VANET security enhancement in cloud navigation with Internet of Things-based trust model in deep learning architecture

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 data continuity among crucial transportation due to the high level of mobility realistic environment. The vehicles' wireless is also unrestricted open, making them more vulnerable security threats compromising improper uses. This research propose novel technique in improvement VANET with cloud-based navigation trust model using deep learning techniques. carried out cloud integrated IoT, its transmission base station analysed. Then, navigated enhanced trust-based federated transfer quadratic authentication system. experimental analysis based on number vehicles as well enhancement. parameters analysed throughput, rate, latency, traffic analysis, scalability. proposed attained throughput 95%, rate 67%, latency 56%, 76%, scalability 75%.