# A Hierarchical Resource Allocation Scheme Based on Nash Bargaining Game in VANET
## 1 Introduction  
With the rapid increase of the number of vehicles, traffic accidents and congestion are becoming more and more serious, which has attracted worldwide attention [1]. The concept of Vehicular Ad-hoc Networks (VANET) is considered to be an effective way to solve this problem. Through modern information and communications technology, the vehicles can access the network and interact with the cloud resource pool in time [2]. In addition, edge cloud computing has been applied to the VANET for data acquisition considering the simple deployment of edge cloud infrastructure [3]. For example, Autonomous Vehicular Clouds was proposed to offer potential applications to VUEs, which ensures the safe operation of autonomous vehicles and provides the services required in the running process of vehicles [4]. VANET clouds usually comprises three types of clouds, which are Central cloud, RSU cloud, and Vehicular cloud [5]. One of the main advantages of VANET clouds is that no additional infrastructure is required. The central cloud is a centralized resource pool that provides the resources needed for RSUs and vehicles. The RSU cloud provides services for vehicle access networks. The vehicular cloud consists of vehicles on the road, providing services for data transmission between vehicles. As seen in Figure 1, the role of central cloud is to achieve centralized scheduling of spectrum resources. Multiple RSUs are connected to the central cloud through the forward link and the management interface, different RSUs' wireless resources are abstracted into a virtual resource pool. VUEs request resources from RSUs within the communication range of each RSU. However, due to the high mobility of vehicles, the topological structure of the VANET is constantly changing, and the number of vehicles within the coverage area of RSU is constantly changing, which increases the difficulty of resource allocation and results in tidal phenomena in VANET [6]. On the other hand, the selfishness of VUEs brings new challenges and difficulties to the rational resource allocation in vehicular networks [7]. For example, the uncertainty of vehicle motion and the selfish behaviors of vehicles may lead to the cloud resource competition among the VUEs and result in network congestion, which occurs in the data access through the RSUs cloud and central cloud. Considering the sparse deployment of RSU, when the number of vehicle users increases rapidly within the coverage of an RSU, the spectrum resources of the RSU can not meet the needs of all vehicle users. Therefore, how to fairly and efficiently allocate spectrum resources to VUEs has become an urgent problem to be solved. At this stage, some scholars have put forward some resources allocation mechanisms in VANET. A spectrum resource acquisition method based on game theory is proposed, which is modeled as a non-cooperative congestion game to ensure the fair resource allocation [8]. Some research has transformed the problem of spectrum resource allocation into a semi-Markov decision-making process to achieve reasonable and effective resource allocation [9]. In [10], a repeated game scheme based on Gauss-Seidel (G-S) iteration method is proposed to solve the resource allocation problem in VANET. Considering the selfishness of the vehicle, a punishment strategy is proposed to avoid the irrational behavior of the vehicles [11]. However, the above research has some limitations and only considers the spectrum resource allocation within the coverage of a single RSU. Considering the tidal phenomenon caused by vehicle movement [12], the above research fails to realize the reasonable allocation of spectrum resources in the central cloud, resulting in the waste of spectrum resources. In non-hot spots of cities, considering the deployment cost, RSUs cannot be deployed on a large scale, which means they cannot form a seamless coverage network [13].