A Survey on Digital Twins: Enabling Technologies, Use Cases, Application, Open Issues, and More

Digital Twins, sophisticated digital replicas of physical entities, have been gaining significant attention, especially after NASA's endorsement, and are poised to revolutionize numerous fields, such as medicine and construction. These advanced models offer dynamic, real-time simulations, leveraging enabling technologies, such as artificial intelligence, machine learning, IoT, cloud computing, and Big Data analytics to enhance their functionality and applicability. In the medical field, Digital Twins facilitate personalized treatment plans and predictive maintenance of medical equipment by simulating human organs with precision. In construction, they enable efficient building design and urban planning, optimizing resource use, and reducing costs through predictive maintenance. Startups are innovatively employing Digital Twins in various sectors, from smart cities—where they optimize traffic flow, energy consumption, and waste management—to industrial machinery, ensuring predictive maintenance and minimizing downtime. This survey delves into the diverse use cases, market potential, and challenges of Digital Twins, such as data security and interoperability, while emphasizing their transformative impact on industries. The future prospects are promising, with continuous advancements in AI, ML, IoT, and cloud computing driving further expansion and application of Digital Twin technologies.

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Publication Details

Journal
IEEE Journal of Selected Areas in Sensors
Published
2024-12-30
DOI
https://doi.org/10.1109/jsas.2024.3523856
Citations
11
Primary Topic
Digital Transformation in Industry
Type
article
Field-Weighted Citation Impact
3.84
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article

A Survey on Digital Twins: Enabling Technologies, Use Cases, Application, Open Issues, and More

Vinay Chamola, Amit Pandey, Kuldip Singh Sangwan, Vikas Hassija et al.
11 citations
IEEE Journal of Selected Areas in Sensors
Digital Transformation in Industry
3.84
article

A Survey on Digital Twins: Enabling Technologies, Use Cases, Application, Open Issues, and More

Vinay Chamola, Amit Pandey, Kuldip Singh Sangwan, Vikas Hassija, Soham Das, A. Chakrabarti, Rajdipta De
article en
11 citations

Abstract

Digital Twins, sophisticated digital replicas of physical entities, have been gaining significant attention, especially after NASA's endorsement, and are poised to revolutionize numerous fields, such as medicine and construction. These advanced models offer dynamic, real-time simulations, leveraging enabling technologies, such as artificial intelligence, machine learning, IoT, cloud computing, and Big Data analytics to enhance their functionality and applicability. In the medical field, Digital Twins facilitate personalized treatment plans and predictive maintenance of medical equipment by simulating human organs with precision. In construction, they enable efficient building design and urban planning, optimizing resource use, and reducing costs through predictive maintenance. Startups are innovatively employing Digital Twins in various sectors, from smart cities—where they optimize traffic flow, energy consumption, and waste management—to industrial machinery, ensuring predictive maintenance and minimizing downtime. This survey delves into the diverse use cases, market potential, and challenges of Digital Twins, such as data security and interoperability, while emphasizing their transformative impact on industries. The future prospects are promising, with continuous advancements in AI, ML, IoT, and cloud computing driving further expansion and application of Digital Twin technologies.

IEEE Journal of Selected Areas in SensorsVol. 2
Bennett University (IN), KIIT University (IN), Birla Institute of Technology and Science, Pilani (IN)
Openalex Percentile: Top 6%
Digital Transformation in Industry
3.84
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A Survey on Digital Twins: Enabling Technologies, Use Cases, Application, Open Issues, and More — Vinay Chamola, Amit Pandey, et al. · IEEE Journal of Selected Areas in Sensors (2024) | TGRS Research Map | TGRS