Publication detail

Vehicle-to-Everything (V2X) Datasets for Machine Learning-Based Predictive Quality of Service

SKOCAJ, M. DI CICCO, N. ZUGNO, T. BOBAN, M. BLUMENSTEIN, J. PROKEŠ, A. MIKULÁŠEK, T. VYCHODIL, J. MIKHAYLOV, K. TORNATORE, M. DEGLI ESPOSTI, V.

Original Title

Vehicle-to-Everything (V2X) Datasets for Machine Learning-Based Predictive Quality of Service

Type

journal article in Web of Science

Language

English

Original Abstract

We present two datasets for Machine Learning (ML)-based Predictive Quality of Service (PQoS) comprising Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) radio channel measurements. As V2V and V2I are both indispensable elements for providing connectivity in Intelligent Transport Systems (ITS), we argue that a combination of the two datasets enables the study of Vehicle-to-Everything (V2X) connectivity in its entire complexity. We describe in detail our methodologies for performing V2V and V2I measurement campaigns, and we provide illustrative examples on the use of the collected data. Specifically, we showcase the application of approximate Bayesian Methods using the two presented datasets to portray illustrative use cases of uncertainty-aware Quality of Service and Channel State Information forecasting. Finally, we discuss novel exploratory research direction building upon our work. The V2I and V2V datasets are available on IEEE Dataport, and the code utilized in our numerical experiments is publicly accessible via CodeOcean.

Keywords

Vehicle-to-infrastructure; Power control; Vehicular ad hoc networks; Quality of service; Machine learning; Prediction algorithms; Particle measurements

Authors

SKOCAJ, M.; DI CICCO, N.; ZUGNO, T.; BOBAN, M.; BLUMENSTEIN, J.; PROKEŠ, A.; MIKULÁŠEK, T.; VYCHODIL, J.; MIKHAYLOV, K.; TORNATORE, M.; DEGLI ESPOSTI, V.

Released

1. 9. 2023

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

Location

PISCATAWAY

ISBN

1558-1896

Periodical

IEEE COMMUNICATIONS MAGAZINE

Year of study

61

Number

9

State

United States of America

Pages from

106

Pages to

112

Pages count

7

URL

https://ieeexplore.ieee.org/abstract/document/10268872

BibTex

@article{BUT185064,
  author="Marco {Skocaj} and Nicola {Di Cicco} and Tommaso {Zugno} and Mate {Boban} and Jiří {Blumenstein} and Aleš {Prokeš} and Tomáš {Mikulášek} and Josef {Vychodil} and Konstantin {Mikhaylov} and Massimo {Tornatore} and Vittorio {Degli Esposti}",
  title="Vehicle-to-Everything (V2X) Datasets for Machine Learning-Based Predictive Quality of Service",
  journal="IEEE COMMUNICATIONS MAGAZINE",
  year="2023",
  volume="61",
  number="9",
  pages="106--112",
  doi="10.1109/MCOM.004.2200723",
  issn="1558-1896",
  url="https://ieeexplore.ieee.org/abstract/document/10268872"
}