Blockchain for Scalable Data and Security in IoV

Abstract

The rapid evolution of the Internet of Vehicles (IoV) introduces major challenges in terms of scalability, data integrity, and security. Traditional centralized systems and single-layer blockchain architectures struggle to handle the high volume of real-time vehicular data efficiently. This thesis proposes an IoV framework that applies the recent concept of Layer 2 blockchain along with a machine learning (ML) model to address these limitations. Layer 1 ensures decentralized trust and secure storage, while Layer 2 (zkSync) significantly improves transaction throughput and reduces gas fees through off-chain processing. A machine learning model deployed at Roadside Units (RSUs) validates events in real-time, distinguishing between valid and malicious data. Smart contracts then manage a reputation score for each vehicle, rewarding honest behavior and penalizing false reporting. The system was implemented using Hyperledger Geth, Docker, and smart contracts, with performance benchmarking conducted via Caliper. Experimental results demonstrate a substantial improvement in system performance when using Layer 2 (zkSync) compared to Layer 1: throughput more than tripled, latency was reduced by approximately 64.63%, CPU usage decreased by around 33% for 3000 transactions, and gas fees were lowered by 42.91%. This integrated approach demonstrates that combining Layer 2 blockchain and ML enables scalable, secure, and intelligent vehicular networks.