Associative Receptive Sensor Network Routing Protocol for VANETs

Abstract

The Vehicular Ad hoc Networks are a different type of ad hoc network, which contain communicating entities that are in motion with different velocity along with lacking in infrastructure. Hence, this work needs the establishment of more consistent end-to-end communicating pathways and efficiently transferring data. We have presented a new security-aware routing algorithm called Deep learning-based prediction. The presented scheme is more efficient and reliable against different kinds of attacks such as black hole and malicious node penetration attempts to the entire network. It basically depends upon route link error recover and shortest path by using Dijkstra’s algorithm. The aim of this scheme is to identify malicious data and black hole nodes. The simulation results of eDC-NC are compared with already existing techniques called COPE in terms of Energy consumption (EC), throughput and network lifetime (NLT). This helps to achieve better performance and filter unwanted data by applying deep learning filtering technique.