Abstract
The Internet of Things (IoT) connects massive smart devices to collect big data and carry out the monitoring and control of numerous things in cyber-physical systems (CPS). By leveraging machine learning (ML) and deep learning (DL) techniques to analyze the collected data, physical systems can be monitored and controlled effectively. Along with the development of IoT and data analysis technologies, a number of CPS (smart grid, smart transportation, smart manufacturing, smart cities, etc.) adopt IoT and data analysis technologies to improve their performance and operations. Nonetheless, directly manipulating or updating the real system has inherent risks. Thus, creating a digital clone of a real physical system, denoted as a Digital Twin (DT), is a viable strategy. Generally speaking, a DT is a data-driven software and hardware emulation platform, which is a cyber replica of physical systems. Meanwhile, a DT describes a specific physical system and tends to achieve the functions and use cases of physical systems. Since DT is a complex digital system, finding a way to effectively represent a variety of things in timely and efficient manner poses numerous challenges to the networking, computing, and data analytics for IoT. Furthermore, the design of a DT for IoT systems must consider numerous exceptional requirements (e.g., latency, reliability, safety, scalability, security, and privacy). To address such challenges, the thoughtful design of DTs offers opportunities for novel and interdisciplinary research efforts. To address the aforementioned problems and issues, in this paper, we first review the architectures of DTs, data representation, and communication protocols. We then review existing efforts on applying DT into IoT data-driven smart systems, including the smart grid, smart transportation, smart manufacturing, and smart cities. Further, we summarize the existing challenges from CPS, data science, optimization, and security and privacy perspectives. Finally, we outline possible future research directions from the perspectives of performance, new DT-driven services, model and learning, and security and privacy.
Highlights
The technological trend of the Internet of Things (IoT) has led to a massive increase in the number of smart devices that are connected to cyberspace [1,2,3]
While advanced networking, computing, and data analysis technologies can help the realization of Digital Twin (DT), there are a number of issues that need to be addressed, including how to define the theoretical foundation and modeling techniques such that DT accurately and reliably reflect the states of things, how to design machine learning (ML)/deep learning (DL) models to achieve real-time big data processing, and and how to secure DT and protect privacy-sensitive information collection and publishing
Physical systems, it is critical that the DT obtains the real-time state information of physical systems; this is realized by using IoT sensors and networking technologies
Summary
The technological trend of the Internet of Things (IoT) has led to a massive increase in the number of smart devices that are connected to cyberspace [1,2,3]. Leveraging big data can establish a virtual smart grid environment to simulate real accidents, to investigate and develop mitigation plans Another example is related to wireless sensors networks, which can be used in different environmental monitoring systems. While advanced networking, computing, and data analysis technologies can help the realization of DT, there are a number of issues that need to be addressed, including how to define the theoretical foundation and modeling techniques such that DT accurately and reliably reflect the states of things, how to design ML/DL models to achieve real-time big data processing, and and how to secure DT and protect privacy-sensitive information collection and publishing. Based on the architecture and its applications to smart-world systems, we discuss challenges that arise from four perspectives: CPS, data science, optimization, and security and privacy.
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