Blockchain-Enabled Edge Computing for Secure and Intelligent Decision-Making in Vehicular Ad Hoc Networks Using Deep Deterministic Policy Gradient

Abstract Vehicular Ad-hoc Networks (VANETs) are referred to as an effective solution for enhancing the competence and security is the standard transportation framework. Additionally, it offers several opportunities to create secure and effective traffic management protocols. Recently, data security and privacy preservation in VANETs have gained greater attention. Yet, VANET is an open framework that often performs data transmission procedures, and the consumers of the model are sensitive towards privacy and security attacks. The traditional approaches often concentrate on the identity authentication of the automobiles within the Vehicle Enterprises (VEs). In this research, a blockchain-enabled edge computing framework integrated with deep learning is proposed for secure and efficient decision-making in VANETs. The model employs a consensus algorithm for managing blockchain security to ensure data integrity and secure communication among vehicles. For decision-making, the proposed framework utilizes a Deep Deterministic Policy Gradient (DDPG) model to enable robust communication in dynamic VANET environments. To further enhance security, data encryption is performed using ElGamal with Rivest–Shamir–Adleman (E-RSA). The proposed approach is evaluated for its performance in terms of security, latency, throughput, and decision-making accuracy, and it is compared against existing state-of-the-art methods to validate its effectiveness in improving blockchain security in the VANET environment.