# Vehicle Cooperative Network Model Based on Hypergraph in Vehicular Fog Computing
## 5 Model Analysis
distribution function, but a certain limited combination of multiple distributions. It can be seen from Figure 2d that, when \(\varphi\) approaches 1, the network exhibits a power-law distribution. At \(t=1000\), \(m=3\), \(\varphi=0.5\) and \(k1\): \(k2=1/3\), 1/2, 2 and 3 respectively a comparison with \(k1\):\(k2=1\) super-degrees distributed in double logarithmic coordinates are shown in Figure 3.  
It can be seen from Figure 3 that, when \(\varphi\)= 0.5, the probability of random connection and preferred connection in the network is equal, and the characteristics displayed by the network should be similar to the dotted lines in Figure 2b,c. As \(k1\)/\(k2\) is worth changing, the nature of the network presentation is slightly different. The larger the value of \(k1\)/\(k2\) is, the more nodes are newly added to the network, and the vast majority of nodes can only be connected randomly, so the network exhibits random network characteristics. At \(t=1000\), \(\varphi=0.5\), \(k1=k2=3\), \(m=2\), 3, 4 and 5, respectively, comparison with \(m=1\) and 2 super-degrees distributed in double logarithmic coordinates are shown in Figure 4.  
It can be seen from Figure 4 that, when \(\varphi\), \(k1\) and \(k2\) are certain, the properties of the network presentation are the same, but the scale-free characteristics are different, but they all have the same power law. The larger \(m\) is, the more super-edges are formed in a certain period of time, and the over-degree value of the non-scaling characteristics of the nodes in the super-degree distribution is larger. When a network node is added in a certain time in the network, the more nodes that are needed to form a super-edge, the more obvious the characteristics of the network will be in the place where the node is too large, but all have the same power law. From the above analysis, we can know that the characteristics of the vehicle collaboration network for vehicular fog computing are affected by many aspects, mainly by the network connection mechanism and network growth mechanism. Traditional vehicular fog computing only considers offloading tasks to roadside infrastructure for processing, and roadside infrastructure is currently not deployed on a large scale, due to its high cost, which cannot be guaranteed to be available everywhere. Because of this, we let vehicle nodes with a lot of idle resources act as fog nodes, to improve resource utilization and offloading coverage. This model can predict the behavior of unloaded nodes from time to space, to expand vehicles. This not only broadens the extension of vehicular fog computing, but also improves resource utilization. Computing and storage capabilities of vehicular fog computing sinks to the user's surroundings. As the user is closer to the user, the user's request no longer needs to be processed through a long  
Figure 3: The characteristics with different \(k1:k2\). transmission network to the distant core network. The fog server offloads part of the traffic and directly processes and responds to users, so the communication delay will be greatly reduced. The latency-saving feature of the fog server is particularly evident in latency-sensitive-related applications such as video transmission and VR. Take video transmission as an example. In the traditional vehicular fog computing method, when each user terminal initiates a video content call request, it first needs to pass through the roadside unit and then access it through the base station, and then connect to the target content through the core network, and then layer by layer. By performing backhaul and finally completing the interaction between the terminal and the target content, it is conceivable that such a connection and layer-by-layer acquisition method is very time consuming.