Indoor positioning using Wi-Fi and machine learning for Industry 5.0

Inoj Neupane; Belal Alsinglawi; Khaled Rabie

doi:10.1109/PerComWorkshops56833.2023.10150346

Indoor positioning using Wi-Fi and machine learning for Industry 5.0

Inoj Neupane, Belal Alsinglawi, Khaled Rabie

Manchester Metropolitan University

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

4 Citations (Scopus)

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.

Original language	English
Title of host publication	2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops 2023), Atlanta, Georgia, USA, 13-17 March 2023
Place of Publication	U.S.
Publisher	IEEE
Pages	359-362
Number of pages	4
ISBN (Electronic)	9781665453813
DOIs	https://doi.org/10.1109/PerComWorkshops56833.2023.10150346
Publication status	Published - 2023
Event	IEEE International Conference on Pervasive Computing and Communications - Atlanta, United States Duration: 13 Mar 2023 → 17 Mar 2023

Conference

Conference	IEEE International Conference on Pervasive Computing and Communications
Country/Territory	United States
City	Atlanta
Period	13/03/23 → 17/03/23

Keywords

Indoor Positioning System
Industry 5.0
Internet of Things
Machine Learning
Wi-Fi

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/PerComWorkshops56833.2023.10150346

Cite this

Neupane, I., Alsinglawi, B., & Rabie, K. (2023). Indoor positioning using Wi-Fi and machine learning for Industry 5.0. In 2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops 2023), Atlanta, Georgia, USA, 13-17 March 2023 (pp. 359-362). IEEE. https://doi.org/10.1109/PerComWorkshops56833.2023.10150346

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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.",

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author = "Inoj Neupane and Belal Alsinglawi and Khaled Rabie",

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booktitle = "2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops 2023), Atlanta, Georgia, USA, 13-17 March 2023",

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note = "IEEE International Conference on Pervasive Computing and Communications ; Conference date: 13-03-2023 Through 17-03-2023",

}

Neupane, I, Alsinglawi, B & Rabie, K 2023, Indoor positioning using Wi-Fi and machine learning for Industry 5.0. in 2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops 2023), Atlanta, Georgia, USA, 13-17 March 2023. IEEE, U.S., pp. 359-362, IEEE International Conference on Pervasive Computing and Communications, Atlanta, Georgia, United States, 13/03/23. https://doi.org/10.1109/PerComWorkshops56833.2023.10150346

Indoor positioning using Wi-Fi and machine learning for Industry 5.0. / Neupane, Inoj; Alsinglawi, Belal; Rabie, Khaled.
2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops 2023), Atlanta, Georgia, USA, 13-17 March 2023. U.S.: IEEE, 2023. p. 359-362.

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

TY - GEN

T1 - Indoor positioning using Wi-Fi and machine learning for Industry 5.0

AU - Neupane, Inoj

AU - Alsinglawi, Belal

AU - Rabie, Khaled

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - Indoor Positioning System

KW - Industry 5.0

KW - Internet of Things

KW - Machine Learning

KW - Wi-Fi

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ER -

Indoor positioning using Wi-Fi and machine learning for Industry 5.0

Abstract

Conference

Keywords

UN SDGs

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