Machine Learning based Analysis of VANET Communication Protocols in Wireless Sensor Networks

Abstract

Both the world and technology are changing. Autonomous vehicles are already common on several nations' roadways thanks to advancements in electronics. We're getting closer to a time when everyone will drive safer, greener vehicles. A dedicated vehicular ad hoc network named VANET was developed for this reason. Routing protocols are among the most crucial components for network dependability. The most well-known VANET routing protocols are examined in this work. These three are DS R (Dynamic Source Routing), DSDV (Destination Sequence Distance Vector) and AODV (Ad hoc on Demand Distance Vector). In vehicular ad hoc networks, as well as in autonomous and connected vehicles, there are numerous cutting-edge techniques for intrusion detection. An intrusion detection system's primary task is to find and report attacks (IDS). IDS is improved with deep learning to make it smarter and more precise. On the other side, it suggests additional difficulties. This research compares the effectiveness and efficiency of the proposed IDS -based deep learning systems.