## 4 Results and Analysis  
Firstly, we simulated five motion indicators of three Mobility models and obtained the following performance curves. The communication radius R varies between 150 m and 290 m. Figure 6 shows that as the communication radius increases, the changing trends of the five parameters are different. From Figure 5(a), it can be seen that there is no spatial correlation between GM and RWP, which is consistent with the conclusion of [32]. The relative speed remains basically unchanged trend with the increase in node communication radius. However, there are significant differences in this parameter among the three mobility models: the GM with time dependence has the lowest relative speed for a single node, as its motion is time-correlated; the RWP with completely independent motion at each time interval has the highest relative velocity. The above results show that the performance of the group mobility model RPGM is superior to that of the individual mobility models RWP and GM. In this experimental scenario, 40 motion nodes were divided into 4 groups with 10 nodes in each group, and 9 of them randomly moved around a central node. The nodes were relatively concentrated, and the network's connectivity performance was good. As the communication radius of nodes increases, the sensing range of nodes becomes larger. The degree of the partitioning of mobility models greatly decreases, available paths increase, and the link duration of mobility models increases, with RPGM greater than RWP and GM. Because GM and RWP nodes are independent of each other, the average spatial dependence of the two is much smaller than that of the group mobility model RPGM which depends on the motion of the head of the node group. The above indicates that the parameters we selected are basically in line with the motion characteristics of the nodes during the motion process, and the differences between different models can also be reflected in the parameters. Next, we will start training and testing the neural network. Firstly, we collected data from the GM model based on the motion scenario set in Section 3.1. The data were normalized and input into the neural network. The results are shown in Fig. 5.  
Figure 6: Five motion indicators’ performance of three mobility models: (**a**) average spatial dependency performance of three mobility models; (**b**) average relative speed performance of three mobility models; (**c**) average link duration performance of three mobility models; (**d**) average path availability performance of three mobility models; (**e**) average partitioning degree performance of three mobility models. network. The performance of the training and validation sets during the neural network training process is shown in Figure 7.  
As the number of training epochs increases, the loss of the test and validation sets shows a decreasing trend and remains stable after 4300 epochs. The maximum value of training accuracy was 93.19305%, and the maximum value of validation accuracy was 93.02666%. In order to verify the generalization of the model, we saved the network parameters with the highest accuracy rate in the training process and then used different motion speeds and random seeds to generate a batch of new GM mobility scenes. After processing the data according to the above process, we used them as the test dataset. After using the test data as the input of the neural network, the accuracy of network performance classification is shown in Figure 8.  
Figure 8: Accuracy of GM test dataset. Figure 7: The performance of the training and validation datasets during the BPNN training process: (**a**) the performance of training loss and validation loss; (**b**) the performance of training accuracy and validation accuracy.