# A Terminal-Oriented Distributed Traffic Flow Splitting Strategy for Multi-Service of V2X Networks
## 5 Simulation Results
### Two-Level Distributed Offloading Scheme  
In this section, we develop a performance analysis of the traffic flow distribution scheme for multi-service of the V2X network by formulating a specific 5G networking application scenario, and compare the proposed scheme with the adaptive shunting strategy for a single type of service.  
V2 Ts in operating vehicles (such as public buses) in cities improve the ability to operate content by expanding local storage. The main service types of V2 Ts include high-bandwidth services such as online music, games, and video. The interactive service requirements of the vehicle terminal and the 5G base station are relatively small. The simulation parameter settings in this scenario are presented in Table 2. This scenario adapts to urban traffic sections, and guarantees vehicular information services (in-vehicle of V2T, V2 MT, and V2B services), as well as out-of-vehicle road condition perception (V2E and V2R services out-of-vehicle), and belongs to the service type with high delay requirements.  
Figure 7 shows the comparison results of delay-sensitive service utility values for multi-type service offloading and single-type service offloading when the service rate requirements of the terminal are different. When the terminal service rate demand is low, the F-utility value of the single type service split is better. When the terminal service rate demand exceeds about 3.8 Mbps, the F-utility value of the multi-type, multi-service split algorithm is higher. The performance optimization effect is more obvious, i.e., the classification performance of the adaptive shunt algorithm is better reflected.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Service Type** & **Service Characteristics** & **Service Proportion** \\ \hline V2T (IN-V) & & 10\% \\ V2 MT (IN-V) & & 20\% \\ V2B (IN-V) & (real-time) & 20\% \\ V2E (OUT-V) & & 10\% \\ V2R(OUT-V) & & 20\% \\ \hline V2E (IN-V) & Cost-sensitive (not & 10\% \\ V2V (OUT-V) & real-time) & 10\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Multi-service splitting parameters (1).  
Figure 7: Delay-sensitive service utility value comparison results.  
When the vehicle terminal in the Internet of Vehicles needs to communicate directly with the 5G base station to obtain its real-time location, traffic conditions, online video, etc., the service data generated by the vehicle terminal is mainly cost-sensitive. The simulation parameters are set as listed in Table 3, and the simulation results are shown in Figure 8. This scenario is suitable for expressway areas. In this scenario, priority should be given to vehicle condition monitoring, perception, and communication services involved in safe driving (which belong to delay-sensitive services). Large-bandwidth data services such as user information services (in-vehicle V2 MT and V2B services) and in-vehicle security monitoring (in-vehicle V2E service) are cost-sensitive services.  
The load-separation results of the cost-sensitive service in both the single-type and multi-type service scenarios when the service data rate requirements of the vehicle-mounted terminal are different are shown in Figure 8. When the service rate requirement of the vehicle terminal is lower than 3 Mbps, the traffic splitting effect for a single type of service is relatively good. However, when the service rate demand exceeds 3 Mbps, the F-utility value of the multi-type service shunt is better, and the utility effect becomes increasingly obvious.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Service Type** & **Service Characteristics** & **Service Proportion** \\ \hline V2T (IN-V) & & 5\% \\ V2E (OUT-V) & Delay-sensitive & 5\% \\ V2R (OUT-V) & (real-time) & 5\% \\ V2V (OUT-V) & & 5\% \\ \hline V2 MT (IN-V) & & 30\% \\ V2B (IN-V) & Cost-sensitive (not & 20\% \\ V2E (IN-V) & real-time) & 30\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Multi-service splitting parameters (2).  
Figure 8: Cost-sensitive service utility value comparison results.