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

作     者：Christos L.STERGIOU Kostas E.PSANNIS 

作者机构：Department of Applied InformaticsUniversity of MacedoniaGreece 

出 版 物：《Virtual Reality & Intelligent Hardware》 (虚拟现实与智能硬件（中英文）)

年 卷 期：2022年第4卷第4期

页      面：279-291页

核心收录：

学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Machine learning IoT Big data Cloud computing Management Analytics Digital twin Scenario Energy efficiency 

摘      要：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 *** 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 ***,reinforcement learning,which is a novel technique that allows large cloud-based data centers,to allocate more energy-efficient resources is ***,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 *** 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 ***,we propose an algorithm for determining the energy consumption of the infrastructure by evaluating the EEIBDM ***,future directions for the expansion of this research are discussed.