## 5 Simulation Experiment  
### Simulation Experiment Environment  
In this paper, Matlab is used to verify the algorithm of unloading edge computing tasks, including modeling application scenarios, and supporting various work on vehicle edge workflow, including model accuracy calibration, testing, etc. This experiment is based on Matlab 2019a for the Windows10 operating system. The scenario is set to an urban road intersection, the area is A\(=1000\text{ m}\times 1000\text{ m}\), there are \(J=4\) RSU, each of which connects to one MEC server, the computing capability of the server is set to \(\Gamma_{mcc}=8\text{ GHz}\), and the maximum bandwidth and power resources of the server are set to \(B_{max}=2.5/\text{Mbit},P_{max}=5.8/\text{kw}\). The vehicle has a certain computing capability \(\Gamma_{vi}^{local}=3\text{ GHz},\forall i\in N\), and the number of vehicle tasks and the maximum allowable delay are evenly distributed in [50, 820]/Kb and [10, 50]/s. Finally, the minimum error threshold \(\xi_{min}=0.001\) is specified in order to confirm the algorithm's convergence.  
Several other benchmark schemes are presented in order to assess the effectiveness of the suggested algorithm more accurately.  
(1) Service node selection  
HDBSACAN: In reference [14], an improved hierarchical clustering algorithm (HDBSACAN) is proposed. Considering four performance indexes, an evaluation expression for surrounding service vehicles is proposed in order to find suitable service vehicles for task vehicles and improve task unloading efficiency.  
Random selection (RS): As the intersection is special, the same vehicle can be in the communication range of multiple RSUs at the same time, and the vehicle randomly selects one of them as the service node.  
F-SNFLC: In the fuzzy logic selection algorithm proposed in this paper, a fixed value is set for the coefficient of the output linear equation of the logic rule \(F(\tau)\).  
(2) Resource allocation strategy algorithm  
Average distribution (AD): Bandwidth \(B_{max}\) and power \(P_{max}\) are equally distributed to each vehicle. Task uninstallation ratio \(a_{vi}^{\tau}=0.5\).  
Random unloading allocation (RUA): A random seed function \(rand\) is introduced to calculate the optimization objective so that the unloading ratio and resource allocation are randomly generated.  
Genetic ant colony algorithm (G-ACA): the literature [21] considers a delay and energy consumption research uninstall strategy, and the algorithm integrates the benefits of the two basic algorithms. The improved shortcoming of the single algorithm easily falls into a local optimal solution, but it is insufficient, and the researchers will only use the algorithm in the single objective optimization scenario. Its performance in multi-objective optimization scenario is unknown.  
Nonorthogonal multiple access (NOMA): according to the literature [2], the federation server allocation and power allocation, along with computing the allocation of resources, time distribution, and channel allocation, can minimize the total delay of all tasks. However, the authors did not take into consideration the unloading ratio and whether there is a coupling relationship between the resource allocation, which could be directly related to the ideal and displayed as a single variable.