# Rogue Node Detection Based on a Fog Network Utilizing Parked Vehicles
## 5 Simulation Experiment
When the number of mobile vehicles increases, the decrease of the TPR of our scheme is smaller than that of the other two comparison schemes. The FPR value can also achieve about 5%, which shows that our scheme is more accurate in abnormal node detection. Meanwhile, it can be seen from Figure 13 that, with the increase of the ratio of rogue nodes, the proposed scheme can always achieve a relatively high TPR value. The main reason is that we use as many fog nodes as possible to discover rogue nodes, especially when the traffic is heavy. In addition, the fog network in this paper is very stable, which can face complex urban traffic conditions. In all, the algorithm proposed in this paper has higher accuracy than the other two schemes, which can identify more rogue nodes and achieve high detection accuracy. Figure 11: Variation of TPR vs. the number of mobile vehicles (30%). Figure 10: Variation of FPR vs. the number of mobile vehicles (20%). In order to further verify the stability of the proposed algorithm, we assume that the number of moving vehicles is 200, and we compare the performance of the three schemes when changing the speed of moving vehicles. In the following experiment, assuming that the number of RSU devices is three and the proportion of rogue nodes is 20%, we explore the changes of the TPR values of the three algorithms when the standard deviation of moving vehicles' speeds is within the range of 0.2-2. As it can be seen from Figure 13, when the speed standard deviation of a moving vehicle is small, the TPR values of the three algorithms are high. However, as the standard deviation is greater than 1, it can be clearly seen that the algorithm in this paper has better stability than the other two algorithms. In particular, for the F-RouND strategy in literature [10], when the speed standard deviation of a moving vehicle is greater than 1, the TPR value decreases significantly. The reason, mainly, is this research depends too much on the physical distance of mobile vehicles on the road. When the average physical distance of the moving vehicle is long, the stability of the algorithm cannot be guaranteed. In contrast, the scheme proposed in this paper has a higher TPR value when varying the speed standard deviation and it is more stable than the other two schemes. Figure 12: Variation of FPR vs. the number of mobile vehicles (30%). Figure 13: Variation of TPR vs. speed standard deviation of moving vehicles. Next, we assume that the vehicles' speeds follow the standard normal distribution and the standard deviation is 1. Further, the number of RSU devices is within the range of 1-5. The TPR value changes of the three algorithms are explored and shown in Figure 14.  
Moreover, assuming that the number of RSU devices is 3, the vehicles' speed values follow the standard normal distribution, and the standard deviation is 1, the TPR values of the 3 algorithms vs. the proportion of rogue nodes is shown in Figure 15.  
As shown in Figures 14 and 15, the algorithm proposed in this paper always has higher accuracy than the other two detection algorithms. In Figure 14, we know that, when the number of RSUs is small, the RSU-based scheme achieves the lowest TPR among these schemes. It can be seen from Figure 15 that, with the increase of the ratio of rogue nodes, the proposed scheme can always achieve a relatively high TPR value. It can identify more rogue nodes and can also identify normal nodes more accurately. The reason is our scheme relies on stable parked vehicles to detect rogue nodes, and the protocol performance is little affected by traffic environment changes. Figure 14: Variation of TPR vs. number of RSUs. Figure 15: Variation of TPR vs. the proportion of rogue nodes. In Figure 16, the running times of the three schemes are compared. When the number of mobile vehicles is less than 75, the running times of the 3 schemes have little difference, which is less than 2000 ms.