Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/82121
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Title: Resource management framework based on the Stackelberg Game in vehicular edge computing
Authors: Li, GS 
Zhang, Y
Wang, ML
Wu, JH
Lin, QY
Sheng, XF
Issue Date: 2020
Source: Complexity, 2020, v. 2020, 8936064
Abstract: With 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.
Publisher: Hindawi
Journal: Complexity 
ISSN: 1076-2787
EISSN: 1099-0526
DOI: 10.1155/2020/8936064
Rights: Copyright © 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.
The 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/10.1155/2020/8936064
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