A novel fuzzy-logic-based adaptive gate-controlled scheduling algorithm for time-aware shaper in TSN

Time-sensitive networking (TSN) is critical for real-time, industrial, and mission-critical applications that require deterministic communication. Scheduling time-triggered flows in TSN’s time-aware shaper (TAS) mechanism constitute an NP-hard problem, where the inherent trade-off between computational complexity and scheduling optimality persists. Exact algorithms achieve precision via exhaustive search mechanisms at prohibitive costs, while heuristic algorithms sacrifice fidelity to accelerate execution under complex network scenarios. This paper addresses these challenges through a novel rule-based framework that employs a fuzzy logic system to dynamically select algorithms, ensuring adaptation to complex requirements in diverse scenarios. In addition, a dynamic switching algorithm is proposed to intelligently select the most suitable scheduling method based on real-time network conditions and task requirements. Compared with traditional exact algorithms, our approach reduces computation time by over 35% in large-scale networks while meeting time constraints. In small-scale networks, it increases the scheduling success ratio by 20% compared to heuristic methods, particularly when higher accuracy is required. The proposed framework establishes an innovative analytical perspective for TAS traffic scheduling challenges by enabling self-adaptive algorithm matching across varying scheduling demands, rather than constraining specific algorithms to predefined operational scenarios.