Privacy Preserving Reliable Data Transmission in Cluster Based Vehicular Adhoc Networks

Abstract

VANETs are a subclass of mobile ad hoc networks (MANETs) that enable efficient data transmission between vehicles and other vehicles, road side units (RSUs), and infrastructure. The purpose of VANET is to enhance security, road traffic management, and traveler services. Due to the nature of real-time issues such as reliability and privacy, messages transmitted via the VANET must be secret and confidential. As a result, this study provides a method for privacy-preserving reliable data transmission in a cluster-based VANET employing Fog Computing (PPRDA-FC). The PPRDA-FC technique suggested here seeks to ensure reliable message transmission by utilising FC and an optimal set of cluster heads (CH). The proposed PPRDA-FC technique utilizes a moth flame optimization with levy flight based clustering (MFO-LFC) process to identify and form clusters from a suitable set of CHs. The CHs are responsible for monitoring each vehicle in their respective clusters. Simultaneously, the CHs provide the most efficient and secure pathways for message transmission. Finally, a deep neural network (DNN) is used as a classification tool to distinguish between attacker-controlled and real-world automobiles. To evaluate the suggested PPRDA-FC technique’s increased performance, a series of simulations were run and the results analyzed using a variety of metrics. The acquired experimental findings illustrate the suggested PPRDA-FC technique’s superiority to recent state-of-the-art procedures.