# SROR: A Secure and Reliable Opportunistic Routing for VANETs
## 6 Simulation Results
### Comparison with Benchmarks
#### 6.4.2 Impact of the Percentage of Malicious Vehicles  
In this simulation, vehicle density is fixed at 40. Figure 10 shows the simulation results where the \(\sigma\) ranges from 5% to 30%.  
Figure 10a illustrates the PDR of SPEA, QL-TRT, PROR, and SROR algorithms. It is observed that the PDR descends with the increase of the \(\sigma\). The reason lies in the increase of attacks when the \(\sigma\) increases. More attacks result in the increase of packets dropped by the attackers, and the PDR decreases in the end.  
In addition, it is observed that the PDR of SROR algorithm is the highest, while the PDR of SPEA and PROR algorithm are the lowest, and their PDR are similar. The PDR of the SPEA and PROR algorithms declined quickly since they did nothing to prevent attackers from forwarding packet. This means they are not robust against attacks.  
Figure 10b compares the EED of the SPEA, QL-TRT, PROR, and SROR algorithms. As known from Figure 10b, the EED increases when the \(\sigma\) increases. The is due to the fact that as the \(\sigma\) increases, the number of packets dropped by the attackers increases. Once the destination fails to received the packet, the process of route discovery is restarted, which results in the increase of transmission delay.  
Furthermore, as Figure 10b shows, the SROR algorithm outperforms SPEA in term of EED, which contributes to the detection of attacks. In addition, SROR combines the opportunistic and geographic routing. So, the EED of SROR algorithm is very low even when the \(\sigma\) is high. However, as the \(\sigma\) is more than 15, the EED of SROR algorithm is a little bigger than that of the QL-TRT algorithm. This is due to the fact that the Expected Transmission Time (ETT) is taken into consideration in the route discovery of QL-TRT algorithm.  
The numerical results for the attack success ratio are shown in Figure 10c. As shown in Figure 10c, the attack success ratio increases as the \(\sigma\) increases. However, for the PROR and SPEA algorithms, the attack success ratios grow slowly. Nevertheless, for the QL-TRT and SROR algorithms, the attack success ratio of them grow quickly when the \(\sigma\) is varied from 5 to 30. The main reason is that both of PROR and SPEA algorithms did not detect the attacks, and the attack success ratio of them do not really vary with \(\sigma\). Fortunately, in regard to the attack success ratio, the SROR algorithm shows an enhancement of about 0.62, 0.62, and 0.34 compared with SPEA, PROR, and QL-TRT algorithms when the \(\sigma\) is 20.  
Table 6 clearly display the average PDR, EED, and ASR of four protocol, where vehicle density is fixed at 40, and the \(\sigma\) ranges from 5% to 30%. As Table 6 shows, the average PDF of the proposed SROR algorithm is up to 0.82 as the \(\sigma\) ranges from 5% to 30%. Compared with the benchmark protocols, our methodology improves the PDF by 12%. Additionally, the SROR algorithm also lowers the ASR because it prohibits attackers from routing packets. Compared with the QL-TRT protocol, SROR lowers the ASR by about 26%. The SROR algorithm outperforms the SPEA and PROR protocols in term of EED.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Protocol** & **Packet Delivery Ratio** & **End-to-End Delay (s)** & **Attack Success Ratio** \\ \hline SPEA & 0.80 & 0.28 & 0.78 \\ \hline QL-TRT & 0.73 & 0.37 & 0.42 \\ \hline PROR & 0.81 & 0.28 & 0.80 \\ \hline SROR & 0.82 & 0.26 & 0.37 \\ \hline \hline \end{tabular}
\end{table}
Table 5: PDR, EED, and ASR performance under different vehicle densities.