Fusion of Digital Twin and Blockchain for Secure and Efficient IoV Networks

Abstract

The emergence of digital twin technology has spurred numerous endeavors to construct smart cities while simultaneously driving the development of smart cars that boast enhanced driver safety and convenience by integrating advanced information technology into automotive systems. This study delves into the challenges encountered within the Internet of Vehicles (IoV) when employing edge computing, including computation complexity, communication latency, and security issues. IoV systems leveraging edge computing often grapple with deficiencies in quality of service and quality of experience due to resource constraints, while offloading services to the cloud exacerbates latency and often leads to bandwidth constraints during data transmission. Moreover, a notable concern arises from edge computing devices' substantially lower physical security level than their counterparts at core cloud sites. Given these challenges, this study explores utilizing blockchain and digital twin technologies as potential solutions. We first analyze recent research to address these issues. We propose an optimized offloading decision -making framework that combines value function approximation utilizing digital twin -based deep learning and reinforcement learning methodologies. Subsequently, to mitigate security vulnerabilities, we delve into the deployment of blockchain technology. Our proposed system demonstrates its feasibility and superior efficiency compared to alternative solutions.