Advanced Dynamic Vehicular Networks with Enhanced Privacy and Real-Time Intelligent Traffic Management - Vijayalakshmi V (International Journal of Intelligent Systems and Applications in Engineering, 2024)
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.