Long-term wireless sensor network deployments in industry and office scenarios

Autor(en): Hanel, T.
Bruggemann, L.
Loske, F.
Aschenbruck, N. 
Stichwörter: Burst Evaluation; Commercial off-the-shelf hardwares; Comparative evaluations; Digital transformation; Industrial environments; Industrial Internet of Things; Office environments; Performance Evaluation; Time dependent; Wireless communications; Wireless Sensor Network; Wireless sensor network deployment, Wireless sensor networks
Erscheinungsdatum: 2021
Herausgeber: Institute of Electrical and Electronics Engineers Inc.
Journal: Proceedings - 2021 IEEE 22nd International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2021
Startseite: 109
Seitenende: 118
Zusammenfassung: 
Wireless networks are indispensable for current and future elements of industry 4.0 and the digital transformation of companies. Industrial environments with arrays of machines mostly made of metal differ significantly from home and office environments, with consequential effects on wireless communication. We deployed wireless sensor networks built with commercial-off-the-shelf hardware in industrial and office scenarios and evaluated various aspects of wireless communications with different technologies and parameters. In this paper, we present our measurement architecture and the core findings. The results show highly spatial- and time-dependent differences between the two scenarios and also provide a comparative evaluation of the applied technologies. In addition, we also share parameters for established simulation models making future simulative evaluations particularly easy. © 2021 IEEE.
Beschreibung: 
Conference of 22nd IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2021 ; Conference Date: 7 June 2021 Through 11 June 2021; Conference Code:170777
ISBN: 9781665422635
DOI: 10.1109/WoWMoM51794.2021.00024
Externe URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112431884&doi=10.1109%2fWoWMoM51794.2021.00024&partnerID=40&md5=de918d01df421ee74906279a87d39617

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