Blockchain-based Federated Approach for Privacy-Preserved IoT-enabled Smart Vehicular Networks
Sushil Kumar Singh, Laihyuk Park, Jong Hyuk Park
Abstract
Over the last few years, smart vehicles have continuously grown and connected to the Internet of Things (IoT), sensors, and advanced communication technologies. Then, it creates a cluster of distributed networks known as IoT-enabled Smart Vehicular Networks. Integrating smart vehicular networks, IoT, and the Internet of Vehicles (IoV) provide interactive solutions such as traffic efficiency, driving safety, autonomous driving, and robust information exchange in the smart city infrastructure. Still, Smart vehicular networks have challenges, such as privacy preservation, security, data authentication, communication bandwidth, and centralization due to vehicles and networks-related data directly stored in the traditional cloud. Motivated by advanced technologies, including Blockchain and Federated Learning, we propose an approach for Privacy-Preserved IoT-enabled Smart Vehicular Networks to address these challenges. The concept of Blockchain and Federated Learning is leveraged in the middle layer of the proposed work for privacy preservation and smart vehicle data authentication, stored at the cloud layer. Furthermore, we show the technological flow of the proposed approach for the IoT-enabled smart vehicular networks in the smart city.