Resource Management Framework Based on the Stackelberg Game in Vehicular Edge Computing
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 com...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/8936064 |
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| _version_ | 1832551888578412544 |
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| author | Guang-Shun Li Ying Zhang Mao-Li Wang Jun-Hua Wu Qing-Yan Lin Xiao-Fei Sheng |
| author_facet | Guang-Shun Li Ying Zhang Mao-Li Wang Jun-Hua Wu Qing-Yan Lin Xiao-Fei Sheng |
| author_sort | Guang-Shun Li |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-85342d8bcd4a4cf99a01042e04e11af7 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-85342d8bcd4a4cf99a01042e04e11af72025-02-03T06:00:11ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/89360648936064Resource Management Framework Based on the Stackelberg Game in Vehicular Edge ComputingGuang-Shun Li0Ying Zhang1Mao-Li Wang2Jun-Hua Wu3Qing-Yan Lin4Xiao-Fei Sheng5School of Information Science and Engineering, Qufu Normal University, RiZhao 276800, ChinaSchool of Information Science and Engineering, Qufu Normal University, RiZhao 276800, ChinaSchool of Information Science and Engineering, Qufu Normal University, RiZhao 276800, ChinaSchool of Information Science and Engineering, Qufu Normal University, RiZhao 276800, ChinaSchool of Information Science and Engineering, Qufu Normal University, RiZhao 276800, ChinaSchool of Information Science and Engineering, Qufu Normal University, RiZhao 276800, ChinaWith 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.http://dx.doi.org/10.1155/2020/8936064 |
| spellingShingle | Guang-Shun Li Ying Zhang Mao-Li Wang Jun-Hua Wu Qing-Yan Lin Xiao-Fei Sheng Resource Management Framework Based on the Stackelberg Game in Vehicular Edge Computing Complexity |
| title | Resource Management Framework Based on the Stackelberg Game in Vehicular Edge Computing |
| title_full | Resource Management Framework Based on the Stackelberg Game in Vehicular Edge Computing |
| title_fullStr | Resource Management Framework Based on the Stackelberg Game in Vehicular Edge Computing |
| title_full_unstemmed | Resource Management Framework Based on the Stackelberg Game in Vehicular Edge Computing |
| title_short | Resource Management Framework Based on the Stackelberg Game in Vehicular Edge Computing |
| title_sort | resource management framework based on the stackelberg game in vehicular edge computing |
| url | http://dx.doi.org/10.1155/2020/8936064 |
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