# Vehicle Cooperative Network Model Based on Hypergraph in Vehicular Fog Computing
## 2 Related Work  
Some studies have been performed to leverage vehicle resources as major providers of roadside services in various domains and interests [19]. In [20], the authors considered how to coordinate computation resources in both the cloud and smart vehicles to solve the task-scheduling problem in the Vehicular Cloud. The authors of [21] put forward a feasible solution that enables offloading for real-time traffic management in fog-based IoV systems, aiming to minimize the average response time for events reported by vehicles. Utilizing the idle resources of parked vehicles reduces the communication service delay of the vehicle connected network effectively, and a solution capable of offloading real-time traffic management is proposed to minimize the average response time of vehicle incident reports. Through the networking of parked vehicles as edge nodes, a distributed urban traffic management system is constructed, which reduces the data transmission pressure of the backhaul link and effectively reduces network delay. The authors of [22] propose a collaborative task offloading and output transmission mechanism to guarantee low latency as well as the application- level performance. However, in various applications of the future vehicle connected networks, users are extremely sensitive to delay requirements. For applications such as collision warning and departure warning, if the task is migrated to the base station, the task migration method in which the base station performs the task and then returns the results will inevitably increase the network delay [23]. Although a hybrid communication solution that combines V2V and V2I functionality will inherently provide the most efficient and powerful solution, purely infrastructure-based communication will not limit the application level of data distribution and processing to a specific centralized architecture. In fact, centralized architecture will inevitably lead to scalability issues, so V2I infrastructure does not necessarily use centralized architecture. To cope with the information needs of thousands of vehicles in large cities, although multiple distributed subsystems can be deployed to achieve better scalability, a fully decentralized peer-to-peer (P2P) approach is more attractive. Reference [24] introduces the D4V architecture, based on opportunistic mechanisms for the dissemination of data generated by vehicle sensors and drivers. This architecture relies on a peer-to-peer (P2P) overlay scheme. The Cooperative Intelligent Transportation System (C-ITS) mainly uses vehicles and passengers in a certain range, and roadside pedestrians and roadside infrastructure, to improve the extension and service quality of the intelligent transportation system. Research applications include collision avoidance [25], emergency vehicle warning systems [26], traffic mitigation [27], and criminal vehicle surveillance investigations [28]. The information content under consideration can have local validity, that is, spatial range utility, and its explicit lifetime, that is, its time range validity [29]. Reference [30] summarizes the research on the telematics of vehicles based on smartphones in the past ten years, focusing on the use of the mobility of smartphones and sensors on vehicles to achieve friendly and cooperative applications of vehicles. In addition, we focused on the differences between traditional and smartphone-based car navigation, and conducted surveys based on the latest technology classifications in smartphone-based transportation modes, vehicle self-organizing networks, cloud computing, driver classification and road-condition monitoring. Future developments are expected to be driven by advances in sensor technology, evidence of the social benefits of current implementation, and the establishment of assessments of sensor fusion and vehicle behavior. [PERSON], [PERSON] et al.