Elif Ak
AI-enabled data management for digital twin networks
Ak, Elif; Yurdakul, Gökhan; Al-Dubai, Ahmed; Canberk, Berk
Authors
Gökhan Yurdakul
Prof Ahmed Al-Dubai A.Al-Dubai@napier.ac.uk
Professor
Prof Berk Canberk B.Canberk@napier.ac.uk
Professor
Contributors
Hamed Ahmadi
Editor
Trung Q. Duong
Editor
Avishek Nag
Editor
Vishal Sharma
Editor
Prof Berk Canberk B.Canberk@napier.ac.uk
Editor
Octavia A. Dobre
Editor
Abstract
As we have discussed in previous chapters, digital twins establish contextual relationships with the surrounding entities, providing a holistic view of interconnected systems and environments. With the growing complexity and abundance of data generated by digital twins, effective data management strategies have become paramount. This chapter delves into AI-enabled data management for digital twins, exploring how artificial intelligence techniques empower the collecting, storing, integrating, analyzing, and utilizing of diverse and voluminous data within the digital twin ecosystem. We will see how 6G and IoT networks can unlock valuable insights and optimize operational processes in many aspects by leveraging AI.
Citation
Ak, E., Yurdakul, G., Al-Dubai, A., & Canberk, B. (2024). AI-enabled data management for digital twin networks. In H. Ahmadi, T. Q. Duong, A. Nag, V. Sharma, B. Canberk, & O. A. Dobre (Eds.), . Institution of Engineering and Technology (IET). https://doi.org/10.1049/pbte109e_ch3
Online Publication Date | Jun 5, 2024 |
---|---|
Publication Date | 2024 |
Deposit Date | Oct 11, 2024 |
Publisher | Institution of Engineering and Technology (IET) |
Peer Reviewed | Peer Reviewed |
Pages | 49-81 |
Series Title | Digital Twins for 6G: Fundamental theory, technology and applications |
ISBN | 9781839537455 |
DOI | https://doi.org/10.1049/pbte109e_ch3 |
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