## 7 Future Recommendations  
Integration of artificial intelligence (AI) and machine learning (ML): Incorporating AI and ML techniques can further enhance the performance and efficiency of the proposed authentication and communication protocol. By leveraging AI and ML algorithms, the protocol can adapt dynamically to changing traffic conditions, optimize resource allocation, and improve decision-making processes for routing and communication.  
* As security and privacy concerns remain critical in VANETs, future work should focus on developing advanced encryption and cryptographic techniques to safeguard sensitive information and prevent unauthorized access. Additionally, privacy-preserving mechanisms, such as secure data aggregation and anonymization, should be explored to protect the privacy of vehicle and driver identities.
* Conducting real-world deployments and field testing of the proposed protocol will provide valuable insights into its practical feasibility and effectiveness. Evaluating the protocol's performance under various traffic scenarios, road conditions, and network densities will help identify potential limitations and areas for further improvement.  
* In order to achieve uninterrupted communication and practical cooperation between diverse vehicles and infrastructure systems, it is imperative to establish interoperability standards. These standards will promote compatibility and collaboration among various Vehicular AdHoc Networks (VANETs), facilitating enhanced traffic management and safer interactions between vehicles and infrastructure components.
* Energy Efficiency Considerations: Energy efficiency is crucial to VANETs, as vehicles operate on limited power resources. Future research should explore energy-aware strategies and mechanisms to optimize power consumption, prolong the network's lifetime, and reduce the carbon footprint associated with vehicular communication.
* As urban areas continue to expand, the scalability and robustness of the proposed protocol need to be thoroughly investigated. Future work should address the challenges of large-scale VANET deployments, including managing a vast number of vehicles, efficient cluster formation, and effective communication in highly dynamic environments.  
Conceptualization, R.M.A.L., M.J., J.H. and M.F.; methodology, R.M.A.L.; software, R.M.A.L.; validation R.M.A.L. and M.J.; formal analysis, R.M.A.L.; investigation, M.J.; resources, J.H.; data curation, M.J.; writing--original draft preparation, R.M.A.L. and M.J.; writing--review and editing, J.H.; visualization, J.H.; supervision, J.H. and M.F.; project administration, J.H.; funding acquisition, J.H. All authors have read and agreed to the published version of the manuscript.  
This study was founded by National Natural Science Foundation of China, grant number 42171214.  
The data supporting the findings of this study are available upon reasonable request. [[https://github.com/Rana64/Urban-Traffic-Monitoring-in-VANETs-Based-on-Cluster-Management](https://github.com/Rana64/Urban-Traffic-Monitoring-in-VANETs-Based-on-Cluster-Management)]([https://github.com/Rana64/Urban-Traffic-Monitoring-in-VANETs-Based-on-Cluster-Management](https://github.com/Rana64/Urban-Traffic-Monitoring-in-VANETs-Based-on-Cluster-Management)) accessed on 2 June 2023.  
The authors declare that they have no conflict of interest.