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Contract-theoretic Approach for Delay Constrained Offloading in Vehicular Edge Computing Networks
University of Electronic Science and Technology of China, Chengdu, China.ORCID iD: 0000-0003-3081-7751
University of Electronic Science and Technology of China, Chengdu, China.
University of Electronic Science and Technology of China, Chengdu, China.
Simula Research Laboratory, Fornebu, Norway & University of Oslo, Oslo, Norway.
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2018 (English)In: Journal on spesial topics in mobile networks and applications, ISSN 1383-469X, E-ISSN 1572-8153Article in journal (Refereed) Epub ahead of print
Abstract [en]

Mobile Edge Computing (MEC) is a promising solution to improve vehicular services through offloading computation to cloud servers in close proximity to mobile vehicles. However, the self-interested nature together with the high mobility characteristic of the vehicles make the design of the computation offloading scheme a significant challenge. In this paper, we propose a new Vehicular Edge Computing (VEC) framework to model the computation offloading process of the mobile vehicles running on a bidirectional road. Based on this framework, we adopt a contract theoretic approach to design optimal offloading strategies for the VEC service provider, which maximize the revenue of the provider while enhancing the utilities of the vehicles. To further improve the utilization of the computing resources of the VEC servers, we incorporate task priority distinction as well as additional resource providing into the design of the offloading scheme, and propose an efficient VEC server selection and computing resource allocation algorithm. Numerical results indicate that our proposed schemes greatly enhance the revenue of the VEC provider, and concurrently improve the utilization of cloud computing resources.

Place, publisher, year, edition, pages
New York, NY: Springer-Verlag New York, 2018.
Keywords [en]
Vehicular network, Cloud, Mobile edge computing, Contract theory
National Category
Communication Systems Telecommunications
Identifiers
URN: urn:nbn:se:hh:diva-36337DOI: 10.1007/s11036-018-1032-0OAI: oai:DiVA.org:hh-36337DiVA, id: diva2:1184733
Note

Funding: The National Natural Science Foundation of China under Grant No.61374189, the joint fund of the Ministry of Education of P.R. China and China Mobile under Grant MCM20160304.

Available from: 2018-02-22 Created: 2018-02-22 Last updated: 2018-03-23

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Zhang, KeVinel, Alexey

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  • apa
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