Advanced Dynamic Vehicular Networks with Enhanced Privacy and Real-Time Intelligent Traffic Management
Keywords:
Dynamic Network Management, Enhanced Privacy Mechanism, Hybrid Encryption Scheme, Intelligent Traffic Management, Real-Time Notifications, VANET (Vehicular Ad Hoc Networks)Abstract
In vehicular communication systems, the evolution of Vehicular Ad Hoc Networks (VANETs) provides the door for novel solutions and expanded features. This proposed study presents an upgraded framework for Vehicular Ad Hoc Networks (VANETs), focusing on better privacy measures, real-time intelligent traffic management, and augmented safety features. Our framework leverages an adaptive network architecture that dynamically responds to varying vehicular conditions, ensuring uninterrupted communication and optimal performance. To secure user privacy, we have incorporated a powerful privacy-preserving system utilizing modern encryption algorithms, assuring the security of sensitive information while facilitating vital data interchange for vehicle operations. Specifically, we have presented a hybrid encryption technique that leverages the Advanced Encryption Standard (AES-128) coupled with digital signatures to protect the content of Content-Centric Vehicular Networks (CCVN) during inter-vehicle communication. This hybrid technique secures data confidentiality and integrity, delivering a complete security solution for vehicle communication. Furthermore, our system provides real-time intelligent traffic management capabilities, including dynamic route optimization and congestion prediction, boosting traffic flow and minimizing travel times. This is accompanied by a comprehensive warning system that gives drivers real-time information about speed restrictions, dangers, and traffic conditions, enhancing driver awareness and overall traffic safety. The warning system is designed to be all-encompassing, ensuring that drivers are well-informed about potential dangers on the road. Network administration is handled using a decentralized architecture, guaranteeing robust and dependable communication without depending on centralized infrastructure. Additionally, our system contains machine learning algorithms that continually learn and adapt to traffic patterns, further enhancing the network’s speed and dependability. Our proposed framework dramatically enhances the capabilities of current VANETs, providing a safe, intelligent, and privacy-preserving alternative for contemporary vehicular communication systems. This revolutionary strategy intends to increase vehicular communication and boost traffic management systems' overall efficiency and safety.
Downloads
References
S. Babu, A. Raj Kumar P, A comprehensive survey on simulators, emulators, and testbeds for VANETs, Int. J. Commun. Syst. 35 (8) (2022) e5123.
T. Yu, Z. Zhiyi, E. Newberry, A. Afanasyev, G. Pau, L. Wang, L. Zhang, Names to Rule Them All: Unifying Mobile Networking via Named Secured Data, Technical Report NDN-0072 (Rev.1). NDN, 2022, http://named-data.net/techreports.html. [Online]. Accessed: 02 May 2023.
S.S. Magdum, M. Sharma, S.M. Kala, A. Antony Franklin, B.R. Tamma, Evaluating DTN routing schemes for application in vehicular networks, in: 2019 11th International Conference on Communication Systems & Networks, COMSNETS, 2019.
H. Shahwani, S. Attique Shah, M. Ashraf, M. Akram, J.P. Jeong, J. Shin, A comprehensive survey on data dissemination in vehicular ad hoc networks, Veh. Commun. 34 (2022) 100420
A. Studer, F. Bai, B. Bellur, A. Perrig, A flexible, extensible, and efficient VANET authentication, in Special Issue on Secure Wireless Networks, J. Commun. Netw. 11 (6) (Dec. 2009) 574–588.
L. Liu, Y. Wang, J. Zhang and Q. Yang, “A Secure and Efficient Group Key Agreement Scheme for VANET”, Sensors, Vol. 19, No. 3, pp. 482-494, 2019.
Y. Agarwal, K. Jain and O. Karabasoglu, “Smart Vehicle Monitoring and Assistance using Cloud Computing in Vehicular Ad Hoc Networks”, International Journal of Transportation Science and Technology, Vol. 7, No. 1, pp. 60-73, 2018.
P. Kumar, R. Merzouki, B. Conrard and V. Coelen, “Multilevel Modeling of the Traffic Dynamic”, IEEE Transactions on Intelligent Transportation Systems, Vol. 15, No. 3, pp. 1066-1082, 2014.
M.B. Mansour, C. Salama, H.K. Mohamed and S.A. Hammad, “VANET Security and Privacy-An Overview”, International Journal of Network Security and Its Applications, Vol. 10, No. 2, pp. 13-34, 2018.
P. Vijayakumar, M. Azees, A. Kannan and L.J. Deborah, “Dual Authentication and Key Management Techniques for Secure Data Transmission in Vehicular Ad Hoc Network”, IEEE Transactions on Intelligent Transportation Systems, Vol. 17, No. 4, pp. 1015-1028, 2016.
A. Sumathi, “Dynamic Handoff Decision based on Current Traffic Level and Neighbor Information in Wireless Data Networks”, Proceedings of International Conference on Advanced Computing, pp. 1-5, 2012.
Bernardini, C., Marchal, S., Asghar, M. R., & Crispo, B. (2019). PrivICN: Privacy-preserving content retrieval in information-centric networking. Computer Networks, 149,
https://doi.org/10.1016/j. comnet.2018.11.012 7. Li, B., Huang, D., Wang, Z., & Zhu, Y. (2016). Attribute-based access control for ICN naming scheme. IEEE Transactions on Dependable and Secure Computing, 15(2), 194. https://doi.org/10.1109/TDSC. 2016.2550437
Ghali, C., Tsudik, G. & Wood, C. A. (2017). When encryption is not enough: Privacy attacks in content-centric networking. In Proceedings of the 4th ACM conference on information-centric networking (pp. 1–10). https://doi.org/10.1145/3125719.3125723.
Abdel-Halim IT, Fahmy HMA (2018) Prediction-based protocols for vehicular ad hoc networks: Survey and taxonomy. Comput Netw 130:34–50
Gupta R, Tanwar S, Tyagi S, Kumar N (2020) Machine learning models for secure data analytics: A taxonomy and threat model. Comput Commun 153:406–440. https://doi.org/10.1016/ j.comcom.2020.02.008. http://www.sciencedirect.com/science/ article/pii/S0140366419318493
Sumi, L.; Ranga, V. Intelligent traffic management system for prioritizing emergency vehicles in a smart city. Int. J. Eng. 2018, 31, 278–283.
Wang, X.; Ning, Z.; Hu, X.; Wang, L.; Hu, B.; Cheng, J.; Leung, V.C. Optimizing content dissemination for real-time traffic management in large-scale internet of vehicle systems. IEEE Trans. Veh. Technol. 2018, 68, 1093–1105.
Ho, G.T.S.; Tsang, Y.P.; Wu, C.H.; Wong, W.H.; Choy, K.L. A computer vision-based roadside occupation surveillance system for intelligent transport in smart cities. Sensors 2019, 19, 1796.
Gunda, P.K. Network-Wide Traffic Congestion Visual Analytics: A Case Study for Brisbane Bluetooth MAC Scanner Data. Ph.D. Thesis, Queensland University of Technology, Brisbane City, QLD, Australia, 2021.
Lee, C.; Kim, Y.; Jin, S.; Kim, D.; Maciejewski, R.; Ebert, D.; Ko, S. A visual analytics system for exploring, monitoring, and forecasting road traffic congestion. IEEE Trans. Vis. Comput. Graph. 2019, 26, 3133–3146.
Nallaperuma, D.; Nawaratne, R.; Bandaragoda, T.; Adikari, A.; Nguyen, S.; Kempitiya, T.; De Silva, D.; Alahakoon, D.; Pothuhera, D. Online incremental machine learning platform for big data-driven smart traffic management. IEEE Trans. Intell. Transp. Syst. 2019, 20, 4679–4690.
Hussain N, Rani P, Chouhan H, Gaur U. Cyber security and privacy of connected and automated vehicles (CAVs)-based federated learning: challenges, opportunities, and open issues. Federated learning for IoT applications. Springer; 2022. p. 169–83.
Rani P, Hussain N, Khan RAH, Sharma Y, Shukla PK. Vehicular intelligence system: time-based vehicle next location predicion in software-defined internet of vehicles (SDN-IOV) for the smart cities. Intelligence of things: AI-IoT based critical-applications and innovations. Cham: Springer International Publishing; 2021. p. 35–54. https://doi.org/10.1007/978-3-030-82800-4_2.
Wahab OA, Mourad A, Otrok H, Bentahar J. CEAP: SVM-based intelligent detection model for clustered vehicular adhoc networks. Expert Syst Appl 2016;50: 40–54. https://doi.org/10.1016/j.eswa.2015.12.006.
Yang J, Fei Z. Broadcasting with prediction and selective forwarding in vehicular networks. Int J Distrib Sens Netw 2013;9(12):309041. https://doi.org/ 10.1155/2013/309041.
Song HM, Woo J, Kim HK. In-vehicle network intrusion detection using deep convolutional neural network. Veh. Commun. 2020;21:100198. https://doi.org/ 10.1016/j.vehcom.2019.100198.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
