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Proactive and hybrid wireless network access strategy for Vehicle Cloud networks: An evolutionary game approach

机译：车辆云网络的主动和混合无线网络访问策略：演化博弈方法

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Vehicular cloud computing is a new paradigm that integrates VANET and cloud computing. It refers to the vehicles' use of outside resources: they access to the conventional cloud or cooperate with other vehicles by sharing their resources (i.e. formation of a temporary vehicular cloud). Both these two ways induce different costs such as expensive cellular links in the case of access to the conventional cloud and intermittent links in the case of vehicles' cooperation. In this paper, we focus on the proactive decision made by the vehicle (i.e. choose the access to the vehicular cloud or to the cloud computing), where this decision depends on many parameters hardly to control. In other words, our goal is to determine the best technology to access the cloud (i.e wifi/LTE). We propose to model the evolution of the vehicles' strategy as an evolutionary game. To avoid the utilization of a centralized controller, we propose a distributed Q-Learning based Vehicular Cloud Access algorithm (QL-VCA) that allows each vehicle to select the way of access independently. The simulation results show that the proposed Q-learning algorithm provides users with better throughput and lower packet loss rate and delay compared to the other scenarios (i.e. using only the LTE or the wifi).
机译：车载云计算是将VANET和云计算集成在一起的新范例。它指的是车辆对外部资源的利用：它们访问常规云或通过共享其资源（即形成临时车辆云）与其他车辆合作。这两种方式都会产生不同的成本，例如在访问常规云时使用昂贵的蜂窝链接，在车辆合作时使用间歇性链接。在本文中，我们专注于车辆做出的主动决策（即选择对车辆云或云计算的访问），其中该决策取决于难以控制的许多参数。换句话说，我们的目标是确定访问云的最佳技术（即wifi / LTE）。我们建议将车辆策略的演变建模为进化博弈。为了避免使用集中式控制器，我们提出了一种基于Q学习的分布式车辆云访问算法（QL-VCA），该算法允许每辆车独立选择访问方式。仿真结果表明，与其他方案（即仅使用LTE或wifi）相比，提出的Q学习算法为用户提供了更好的吞吐量，更低的丢包率和延迟。

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