Joint Routing and Scheduling Optimization with Swarm Intelligence in Time-Sensitive Networking

IEEE 802.1 Time-Sensitive Networking (TSN) is an emerging and promising communication solution offering benefits for Industrial Internet. TSN can provide deterministic latency and ultra-reliability guarantee for automation control information in multi-traffics scenario. However, the routing and scheduling methods, which have important effects on system performance, are not covered by TSN standards. Therefore, in this paper, a joint routing and scheduling model based on K-shortest-path (KSP) and swarm intelligence is proposed. The model effectually reduces end-to-end latency caused by link congestion and improves scheduling feasibility for multiple traffics in TSN domain. By contrast experiments under different circumstances, Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) are analyzed and compared. The simulation results indicate that the proposed model based on improved ACO achieves better performance on scheduling success rate and end-to-end latency guarantee, while PSO has higher scheduling efficiency in terms of optimization results and iterative convergence rate.