# Vehicle Cooperative Network Model Based on Hypergraph in Vehicular Fog Computing
## 3 The Architecture of Vehicular Fog Computing  
Fog computing [36] involves many technologies, such as resource management, privacy protection [37], network connection, etc. The development of fog computing depends on the continuous advancement of these technologies, and architecture is the cornerstone of all technologies [38]. Many experts and scholars have carried out research on the architecture of fog computing [39]. Existing fog computing architecture research can generally be divided into two categories: cloud-fog-terminal three-level architecture and fog-terminal two-level architecture [40]. The former consists of a cloud layer, a fog layer and a terminal layer. The fog layer provides temporary storage and real-time analysis of the collected data and sends data to the cloud layer on a regular basis. The latter consists of a fog layer and a terminal layer. Cloud servers participate in cooperation with each other, to provide various services, such as distributed vehicle navigation, intelligent traffic lights and local content distribution. Fog computing is an extension of cloud computing. The fog-based radio access network is proposed to solve the large-scale collaborative signal processing of cloud-based radio access network (C-RAN) architecture, with large delays and no separation of services and control. The fog-based radio access network (F-RAN) was developed on the basis of C-RAN and Heterogeneous cloud-based radio access networks (H-CRAN) and their architectures are basically similar. The main difference is that F-RAN uses software-defined network technology, edge buffer technology, cooperative radio signal processing technology and cooperative radio. Fog computing access points have evolved into management points, and multiple wireless resource management technologies have been sunk into the edge network. This allows business traffic to be transferred from the cloud server to local network equipment, reducing the burden on the fronthaul link, and also reducing delays in small business processing. Adjacent fog nodes communicate via D2D mode or relay mode, to improve spectrum efficiency. Because the communication delay between the end-to-end communication distance is positively correlated, the fog computing usually only considers the two hops between the fog nodes. The F-RAN architecture integrates wireless network technology, software-defined network technology, social networking and network virtualization computing into fog computing [41]. It is a service processing solution provided in mobile networks close to users. Many features are similar to the MEC model. It has the advantages of distributed deployment, low latency, high bandwidth, high privacy and high security. However, the difference between the two is also obvious. Edge computing mainly uses edge devices near the user, such as edge servers, to share part of the processing tasks. Fog computing can also use fog nodes to cooperate and communicate with each other, to form an edge distributed network. The server shares part of the processing tasks, and it can also use a nearby fog node to take part of the processing tasks. Fog computing is very suitable for distributed computing with a large amount of computing and extremely low latency requirements, which can effectively save server resources, and at the same time, share idle resources of fog nodes and improve resource utilization of the entire environment  
F-RAN research is in the early stages of research. Although there are some key technologies to support it, there are still some issues that need to be resolved, such as the choice of transmission mode based on mobile speed, communication distance, location, etc. There are serious interference problems; software-defined networking technologies, social awareness, computing offloading and network virtualization are still immature, and there are even many computer technology that cannot be directly applied to F-RAN. The F-RAN architecture also needs to be further improved.