基于数字孪生的5G融合造船网络选择与QoS管理算法

Bin Huang, Dongyao Wang, He Li, Cheng-lin Zhao
{"title":"基于数字孪生的5G融合造船网络选择与QoS管理算法","authors":"Bin Huang, Dongyao Wang, He Li, Cheng-lin Zhao","doi":"10.1109/ICIET55102.2022.9779044","DOIUrl":null,"url":null,"abstract":"With the continuous development of intelligent manufacturing technology, in order to meet the diversified needs of network and computing power for intelligent applications in the shipbuilding process, 5G technology and edge computing technology are applied in the traditional 4G/WLAN networks, thus forming the 5G converged shipbuilding network. In order to support massive intelligent services under the converged network in the shipyard, the mechanism for access control and QoS (Quality of Services) management need further researches. In this paper, the 5G converged shipbuilding network based on digital twin is studied, and an efficient and reliable method for multi-mode terminal access and multi-QoS management is proposed. This method continuously constructs the communication environment of digital-twin network through data collection from the physical network, and performs detailed network simulation and mapping through RL (Reinforcement Learning) in digital-twin network to achieve scheduling optimization. The decisions made in digital-twin network are delivered to the physical network to achieve efficient and reliable multi-mode terminal access and multi-QoS management. The simulation results show that the proposed new architecture and method effectively can improve the efficiency of 5G converged network in the shipyard.","PeriodicalId":371262,"journal":{"name":"2022 10th International Conference on Information and Education Technology (ICIET)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Network Selection and QoS Management Algorithm for 5G Converged Shipbuilding Network Based on Digital Twin\",\"authors\":\"Bin Huang, Dongyao Wang, He Li, Cheng-lin Zhao\",\"doi\":\"10.1109/ICIET55102.2022.9779044\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the continuous development of intelligent manufacturing technology, in order to meet the diversified needs of network and computing power for intelligent applications in the shipbuilding process, 5G technology and edge computing technology are applied in the traditional 4G/WLAN networks, thus forming the 5G converged shipbuilding network. In order to support massive intelligent services under the converged network in the shipyard, the mechanism for access control and QoS (Quality of Services) management need further researches. In this paper, the 5G converged shipbuilding network based on digital twin is studied, and an efficient and reliable method for multi-mode terminal access and multi-QoS management is proposed. This method continuously constructs the communication environment of digital-twin network through data collection from the physical network, and performs detailed network simulation and mapping through RL (Reinforcement Learning) in digital-twin network to achieve scheduling optimization. The decisions made in digital-twin network are delivered to the physical network to achieve efficient and reliable multi-mode terminal access and multi-QoS management. The simulation results show that the proposed new architecture and method effectively can improve the efficiency of 5G converged network in the shipyard.\",\"PeriodicalId\":371262,\"journal\":{\"name\":\"2022 10th International Conference on Information and Education Technology (ICIET)\",\"volume\":\"26 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-04-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 10th International Conference on Information and Education Technology (ICIET)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIET55102.2022.9779044\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 10th International Conference on Information and Education Technology (ICIET)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIET55102.2022.9779044","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

随着智能制造技术的不断发展,为了满足造船过程智能化应用对网络和计算能力的多样化需求,在传统的4G/WLAN网络中应用5G技术和边缘计算技术,形成5G融合造船网络。为了支持融合网络下船厂的海量智能业务,访问控制机制和服务质量(QoS)管理有待进一步研究。本文对基于数字孪生的5G融合造船网络进行了研究,提出了一种高效可靠的多模终端接入和多qos管理方法。该方法通过从物理网络中采集数据,不断构建数字孪生网络的通信环境,并通过数字孪生网络中的RL (Reinforcement Learning)进行详细的网络仿真和映射,实现调度优化。将数字孪生网络中的决策传递到物理网络中,实现高效可靠的多模终端接入和多qos管理。仿真结果表明,所提出的新架构和方法能够有效提高船厂5G融合网络的效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Network Selection and QoS Management Algorithm for 5G Converged Shipbuilding Network Based on Digital Twin
With the continuous development of intelligent manufacturing technology, in order to meet the diversified needs of network and computing power for intelligent applications in the shipbuilding process, 5G technology and edge computing technology are applied in the traditional 4G/WLAN networks, thus forming the 5G converged shipbuilding network. In order to support massive intelligent services under the converged network in the shipyard, the mechanism for access control and QoS (Quality of Services) management need further researches. In this paper, the 5G converged shipbuilding network based on digital twin is studied, and an efficient and reliable method for multi-mode terminal access and multi-QoS management is proposed. This method continuously constructs the communication environment of digital-twin network through data collection from the physical network, and performs detailed network simulation and mapping through RL (Reinforcement Learning) in digital-twin network to achieve scheduling optimization. The decisions made in digital-twin network are delivered to the physical network to achieve efficient and reliable multi-mode terminal access and multi-QoS management. The simulation results show that the proposed new architecture and method effectively can improve the efficiency of 5G converged network in the shipyard.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Reforms to Teaching Practices in College Human Resource Management Courses Based on Integration with Regional Industries Recognizing Objects from on-Board Vehicle Footage to Build an Educational Foundation for Undergraduate Research in Computer Vision An AI Mock-interview Platform for Interview Performance Analysis Gender Inequity in Engineering Higher Education: A Case Study of an American University in a Middle Eastern Country Designing a Teaching Model in Pharmacotherapeutics Course to Improve Learning Outcomes Through Web-Based Learning for Pharmacy Students
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1