Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/82121
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dc.contributorDepartment of Computingen_US
dc.creatorLi, GSen_US
dc.creatorZhang, Yen_US
dc.creatorWang, MLen_US
dc.creatorWu, JHen_US
dc.creatorLin, QYen_US
dc.creatorSheng, XFen_US
dc.date.accessioned2020-05-05T05:58:45Z-
dc.date.available2020-05-05T05:58:45Z-
dc.identifier.issn1076-2787en_US
dc.identifier.urihttp://hdl.handle.net/10397/82121-
dc.language.isoenen_US
dc.publisherHindawien_US
dc.rightsCopyright © 2020 Guang-Shun Li et al. This is an open access article distributed under the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en_US
dc.rightsThe following publication Li, G. S., Zhang, Y., Wang, M. L., Wu, J. H., Lin, Q. Y., & Sheng, X. F. (2020). Resource Management Framework Based on the Stackelberg Game in Vehicular Edge Computing. Complexity, 2020 is available at https://doi.org/10.1155/2020/8936064en_US
dc.titleResource management framework based on the Stackelberg Game in vehicular edge computingen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume2020en_US
dc.identifier.doi10.1155/2020/8936064en_US
dcterms.abstractWith the emergence and development of the Internet of Vehicles (IoV), quick response time and ultralow delay are required. Cloud computing services are unfavorable for reducing delay and response time. Mobile edge computing (MEC) is a promising solution to address this problem. In this paper, we combined MEC and IoV to propose a specific vehicle edge resource management framework, which consists of fog nodes (FNs), data service agents (DSAs), and cars. A dynamic service area partitioning algorithm is designed to balance the load of DSA and improve the quality of service. A resource allocation framework based on the Stackelberg game model is proposed to analyze the pricing problem of FNs and the data resource strategy of DSA with a distributed iteration algorithm. The simulation results show that the proposed framework can ensure the allocation efficiency of FN resources among the cars. The framework achieves the optimal strategy of the participants and subgame perfect Nash equilibrium.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationComplexity, 2020, v. 2020, 8936064en_US
dcterms.isPartOfComplexityen_US
dcterms.issued2020-
dc.identifier.isiWOS:000514163700013-
dc.identifier.scopus2-s2.0-85078996319-
dc.identifier.eissn1099-0526en_US
dc.identifier.artn8936064en_US
dc.description.validate202006 bcmaen_US
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.pubStatusPublisheden_US
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