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Cyber Physical Systems (CPS) consist of integration of cyber and physical spaces through computing, communication, and control operations. In vehicular CPS, modern vehicles with multiple Electronic Control Units (ECUs) and networking with other vehicles help autonomous driving. Vehicular CPS is vulner-able to multitude of cyber attacks, including false data injection attacks. This paper presents an Asynchronous Federated Learning (AFL) with a Gated Recurrent Unit (GRU) model for identifying False Data Injection (FDI) attacks in a VCPS. The AFL model continuously monitors the network and constructs a digital twin using the data obtained from a VCPS for intrusion detection. The proposed model is evaluated using different evaluation metrics. Numerical results show that the AFL model outperforms other existing models. 

In the rapidly growing consumer electronics industry, continuous innovation drives increasing demand for smart devices and advanced gadgets. However, this sector faces changing demands and complex supply chains due to the management of rapid technological advancements and consumer expectations. Seamless communication between suppliers and consumers is essential to optimize production processes, minimize waste, and enhance overall customer satisfaction. In response to these demands, this paper presents a solution that combines Digital Twins (DT) and blockchain to improve security and efficiency in metaverse-inspired consumer-oriented supply chains. Herein, DT is used to represent products in virtual spaces and blockchain secures sensitive information using encryption and access controls. Our objective is to create a transparent, secure, and user-friendly system where consumers and suppliers can interact in real-time to verify product details and access important information of featured tasks like warranties and payment settlement. Smart contracts automates these tasks to make processes faster and more reliable. Through experiments, we tested how well the system maintains product integrity, authenticates transactions, and supports consumer-oriented supply chain (CSC) operations. Comparative analysis shows that our approach improves security, performance, and scalability over existing methods. Furthermore, the proposed system not only enhances security, trust, and transparency in CSC but also sets a higher standard for consumer demands and satisfaction. The findings point to the potential solution for future innovations in metaverse-driven CSC management systems. 

Fault tolerance, energy management, and quality of service (QoS) are essential aspects for the design of real-time embedded systems. In this work, we focus on exploring methods that can simultaneously address the above three critical issues under standby-sparing. The standby-sparing mechanism adopts a dual-processor architecture in which each processor plays the role of the backup for the other one dynamically. In this way, it can provide fault tolerance subject to both permanent and transient faults. Due to its duplicate executions of the real-time jobs/tasks, the energy consumption of a standby-sparing system could be quite high. With the purpose of reducing energy under standby-sparing, we proposed three novel scheduling schemes: The first one is for (1, 1)-constrained tasks, and the second one and the third one (which can be combined into an integrated approach to maximize the overall energy reduction) are for general (m,k)-constrained tasks that require that among anykconsecutive jobs of a task no more than (k-m) out of them could miss their deadlines. Through extensive evaluations and performance analysis, our results demonstrate that compared with the existing research, the proposed techniques can reduce energy by up to 11% for (1, 1)-constrained tasks and 25% for general (m,k)-constrained tasks while assuring (m,k)-constraints and fault tolerance as well as providing better user perceived QoS levels under standby-sparing.