UbiMeta:面向元宇宙的泛在操作系统模型

Q2 Decision Sciences International Journal of Crowd Science Pub Date : 2023-12-22 DOI:10.26599/IJCS.2023.9100028
Yiqiang Chen;Wuliang Huang;Xinlong Jiang;Teng Zhang;Yi Wang;Bingjie Yan;Zhirui Wang;Qian Chen;Yunbing Xing;Dong Li;Guodong Long
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引用次数: 0

摘要

元宇宙意味着通过人机交互将虚拟和有形领域融合在一起。在泛在计算中实现人、网络和环境的无缝整合,对充分利用元宇宙的能力起着至关重要的作用。然而,元宇宙操作系统在访问泛在资源、处理信息的同时保护隐私和安全,以及为下游应用提供人工智能功能等方面面临着巨大的障碍。为了应对这些挑战,本文介绍了 UbiMeta 模型,这是一种专门为元宇宙设计的泛在操作系统。它扩展了传统泛在操作系统的功能,重点是调整下游模型和运行能力,以便在元宇宙中有效发挥作用。UbiMeta 由四层组成:泛在资源管理层(URML)、自主信息掌握层(AIML)、通用智能机制层(GIML)和元宇宙生态模型层(MEML)。URML 有助于无缝集成和管理各种外部设备和资源。它为整合和控制这些资源提供了一个框架,包括虚拟化、抽象化和重用。AIML 负责感知信息,并在存储和处理过程中保护隐私和安全。GIML 利用大规模预训练深度学习特征提取器来获取有效的信息处理特征。MEML侧重于利用模型即服务(MaaS)原则和OODA循环(观察、定位、决策、行动)构建元宇宙应用。它利用 URML 层和 AIML 层收集的大量信息,构建了一个强大的元宇宙生态系统。此外,本研究还探讨了 UbiMeta 如何增强用户体验并促进各种元数据领域的创新。它强调了 UbiMeta 在元宇宙中革新医疗保健、工业实践、教育和农业的潜力。
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UbiMeta: A Ubiquitous Operating System Model for Metaverse
The metaverse signifies the amalgamation of virtual and tangible realms through human-computer interaction. The seamless integration of human, cyber, and environments within ubiquitous computing plays a pivotal role in fully harnessing the metaverse's capabilities. Nevertheless, metaverse operating systems face substantial hurdles in terms of accessing ubiquitous resources, processing information while safeguarding privacy and security, and furnishing artificial intelligence capabilities to downstream applications. To tackle these challenges, this paper introduces the UbiMeta model, a specialized ubiquitous operating system designed specifically for the metaverse. It extends the capabilities of traditional ubiquitous operating systems and focuses on adapting downstream models and operational capacity to effectively function within the metaverse. UbiMeta comprises four layers: the Ubiquitous Resource Management Layer (URML), the Autonomous Information Mastery Layer (AIML), the General Intelligence Mechanism Layer (GIML), and the Metaverse Ecological Model Layer (MEML). The URML facilitates the seamless incorporation and management of various external devices and resources. It provides a framework for integrating and controlling these resources, including virtualization, abstraction, and reuse. The AIML is responsible for perceiving information and safeguarding privacy and security during storage and processing. The GIML leverages large-scale pre-trained deep-learning feature extractors to obtain effective features for processing information. The MEML focuses on constructing metaverse applications using the principles of Model-as-a-Service (MaaS) and the OODA loop (Observation, Orientation, Decision, Action). It leverages the vast amount of information collected by the URML and AIML layers to build a robust metaverse ecosystem. Furthermore, this study explores how UbiMeta enhances user experiences and fosters innovation in various metaverse domains. It highlights the potential of UbiMeta in revolutionizing medical healthcare, industrial practices, education, and agriculture within the metaverse.
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来源期刊
International Journal of Crowd Science
International Journal of Crowd Science Decision Sciences-Decision Sciences (miscellaneous)
CiteScore
2.70
自引率
0.00%
发文量
20
审稿时长
24 weeks
期刊最新文献
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