# Enhancing Heterogeneous Communication for Foggy Highways Using Vehicular Platoons and SDN
## 4 Performance Evaluation
### Routing Overhead Ratio (RoR)  
RoR is the ratio between the number of control packets and the entire network packets. The pattern for all the cases shows an increase in overhead due to an increase in vehicular density as can be seen in Figure 8. Because the number of nodes increases, the overhead increases to ensure connectivity. Case (_iii_) shows high overhead due to finding a relay node within range. It needs to send control packets until it reaches any LC or HV. Case (_i_) shows high connectivity, which means less overhead as compared to the other two cases. In case (_ii_), the RoR is higher due to the need to exchange more packets in order to avoid collisions. If there are no SDN local controllers, then the exchange of warning messages is in the form of broadcasting, which leads to high overhead, as can be seen in the reference schemes. To sum up, we achieved high performance in all four parameters in comparison to the reference schemes, thus ensuring that the use of SDN in vehicular networks during foggy weather makes it possible for vehicles to travel safely by finding an optimal routing path.  
To justify our preference for DTC in our scheme, we examined another set of comparisons for all four parameters in Figure 9. The comparison between the performance  
Figure 8: Performance comparison in terms of routing overhead ratio as a function of vehicular density.  
Figure 7: Performance comparison in terms of average number of hops as a function of vehicular density.  
of DTC and the gradient descent (GD) algorithm is made to assess their ability to predict LCs based on the number of vehicles. Both models were trained and implemented in our code to select LCs. The dataset was produced by randomly generating vehicle data. The features included vehicle speed, vehicle density, and the positions of RSUs to determine which LCs should be selected. Due to their position, different RSUs have different road traffic. Traffic signals always have a high density when they are red; therefore, position plays a significant role in selecting the LCs. Likewise, speed is crucial as it reflects how long a vehicle stays within the communication range of an RSU. Table 2 illustrates the use of multiple metrics to evaluate the effectiveness of the models. After calculating the accuracy of both models, we observed that DTC achieved 90% accuracy while GD achieved 82.2% accuracy. Hence, DTC was more preferred in our scheme to choose LCs than GD for this reason. DTC is able to handle non-linear relationships among features with minimal computational requirements, which is typically the case in highly dynamic environments like VANETs. Figure 9 illustrates the high connectivity situation of platoons on highways where the patterns for both algorithms are almost the same. However, the DTC exhibits superior performance in each PDR, delay, hops, and RoR, which demonstrates its high accuracy when compared to GD.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Gradient Descent & 82.2\% & 86.0\% & 83.0\% & 84.5\% \\ \hline Decision Tree Classifier & 90\% & 94.1\% & 88.9\% & 91.4\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance metrics for different classifiers.  
Figure 9: _Cont_.