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

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...

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Vydané v:IEEE internet of things journal Ročník 6; číslo 5; s. 7635 - 7647
Hlavní autori: Zhang, Ke, Zhu, Yongxu, Leng, Supeng, He, Yejun, Maharjan, Sabita, Zhang, Yan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 01.10.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Cellular networks
Computational modeling
Data transmission
Deep learning
Driving
Edge computing
Electronic devices
Internet of Things
Mobile computing
Offloading
Optimization
Q-learning
Reliability
Reliability analysis
Servers
Task analysis
Traffic information
Urban environments
Utilities
vehicular edge computing
ISSN:2327-4662, 2327-4662
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Shrnutí: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 algorithm to improve task offloading reliability in the case of vehicular data transmission failure. We evaluate the proposed schemes based on real traffic data. Results indicate that our offloading schemes have great advantages in optimizing system utilities and improving offloading reliability.
Bibliografia:ObjectType-Article-1
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ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2019.2903191