# Coordination of Congestion and Awareness Control in Vehicular Networks
## 4 Evaluation
### Congestion and Awareness Control  
Figure 5 plots the average spatial distribution of the \(CBR\) and the \(D_{p}\) metric as a function of the distance to the intersection; the vertical lines represent the 5 th and 95 th percentiles. The \(D_{p}\) metric represents the difference between the number of packets demanded by the application (i.e., packet reception frequency) and the number of packets that are actually received per second at the communication range required by the application. An application is then satisfactorily supported if \(D_{p}>0\). The \(D_{p}\) values reported have been calculated taking into account packet losses due to propagation conditions. The results depicted in Figure 5a show that A-DCC successfully maintains the \(CBR\) below \(CBR_{max}=0.6\) irrespective of the traffic density. This trend was indeed expected since A-DCC has been designed to control the channel \begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Packet size [Bytes] & 250 \\ Min. and Max. transmission power [dBm] & \(-10\) and 23 \\ Min. and Max. packet \(\kappa\) frequency [Hz] & 1 and 20 \\ Min. and Max. \(\Delta T_{f}\) [Hz] & 1 and 3 \\ Data rate [Mbps] & 6 \\ Carrier sense threshold [dBm] & \(-90\) \\ Reception threshold [dBm] & \(-82\) \\ Target channel busy ratio (\(CBR_{max}\)) & 0.6 \\ \(CBR\) measurement period [ms] & 250 \\ Communication range required (\(CR\)) [m] & 50-200 \\ Packet reception frequency required (\(R\)) [Hz] & 1–10 \\ Traffic density [vehicles/km/lane] & 50, 75, 100 \\ Simulation time [s] and simulation runs & 150 and 10 \\ \hline \end{tabular}
\end{table} 
Table 1: Communications and simulation parameters. load. Congestion control protocols do not take into account that vehicles may have varying application requirements when setting the vehicles' communication parameters. The results depicted in Figure 5e show that A-DCC does not always satisfy the applications requirements (\(D_{p}<0\)). Only for low traffic densities, vehicles located at long distances to the intersection can achieve positive \(D_{p}\) values since they can use high packet frequencies compared to vehicles close to the intersection. The results obtained show that the \(CBR\) experienced with MINT (Figure 5b) increases with the traffic density, especially at short distances to the intersection. This is the case because MINT configures each vehicle with the minimum packet transmission frequency and power required to satisfy its application requirements, and the protocol does not control the channel load. This results in that the application requirements are satisfied with a constant \(D_{p}\) metric independently of the traffic density (see Figure 5f). Under low and medium traffic densities, the use of the minimum required transmission settings results in that the channel is not fully utilized since the maximum experienced \(CBR\) is below \(CBR_{max}\). These results demonstrate that, even if A-DCC does not always satisfy the application requirements, there is sufficient channel capacity available in the simulated scenario to satisfy the application requirements while maintaining the \(CBR\) below \(CBR_{max}\). The experienced \(CBR\) with MINT only surpasses \(CBR_{max}\) under the highest simulated traffic density and for vehicles close to the intersection. In this case, the traffic density is so high that there is not sufficient channel capacity to satisfy the requirements of all vehicles without exceeding \(CBR_{max}\) at short distances to the intersection. The results depicted in Figure 5c,g show that INTERN is able to satisfy the application requirements of all vehicles (\(D_{p}>0\)) while maintaining the \(CBR\) below \(CBR_{max}\), except for the highest traffic density scenario. In this scenario, the requirements of all vehicles cannot be satisfied without exceeding \(CBR_{max}\), as it was also observed for MINT in Figure 5b. The direct comparison of INTERN and MINT shows that INTERN increases the use of the available bandwidth and augments the experienced \(D_{p}\) compared to MINT.