Privacy Preserving Reliable Data Transmission in Cluster Based Vehicular Adhoc Networks

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