Empirical IIoT Data Traffic Analysis and Comparison to 3GPP 5G Models

Rasmus Suhr Mogensen; Ignacio Rodriguez; Gilberto Berardinelli; Guillermo Pocovi; Troels Kolding

doi:10.1109/VTC2021-Fall52928.2021.9625319

Empirical IIoT Data Traffic Analysis and Comparison to 3GPP 5G Models

Rasmus Suhr Mogensen, Ignacio Rodriguez, Gilberto Berardinelli, Guillermo Pocovi, Troels Kolding

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

6 Citationer (Scopus)

195 Downloads (Pure)

Abstract

5G New Radio (NR) technology is expected to enable the wireless transition of manufacturing processes in modern factories. In order to proper dimension the 5G system, accurate models of the data and control traffic in industrial use cases are needed. In this paper, we analyze the industrial traffic empirically measured in different Danish factories and compare its characteristics to the modeling assumptions used in the 3GPP for 5G NR performance analyses. In particular, three relevant use cases are studied: a unit test cell for actuator calibration, a visual inspection cell, and the control of an autonomous mobile robot (AMR). Our results indicate that some of the relevant 3GPP assumptions for industrial traffic such as exponential packet interarrival time (PIAT) for aperiodic traffic and fixed packet sizes are only partially corroborated by experimental evidence. Moreover, industrial traffic can exhibit a large burstiness that may lead to conservative admission control and radio resource allocation.

Originalsprog	Engelsk
Titel	2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)
Antal sider	7
Forlag	IEEE
Publikationsdato	sep. 2021
Artikelnummer	9625319
ISBN (Trykt)	978-1-6654-1369-5
ISBN (Elektronisk)	978-1-6654-1368-8
DOI	https://doi.org/10.1109/VTC2021-Fall52928.2021.9625319
Status	Udgivet - sep. 2021
Begivenhed	IEEE 94th Vehicular Technology Conference: VTC2021-Fall - Online Varighed: 27 sep. 2021 → 30 sep. 2021 https://events.vtsociety.org/vtc2021-fall/

Konference

Konference	IEEE 94th Vehicular Technology Conference: VTC2021-Fall
Lokation	Online
Periode	27/09/2021 → 30/09/2021
Internetadresse	https://events.vtsociety.org/vtc2021-fall/

Navn	IEEE Vehicular Technology Conference. Proceedings
ISSN	1090-3038

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10.1109/VTC2021-Fall52928.2021.9625319

VTCFall2021_traffic_models_5GForlagets udgivne version, 2,41 MB

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title = "Empirical IIoT Data Traffic Analysis and Comparison to 3GPP 5G Models",

abstract = "5G New Radio (NR) technology is expected to enable the wireless transition of manufacturing processes in modern factories. In order to proper dimension the 5G system, accurate models of the data and control traffic in industrial use cases are needed. In this paper, we analyze the industrial traffic empirically measured in different Danish factories and compare its characteristics to the modeling assumptions used in the 3GPP for 5G NR performance analyses. In particular, three relevant use cases are studied: a unit test cell for actuator calibration, a visual inspection cell, and the control of an autonomous mobile robot (AMR). Our results indicate that some of the relevant 3GPP assumptions for industrial traffic such as exponential packet interarrival time (PIAT) for aperiodic traffic and fixed packet sizes are only partially corroborated by experimental evidence. Moreover, industrial traffic can exhibit a large burstiness that may lead to conservative admission control and radio resource allocation.",

keywords = "5G, 3GPP, Industrial IoT, Industry 4.0, Measurements, Traffic models, Industrial Applications",

author = "Mogensen, {Rasmus Suhr} and Ignacio Rodriguez and Gilberto Berardinelli and Guillermo Pocovi and Troels Kolding",

year = "2021",

month = sep,

doi = "10.1109/VTC2021-Fall52928.2021.9625319",

language = "English",

isbn = "978-1-6654-1369-5",

series = "IEEE Vehicular Technology Conference. Proceedings",

publisher = "IEEE",

booktitle = "2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)",

address = "United States",

note = "IEEE 94th Vehicular Technology Conference: VTC2021-Fall ; Conference date: 27-09-2021 Through 30-09-2021",

url = "https://events.vtsociety.org/vtc2021-fall/",

}

Mogensen, RS, Rodriguez, I, Berardinelli, G, Pocovi, G & Kolding, T 2021, Empirical IIoT Data Traffic Analysis and Comparison to 3GPP 5G Models. i 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)., 9625319, IEEE, IEEE Vehicular Technology Conference. Proceedings, IEEE 94th Vehicular Technology Conference: VTC2021-Fall, 27/09/2021. https://doi.org/10.1109/VTC2021-Fall52928.2021.9625319

Empirical IIoT Data Traffic Analysis and Comparison to 3GPP 5G Models. / Mogensen, Rasmus Suhr; Rodriguez, Ignacio; Berardinelli, Gilberto et al.
2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall). IEEE, 2021. 9625319 (IEEE Vehicular Technology Conference. Proceedings).

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

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T1 - Empirical IIoT Data Traffic Analysis and Comparison to 3GPP 5G Models

AU - Mogensen, Rasmus Suhr

AU - Rodriguez, Ignacio

AU - Berardinelli, Gilberto

AU - Pocovi, Guillermo

AU - Kolding, Troels

PY - 2021/9

Y1 - 2021/9

N2 - 5G New Radio (NR) technology is expected to enable the wireless transition of manufacturing processes in modern factories. In order to proper dimension the 5G system, accurate models of the data and control traffic in industrial use cases are needed. In this paper, we analyze the industrial traffic empirically measured in different Danish factories and compare its characteristics to the modeling assumptions used in the 3GPP for 5G NR performance analyses. In particular, three relevant use cases are studied: a unit test cell for actuator calibration, a visual inspection cell, and the control of an autonomous mobile robot (AMR). Our results indicate that some of the relevant 3GPP assumptions for industrial traffic such as exponential packet interarrival time (PIAT) for aperiodic traffic and fixed packet sizes are only partially corroborated by experimental evidence. Moreover, industrial traffic can exhibit a large burstiness that may lead to conservative admission control and radio resource allocation.

AB - 5G New Radio (NR) technology is expected to enable the wireless transition of manufacturing processes in modern factories. In order to proper dimension the 5G system, accurate models of the data and control traffic in industrial use cases are needed. In this paper, we analyze the industrial traffic empirically measured in different Danish factories and compare its characteristics to the modeling assumptions used in the 3GPP for 5G NR performance analyses. In particular, three relevant use cases are studied: a unit test cell for actuator calibration, a visual inspection cell, and the control of an autonomous mobile robot (AMR). Our results indicate that some of the relevant 3GPP assumptions for industrial traffic such as exponential packet interarrival time (PIAT) for aperiodic traffic and fixed packet sizes are only partially corroborated by experimental evidence. Moreover, industrial traffic can exhibit a large burstiness that may lead to conservative admission control and radio resource allocation.

KW - 5G

KW - 3GPP

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KW - Industry 4.0

KW - Measurements

KW - Traffic models

KW - Industrial Applications

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M3 - Article in proceeding

SN - 978-1-6654-1369-5

T3 - IEEE Vehicular Technology Conference. Proceedings

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T2 - IEEE 94th Vehicular Technology Conference: VTC2021-Fall

Y2 - 27 September 2021 through 30 September 2021

ER -

Empirical IIoT Data Traffic Analysis and Comparison to 3GPP 5G Models

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