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一种基于公共物品博弈论的异质网络建模方法,用于探索 VANETs 中的合作行为。

A Heterogeneous Network Modeling Method Based on Public Goods Game Theory to Explore Cooperative Behavior in VANETs.

机构信息

School of Cyberspace, Hangzhou Dianzi University, Hangzhou 310018, China.

出版信息

Sensors (Basel). 2020 Mar 24;20(6):1802. doi: 10.3390/s20061802.

Abstract

Cooperative vehicular networking has been widely studied in recent years. Existing evolution game theoretic approaches to study cooperative behavior in Vehicular Ad hoc Network (VANET) are mainly based on the assumption that VANET is constructed as a homogeneous network. This modeling method only extracts part attributes of vehicles and does not distinguish the differences between strategy and attribute. In this paper, we focus on the heterogeneous network model based on the public goods game theory for VANET. Then we propose a Dynamic Altruism Public Goods Game (DAPGG) model consisting of rational nodes, altruistic nodes, and zealots to more realistically characterize the real VANET. Rational nodes only care about their own benefits, altruistic nodes comprehensively consider the payoffs in the neighborhood, while zealots insist on behaving cooperatively. Finally, we explore the impacts of these attributes on the evolution of cooperation under different network conditions. The simulation results show that only adding altruistic nodes can effectively improve the proportion of cooperators, but it may cause conflicts between individual benefits and neighborhood benefits. Altruistic nodes together with zealots can better improve the proportion of cooperators, even if the network conditions are not suitable for the spread of cooperative behavior.

摘要

近年来,协作式车辆网络得到了广泛的研究。现有的基于演化博弈论的方法主要基于 VANET 被构建为同质网络的假设。这种建模方法仅提取了车辆的部分属性,没有区分策略和属性之间的差异。本文基于公共物品博弈论,关注 VANET 的异质网络模型。然后,我们提出了一个由理性节点、利他节点和狂热者组成的动态利他公共物品博弈(DAPGG)模型,以更真实地描述真实的 VANET。理性节点只关心自己的利益,利他节点全面考虑邻居的收益,而狂热者则坚持合作。最后,我们探讨了这些属性在不同网络条件下对合作进化的影响。仿真结果表明,仅增加利他节点可以有效地提高合作者的比例,但可能会导致个体利益和邻居利益之间的冲突。利他节点和狂热者的结合可以更好地提高合作者的比例,即使网络条件不适合合作行为的传播。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d176/7146436/4c72d9d880e5/sensors-20-01802-g001.jpg

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