Digital twin intelligent system for industrial internet of things-based big data management and analysis in cloud environments

Q1 Computer Science
Christos L. Stergiou, Kostas E. Psannis
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引用次数: 9

Abstract

This work surveys and illustrates multiple open challenges in the field of industrial Internet of Things (IoT)-based big data management and analysis in cloud environments. Challenges arising from the fields of machine learning in cloud infrastructures, artificial intelligence techniques for big data analytics in cloud environments, and federated learning cloud systems are elucidated. Additionally, reinforcement learning, which is a novel technique that allows large cloud-based data centers, to allocate more energy-efficient resources is examined. Moreover, we propose an architecture that attempts to combine the features offered by several cloud providers to achieve an energy-efficient industrial IoT-based big data management framework (EEIBDM) established outside of every user in the cloud. IoT data can be integrated with techniques such as reinforcement and federated learning to achieve a digital twin scenario for the virtual representation of industrial IoT-based big data of machines and room temperatures. Furthermore, we propose an algorithm for determining the energy consumption of the infrastructure by evaluating the EEIBDM framework. Finally, future directions for the expansion of this research are discussed.

基于工业物联网的云环境下大数据管理分析的数字孪生智能系统
这项工作调查并说明了云环境下基于工业物联网(IoT)的大数据管理和分析领域的多个开放挑战。阐述了云基础设施中的机器学习、云环境中用于大数据分析的人工智能技术以及联合学习云系统等领域所面临的挑战。此外,强化学习是一种允许大型基于云的数据中心分配更节能资源的新技术。此外,我们提出了一种架构,试图结合几家云提供商提供的功能,以实现在云中的每个用户之外建立的节能的基于工业物联网的大数据管理框架(EEIBDM)。物联网数据可以与强化和联邦学习等技术集成,以实现基于工业物联网的机器和室温大数据的虚拟表示的数字孪生场景。此外,我们提出了一种通过评估EEIBDM框架来确定基础设施能耗的算法。最后,对未来研究的发展方向进行了展望。
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
Virtual Reality  Intelligent Hardware
Virtual Reality Intelligent Hardware Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
6.40
自引率
0.00%
发文量
35
审稿时长
12 weeks
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