Design of compound data acquisition gateway based on 5G network

IF 0.2 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Web Intelligence Pub Date : 2023-08-02 DOI:10.3233/web-220071

Jufen Hu, G. Lorenzini

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引用次数: 0

Abstract

With the wide application of industrial Internet of Things, the increasing amount of data and the complexity of data types, higher requirements are put forward for the performance of data acquisition gateway. In order to reduce the data acquisition time of the gateway and improve the data retrieval coverage of the gateway, a novel design method of composite data acquisition gateway based on 5G network is proposed. Based on the analysis of related technologies, the functional requirements of the composite data acquisition gateway are summarized, and the overall design of the gateway is completed. On this basis, the gateway hardware environment is constructed by designing the main control module, 5G module and FPGA program, and then the software program is designed by designing the data acquisition driver, 5G module driver, embedded software and protocol conversion process. The experimental results show that the data retrieval coverage of the gateway designed by this method is always above 92%, which is 6% higher than that of method 1. This shows that the method significantly improves the coverage of data search, speeds up the efficiency of data collection, and improves the performance of the data collection gateway, which proves the effectiveness and feasibility of the method and is conducive to promoting the intelligent development of the data collection gateway technology.

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基于5G网络的复合数据采集网关设计

随着工业物联网的广泛应用，数据量越来越大，数据类型越来越复杂，对数据采集网关的性能提出了更高的要求。为了减少网关的数据采集时间，提高网关的数据检索覆盖率，提出了一种基于5G网络的复合数据采集网关设计方法。在分析相关技术的基础上，总结了复合数据采集网关的功能需求，完成了网关的总体设计。在此基础上，通过设计主控模块、5G模块和FPGA程序构建网关硬件环境，然后通过设计数据采集驱动程序、5G模块驱动程序、嵌入式软件和协议转换流程进行软件程序设计。实验结果表明，该方法设计的网关的数据检索覆盖率始终在92%以上，比方法1提高了6%。由此可见，该方法显著提高了数据搜索的覆盖范围，加快了数据采集的效率，提高了数据采集网关的性能，证明了该方法的有效性和可行性，有利于促进数据采集网关技术的智能化发展。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Web Intelligence COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-

CiteScore

0.90

自引率

0.00%

发文量

期刊介绍： Web Intelligence (WI) is an official journal of the Web Intelligence Consortium (WIC), an international organization dedicated to promoting collaborative scientific research and industrial development in the era of Web intelligence. WI seeks to collaborate with major societies and international conferences in the field. WI is a peer-reviewed journal, which publishes four issues a year, in both online and print form. WI aims to achieve a multi-disciplinary balance between research advances in theories and methods usually associated with Collective Intelligence, Data Science, Human-Centric Computing, Knowledge Management, and Network Science. It is committed to publishing research that both deepen the understanding of computational, logical, cognitive, physical, and social foundations of the future Web, and enable the development and application of technologies based on Web intelligence. The journal features high-quality, original research papers (including state-of-the-art reviews), brief papers, and letters in all theoretical and technology areas that make up the field of WI. The papers should clearly focus on some of the following areas of interest: a. Collective Intelligence[...] b. Data Science[...] c. Human-Centric Computing[...] d. Knowledge Management[...] e. Network Science[...]

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