Optimizing handover in time-sensitive Wi-Fi networks through machine learning
- Author
- Pablo Avila-Campos (UGent) , Jetmir Haxhibeqiri (UGent) , Xianjun Jiao (UGent) , Ingrid Moerman (UGent) and Jeroen Hoebeke (UGent)
- Organization
- Project
- Abstract
- Time-Sensitive Networking (TSN) plays a crucial role in ensuring determinism and low latency, vital for the demands of industrial applications. Integrating the benefits of wire-less networks, including mobility, presents a significant challenge in such environments. In this study, we propose a novel solution to address this challenge by introducing handover capabilities into wireless Time-Sensitive Networking (W-TSN). Through real-world development and testing, we present an optimized approach for minimizing handover delay and leveraging machine learning to select the optimal handover time and space moment in a two-dimensional environment, with low effect on time-sensitive traffic. Our findings demonstrate that our mechanism reduces handover delay below 10 milliseconds and optimizes the handover moment selection, leading to improvements in critical network parameters such as bandwidth and jitter.
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01JADB7ZDS4SNWRYS5HAKS4XR8
- MLA
- Avila-Campos, Pablo, et al. “Optimizing Handover in Time-Sensitive Wi-Fi Networks through Machine Learning.” 2024 IEEE 29TH INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION, ETFA 2024, IEEE, 2024, pp. 1–8, doi:10.1109/etfa61755.2024.10710709.
- APA
- Avila-Campos, P., Haxhibeqiri, J., Jiao, X., Moerman, I., & Hoebeke, J. (2024). Optimizing handover in time-sensitive Wi-Fi networks through machine learning. 2024 IEEE 29TH INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION, ETFA 2024, 1–8. https://doi.org/10.1109/etfa61755.2024.10710709
- Chicago author-date
- Avila-Campos, Pablo, Jetmir Haxhibeqiri, Xianjun Jiao, Ingrid Moerman, and Jeroen Hoebeke. 2024. “Optimizing Handover in Time-Sensitive Wi-Fi Networks through Machine Learning.” In 2024 IEEE 29TH INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION, ETFA 2024, 1–8. IEEE. https://doi.org/10.1109/etfa61755.2024.10710709.
- Chicago author-date (all authors)
- Avila-Campos, Pablo, Jetmir Haxhibeqiri, Xianjun Jiao, Ingrid Moerman, and Jeroen Hoebeke. 2024. “Optimizing Handover in Time-Sensitive Wi-Fi Networks through Machine Learning.” In 2024 IEEE 29TH INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION, ETFA 2024, 1–8. IEEE. doi:10.1109/etfa61755.2024.10710709.
- Vancouver
- 1.Avila-Campos P, Haxhibeqiri J, Jiao X, Moerman I, Hoebeke J. Optimizing handover in time-sensitive Wi-Fi networks through machine learning. In: 2024 IEEE 29TH INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION, ETFA 2024. IEEE; 2024. p. 1–8.
- IEEE
- [1]P. Avila-Campos, J. Haxhibeqiri, X. Jiao, I. Moerman, and J. Hoebeke, “Optimizing handover in time-sensitive Wi-Fi networks through machine learning,” in 2024 IEEE 29TH INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION, ETFA 2024, Padova, Italy, 2024, pp. 1–8.
@inproceedings{01JADB7ZDS4SNWRYS5HAKS4XR8,
abstract = {{Time-Sensitive Networking (TSN) plays a crucial role in ensuring determinism and low latency, vital for the demands of industrial applications. Integrating the benefits of wire-less networks, including mobility, presents a significant challenge in such environments. In this study, we propose a novel solution to address this challenge by introducing handover capabilities into wireless Time-Sensitive Networking (W-TSN). Through real-world development and testing, we present an optimized approach for minimizing handover delay and leveraging machine learning to select the optimal handover time and space moment in a two-dimensional environment, with low effect on time-sensitive traffic. Our findings demonstrate that our mechanism reduces handover delay below 10 milliseconds and optimizes the handover moment selection, leading to improvements in critical network parameters such as bandwidth and jitter.}},
author = {{Avila-Campos, Pablo and Haxhibeqiri, Jetmir and Jiao, Xianjun and Moerman, Ingrid and Hoebeke, Jeroen}},
booktitle = {{2024 IEEE 29TH INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION, ETFA 2024}},
isbn = {{9798350361247}},
issn = {{1946-0740}},
language = {{eng}},
location = {{Padova, Italy}},
pages = {{1--8}},
publisher = {{IEEE}},
title = {{Optimizing handover in time-sensitive Wi-Fi networks through machine learning}},
url = {{http://doi.org/10.1109/etfa61755.2024.10710709}},
year = {{2024}},
}
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