# Vehicle Cooperative Network Model Based on Hypergraph in Vehicular Fog Computing
## 3 The Architecture of Vehicular Fog Computing
For example, resource classification needs a perfect algorithm, and resource sharing and offloading mechanisms need a perfect mechanism. In [42], a fog computing platform was developed, to extend the cloud computing paradigm to the edge of the machine-to-machine network, to support the internet of things. The proposed four-layer fog computing architecture supports the quick response at neighborhood-wide, communitywide, and city-wide levels, providing high computing performance and intelligence in future smart cities. Vehicular fog computing is an effective fusion of fog computing technology and vehicle networking to efficiently provide computing, storage, and processing functions close to the user, so as to meet the application needs of rapidly developing compute-intensive services. Aiming at the combination of these two types of technologies, the overall network framework design has become the primary problem to be solved for the large-scale application of fog computing in the Internet of Vehicles. When fog computing is integrated with the Internet of Vehicles, fog computing technology can be used to process business data of vehicle users in real time, making it extremely suitable for servicing high-speed vehicles. The entire network framework can be roughly divided into three layers, from bottom to top: the user layer, the fog computing layer and the cloud computing layer. The vehicle user is responsible for data collection and preprocessing at the lowest user level and only makes decisions at the vehicle level; the middle layer is the fog computing layer, which is mainly responsible for real-time data fusion and localization processing, and undertakes decision-making tasks at the regional level; and the cloud computing layer at the core of the network has long been committed to network data development and macro analysis, and plays a role in network-level decision-making. In particular, roadside units distributed at different locations on the road can be upgraded through hardware upgrades and can become a fog node that makes localized decisions for nearby vehicle users. By deploying and making full use of these fog nodes that are widely distributed around vehicle users, the fog computing-based vehicle networking edge computing solution is also suitable for real-time communication management for smart city scenarios; it greatly reduces the network burden of the traditional vehicle networking core network, while significantly reducing the response delay in the vehicle communication process. In order to improve the performance of vehicular fog computing and minimize the real-time response time of the vehicle networking system, we further studied the vehicular fog computing architecture in our previous research. We integrated the advantages and disadvantages of different edge computing technologies, and based on the focus on edge cloud collaboration, we proposed an edge computing framework for the Internet of Vehicles that integrates a comprehensive sense of collaboration between different network layers and different edge servers. From Figure 1, it can be seen that, when the vehicle needs assistance, the vehicle will send out a help signal. If there are several facilities around, the infrastructure will assist the vehicle to complete the task; if there is no roadside infrastructure around the vehicle, you can pass the surrounding vehicles, to assist in completing the corresponding tasks. Vehicle assistance can be selected based on the vehicle's historical assistance data. As shown in Figure 1, the architecture is divided into the following layers. The cloud services layer is uniformly deployed at the top level of the architecture. It is similar to a traditional cloud computing center. It centralizes most application services in the Internet of Vehicles. The cloud service center includes a cloud data center and a cloud composed of high-performance server clusters. The control center can store and analyze a large amount of data collected by various terminal devices.