K. Horihata, K. Kanai, Rei Hasegawa, Y. Koyanagi, Y. Ichikawa
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A Study of Machine Learning Using Wireless and Physical Environment Data at a Factory
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.
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