An Efficient and Secured Data Transmission in Vanet Using LBK Interfaced AI-Optimized Routing Algorithm

The vehicular network, which is supported by attacker-less authorization, is an assembly of automobiles and other components that may connect to a variety of sensors. Vehicle-to-vehicle Ad hoc Networks (VANETs) can broadcast Emergency Messages (EMs) ahead of time between cars, therefore mitigating traffic-related accidents. Current authentication techniques prioritize security or the lightweight feature. However, confidentiality throughout an authorization process is crucial. Furthermore, existing techniques suffer from the infrastructure-less situation since they rely on a trusted authority to verify the authenticity of a communication node. This paper uses the GWO-based ANFIS to propose a lightweight, secure, privacy-preserving, and less vulnerable to attacks authentication mechanism for VANETs. The Artificial Neural Network (ANN) or Adaptive Neuro-Fuzzy Inference System (ANFIS) is a kind of Artificial Intelligence (AI). Neural network and fuzzy logic concepts are combined by ANFIS, which may leverage both inside a single framework. The suggested approach, known as ANFIS- Grey Wolf Optimization (GWO), is divided into two phases. Using the training set, GWO is employed in the first step to learn the ANFIS parameters. In the meantime, the performance of the suggested ANFIS-GWO approach is assessed using the testing set in the second stage. Furthermore, a thorough performance study using the current systems for authorization demonstrates that the suggested algorithm has lower compute, communication, and energy costs than the existing schemes. NS2 software is used to implement this project. ANFIS-GWO yielded an accuracy comparison of 91.5% and an energy consumption of 115J.