Journal
IEEE INTERNET OF THINGS JOURNAL
Volume 6, Issue 5, Pages 7635-7647Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2019.2903191
Keywords
Offloading; Q-learning; reliability; vehicular edge computing
Categories
Funding
- Fundamental Research Funds for the Central Universities, China [2672018ZYGX2018J001]
- European Union [824019]
- Xi'an Key Laboratory of Mobile Edge Computing and Security [201805052-ZD3CG36]
- Key Research and Development Plan of Shaanxi province [2017ZDCXL-GY-05-01]
- Science and Technology Program of Sichuan Province [2019YFH0007]
- National Natural Science Foundation of China [61374189, 61871433, 61828103]
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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 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.
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