Exploiting Channel Diversity to Improve BLE Range-Finding Accuracy

Samuel Leitch; Qasim Zeeshan Ahmed; Pavlos Lazaridis; Maryam Hafeez

doi:10.1007/978-3-031-49413-0_44

Exploiting Channel Diversity to Improve BLE Range-Finding Accuracy

Samuel Leitch, Qasim Zeeshan Ahmed, Pavlos Lazaridis, Maryam Hafeez

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

1 Citation (Scopus)

Abstract

Accurate distance estimation techniques are one of the major requirements for 6G systems. Traditional techniques when using Bluetooth Low Energy (BLE) technology, are based on log-normal shadowing model. However, these models have proven to be incapable of providing the needed accuracy. This paper utilizes a novel method of distance estimation by using a Naïve Bayesian (NB) Classifier to fuse information present in packets from three different advertising channels and classify the distance. This method of distance estimation was tested on a custom dataset and found to have a Mean Absolute Error (MAE) of 0.0234 m. It was compared to the traditional log-normal shadowing curve estimation method and the NB method of fusing channel information was found to outperform it.

Original language	English
Title of host publication	Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 1
Editors	Andrew D. Ball, Huajiang Ouyang, Jyoti K. Sinha, Zuolu Wang
Publisher	Springer, Cham
Pages	591-599
Number of pages	9
Volume	151
ISBN (Electronic)	9783031494130
ISBN (Print)	9783031494123, 9783031494154
DOIs	https://doi.org/10.1007/978-3-031-49413-0_44
Publication status	Published - 30 May 2024
Event	The UNIfied Conference of DAMAS, InCoME and TEPEN Conferences - Huddersfield, United Kingdom, Huddersfield, United Kingdom Duration: 29 Aug 2023 → 1 Sept 2023 https://unified2023.org/

Publication series

Name	Mechanisms and Machine Science
Publisher	Springer
Volume	151 MMS
ISSN (Print)	2211-0984
ISSN (Electronic)	2211-0992

Conference

Conference	The UNIfied Conference of DAMAS, InCoME and TEPEN Conferences
Abbreviated title	UNIfied 2023
Country/Territory	United Kingdom
City	Huddersfield
Period	29/08/23 → 1/09/23
Internet address	https://unified2023.org/

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Cite this

Leitch, S., Ahmed, Q. Z., Lazaridis, P., & Hafeez, M. (2024). Exploiting Channel Diversity to Improve BLE Range-Finding Accuracy. In A. D. Ball, H. Ouyang, J. K. Sinha, & Z. Wang (Eds.), Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 1 (Vol. 151, pp. 591-599). (Mechanisms and Machine Science; Vol. 151 MMS). Springer, Cham. https://doi.org/10.1007/978-3-031-49413-0_44

Leitch, Samuel ; Ahmed, Qasim Zeeshan ; Lazaridis, Pavlos et al. / Exploiting Channel Diversity to Improve BLE Range-Finding Accuracy. Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 1. editor / Andrew D. Ball ; Huajiang Ouyang ; Jyoti K. Sinha ; Zuolu Wang. Vol. 151 Springer, Cham, 2024. pp. 591-599 (Mechanisms and Machine Science).

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abstract = "Accurate distance estimation techniques are one of the major requirements for 6G systems. Traditional techniques when using Bluetooth Low Energy (BLE) technology, are based on log-normal shadowing model. However, these models have proven to be incapable of providing the needed accuracy. This paper utilizes a novel method of distance estimation by using a Na{\"i}ve Bayesian (NB) Classifier to fuse information present in packets from three different advertising channels and classify the distance. This method of distance estimation was tested on a custom dataset and found to have a Mean Absolute Error (MAE) of 0.0234 m. It was compared to the traditional log-normal shadowing curve estimation method and the NB method of fusing channel information was found to outperform it.",

keywords = "BLE, Distance estimation, Localization, Na{\"i}ve Bayes classifier",

author = "Samuel Leitch and Ahmed, {Qasim Zeeshan} and Pavlos Lazaridis and Maryam Hafeez",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; The UNIfied Conference of DAMAS, InCoME and TEPEN Conferences, UNIfied 2023 ; Conference date: 29-08-2023 Through 01-09-2023",

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Leitch, S, Ahmed, QZ , Lazaridis, P & Hafeez, M 2024, Exploiting Channel Diversity to Improve BLE Range-Finding Accuracy. in AD Ball, H Ouyang, JK Sinha & Z Wang (eds), Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 1. vol. 151, Mechanisms and Machine Science, vol. 151 MMS, Springer, Cham, pp. 591-599, The UNIfied Conference of DAMAS, InCoME and TEPEN Conferences, Huddersfield, United Kingdom, 29/08/23. https://doi.org/10.1007/978-3-031-49413-0_44

Exploiting Channel Diversity to Improve BLE Range-Finding Accuracy. / Leitch, Samuel; Ahmed, Qasim Zeeshan ; Lazaridis, Pavlos et al.
Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 1. ed. / Andrew D. Ball; Huajiang Ouyang; Jyoti K. Sinha; Zuolu Wang. Vol. 151 Springer, Cham, 2024. p. 591-599 (Mechanisms and Machine Science; Vol. 151 MMS).

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

TY - GEN

T1 - Exploiting Channel Diversity to Improve BLE Range-Finding Accuracy

AU - Leitch, Samuel

AU - Ahmed, Qasim Zeeshan

AU - Lazaridis, Pavlos

AU - Hafeez, Maryam

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

PY - 2024/5/30

Y1 - 2024/5/30

N2 - Accurate distance estimation techniques are one of the major requirements for 6G systems. Traditional techniques when using Bluetooth Low Energy (BLE) technology, are based on log-normal shadowing model. However, these models have proven to be incapable of providing the needed accuracy. This paper utilizes a novel method of distance estimation by using a Naïve Bayesian (NB) Classifier to fuse information present in packets from three different advertising channels and classify the distance. This method of distance estimation was tested on a custom dataset and found to have a Mean Absolute Error (MAE) of 0.0234 m. It was compared to the traditional log-normal shadowing curve estimation method and the NB method of fusing channel information was found to outperform it.

AB - Accurate distance estimation techniques are one of the major requirements for 6G systems. Traditional techniques when using Bluetooth Low Energy (BLE) technology, are based on log-normal shadowing model. However, these models have proven to be incapable of providing the needed accuracy. This paper utilizes a novel method of distance estimation by using a Naïve Bayesian (NB) Classifier to fuse information present in packets from three different advertising channels and classify the distance. This method of distance estimation was tested on a custom dataset and found to have a Mean Absolute Error (MAE) of 0.0234 m. It was compared to the traditional log-normal shadowing curve estimation method and the NB method of fusing channel information was found to outperform it.

KW - BLE

KW - Distance estimation

KW - Localization

KW - Naïve Bayes classifier

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M3 - Conference contribution

AN - SCOPUS:85197172321

SN - 9783031494123

SN - 9783031494154

VL - 151

T3 - Mechanisms and Machine Science

SP - 591

EP - 599

BT - Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 1

A2 - Ball, Andrew D.

A2 - Ouyang, Huajiang

A2 - Sinha, Jyoti K.

A2 - Wang, Zuolu

PB - Springer, Cham

T2 - The UNIfied Conference of DAMAS, InCoME and TEPEN Conferences

Y2 - 29 August 2023 through 1 September 2023

ER -

Leitch S, Ahmed QZ , Lazaridis P, Hafeez M. Exploiting Channel Diversity to Improve BLE Range-Finding Accuracy. In Ball AD, Ouyang H, Sinha JK, Wang Z, editors, Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 1. Vol. 151. Springer, Cham. 2024. p. 591-599. (Mechanisms and Machine Science). Epub 2024 May 29. doi: 10.1007/978-3-031-49413-0_44

Exploiting Channel Diversity to Improve BLE Range-Finding Accuracy

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