A Study of Machine Learning Using Wireless and Physical Environment Data at a Factory

Autor:	Yoshio Koyanagi, Rei Hasegawa, Yasufumi Ichikawa, Kenshi Horihata, Kazuki Kanai
Rok vydání:	2020
Předmět:	Flexibility (engineering) business.industry Computer science Machine learning computer.software_genre Power (physics) Wide area Signal strength Terminal (electronics) Wireless Factory (object-oriented programming) Artificial intelligence business Wireless sensor network computer
Zdroj:	2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting.
DOI:	10.1109/ieeeconf35879.2020.9329751
Popis:	Wireless communication is expected to improve the flexibility of equipment layout or of the factory IoT (Internet of Things). In this paper, we show the result of constructing IoT sensor network using LPWA (Low Power Wide Area) in a factory, performing machine learning, and analyzing the correlation between wireless and physical environment. As a result, it has been shown that RSSI (received signal strength indicator) fluctuation of a terminal could be estimated from sensor data that recorded physical environment around the terminal.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::04ec86fd23ee686039f566ac9292b00f https://doi.org/10.1109/ieeeconf35879.2020.9329751 Zobrazit plný text záznamu