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Summary
This summary is machine-generated.

A new distributed algorithm, 2PDGA, optimizes Time-Sensitive Networking (TSN) scheduling for Industrial Sensor Networks (ISNs). It improves scalability and hardware deployability for real-time communication in Industry 4.0.

Keywords:
IEEE 802.1QbvIndustry 4.0Time-Aware Shaper (TAS)Time-Sensitive Networking (TSN)edge computinggenetic algorithm (GA)

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Time-Sensitive Networking (TSN) and Time-Aware Shaper (TAS) are crucial for real-time industrial communication.
  • Existing TAS scheduling methods face scalability issues and generate fragmented Gate Control Lists (GCLs), limiting deployment on resource-constrained switches.

Purpose of the Study:

  • To propose a novel, distributed algorithm for TAS scheduling that overcomes the limitations of centralized approaches.
  • To enhance the deployability of TAS in Industrial Sensor Networks (ISNs) by addressing GCL size constraints.

Main Methods:

  • A two-phase distributed genetic-based algorithm (2PDGA) was developed.
  • Phase I: Network-level genetic algorithm (GA) for routing and baseline schedule creation.
  • Phase II: Per-switch local refinement to merge windows and enforce GCL caps with minimal coordination.

Main Results:

  • 2PDGA achieved 92.9% (CAP@8) and 99.8% (CAP@16) compliance at 0 ns guard bands, with a median latency of 42.1 μs.
  • Phase II reduced average maximum per-port GCL entries by 7.7%, improving hardware deployability.
  • The algorithm was evaluated across 1512 configurations with varying topologies and switch counts.

Conclusions:

  • The 2PDGA algorithm offers an effective distributed solution for TAS scheduling in ISNs.
  • It enhances scalability and practical deployment by managing GCL fragmentation and respecting switch limitations.
  • This work supports the advancement of real-time communication in Industry 4.0 applications.