@inproceedings{0f75d00c3dcc4a8f8a8a40cfa37441c5,
title = "Indoor Positioning Using Wi-Fi and Machine Learning for Industry 5.0",
abstract = "Humans and robots working together in an environment to enhance human performance is the aim of Industry 5.0. Although significant progress in outdoor positioning has been seen, indoor positioning remains a challenge. In this paper, we introduce a new research concept by exploiting the potential of indoor positioning for Industry 5.0. We use Wi-Fi Received Signal Strength Indicator (RSSI) with trilateration using cheap and easily available ESP32 Arduino boards for positioning as well as sending effective route signals to a human and a robot working in a simulated-indoor factory environment in real-time. We utilized machine learning models to detect safe closeness between two co-workers (a human subject and a robot). Experimental data and analysis show an average deviation of less than 1m from the actual distance while the targets are mobile or stationary.",
keywords = "Indoor Positioning System, Industry 5.0, Internet of Things, Machine Learning, Wi-Fi",
author = "Inoj Neupane and Belal Alsinglawi and Khaled Rabie",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 21st IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2023 ; Conference date: 13-03-2023 Through 17-03-2023",
year = "2023",
doi = "10.1109/PerComWorkshops56833.2023.10150346",
language = "English",
series = "2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "359--362",
booktitle = "Proceedings - 2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2023",
address = "United States",
}
