# Evaluation of the Use of Compressed Sensing in Data Harvesting for Vehicular Sensor Networks
## 5 Evaluation
### Simulations  
In the literature, we have found different proposals that applies CS to the VSN domain. Nevertheless, they analyze this aspect from a theoretical point of view or do only consider the compression aspect using the vehicles as ferries, without considering the mobility of the nodes and their effects on the vehicular network. Therefore, we have opted to compare our solution with another proposed solution called DB-VDG already introduced in Section 3 which also has presented interesting results from the point of view of efficiency and network overload.  
We have evaluated our proposal's performance through simulations using the Network Simulator NS-2 ([[https://www.isi.edu/nsnam/ns/](https://www.isi.edu/nsnam/ns/)]([https://www.isi.edu/nsnam/ns/](https://www.isi.edu/nsnam/ns/))), version 2.33. For this purpose we have developed an urban scenario with the SUMO tool ([[http://sumo.sourceforge.net/](http://sumo.sourceforge.net/)]([http://sumo.sourceforge.net/](http://sumo.sourceforge.net/))) taken from the main streets and highways of the city of Murcia, Spain. Particularly, for our simulations, we have selected the most relevant streets, so our scenario consists of 53 streets and 28 junctions as shown in Figure 11. Vehicles move through 20 predefined routes at a maximum speed of 13.98 m/s (50 km/h) inside the city, and 22.22 m/s (80 km/h) on the highway that crosses the scenario during 445s. In this scenario, the following vehicles' densities: \(1/50,1/45,1/40,1/35,1/30,1/25,1/20,1/15,1/10\) and \(1/5~{}veh/route/s\) have been defined. This means that, for instance, for the less dense scenario which corresponds to the first value (1/50), in each of the defined route, a vehicle is injected in the scenario every 50 seconds. This means that, for a total of 10 routes defined for this scenario, the density of vehicles corresponds to the one described in Table 6.  
Regarding the signal propagation model, we have used TwoRayGround for our simulations defining a coverage range of 250 m. Besides, we have run 10 different executions per scenario and vehicle's density whose results are presented in graphs where 95% confidence interval are also included.  
Since in the harvesting process there is only one FC, we have varied the random component seed, which is applied to timers making different vehicles to answer first in each execution of the simulation.  
So, under the conditions specified above, we have evaluated the performance of our proposed solution performance against another gathering scheme, DB-VDG. Additionally, we compare variants of our proposed solution.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Density (veh/route/s)** & **veh/s** & **Total Injected Vehs** \\ \hline
50 & \(1/50\) & 0.4 & 178 \\ \hline
45 & \(1/45\) & 0.44 & 197 \\ \hline
40 & \(1/40\) & 0.50 & 222 \\ \hline
35 & \(1/35\) & 0.57 & 254 \\ \hline
30 & \(1/30\) & 0.67 & 296 \\ \hline
25 & \(1/25\) & 0.80 & 356 \\ \hline
20 & \(1/20\) & 1.00 & 445 \\ \hline
15 & \(1/15\) & 1.33 & 593 \\ \hline
10 & \(1/10\) & 2 & 890 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Detailed information about the different traffic densities generated for the simulations.  
Figure 11: Urban scenario of the city of Murcia used in our simulations.