Toward an AI-enabled Connected Industry: AGV Communication and Sensor Measurement Datasets
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866916206001258496 |
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| author | Hernangómez, Rodrigo Palaios, Alexandros Watermann, Cara Schäufele, Daniel Geuer, Philipp Ismayilov, Rafail Parvini, Mohammad Krause, Anton Kasparick, Martin Neugebauer, Thomas Ramos-Cantor, Oscar D. Tchouankem, Hugues Calvo, Jose Leon Chen, Bo Fettweis, Gerhard Stańczak, Sławomir |
| author_facet | Hernangómez, Rodrigo Palaios, Alexandros Watermann, Cara Schäufele, Daniel Geuer, Philipp Ismayilov, Rafail Parvini, Mohammad Krause, Anton Kasparick, Martin Neugebauer, Thomas Ramos-Cantor, Oscar D. Tchouankem, Hugues Calvo, Jose Leon Chen, Bo Fettweis, Gerhard Stańczak, Sławomir |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2301_03364 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Toward an AI-enabled Connected Industry: AGV Communication and Sensor Measurement Datasets Hernangómez, Rodrigo Palaios, Alexandros Watermann, Cara Schäufele, Daniel Geuer, Philipp Ismayilov, Rafail Parvini, Mohammad Krause, Anton Kasparick, Martin Neugebauer, Thomas Ramos-Cantor, Oscar D. Tchouankem, Hugues Calvo, Jose Leon Chen, Bo Fettweis, Gerhard Stańczak, Sławomir Networking and Internet Architecture Artificial Intelligence Machine Learning 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. |
| title | Toward an AI-enabled Connected Industry: AGV Communication and Sensor Measurement Datasets |
| topic | Networking and Internet Architecture Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2301.03364 |