# Impact of Direction Parameter in Performance of Modified AODV in VANET
## 5 Results and Discussion  
To compare our results with published articles, we used the map of Erlangen in SUMO to generate the traffic information. This map is used primarily in VANET simulations in most of the state-of-the-art methods. The mobility model was generated using OMNeT++, and then the simulations were run by varying the number of vehicles. The simulation was run 20 times to have a statistically significant output.  
Figure 6 shows the roads and the existing infrastructure of the area. The black lines are the roads, and the red-colored areas indicate the infrastructure.  
The simulation result in OMNeT++ is shown in Figure 7, where the blue dots are the moving nodes in the same area in SUMO. A summary of the simulation parameters is provided in Table 1.  
The percentage of packet loss versus the number of cars is presented in Figure 8. It can be seen from the plot that the packet loss increased with an increase in the number of cars.  
Figure 5: Roadside unit (RSU) model.  
Figure 6: View of Erlangen map in SUMO.  
Figure 8: Percentage of packet loss vs. the number of cars.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Network simulator & OMNeT++ \\ Simulation time & 1000 s \\ Number of nodes & 100–800 \\ Simulation coverage volume & 2500 m (L) \(\times\) 2500 m (W) \(\times\) 50 m (H) \\ MAC layer & 802.11 p \\ Propagation model & Two ray interferences \\ Mobility model & Random trip and Intelligent car \\ Number of runs & 20 \\ Velocity & 20–40 mps \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of simulation parameters.  
Figure 7: Simulation in OMNeT++.  
The throughput vs. the number of cars is shown in Figure 9. It can be seen from this graph that the throughput increased with the number of cars due to the high network activities with more cars.  
We examined the performance regarding packet loss and packet delivery ratios during the simulation; and the results are provided in Table 2 with 100 nodes and in Table 3 with 200 nodes, along with the results of other methods [4; 5; 6; 9]. In both cases, the inclusion of the direction parameter outperformed the others by about 1.3% under the same environmental conditions.  
The comparison of an average E2E delay is presented in Table 4. As shown, our method displayed a decrease of 11% in delay compared with the other methods.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Methodology** & **Average E2E Delay** \\ \hline _Our method_ & _178 ms_ \\ [PERSON] et al. [5] & 200 ms \\ [PERSON] et al. [7] & 400 ms \\ [PERSON] et al. [9] & 300 ms \\ [PERSON] et al. [10] & 225 ms \\ [PERSON] et al. [11] & 250 ms \\ \hline \hline \end{tabular}
\end{table}
Table 4: E2E delay performance.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methodology** & **Packet Loss** & **Packet Delivery Ratio** \\ \hline _Our method_ & 1.4\% & _98.6\%_ \\ [PERSON] et al. [5] & 2.75\% & 97.25\% \\ [PERSON] et al. [7] & 10\% & 90\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Percentage of packet loss with 100 nodes.  
Figure 9: Throughput vs. the number of cars.  
The improved performance is the result of using two filtering steps in route discovery and route selection procedures. In this case, each node has the most updated routing information; therefore, it also has fewer broken links. The packet drop rate and end-to-end delay also decreased due to a lower number of broken links in a stable route. These processes also reduced the number of RREQ and RREP messages, which increased throughput.