The application of Digital Twin (DT) technology in conjunction with sixth-generation (6G) networks can enable advanced real-time analytics, predictive control, an‎d secure cyber-physical integration. In this work, we present a modular framework that incorporates Edge AI an‎d blockchain technology to tackle major barriers of DTs: real-time synchronization, scalability, an‎d data-related security issues. The use of localized AI inference engines gives sub-millisecond latency an‎d robustness to system changes, while blockchain provides tamper-proof audit trails an‎d decentralized access permissions. The system architecture is composed of: physical assets, AI driven edge nodes, a transaction layer which is secured by blockchain, an‎d a simulated 6G environment modeled in NS-3. Important metrics include: latency, synchronization accuracy, throughput, an‎d security breach probability. The simulations chosen for the research focus on ease of replication an‎d resource deman‎ds as well as integration with open-source software which is outside proprietary ecosystems. The results are anticipated to show that the hybrid architecture can support over 10,000 DT instances while keeping latency under 50 ms, thus enabling more scalable an‎d secure architectures for DTs in 6G networks. The model supports intelligent, resilient, an‎d distributed cyber-physi systems an‎d provides a foundation for future reproducible research.

6/10/2025 12:00:00 AM

87024

1404/04/15

A SURVY ON Digital Twins in 6G Technology

Digital Twins , 6G Networks , Edge AI , Blockchain , Real-Time Synchronization