A comprehensive survey on digital twin for future networks and emerging Internet of Things industry

Akram Hakiri*, Aniruddha Gokhale, Sadok Ben Yahia, Nedra Mellouli

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Abstract

The rapid growth of industrial digitalization in the Industry 4.0 era is fundamentally transforming the industrial sector by connecting products, machines, and people, offering real-time digital models to allow self-diagnosis, self-optimization and self-configuration. However, this uptake in such a digital transformation faces numerous obstacles. For example, the lack of real-time data feeds to perform custom closed-loop control and realize common, powerful industrial systems, the complexity of traditional tools and their inability in finding effective solutions to industry problems, lack of capabilities to experiment rapidly on innovative ideas, and the absence of continuous real-time interactions between physical objects and their simulation representations along with reliable two-way communications, are key barriers towards the adoption of such a digital transformation. Digital twins hold the promise of improving maintainability and deployability, enabling flexibility, auditability, and responsiveness to changing conditions, allowing continuous learning, monitoring and actuation, and allowing easy integration of new technologies in order to deploy open, scalable and reliable Industrial Internet of Things (IIoT). A critical understanding of this emerging paradigm is necessary to address the multiple dimensions of challenges in realizing digital twins at scale and create new means to generate knowledge in the industrial IoT. To address these requirements, this paper surveys existing digital twin along software technologies, standardization efforts and the wide range of recent and state-of-the-art digital twin-based projects; presents diverse use cases that can benefit from this emerging technology; followed by an in-depth discussion of the major challenges in this area drawing upon the research status and key trends in Digital Twins.

OriginalsprogEngelsk
Artikelnummer110350
TidsskriftComputer Networks
Vol/bind244
Antal sider26
ISSN1389-1286
DOI
StatusUdgivet - maj 2024

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© 2024 Elsevier B.V.

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