1.
Nazar, K., Saeed, Y., Ali, A., Algarni, A. D., Soliman, N. F., Ateya, A. A., Jamil, F. (2022). Towards Intelligent Zone-Based Content Pre-Caching Approach in VANET for Congestion Control. Sensors, 22(23), 9157.
2.
Maaroufi, S., & Pierre, S. (2021). BCOOL: a novel blockchain congestion control architecture using dynamic service function chaining and machine learning for next generation vehicular networks. IEEE Access, 9, 53096-53122.
3.
Chandrasekharan, P. (2023). Transmission Power Based Congestion Control Using Q-Learning Algorithm in Vehicular Ad Hoc Networks (VANET) (Doctoral dissertation, University of Windsor (Canada)).
4.
Khatri, S., Vachhani, H., Shah, S., Bhatia, J., Chaturvedi, M., Tanwar, S., & Kumar, N. (2021). Machine learning models and techniques for VANET based traffic management: Implementation issues and challenges. Peer-to-Peer Networking and Applications, 14, 1778-1805.
5.
Ahamed, V. N., Prakash, A., & Ziyath, M. (2023). TCC-HDL: a hybrid deep learning based traffic congestion control system for VANET. Indian journal of science and technology, 16(32), 2548-2559.
6.
Liu, X., Amour, B. S., & Jaekel, A. (2023). A Reinforcement Learning-Based Congestion Control Approach for V2V Communication in VANET. Applied Sciences, 13(6), 3640.
7.
Gopi, R., Mathapati, M., Prasad, B., Ahmad, S., Al-Wesabi, F. N., Alohali, M. A., & Hilal, A. M. (2022). Intelligent DoS Attack Detection with Congestion Control Technique for VANETs. Computers, Materials & Continua, 72(1).
8.
Alsarhan, A., Al-Ghuwairi, A. R., Almalkawi, I. T., Alauthman, M., & Al-Dubai, A. (2021). Machine learning-driven optimization for intrusion detection in smart vehicular networks. Wireless Personal Communications, 117, 3129-3152.
9.
Kothai, G., Poovammal, E., Dhiman, G., Ramana, K., Sharma, A., AlZain, M. A., … & Masud, M. (2021). A new hybrid deep learning algorithm for prediction of wide traffic congestion in smart cities. Wireless Communications and Mobile Computing, 2021, 1-13.
10.
Wischhof, L., & Rohling, H. (2005, October). Congestion control in vehicular ad hoc networks. In IEEE International Conference on Vehicular Electronics and Safety, 2005. (pp. 58-63). IEEE.
11.
Nuthalapati, G. S. (2023). Reinforcement Learning-Based Data Rate Congestion Control for Vehicular Ad-Hoc Networks (Doctoral dissertation, University of Windsor (Canada)).
12.
Vamsi, B., Doppala, B. P., Mahanty, M., Veeraiah, D., Rao, J. N., & Rao, B. S. A Detailed Case Study on Various Challenges in Vehicular Networks for Smart Traffic Control System Using Machine Learning Algorithms. In Artificial Intelligence and Machine Learning for Smart Community (pp. 51-87). CRC Press.
13.
Pholpol, C., & Sanguankotchakorn, T. (2021). Traffic Congestion Prediction using Deep Reinforcement Learning in Vehicular Ad-hoc Networks (vanets). International Journal of Computer Networks & Communications (IJCNC), 13(4), 1-19.
14.
Marwah, G. P. K., & Jain, A. (2022). A hybrid optimization with ensemble learning to ensure VANET network stability based on performance analysis. Scientific Reports, 12(1), 10287.
15.
Kandali, K., Bennis, L., El Bannay, O., & Bennis, H. (2022). An intelligent machine learning based routing scheme for VANET. IEEE Access, 10, 74318-74333.
16.
Liu, X., Amour, B. S., & Jaekel, A. (2022, May). A Q-learning based adaptive congestion control for V2V communication in VANET. In 2022 International Wireless Communications and Mobile Computing (IWCMC) (pp. 847-852). IEEE.
17.
Rawat, G. S., & Singh, K. (2023). Evaluation and Optimization of a Congestion Control Scheme for Vanets. In Advanced Computer Science Applications (pp. 351-363). Apple Academic Press.
18.
Sangaiah, A. K., Ramamoorthi, J. S., Rodrigues, J. J., Rahman, M. A., Muhammad, G., & Alrashoud, M. (2020). LACCVoV: Linear adaptive congestion control with optimization of data dissemination model in vehicle-to-vehicle communication. IEEE transactions on intelligent transportation systems, 22(8), 5319-5328.
19.
Alqahtani, A. S., Mubarakali, A., Saravanan, M., Changalasetty, S. B., Thota, L. S., Parthasarathy, P., & Sivakumar, B. (2023). Enhanced machine learning approach with orthogonal frequency division multiplexing to avoid congestion in wireless communication system. Optical and Quantum Electronics, 55(10), 913.
20.
Balador, A., Cinque, E., Pratesi, M., Valentini, F., Bai, C., Gómez, A. A., & Mohammadi, M. (2022). Survey on decentralized congestion control methods for vehicular communication. Vehicular Communications, 33, 100394.
21.
Gillani, M., Niaz, H. A., & Tayyab, M. (2021). Role of machine learning in WSN and VANETs. International Journal of Electrical and Computer Engineering Research, 1(1), 15-20.
22.
Qureshi, K. N., Abdullah, A. H., Kaiwartya, O., Iqbal, S., Butt, R. A., & Bashir, F. (2018). A dynamic congestion control scheme for safety applications in vehicular ad hoc networks. Computers & Electrical Engineering, 72, 774-788.
23.
Taherkhani, N., & Pierre, S. (2015). Improving dynamic and distributed congestion control in vehicular ad hoc networks. Ad Hoc Networks, 33, 112-125.
24.
Amer, H., Al-Kashoash, H., Khami, M. J., Mayfield, M., & Mihaylova, L. (2020). Non-cooperative game based congestion control for data rate optimization in vehicular ad hoc networks. Ad Hoc Networks, 107, 102181.
25.
Alsarhan, A., Alauthman, M., Alshdaifat, E. A., Al-Ghuwairi, A. R., & Al-Dubai, A. (2021). Machine Learning-driven optimization for SVM-based intrusion detection system in vehicular ad hoc networks. Journal of Ambient Intelligence and Humanized Computing, 1-10.
26.
Sattari, M. R. J., Noor, R. M., & Ghahremani, S. (2013). Dynamic congestion control algorithm for vehicular ad hoc networks. International Journal of Software Engineering and Its Applications, 7(3), 95-108.
27.
Wang, M., Mao, J., Zhao, W., Han, X., Li, M., Liao, C., … & Wang, K. (2024). Smart City Transportation: A VANET Edge Computing Model to Minimize Latency and Delay Utilizing 5G Network. Journal of Grid Computing, 22(1), 25.
28.
Ismail, A. H., Ali, Z. H., Abdellatef, E., Sakr, N. A., & Sedhom, G. G. (2024). Congestion Management Using K-Means for Mobile Edge Computing 5G System. Wireless Personal Communications, 1-20.