ML-based estimation of the number of devices in industrial networks using unlicensed bands: (Best workshop paper)

Oluwatobi Baiyekusi; Haeyoung Lee; Klaus Moessner

doi:10.1109/ICTC55196.2022.9952455

ML-based estimation of the number of devices in industrial networks using unlicensed bands: (Best workshop paper)

Oluwatobi Baiyekusi, Haeyoung Lee, Klaus Moessner

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Abstract

Advanced automation is being adopted by manu-facturing facilities and wireless technologies are set to be a key component in driving the factories of the future. It is expected that private cellular networks and WLAN technologies would be deployed for smart factory operations. Since both wireless technologies can operate on the same channel in unlicensed bands, then efficient resource sharing becomes important. When multiple devices compete for the resource, the estimation of number of devices contending for the channel resource can help the design of an efficient resource sharing scheme. This paper aims to address the challenge of estimating the number of factory devices contending to transmit over the unlicensed channel. We adopt three machine learning (ML) techniques and develop a novel device number estimation system by collating and analysing the idle-time interval between transmission across the channel. By using NS-3 simulation, the performance of the proposed estimation approach is evaluated. The results presented reveal the significance of the chosen features and performance of each ML algorithm used.

Original language	English
Title of host publication	2022 13th International Conference on Information and Communication Technology Convergence (ICTC)
Subtitle of host publication	Accelerating Digital Transformation with ICT Innovation
Place of Publication	Jeju Island, Korea, Republic of
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	519-524
Number of pages	6
ISBN (Electronic)	978-1-6654-9939-2
ISBN (Print)	978-1-6654-9940-8
DOIs	https://doi.org/10.1109/ICTC55196.2022.9952455
Publication status	Published - 21 Oct 2022
Event	2022 13th International Conference on Information and Communication Technology Convergence (ICTC): “Accelerating Digital Transformation with ICT Innovation” - Jeju Island, Korea, Democratic People's Republic of Duration: 19 Oct 2022 → 21 Oct 2022 Conference number: 13 https://ieeexplore.ieee.org/xpl/conhome/9952188/proceeding

Publication series

Name	International Conference on ICT Convergence
Volume	2022-October
ISSN (Print)	2162-1233
ISSN (Electronic)	2162-1241

Conference

Conference	2022 13th International Conference on Information and Communication Technology Convergence (ICTC)
Abbreviated title	ICTC 2022
Country/Territory	Korea, Democratic People's Republic of
City	Jeju Island
Period	19/10/22 → 21/10/22
Internet address	https://ieeexplore.ieee.org/xpl/conhome/9952188/proceeding

Keywords

Radio frequency
Performance evaluation
Wireless communication
Maximum likelihood estimation
Computational modeling
Channel estimation
Prediction algorithms
unlicensed band
Machine learning
number of device estimation
smart factory

Access to Document

10.1109/ICTC55196.2022.9952455Licence: Unspecified

Accepted_Manuscript
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This is the accepted manuscript version of a conference paper which has been published in final form at https://doi.org/10.1109/ICTC55196.2022.9952455
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http://Final_VersionLicence: Unspecified

Cite this

Baiyekusi, O., Lee, H., & Moessner, K. (2022). ML-based estimation of the number of devices in industrial networks using unlicensed bands: (Best workshop paper). In 2022 13th International Conference on Information and Communication Technology Convergence (ICTC): Accelerating Digital Transformation with ICT Innovation (pp. 519-524). Article 9952455 (International Conference on ICT Convergence; Vol. 2022-October). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICTC55196.2022.9952455

Baiyekusi, Oluwatobi ; Lee, Haeyoung ; Moessner, Klaus. / ML-based estimation of the number of devices in industrial networks using unlicensed bands : (Best workshop paper). 2022 13th International Conference on Information and Communication Technology Convergence (ICTC): Accelerating Digital Transformation with ICT Innovation. Jeju Island, Korea, Republic of : Institute of Electrical and Electronics Engineers (IEEE), 2022. pp. 519-524 (International Conference on ICT Convergence).

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title = "ML-based estimation of the number of devices in industrial networks using unlicensed bands: (Best workshop paper)",

abstract = "Advanced automation is being adopted by manu-facturing facilities and wireless technologies are set to be a key component in driving the factories of the future. It is expected that private cellular networks and WLAN technologies would be deployed for smart factory operations. Since both wireless technologies can operate on the same channel in unlicensed bands, then efficient resource sharing becomes important. When multiple devices compete for the resource, the estimation of number of devices contending for the channel resource can help the design of an efficient resource sharing scheme. This paper aims to address the challenge of estimating the number of factory devices contending to transmit over the unlicensed channel. We adopt three machine learning (ML) techniques and develop a novel device number estimation system by collating and analysing the idle-time interval between transmission across the channel. By using NS-3 simulation, the performance of the proposed estimation approach is evaluated. The results presented reveal the significance of the chosen features and performance of each ML algorithm used.",

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Baiyekusi, O, Lee, H & Moessner, K 2022, ML-based estimation of the number of devices in industrial networks using unlicensed bands: (Best workshop paper). in 2022 13th International Conference on Information and Communication Technology Convergence (ICTC): Accelerating Digital Transformation with ICT Innovation., 9952455, International Conference on ICT Convergence, vol. 2022-October, Institute of Electrical and Electronics Engineers (IEEE), Jeju Island, Korea, Republic of, pp. 519-524, 2022 13th International Conference on Information and Communication Technology Convergence (ICTC), Jeju Island, Korea, Democratic People's Republic of, 19/10/22. https://doi.org/10.1109/ICTC55196.2022.9952455

ML-based estimation of the number of devices in industrial networks using unlicensed bands: (Best workshop paper). / Baiyekusi, Oluwatobi; Lee, Haeyoung; Moessner, Klaus.
2022 13th International Conference on Information and Communication Technology Convergence (ICTC): Accelerating Digital Transformation with ICT Innovation. Jeju Island, Korea, Republic of: Institute of Electrical and Electronics Engineers (IEEE), 2022. p. 519-524 9952455 (International Conference on ICT Convergence; Vol. 2022-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Baiyekusi O, Lee H, Moessner K. ML-based estimation of the number of devices in industrial networks using unlicensed bands: (Best workshop paper). In 2022 13th International Conference on Information and Communication Technology Convergence (ICTC): Accelerating Digital Transformation with ICT Innovation. Jeju Island, Korea, Republic of: Institute of Electrical and Electronics Engineers (IEEE). 2022. p. 519-524. 9952455. (International Conference on ICT Convergence). doi: 10.1109/ICTC55196.2022.9952455

ML-based estimation of the number of devices in industrial networks using unlicensed bands: (Best workshop paper)

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Keywords

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