# SROR: A Secure and Reliable Opportunistic Routing for VANETs
## 7 Conclusions, Limitations, and Future Directions  
In this paper, a secure and reliable opportunistic routing named SROR is proposed for selecting a stable route path. The proposed methodology was embraced to address issues regrading selection of relay node and attackers. These attackers often lead to packet drops due to their malicious activities. By employing three important network metrics, some candidate forwarding nodes are selected. The aim of the proposed SROR algorithm is to select relay nodes from the candidate forwarding nodes. In addition, the dynamic exploratory factor and OneCycleLR policy are adopted to improve the reward and achieve a better convergence. Compared with benchmarks, simulation experiments show that the SROR algorithm achieves better routing performance. When the percentage of malicious vehicles ranges from 5% to 30% and the vehicle density is fixed 40, the SROR algorithm improves the average PDR by 12% and lowers the ASR by about 26% compared with QL-TRT, displaying the best overall performance among the SPEA, PROR, and QL-TRT protocols. The comparative analysis illustrated the efficiency of the proposed SROR algorithm.  
Although the effectiveness of the SROR algorithm was verified, it has some limitations. The main limitations of our research are as follows: (1) this study was confined to identifying  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Protocol** & **Packet Delivery Ratio** & **End-to-End Delay (s)** & **Attack Success Ratio** \\ \hline SPEA & 0.73 & 0.56 & 0.84 \\ \hline QL-TRT & 0.72 & 0.44 & 0.58 \\ \hline PROR & 0.72 & 0.48 & 0.84 \\ \hline SROR & 0.82 & 0.44 & 0.46 \\ \hline \hline \end{tabular}
\end{table}
Table 6: PDR, EED, and ASR performance under different percentages of malicious vehicles.  
Figure 10: Comparison of the performance under different \(\sigma\). (**a**) Packet delivery ratio. (**b**) End-to-end delay. (**c**) Attack success ratio.  
BHA and GHA attacks in the routing protocol; (2) this study verified the effectiveness of the SROR algorithm by simulation, and the real-world validation is not discussed.  
Therefore, in future work, we plan to consider multiple different types of attacks simultaneously in the routing protocol. Additionally, the proposed SROR algorithm will be refined to apply to the real vehicular network, and the performance is further analyzed.  
Methodology, H.X.; Software, H.X.; Validation, H.X.; Writing--original draft, H.X.; Writing--review & editing, Y.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Huzhou Natural Science Foundation, Grant No. 2021 YZ20, and the Science and Technology Project of Jiangxi Provincial Department of Education, Grant No. GJJ201818.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.  
The following main abbreviations are used in this manuscript:  
\begin{tabular}{l l} VANETs & Vehicular Ad-Hoc Networks \\ IoT & Internet of Things \\ V2V & Vehicle-to-Vehicle \\ BHA & Black Hole Attack \\ GHA & Gray Hole Attack \\ RL & Reinforcement Learning \\ CFNs & Candidate Forwarding Nodes \\ MDP & Markov Decision Process \\ DRL & Deep Reinforcement Learning \\ DQN & Deep Q-network \\ OBU & On-Board Unit \\ PFR & Packet Forwarding Ratio \\ PDR & Packet Delivery Ratio \\ EED & End-to-end Delay \\ ASR & Attack Success Ratio \\ \end{tabular}