# Coordination of Congestion and Awareness Control in Vehicular Networks
## 4 Evaluation
### Congestion and Awareness Control
Higher \(D_{p}\) levels reduce the risks of random channel variations that could provoke that transmission settings are not capable of guaranteeing the application requirements at the receiving vehicles. Figure 5: Average spatial distribution of the \(CBR\) and \(D_{p}\) metric experienced with the different protocols analyzed. The vertical lines represent the 5 th and 95 th percentiles. The application requirements are satisfied if \(D_{p}>0\). (**a**) A-DCC. (**b**) MINT. (**c**) INTERN. (**d**) COMPASS. (**e**) A-DCC. (**f**) MINT. (**g**) INTERN. (**h**) COMPASS. The results obtained when applying the COMPASS methodology to coordinate A-DCC and MINT are depicted in Figure 5d,h. Figure 5d show that COMPASS is able to maintain the channel load levels below \(CBR_{max}=0.6\) (except for very high traffic densities as it was the case with MINT and INTERN), while satisfying the application requirements of all vehicles (\(D_{p}>0\) in Figure 5h). COMPASS is able to improve the \(D_{p}\) values with respect to INTERN, especially at short distances to the intersection and high traffic densities. Moreover, COMPASS offers better evolution perspectives since it does not require designing a new integrated scheme. This allowed, for example, the straightforward application of COMPASS to coordinate A-DCC and MINT, but also to coordinate other protocols such as PULSAR [23] and MINT. The analysis conducted with PULSAR and MINT yielded similar results as to those here presented when coordinating A-DCC and MINT: the \(CBR\) is maintained below \(CBR_{max}\) while the application requirements are satisfied for all vehicles \(D_{p}>0\). This demonstrates that the proposed COMPASS coordination methodology is independent of the specific protocol implementations, and can be applied to different congestion and awareness control schemes. The convergence and stability of the control processes that are part of A-DCC and MINT is relevant for their correct operation. It is therefore important to analyze whether these properties are maintained when coordinating the operation of the two protocols using COMPASS. Figure 6 depicts for INTERN and COMPASS, the time evolution of the \(CBR\) and \(D_{p}\) metric for two selected vehicles and a traffic density of 75 veh/km/lane. One of these vehicles is located at the intersection (referred to as 'close' in the figure), where the maximum channel load is experienced. The other vehicle is located at around 2400 m from the intersection (referred to as 'far' in the figure). These two vehicles are selected to check the convergence and stability of the protocols under different conditions. Figure 6 shows that both protocols satisfy the application requirements during the simulation time as the \(D_{p}\) is maintained higher than 0. The channel load is also maintained below the required threshold during the simulation run. The results in Figure 6 show that COMPASS can maintain the convergence and stability properties of the protocols it coordinates. After an initial transition period of less than 5 s, the \(CBR\) and \(D_{p}\) metrics converge and their oscillations are kept below \(\pm 5\%\) of their respective average values. The initial transition period is produced because all vehicles in the scenario initiate their operation at the same time (i.e., at simulation time equal to \(t=0\)), which is unlikely to happen in a practical situation. Figures 7 and 8 represent a snapshot of the packet transmission frequency and power used by each vehicle in the scenario (each dot in the figure corresponds to a particular vehicle) after 50 s of simulation time. MINT, INTERN and COMPASS adapt the vehicles' packet transmission frequency and power taking into account their individual application requirements. This results in that vehicles implementing these protocols can utilize different packet transmission frequency and power depending on their specific application requirements.