Multi-Tier Computing-Enabled Digital Twin in 6G Networks

• URL: http://arxiv.org/abs/2312.16999v1
• Date: Thu, 28 Dec 2023 13:02:53 GMT
• Title: Multi-Tier Computing-Enabled Digital Twin in 6G Networks
• Authors: Kunlun Wang, Yongyi Tang, Trung Q. Duong, Saeed R. Khosravirad, Octavia A. Dobre, George K. Karagiannidis,
• Abstract summary: In Industry 4.0, industries such as manufacturing, automotive, and healthcare are rapidly adopting DT-based development. The main challenges to date have been the high demands on communication and computing resources, as well as privacy and security concerns. To achieve low latency and high security services in the emerging DT, multi-tier computing has been proposed by combining edge/fog computing and cloud computing.
• Score: 50.236861239246835
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Digital twin (DT) is the recurrent and common feature in discussions about future technologies, bringing together advanced communication, computation, and artificial intelligence, to name a few. In the context of Industry 4.0, industries such as manufacturing, automotive, and healthcare are rapidly adopting DT-based development. The main challenges to date have been the high demands on communication and computing resources, as well as privacy and security concerns, arising from the large volumes of data exchanges. To achieve low latency and high security services in the emerging DT, multi-tier computing has been proposed by combining edge/fog computing and cloud computing. Specifically, low latency data transmission, efficient resource allocation, and validated security strategies of multi-tier computing systems are used to solve the operational problems of the DT system. In this paper, we introduce the architecture and applications of DT using examples from manufacturing, the Internet-of-Vehicles and healthcare. At the same time, the architecture and technology of multi-tier computing systems are studied to support DT. This paper will provide valuable reference and guidance for the theory, algorithms, and applications in collaborative multi-tier computing and DT.

Related papers

• Sustainable Diffusion-based Incentive Mechanism for Generative AI-driven Digital Twins in Industrial Cyber-Physical Systems [65.22300383287904]
  Industrial Cyber-Physical Systems (ICPSs) are an integral component of modern manufacturing and industries. By digitizing data throughout the product life cycle, Digital Twins (DTs) in ICPSs enable a shift from current industrial infrastructures to intelligent and adaptive infrastructures. mechanisms that leverage sensing Industrial Internet of Things (IIoT) devices to share data for the construction of DTs are susceptible to adverse selection problems.
  arXiv  Detail & Related papers  (2024-08-02T10:47:10Z)
• Efficient Task Offloading Algorithm for Digital Twin in Edge/Cloud Computing Environment [14.14935102383516]
  Digital Twin (DT) is envisioned to empower various areas as a bridge between physical objects and the digital world. Mobile Cloud Computing (MCC) and Mobile Edge Computing (MEC) have become two of the key factors to achieve real-time feedback. We propose a new DT system model considering a heterogeneous MEC/MCC environment.
  arXiv  Detail & Related papers  (2023-07-12T03:31:34Z)
• Enabling Spatial Digital Twins: Technologies, Challenges, and Future Research Directions [13.210510790794006]
  A Digital Twin (DT) is a virtual replica of a physical object or system, created to monitor, analyze, and optimize its behavior and characteristics. A Spatial Digital Twin (SDT) is a specific type of digital twin that emphasizes the geospatial aspects of the physical entity. We are the first to systematically analyze different spatial technologies relevant to building an SDT in layered approach.
  arXiv  Detail & Related papers  (2023-06-11T06:28:44Z)
• Future Computer Systems and Networking Research in the Netherlands: A Manifesto [137.47124933818066]
  We draw attention to CompSys as a vital part of ICT. Each of the Top Sectors of the Dutch Economy, each route in the National Research Agenda, and each of the UN Sustainable Development Goals pose challenges that cannot be addressed without CompSys advances.
  arXiv  Detail & Related papers  (2022-05-26T11:02:29Z)
• Digital Twin Virtualization with Machine Learning for IoT and Beyond 5G Networks: Research Directions for Security and Optimal Control [3.1798318618973362]
  Digital twin (DT) technologies have emerged as a solution for real-time data-driven modeling of cyber physical systems. We establish a conceptual layered architecture for a DT framework with decentralized implementation on cloud computing. We discuss the significance of DT in lowering the risk of development and deployment of innovative technologies on existing system.
  arXiv  Detail & Related papers  (2022-04-05T03:04:02Z)
• Pervasive AI for IoT Applications: Resource-efficient Distributed Artificial Intelligence [45.076180487387575]
  Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of Things (IoT) applications and services. This is driven by the easier access to sensory data and the enormous scale of pervasive/ubiquitous devices that generate zettabytes (ZB) of real-time data streams. The confluence of pervasive computing and artificial intelligence, Pervasive AI, expanded the role of ubiquitous IoT systems.
  arXiv  Detail & Related papers  (2021-05-04T23:42:06Z)
• Machine Learning for Massive Industrial Internet of Things [69.52379407906017]
  Industrial Internet of Things (IIoT) revolutionizes the future manufacturing facilities by integrating the Internet of Things technologies into industrial settings. With the deployment of massive IIoT devices, it is difficult for the wireless network to support the ubiquitous connections with diverse quality-of-service (QoS) requirements. We first summarize the requirements of the typical massive non-critical and critical IIoT use cases. We then identify unique characteristics in the massive IIoT scenario, and the corresponding machine learning solutions with its limitations and potential research directions.
  arXiv  Detail & Related papers  (2021-03-10T20:10:53Z)
• Digital Twins: State of the Art Theory and Practice, Challenges, and Open Research Questions [62.67593386796497]
  This work explores the various DT features and current approaches, the shortcomings and reasons behind the delay in the implementation and adoption of digital twin. The major reasons for this delay are the lack of a universal reference framework, domain dependence, security concerns of shared data, reliance of digital twin on other technologies, and lack of quantitative metrics.
  arXiv  Detail & Related papers  (2020-11-02T19:08:49Z)
• Convergence of Artificial Intelligence and High Performance Computing on NSF-supported Cyberinfrastructure [3.4291439418246177]
  Artificial Intelligence (AI) applications have powered transformational solutions for big data challenges in industry and technology. As AI continues to evolve into a computing paradigm endowed with statistical and mathematical rigor, it has become apparent that single- GPU solutions for training, validation, and testing are no longer sufficient. This realization has been driving the confluence of AI and high performance computing to reduce time-to-insight.
  arXiv  Detail & Related papers  (2020-03-18T18:00:02Z)
• Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks [84.2155885234293]
  We first summarize how to apply data-driven supervised deep learning and deep reinforcement learning in URLLC. To address these open problems, we develop a multi-level architecture that enables device intelligence, edge intelligence, and cloud intelligence for URLLC.
  arXiv  Detail & Related papers  (2020-02-22T14:38:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.