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一种用于WiFi接入TSN的联合调度方案。

A Joint Scheduling Scheme for WiFi Access TSN.

作者信息

Li Zhong, Yang Jianfeng, Guo Chengcheng, Xiao Jinsheng, Tao Tao, Li Chengwang

机构信息

School of Electronic Information, Wuhan University, Wuhan 430072, China.

出版信息

Sensors (Basel). 2024 Apr 16;24(8):2554. doi: 10.3390/s24082554.

Abstract

In the context of Industry 4.0, industrial production equipment needs to communicate through the industrial internet to improve the intelligence of industrial production. This requires the current communication network to have the ability of large-scale equipment access, multiple communication protocols/heterogeneous systems interoperability, and end-to-end deterministic low-latency transmission. Time-sensitive network (TSN), as a new generation of deterministic Ethernet communication technology, is the main development direction of time-critical communication technology applied in industrial environments, and Wi-Fi technology has become the main way of wireless access for users due to its advantages of high portability and mobility. Therefore, accessing WiFi in the TSN is a major development direction of the current industrial internet. In this paper, we model the scheduling problem of TSN and WiFi converged networks and propose a scheme based on a greedy strategy distributed estimation algorithm (GE) to solve the scheduling problem. Compared with the integer linear programming (ILP) algorithm and the Tabu algorithm, the algorithm implemented in this paper outperforms the other algorithms in being able to adapt to a variety of different scenarios and in scheduling optimization efficiency, especially when the amount of traffic to be deployed is large.

摘要

在工业4.0的背景下,工业生产设备需要通过工业互联网进行通信,以提高工业生产的智能化程度。这就要求当前的通信网络具备大规模设备接入能力、多种通信协议/异构系统互操作性以及端到端确定性低延迟传输能力。时间敏感网络(TSN)作为新一代确定性以太网通信技术,是工业环境中时间关键型通信技术的主要发展方向,而Wi-Fi技术因其高便携性和移动性优势,已成为用户无线接入的主要方式。因此,在TSN中接入WiFi是当前工业互联网的一个主要发展方向。本文对TSN与WiFi融合网络的调度问题进行建模,并提出一种基于贪婪策略分布式估计算法(GE)的方案来解决调度问题。与整数线性规划(ILP)算法和禁忌算法相比,本文实现的算法在适应各种不同场景以及调度优化效率方面优于其他算法,尤其是在待部署流量较大时。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ef1/11053665/e64d3e99eacb/sensors-24-02554-g001.jpg

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