Yagmur Yigit
TwinPort: 5G drone-assisted data collection with digital twin for smart seaports
Yigit, Yagmur; Nguyen, Long D.; Ozdem, Mehmet; Kinaci, Omer Kemal; Hoang, Trang; Canberk, Berk; Duong, Trung Q.
Authors
Long D. Nguyen
Mehmet Ozdem
Omer Kemal Kinaci
Trang Hoang
Prof Berk Canberk B.Canberk@napier.ac.uk
Professor
Trung Q. Duong
Abstract
Numerous ports worldwide are adopting automation to boost productivity and modernize their operations. At this point, smart ports become a more important paradigm for handling increasing cargo volumes and increasing operational efficiency. In fact, as ports become more congested and cargo volumes increase, the need for accurate navigation through seaports is more pronounced to avoid collisions and the resulting consequences. To this end, digital twin (DT) technology in the fifth-generation (5G) networks and drone-assisted data collection can be combined to provide precise ship maneuvering. In this paper, we propose a DT model using drone-assisted data collection architecture, called TwinPort, to offer a comprehensive port management system for smart seaports. We also present a recommendation engine to ensure accurate ship navigation within a smart port during the docking process. The experimental results reveal that our solution improves the trajectory performance by approaching the desired shortest path. Moreover, our solution supports significantly reducing financial costs and protecting the environment by reducing fuel consumption.
Citation
Yigit, Y., Nguyen, L. D., Ozdem, M., Kinaci, O. K., Hoang, T., Canberk, B., & Duong, T. Q. (2023). TwinPort: 5G drone-assisted data collection with digital twin for smart seaports. Scientific Reports, 13(1), Article 12310. https://doi.org/10.1038/s41598-023-39366-1
Journal Article Type | Article |
---|---|
Acceptance Date | Jul 24, 2023 |
Online Publication Date | Jul 29, 2023 |
Publication Date | 2023 |
Deposit Date | Aug 7, 2023 |
Publicly Available Date | Aug 7, 2023 |
Journal | Scientific Reports |
Electronic ISSN | 2045-2322 |
Publisher | Nature Publishing Group |
Peer Reviewed | Peer Reviewed |
Volume | 13 |
Issue | 1 |
Article Number | 12310 |
DOI | https://doi.org/10.1038/s41598-023-39366-1 |
Files
TwinPort: 5G drone-assisted data collection with digital twin for smart seaports
(3 Mb)
PDF
Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
Copyright Statement
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
You might also like
Throughput Maximization in RIS-Assisted NOMA-THz Communication Network
(2024)
Journal Article
Distributed TDMA Scheduling for Autonomous Aerial Swarms: A Self-Organizing Approach
(2024)
Journal Article
Downloadable Citations
About Edinburgh Napier Research Repository
Administrator e-mail: repository@napier.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search