# The Traffic Adaptive Data Dissemination (TrAD) Protocol for both Urban and Highway Scenarios
## 4 Performance Evaluation
### GPS Drift  
Some approaches of TrAD are based on vehicle position. However, the GPS system is usually not accurate, particularly in downtowns. This would be affected by several impact factors, such as satellite position and number, signal attenuation and clocking errors. In particular, in urban scenarios, high towers could obstruct the GPS signal. The average deviation of standard GPS is up to 30 m [36]. Therefore, we investigated the GPS drift in terms of data dissemination speed that was defined at the beginning of this section. Error deviations have been injected into the mobility module of Veins framework. We choose five options of error deviations, _i.e._, 0 m, 25 m, 50 m, 75 m and 100 m. The 0 m deviation means the perfect positioning, and other instantaneous deviation values are selected between the plus and minus interval following uniform distribution. For the instance of 25 m deviation, the \(x\) axis and the \(y\) axis of deviation values are obtained randomly between \(-25\) and \(25\) and added to the original correct coordinate. To evaluate the impact of GPS drift on the performance of protocols, the moderate vehicle density, _i.e._, 100 v/km\({}^{2}\), is used in the Clermont-Ferrand and Manhattan scenarios with traffic route 1 (uniform).  
First, the robustness of TrAD to GPS drift is tested in the Clermont-Ferrand and Manhattan scenarios, as shown in Figures 13 and 14. In Figure 13, we can observe that thanks to the regular road topology and building obstacles TrAD maintains a relatively good performance in the Manhattan scenarios except 75 m and 100 m positioning deviations. However, in the case of Clermont-Ferrand,  
Figure 12: Results with a 95% confidence interval in Highway Scenarios: (**a**) PDR; (**b**) Number of Transmissions; (**c**) Delay.  
Figure 14 presents that the data dissemination speed is slower than the one of the Manhattan scenario due to the complex nature of the traffic environment. The 25 m, 50 m and 75 m error deviations impact the performance of TrAD, but the data dissemination speed of these deviations still achieves a moderate performance. The result of 100 m deviation is the worst, but its data coverage also reaches 98.3% taking 31 s after the data dissemination has been started.  
Second, we select the Clermont-Ferrand scenario to evaluate the robustness of TrAD, AMD and UV-CAST to the GPS drift, since the Clermont-Ferrand scenario has more influence on TrAD than the Manhattan one. Error deviations include 0 m, 50 m and 100 m, as shown in Figures 15-17. In Figure 15, the data coverage of TrAD increases rapidly to 100% while the data coverage of UV-CAST climbs up much slower than TrAD and finally reaches 99.1%. In Figure 16, TrAD still keeps a good upward trend and reaches 100% data coverage. It is interesting that, in this case, the GPS drift limits the performance of UV-CAST and let its dissemination speed be lower than the one of AMD at most of time. Finally, UV-CAST and AMD achieve 86% and 83% data coverage, respectively. In Figure 17, although the large positioning deviation hurts TrAD very badly, the data coverage of TrAD still exceeds the other two protocols 17 s after data dissemination starts and achieves 100% at the end. In this simulation, the dissemination speed of UV-CAST is always better than AMD, which is not the case in Figure 16. Finally, UV-CAST and AMD achieve 92% and 75.1% data coverage, respectively. The abovementioned observations reveal that the random GPS drift will cause unreliable results in the protocols with less robustness. Although, for TrAD, the increasing slope of its data dissemination speed gradually decreases with the increasing of error deviations, it always takes the shortest time to reach 100% data coverage.