan, S and Zhang, Y
Abstract

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Led by industrialization of smart cities, numerous interconnected mobile devices, and novel applications have emerged in the urban environment, providing great opportunities to realize industrial automation. In this context, autonomous driving is an attractive issue, which leverages large amounts of sensory information for smart navigation while posing intensive computation demands on resource constrained vehicles. Mobile edge computing (MEC) is a potential solution to alleviate the heavy burden on the devices. However, varying states of multiple edge servers as well as a variety of vehicular offloading modes make efficient task offloading a challenge. To cope with this challenge, we adopt a deep Q-learning approach for designing optimal offloading schemes, jointly considering selection of target server and determination of data transmission mode. Furthermore, we propose an efficient redundant offloading …

KeywordsOffloading; Q-learning; reliability; vehicular edge computing
Year2019
JournalIEEE Internet of Things Journal
Journal citation6 (5), pp. 7635-7647
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISSN2372-2541
Digital Object Identifier (DOI)doi:10.1109/jiot.2019.2903191
Publication dates
PrintOct 2019
Publication process dates
Accepted01 Feb 2019
Deposited25 Jun 2020
Accepted author manuscript
License
CC BY 4.0
File Access Level
Open
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