Digital Twin-Based Cyber Redundancies to Achieve Cost-Effective Fault Tolerance

Modern vehicles, as a cyber-physical system, consist of hundreds of sensors, and due to the safety-critical nature of vehicles, faults in these sensors can result in failures and potentially catastrophic consequences. The traditional approaches employ redundant components for tolerating faults, which incur costs and overheads. These costs and overheads present barriers to adopting fault-tolerant methods in the automotive industry. To address this issue, we propose a solution that replaces redundant sensors with virtual replicas of Digital Twin (DT) technology. We exploit data of these virtual replicas, available through the Internet of Things (IoT) infrastructure to devise cyber redundancies. We refer to these cyber redundancies as Cyber-Twin-based Redundancies (CTRs). Recognizing that CTRs utilize DT data, does not incur additional physical overhead. We applied our approach to throttle-by-wire technology as a case study and conducted fault injection experiments. Our experiments demonstrate that our proposed approach not only provides cost-effective fault tolerance capability but also enhances the number of nines of reliability on average from 4.33N to 4.35N for the 5G network and 4.74N for the 6G network in the first year when vehicles require more maintenance. For future networks with 7N, 8N, and 9N reliability, this metric improves to 5.38N, 5.88N, and 6.17N, respectively. Compared to traditional approaches, our proposed approach achieves these improvements without incurring additional costs and physical overheads.