This paper presents two wireless measurement campaigns in industrial testbeds: industrial Vehicle-to-vehicle (iV2V) and industrial Vehicle-to-infrastructure plus Sensor (iV2I+), together with detailed information about the two captured datasets. iV2V covers sidelink communication scenarios between Automated Guided Vehicles (AGVs), while iV2I+ is conducted at an industrial setting where an autonomous cleaning robot is connected to a private cellular network. The combination of different communication technologies within a common measurement methodology provides insights that can be exploited by Machine Learning (ML) for tasks such as fingerprinting, line-of-sight detection, prediction of quality of service or link selection. Moreover, the datasets are publicly available, labelled and prefiltered for fast on-boarding and applicability.
@article{arxiv.2301.03364,
title = {Toward an AI-enabled Connected Industry: AGV Communication and Sensor Measurement Datasets},
author = {Rodrigo Hernangómez and Alexandros Palaios and Cara Watermann and Daniel Schäufele and Philipp Geuer and Rafail Ismayilov and Mohammad Parvini and Anton Krause and Martin Kasparick and Thomas Neugebauer and Oscar D. Ramos-Cantor and Hugues Tchouankem and Jose Leon Calvo and Bo Chen and Gerhard Fettweis and Sławomir Stańczak},
journal= {arXiv preprint arXiv:2301.03364},
year = {2024}
}
Comments
7 pages, 3 figures. Published at IEEE Communications Magazine. IEEE Copyright protected. Datasets available at https://ieee-dataport.org/open-access/ai4mobile-industrial-wireless-datasets-iv2v-and-iv2i