Time-Sensitive Networking (TSN) provides guaranteed traffic delivery, making it essential for industrial automation, multimedia, automotive systems, and other areas. Although TSN is well-established in wired networks, wireless systems face extra challenges such as delays, interference, and unstable links. Extending TSN to wireless (WTSN) thus adds complexity, particularly in managing traffic. This paper proposes a data-driven solution leveraging Digital Twin (DT) modeling to enhance WTSN management. We focus on mitigating the residual service time (RST) problem introduced by non-TSN traffic generators, which increases link latency. Implemented in a real Wi-Fi TSN-based environment, our implementation demonstrates that delay prediction through WTSN-DT reduces application link latency by up to 96% at the 90th percentile in a real Wi-Fi TSN setting, relative to fixed time slot allocation.
