The IEEE 802.1Qch standard, i.e., Cyclic Queuing and Forwarding (CQF), enhances Time-Sensitive Networking (TSN) by simplifying configurations and increasing flexibility. Moreover, for improving network reliability, TSN supports the transmission of multiple stream copies across disjoint redundant routes via IEEE 802.1CB. However, the use of redundant routes can lead to varying receiving times for one stream, introducing undesirable receiving jitter and adversely affecting Quality-of-Service. To tackle this issue, we introduce a novel scheduling strategy aimed at reducing the maximum receiving jitter in CQF. Our approach includes a multi-objective function designed to simultaneously minimize the average jitter across all streams and reduce slot length. We utilize an Integer Linear Programming (ILP) solver to find optimal injection time slots within CQF constraints. To address the computational intensity of the ILP solution, we also propose a heuristic method that uses an injection time slot selection indicator. This indicator helps efficiently identify lower-utilization slots on each redundant route, thereby easing network congestion and further reducing jitter. In addition, a sophisticated proposed slot reduction procedure is also employed to further reduce the receiving and avoid negatively affecting the schedulability. Experimental results validate the superiority of our approach compared to existing methods.

The velocity of the luge is too fast for athletes to feel the state of sliding accurately. The sliding state should be measured accurately through available sensors to help improve athletes’ performance. However, only certain types of sensors usually work at Yanqing National Sliding Center, i.e., inertial measurement unit, ultra-wide band, and airspeed head. Furthermore, the precision and synchronization of all the above sensors are inferior; thus, accurate state estimation cannot be obtained through either single type of sensor. This paper proposes a sensor fusion method using asynchronous low-quality data to make high-precision state estimations based on the Kalman filter. First, we analyze the uncertain time offset of multiple sensors and extend the Kalman filter to interval fusion to fit it. Second, we use finite checkpoints from three photogates and tracks to evaluate the characteristics of luge motion. Third, particle swarm optimization is established to find an optimal offset to generate a state estimation with the lowest cost function. And we speed up the optimization by searching for optimal results from the finite points. Finally, the proposed method is validated with the experimental data at Yanqing National Sliding Center without ground truth and a simulation model incorporating ground truth.