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迈向无线工业数据分发方法的详细评估。

Toward a Detailed Evaluation of Wireless Industrial Data Distribution Approaches.

机构信息

Institute of Informatics and Telematics, National Research Council, 56124 Pisa, Italy.

Department of Computer Science, University of Milan, 20133 Milan, Italy.

出版信息

Sensors (Basel). 2022 Mar 25;22(7):2533. doi: 10.3390/s22072533.

DOI:10.3390/s22072533
PMID:35408155
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9002902/
Abstract

Data distribution is a cornerstone of efficient automation for intelligent machines in Industry 4.0. Although in the recent literature there have been several comparisons of relevant methods, we identify that most of those comparisons are either theoretical or based on abstract simulation tools, unable to uncover the specific, detailed impacts of the methods to the underlying networking infrastructure. In this respect, as a first contribution of this paper, we develop more detailed and fine-tuned solutions for robust data distribution in smart factories on stationary and mobile scenarios of wireless industrial networking. Using the technological enablers of WirelessHART, RPL and the methodological enabler of proxy selection as building blocks, we compose the protocol stacks of four different methods (both centralized and decentralized) for data distribution in wireless industrial networks over the IEEE 802.15.4 physical layer. We implement the presented methods in the highly detailed OMNeT++ simulation environment and we evaluate their performance via an extensive simulation analysis. Interestingly enough, we demonstrate that the careful selection of a limited set of proxies for data caching in the network can lead to an increased data delivery success rate and low data access latency. Next, we describe two test cases demonstrated in an industrial smart factory environment. First, we show the collaboration between robotic elements and wireless data services. Second, we show the integration with an industrial fog node which controls the shop-floor devices. We report selected results in much larger scales, obtained via simulations.

摘要

数据分发是工业 4.0 中智能机器高效自动化的基石。尽管在最近的文献中,已经有几种相关方法的比较,但我们发现,这些比较大多数都是理论上的,或者基于抽象的仿真工具,无法揭示这些方法对底层网络基础设施的具体、详细的影响。在这方面,作为本文的第一个贡献,我们针对无线工业网络的固定和移动场景,为智能工厂中稳健的数据分发开发了更详细和更精细的解决方案。我们使用 WirelessHART、RPL 的技术推动者和代理选择的方法推动者作为构建块,为 IEEE 802.15.4 物理层上的无线工业网络中的数据分发组合了四种不同方法(集中式和分散式)的协议栈。我们在高度详细的 OMNeT++仿真环境中实现了所提出的方法,并通过广泛的仿真分析评估了它们的性能。有趣的是,我们证明了在网络中为数据缓存精心选择有限数量的代理可以提高数据传输成功率和降低数据访问延迟。接下来,我们描述了在工业智能工厂环境中演示的两个测试用例。首先,我们展示了机器人元素和无线数据服务之间的协作。其次,我们展示了与工业雾节点的集成,该节点控制车间设备。我们报告了通过模拟获得的更大规模的选定结果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e46/9002902/242698317d60/sensors-22-02533-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e46/9002902/1572e6af29fc/sensors-22-02533-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e46/9002902/094be625c093/sensors-22-02533-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e46/9002902/420cc7c81618/sensors-22-02533-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e46/9002902/86a7eb1cc334/sensors-22-02533-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e46/9002902/0e1911573b40/sensors-22-02533-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e46/9002902/a909633a3d42/sensors-22-02533-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e46/9002902/d75783c47afe/sensors-22-02533-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e46/9002902/f241ffb279fe/sensors-22-02533-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e46/9002902/242698317d60/sensors-22-02533-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e46/9002902/1572e6af29fc/sensors-22-02533-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e46/9002902/094be625c093/sensors-22-02533-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e46/9002902/420cc7c81618/sensors-22-02533-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e46/9002902/86a7eb1cc334/sensors-22-02533-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e46/9002902/0e1911573b40/sensors-22-02533-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e46/9002902/a909633a3d42/sensors-22-02533-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e46/9002902/d75783c47afe/sensors-22-02533-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e46/9002902/f241ffb279fe/sensors-22-02533-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e46/9002902/242698317d60/sensors-22-02533-g009.jpg

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本文引用的文献

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The Impact of Rate Adaptation Algorithms on Wi-Fi-Based Factory Automation Systems.速率适配算法对基于Wi-Fi的工厂自动化系统的影响。
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Performance Analysis of Latency-Aware Data Management in Industrial IoT Networks.工业物联网网络中基于潜伏期感知的数据管理性能分析。
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Routing and Scheduling Algorithms for WirelessHARTNetworks: A Survey.无线HART网络的路由与调度算法:综述
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