Blockchain-Enhanced Machine Learning for Dynamic Routing and Secure Communications in Autonomous Vehicle Networks

Abstract

The advent of autonomous vehicles (AVs) marks a significant milestone in urban transportation, promising to enhance safety, reduce congestion, and improve environmental sustainability. However, deploying AVs on a mass scale comes with critical challenges related to secure and efficient vehicular communication. This research work proposes a novel framework that combines the security features of blockchain technology with the adaptive capabilities of machine learning (ML) to address these major challenges. Integrating a blockchain-based protocol ensures tamper-proof and transparent communication within AV networks, protecting against a wide array of cyber threats. Concurrently, ML algorithms are employed to optimize real-time routing decisions based on comprehensive traffic data and environmental conditions. Through simulation in realistic urban scenarios, our framework demonstrates a significant improvement in communication security and routing efficiency, indicating a promising avenue for achieving scalable and reliable AV networks. Operational cost assessments further reveal the economic viability of the proposed model, underscoring its potential to deliver long-term savings through enhanced efficiency and reduced human intervention. Thus an efficient solution in terms of security, dynamic routing, and scalability with respect to traditional models.