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在工业传感器网络中用于时间感知形状调度的双相分布式基因算法.

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  • 1Department of Engineering Science and Ocean Engineering, National Taiwan University, Taipei 10617, Taiwan.

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概括
此摘要是机器生成的。

一个新的分布式算法,2PDGA优化了工业传感器网络 (ISN) 的时间敏感网络 (TSN) 调度. 它提高了工业4.0中的实时通信的可扩展性和硬件部署性.

关键词:
这是IEEE 802.1Qbv.工业4.0 工业4.0 工业4.0 工业4.0 工业4.0 是一个时间感知造型器 (TAS)时间敏感网络 (TSN)边缘计算是一种边缘计算.基因算法 (GA) 是一种基因算法.

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科学领域:

  • 计算机科学 计算机科学
  • 电气工程 电气工程
  • 网络工程 网络工程

背景情况:

  • 时间敏感网络 (TSN) 和时间意识形态 (TAS) 对于实时工业通信至关重要.
  • 现有的TAS调度方法面临可扩展性问题,并产生碎片化的Gate Control List (GCL),限制在资源受限的交换机上部署.

研究的目的:

  • 为TAS调度提出一种新的分布式算法,克服集中式方法的局限性.
  • 通过解决GCL大小限制,提高TAS在工业传感器网络 (ISN) 的部署能力.

主要方法:

  • 开发了一种基于基因的双相分布式算法 (2PDGA).
  • 第一阶段:网络级遗传算法 (GA) 用于路由和基线时间表的创建.
  • 第二阶段:每个开关的本地改进,以合并窗口,并在最小的协调下执行GCL上限.

主要成果:

  • 2PDGA在0 ns防护带实现了92.9% (CAP@8) 和99.8% (CAP@16) 的合规性,中位延迟为42.1μs.
  • 第二阶段将每个端口的平均最大GCL输入量减少了7.7%,从而提高了硬件部署能力.
  • 该算法在1512个配置中进行了评估,具有不同的拓和交换机数量.

结论:

  • 2PDGA算法为ISN中的TAS调度提供了一个有效的分布式解决方案.
  • 它通过管理GCL碎片化和尊重交换机限制,提高了可扩展性和实际部署.
  • 这项工作支持在工业4.0应用中推进实时通信.