Machine learning based approach for indoor localization using ultra-wide bandwidth (UWB) system for industrial internet of things (IIoT)

Fuhu Che; Abbas Ahmed; Qasim Zeeshan Ahmed; Syed Ali Raza Zaidi; Muhammad Zeeshan Shakir

doi:10.1109/UCET51115.2020.9205352

Machine learning based approach for indoor localization using ultra-wide bandwidth (UWB) system for industrial internet of things (IIoT)

Fuhu Che, Abbas Ahmed, Qasim Zeeshan Ahmed, Syed Ali Raza Zaidi, Muhammad Zeeshan Shakir

School of Computing, Engineering and Physical Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

34 Citations (Scopus)

79 Downloads (Pure)

Abstract

With the rapid development of wireless communication technology and the emergence of the Industrial Internet of Things (IIoT)s applications, high-precision Indoor Positioning Services (IPS) are urgently required. While the Global Positioning System (GPS) has been a key technology for outdoor localization,
its limitation for indoor environments is well known. Ultra-WideBand (UWB) can help provide a very accurate position or localization for indoor harsh propagation environments. This paper focuses on improving the accuracy of the UWB indoor localization system including the Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) conditions by developing a Machine Learning (ML) algorithm. In this paper, a Naive Bayes (NB) ML algorithm is developed for UWB IPS. The performance of the developed algorithm is evaluated by Receiving Operating Curves (ROC)s. The results indicate that by employing the NB based ML algorithm significantly improves the localization accuracy of the UWB system for both the LoS and NLoS environment.

Original language	English
Title of host publication	Proceedings of the 2020 International Conference on UK-China Emerging Technologies (UCET)
Place of Publication	Piscataway, NJ
Publisher	IEEE
Number of pages	4
ISBN (Electronic)	9781728194882
ISBN (Print)	9781728194899
DOIs	https://doi.org/10.1109/UCET51115.2020.9205352
Publication status	Published - 29 Sept 2020
Event	5th International Conference on the UK - China Emerging Technologies (UCET) 2020 - University of Glasgow, Glasgow, United Kingdom Duration: 20 Aug 2020 → 21 Aug 2020 https://www.gla.ac.uk/events/conferences/ucet/

Conference

Conference	5th International Conference on the UK - China Emerging Technologies (UCET) 2020
Abbreviated title	UCET 2020
Country/Territory	United Kingdom
City	Glasgow
Period	20/08/20 → 21/08/20
Internet address	https://www.gla.ac.uk/events/conferences/ucet/

Keywords

UWB
IPS
localization
ML
naive Bayes

UN SDGs

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

Access to Document

10.1109/UCET51115.2020.9205352

2020 07 28 Che et al Machine acceptedAccepted author manuscript, 247 KB

Cite this

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title = "Machine learning based approach for indoor localization using ultra-wide bandwidth (UWB) system for industrial internet of things (IIoT)",

abstract = "With the rapid development of wireless communication technology and the emergence of the Industrial Internet of Things (IIoT)s applications, high-precision Indoor Positioning Services (IPS) are urgently required. While the Global Positioning System (GPS) has been a key technology for outdoor localization,its limitation for indoor environments is well known. Ultra-WideBand (UWB) can help provide a very accurate position or localization for indoor harsh propagation environments. This paper focuses on improving the accuracy of the UWB indoor localization system including the Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) conditions by developing a Machine Learning (ML) algorithm. In this paper, a Naive Bayes (NB) ML algorithm is developed for UWB IPS. The performance of the developed algorithm is evaluated by Receiving Operating Curves (ROC)s. The results indicate that by employing the NB based ML algorithm significantly improves the localization accuracy of the UWB system for both the LoS and NLoS environment.",

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author = "Fuhu Che and Abbas Ahmed and Ahmed, {Qasim Zeeshan} and Zaidi, {Syed Ali Raza} and Shakir, {Muhammad Zeeshan}",

year = "2020",

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doi = "10.1109/UCET51115.2020.9205352",

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booktitle = "Proceedings of the 2020 International Conference on UK-China Emerging Technologies (UCET)",

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note = "5th International Conference on the UK - China Emerging Technologies (UCET) 2020, UCET 2020 ; Conference date: 20-08-2020 Through 21-08-2020",

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Che, F, Ahmed, A, Ahmed, QZ, Zaidi, SAR & Shakir, MZ 2020, Machine learning based approach for indoor localization using ultra-wide bandwidth (UWB) system for industrial internet of things (IIoT). in Proceedings of the 2020 International Conference on UK-China Emerging Technologies (UCET). IEEE, Piscataway, NJ, 5th International Conference on the UK - China Emerging Technologies (UCET) 2020, Glasgow, United Kingdom, 20/08/20. https://doi.org/10.1109/UCET51115.2020.9205352

Machine learning based approach for indoor localization using ultra-wide bandwidth (UWB) system for industrial internet of things (IIoT). / Che, Fuhu; Ahmed, Abbas; Ahmed, Qasim Zeeshan et al.
Proceedings of the 2020 International Conference on UK-China Emerging Technologies (UCET). Piscataway, NJ: IEEE, 2020.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Machine learning based approach for indoor localization using ultra-wide bandwidth (UWB) system for industrial internet of things (IIoT)

AU - Che, Fuhu

AU - Ahmed, Abbas

AU - Ahmed, Qasim Zeeshan

AU - Zaidi, Syed Ali Raza

AU - Shakir, Muhammad Zeeshan

PY - 2020/9/29

Y1 - 2020/9/29

N2 - With the rapid development of wireless communication technology and the emergence of the Industrial Internet of Things (IIoT)s applications, high-precision Indoor Positioning Services (IPS) are urgently required. While the Global Positioning System (GPS) has been a key technology for outdoor localization,its limitation for indoor environments is well known. Ultra-WideBand (UWB) can help provide a very accurate position or localization for indoor harsh propagation environments. This paper focuses on improving the accuracy of the UWB indoor localization system including the Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) conditions by developing a Machine Learning (ML) algorithm. In this paper, a Naive Bayes (NB) ML algorithm is developed for UWB IPS. The performance of the developed algorithm is evaluated by Receiving Operating Curves (ROC)s. The results indicate that by employing the NB based ML algorithm significantly improves the localization accuracy of the UWB system for both the LoS and NLoS environment.

AB - With the rapid development of wireless communication technology and the emergence of the Industrial Internet of Things (IIoT)s applications, high-precision Indoor Positioning Services (IPS) are urgently required. While the Global Positioning System (GPS) has been a key technology for outdoor localization,its limitation for indoor environments is well known. Ultra-WideBand (UWB) can help provide a very accurate position or localization for indoor harsh propagation environments. This paper focuses on improving the accuracy of the UWB indoor localization system including the Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) conditions by developing a Machine Learning (ML) algorithm. In this paper, a Naive Bayes (NB) ML algorithm is developed for UWB IPS. The performance of the developed algorithm is evaluated by Receiving Operating Curves (ROC)s. The results indicate that by employing the NB based ML algorithm significantly improves the localization accuracy of the UWB system for both the LoS and NLoS environment.

KW - UWB

KW - IPS

KW - localization

KW - ML

KW - naive Bayes

UR - https://www.gla.ac.uk/events/conferences/ucet/

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DO - 10.1109/UCET51115.2020.9205352

M3 - Conference contribution

SN - 9781728194899

BT - Proceedings of the 2020 International Conference on UK-China Emerging Technologies (UCET)

PB - IEEE

CY - Piscataway, NJ

T2 - 5th International Conference on the UK - China Emerging Technologies (UCET) 2020

Y2 - 20 August 2020 through 21 August 2020

ER -

Machine learning based approach for indoor localization using ultra-wide bandwidth (UWB) system for industrial internet of things (IIoT)

Abstract

Conference

Keywords

UN SDGs

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