AI4Mobile Industrial Wireless Datasets: iV2V and iV2I+

Citation Author(s):
Rodrigo
Hernangomez
Fraunhofer Heinrich Hertz Institute
Alexandros
Palaios
Ericsson Research
Cara
Watermann
Ericsson Research
Daniel
Schäufele
Fraunhofer Heinrich Hertz Institute
Philipp
Geuer
Ericsson Research
Rafail
Ismayilov
Fraunhofer Heinrich Hertz Institute
Mohammad
Parvini
Vodafone Chair, Technische Universität Dresden
Anton
Krause
Vodafone Chair, Technische Universität Dresden
Martin
Kasparick
Fraunhofer Heinrich Hertz Institute
Thomas
Neugebauer
Götting KG
Oscar D.
Ramos-Cantor
Robert Bosch GmbH
Hugues
Tchouankem
Robert Bosch GmbH
Jose
Leon Calvo
Ericsson Research
Bo
Chen
Enway GmbH
Slawomir
Stanczak
Fraunhofer Heinrich Hertz Institute
Gerhard
Fettweis
Vodafone Chair, Technische Universität Dresden
Submitted by:
Rodrigo Hernangomez
Last updated:
Tue, 01/10/2023 - 05:42
DOI:
10.21227/04ta-v128
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Abstract 

This dataset provides wireless measurements from two industrial testbeds: iV2V (industrial Vehicle-to-Vehicle) and iV2I+ (industrial Vehicular-to-Infrastructure plus sensor).

iV2V covers 10h of sidelink communication scenarios between 3 Automated Guided Vehicles (AGVs), while iV2I+ was conducted for around 16h at an industrial site where an autonomous cleaning robot is connected to a private cellular network.

The data includes information on physical layer parameters (such as signal strength and signal quality), wireless Quality of Service (QoS) like delay and throughput, and positioning information.

The datasets are labelled and pre-filtered for a fast on-boarding and applicability. The common measurement methodology to both datasets pursues an application to Machine Learning (ML) for tasks such as fingerprinting, line-of-sight detection, QoS prediction or link selection, among others.

Instructions: 

The parquet files for the respective datasets can be directly downloaded and read with Python using Pandas and parquet support via PyArrow or Fastparquet.

For detailed instructions, please refer to the readme.pdf or the available documentation on GitHub.

Funding Agency: 
German Federal Ministry of Education and Research
Grant Number: 
16KIS1170K

Dataset Files

Documentation

AttachmentSize
File readme.pdf1.06 MB