VANET Security Enhancement in Cloud Navigation with Internet of Things Based Trust Model in Deep Learning Architecture

Abstract

Many intelligent services are available for developing sensor-based vehicle-to-vehicle communication systems through vehicular ad hoc network (VANET). Although neighbour locating and interconnected vehicle sensor processes have been improved by geographic routing methods. However, reliability and data continuity among data routing are crucial for developing transportation systems due to the high level of mobility and realistic environment.The vehicles' wireless communication is also unrestricted and open, making them more vulnerable to security threats and compromising data for improper uses.This research propose novel technique in security improvement in VANET with vehicle cloud based navigation and trust model using deep learning techniques. the vehicle network navigation is carried out using cloud network integrated with IoT and its data transmission to the base station is analysed. then the navigated vehicle security is enhanced using trust based federated transfer quadratic authentication system. the experimental analysis is carried out based on number of vehicles in network as well as its security enhancement. the parameters analysed are throughput, data transmission rate, latency, network traffic analysis, scalability. the proposed technique attained throughput of 95%, data transmission rate of 67%, latency of 56%, network traffic analysis of 76%, scalability of 75%