Toward an AI-enabled Connected Industry: AGV Communication and Sensor Measurement Datasets

• URL: http://arxiv.org/abs/2301.03364v5
• Date: Mon, 15 Apr 2024 11:42:03 GMT
• Title: Toward an AI-enabled Connected Industry: AGV Communication and Sensor Measurement Datasets
• Authors: Rodrigo Hernangómez, Alexandros Palaios, Cara Watermann, Daniel Schäufele, Philipp Geuer, Rafail Ismayilov, Mohammad Parvini, Anton Krause, Martin Kasparick, Thomas Neugebauer, Oscar D. Ramos-Cantor, Hugues Tchouankem, Jose Leon Calvo, Bo Chen, Gerhard Fettweis, Sławomir Stańczak,
• Abstract summary: This paper presents two wireless measurement campaigns in industrial testbeds: industrial Vehicle-to-vehicle (iV2V) and industrial Vehicle-to-infrastructure plus Sensor (iV2I+) 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
• Score: 33.89321466798318
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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