Efficient Event Transactions in VANET’s Using Reinforcement Learning Aided Block Chain Architecture

Vehicular Ad-Hoc Networks (VANETs) have emerged as a pivotal technology for enhancing road safety and traffic management through real-time vehicle-to-vehicle (V2V) communication. However, the dynamic and open nature of VANETs introduces challenges related to data security, privacy, and trust among vehicles. To address these challenges, the integration of blockchain technology into VANETs has gained considerable attention. In this study, we introduce Vehicular chain- Reinforcement Learning (RL), a Blockchain-based VANET system that employs artificial intelligence (AI), Deep Reinforcement Learning (DRL), to create a flexible, knowledgeable, collaborative, and secure network for the VANET industry. The framework brings together a wide variety of VANET systems by utilizing Blockchain technology and an intelligent decision-making RL algorithm that operates online. The goal is to optimize the network's behavior in real time, with privacy and security of Vehicles data as primary concerns. The proposed Blockchain Manager (BM) intelligently adjusts blockchain setup to optimize security, latency, and cost. In the realm of Reinforcement Learning (RL), the D3QN framework introduces Deep Q-Network (DQN), Double Deep Q-Network (DDQN) and Dueling DQN (DQDQN) techniques to efficiently solve the Markov Decision Process (MDP) optimization model. The proposed approaches and two heuristic ones are thoroughly compared. The suggested methods achieve real-time adaptation to system state convergence, maximum security, minimal latency, and low cost.