## 5 Simulation Experiment
### Analysis of Experimental Results  
When designing the fuzzy inference system, the researchers adjusted the Gaussian \(\alpha,\beta\) and modified the shape of the membership functions. Finally, a different service node was selected in order to verify this deviation, and the vehicle average cost was set as the evaluation criterion. The vehicle average cost is defined as the energy consumption and delay of accumulative and average \(\cos t=\frac{1}{N}\sum\limits_{l=1}^{N}\left(E_{vl}^{local}+E_{vl,j}^{un}+T_{vl}^{ local}+T_{vl,j}^{un}+T_{vl,j}^{wait}\right)\). The node is not the best option if the vehicle pays a higher fee there. According to Figure 5, if there is a difference between the two parameters, the Gaussian function will exhibit large fluctuations. The membership function of a fuzzy system's input variables can also produce larger deviations. As shown in Figure 5, there is a gap between the two parameters, and the Gaussian function will have large fluctuations. The membership function of the fuzzy system input variables can produce larger deviation and evaluate the performance of the node deviation, which is greater than \(\alpha,\beta=0.5\). The more reasonable the parameters are, the more closely a vehicle can be matched to a service node, which makes it easier to allocate resources for subsequent work. Figure 6 illustrates the differences in the various service node selection algorithms, the vehicle, and the final decision to use the CCP for Unicom. In other words, regardless of the algorithm used to find the best service node for the vehicle, the node and the vehicle must be able to establish a connection for Unicom to continue, and the probability of this happening is relatively low. The average CCP of the system will decrease as the number of vehicles rises, per the results, but the SNFLC-based algorithm suggested in this article performs best overall among all algorithms, followed by HDBSACAN. When clustering vehicles, the algorithm assigns a cluster head to each cluster, and, after computing duties are finished, the cluster head manages all the vehicles in the cluster jointly. When there are few vehicles, the results of the two algorithms are essentially the same, but as the number of vehicles rises, the shortcomings of the latter algorithm gradually become more obvious. This is because it is ignored that each vehicle is dynamically changing and that the evaluation of node performance has its own value. F-SNFLC is the second. Because this algorithm primarily establishes a fixed value in the fuzzy linear output equation that cannot be adjusted adaptively to changes in the system environment, performance degrades over time as the number of vehicles increases. N is the quantity of the car in Figure 6.  
The convergence of the suggested resource distribution algorithm is confirmed, as seen in Figure 7. The algorithm gradually converges at around 700 iterations, and the average energy consumption gradually stabilizes, which is essentially consistent with the system's lowest energy consumption. Figure 5: Influence of Gaussian factor selection on average vehicle cost. The performance of the suggested algorithm is then evaluated in terms of system energy usage in comparison to other algorithms. According to Figure 8, the system's energy consumption rises as the number of vehicles grows. The system load is significant when there are more than 80 system cars. The performance of G-ACA is reduced in the situation of multi-objective and large computation, and is even worse than the performance of the algorithm NOMA. Due to its high computational complexity, the NOMA algorithm performs less well than the LC-IRA algorithm suggested in this article. The efficiency of G-ACA, NOMA, and LC-IRA algorithms increased by 26.5%, 31.37%, and 45.52% in comparison to RUA and AD algorithms, respectively. Figure 6: Comparison of different node selection algorithms. Figure 7: Convergence analysis of LC-IRA algorithm.