Resource Management Framework Based on the Stackelberg Game in Vehicular Edge Computing

Guang-Shun Li, Ying Zhang, Mao-Li Wang, Jun-Hua Wu, Qing-Yan Lin and Xiao-Fei Sheng

Complexity, 2020, vol. 2020, 1-11

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.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:8936064

DOI: 10.1155/2020/8936064

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