k-NN Empowered LoRaWAN Localization for Surface and Underground Scenarios: Work-in-Progress Report

Ekaterina Svertoka; Alexandru Rusu-Casandra; Radim Burget; Jari Nurmi; Aleksandr Ometov

k-NN Empowered LoRaWAN Localization for Surface and Underground Scenarios: Work-in-Progress Report

Ekaterina Svertoka, Alexandru Rusu-Casandra, Radim Burget, Jari Nurmi, Aleksandr Ometov

Electrical Engineering

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

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Abstract

This short paper explores the usability of Long Range Wide Area Network (LoRaWAN) technology for localization within the context of modern Industry 5.0 wireless networks. Traditional localization methods have often fallen short in providing meaningful accuracy in this domain. Our research addresses this gap by investigating the potential of LoRaWAN for localization, synthesizing key findings and advancements. Two primary contributions are presented: the analysis of two underground LoRaWAN datasets, valuable resources for researchers and practitioners, and the proposal of two innovative 𝑘-nearest neighbors (𝑘-NN) algorithms designed to enhance position estimation accuracy through optimized nearest neighbor selection. By integrating preprocessing strategies with these algorithms, an improvement in accuracy of up to 17% is achieved.

Original language	English
Title of host publication	Proceedings of Work-in-Progress in Hardware and Software for Location Computation (WIPHAL 2024)
Editors	Aleksandr Ometov, Jari Nurmi, Elena Simona Lohan, Joaquín Torres-Sospedra, Heidi Kuusniemi
Publisher	CEUR-WS
Number of pages	6
Publication status	Published - 2024
Publication type	A4 Article in conference proceedings
Event	Work-in-Progress in Hardware and Software for Location Computation - Antwerp, Belgium Duration: 25 Jun 2024 → 27 Jun 2024 https://events.tuni.fi/icl-gnss2024/

Publication series

Name	CEUR Workshop Proceedings
Publisher	CEUR-WS
Volume	3719
ISSN (Electronic)	1613-0073

Conference

Conference	Work-in-Progress in Hardware and Software for Location Computation
Abbreviated title	WIPHAL 2024
Country/Territory	Belgium
City	Antwerp
Period	25/06/24 → 27/06/24
Internet address	https://events.tuni.fi/icl-gnss2024/

Keywords

LoRaWAN
localization
accuracy
machine learning (ML)
dataset

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k-NN Empowered LoRaWAN Localization for Surface and Underground Scenarios Work-in-Progress ReportFinal published version, 1.09 MBLicence: CC BY

https://ceur-ws.org/Vol-3719/paper4.pdfLicence: CC BY
https://urn.fi/URN:NBN:fi:tuni-202408027877Licence: CC BY

Cite this

Svertoka, E., Rusu-Casandra, A., Burget, R., Nurmi, J., & Ometov, A. (2024). k-NN Empowered LoRaWAN Localization for Surface and Underground Scenarios: Work-in-Progress Report. In A. Ometov, J. Nurmi, E. S. Lohan, J. Torres-Sospedra, & H. Kuusniemi (Eds.), Proceedings of Work-in-Progress in Hardware and Software for Location Computation (WIPHAL 2024) (CEUR Workshop Proceedings; Vol. 3719). CEUR-WS. https://ceur-ws.org/Vol-3719/paper4.pdf

Svertoka, Ekaterina ; Rusu-Casandra, Alexandru ; Burget, Radim et al. / k-NN Empowered LoRaWAN Localization for Surface and Underground Scenarios : Work-in-Progress Report. Proceedings of Work-in-Progress in Hardware and Software for Location Computation (WIPHAL 2024). editor / Aleksandr Ometov ; Jari Nurmi ; Elena Simona Lohan ; Joaquín Torres-Sospedra ; Heidi Kuusniemi. CEUR-WS, 2024. (CEUR Workshop Proceedings).

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title = "k-NN Empowered LoRaWAN Localization for Surface and Underground Scenarios: Work-in-Progress Report",

abstract = "This short paper explores the usability of Long Range Wide Area Network (LoRaWAN) technology for localization within the context of modern Industry 5.0 wireless networks. Traditional localization methods have often fallen short in providing meaningful accuracy in this domain. Our research addresses this gap by investigating the potential of LoRaWAN for localization, synthesizing key findings and advancements. Two primary contributions are presented: the analysis of two underground LoRaWAN datasets, valuable resources for researchers and practitioners, and the proposal of two innovative 𝑘-nearest neighbors (𝑘-NN) algorithms designed to enhance position estimation accuracy through optimized nearest neighbor selection. By integrating preprocessing strategies with these algorithms, an improvement in accuracy of up to 17% is achieved.",

keywords = "LoRaWAN, localization, accuracy, machine learning (ML), dataset",

author = "Ekaterina Svertoka and Alexandru Rusu-Casandra and Radim Burget and Jari Nurmi and Aleksandr Ometov",

year = "2024",

language = "English",

series = "CEUR Workshop Proceedings",

publisher = "CEUR-WS",

editor = "Aleksandr Ometov and Jari Nurmi and Lohan, {Elena Simona} and Joaqu{\'i}n Torres-Sospedra and Heidi Kuusniemi",

booktitle = "Proceedings of Work-in-Progress in Hardware and Software for Location Computation (WIPHAL 2024)",

note = "Work-in-Progress in Hardware and Software for Location Computation, WIPHAL 2024 ; Conference date: 25-06-2024 Through 27-06-2024",

url = "https://events.tuni.fi/icl-gnss2024/",

}

Svertoka, E, Rusu-Casandra, A, Burget, R, Nurmi, J & Ometov, A 2024, k-NN Empowered LoRaWAN Localization for Surface and Underground Scenarios: Work-in-Progress Report. in A Ometov, J Nurmi, ES Lohan, J Torres-Sospedra & H Kuusniemi (eds), Proceedings of Work-in-Progress in Hardware and Software for Location Computation (WIPHAL 2024). CEUR Workshop Proceedings, vol. 3719, CEUR-WS, Work-in-Progress in Hardware and Software for Location Computation, Antwerp, Belgium, 25/06/24. <https://ceur-ws.org/Vol-3719/paper4.pdf>

k-NN Empowered LoRaWAN Localization for Surface and Underground Scenarios: Work-in-Progress Report. / Svertoka, Ekaterina; Rusu-Casandra, Alexandru; Burget, Radim et al.
Proceedings of Work-in-Progress in Hardware and Software for Location Computation (WIPHAL 2024). ed. / Aleksandr Ometov; Jari Nurmi; Elena Simona Lohan; Joaquín Torres-Sospedra; Heidi Kuusniemi. CEUR-WS, 2024. (CEUR Workshop Proceedings; Vol. 3719).

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

TY - GEN

T1 - k-NN Empowered LoRaWAN Localization for Surface and Underground Scenarios

T2 - Work-in-Progress in Hardware and Software for Location Computation

AU - Svertoka, Ekaterina

AU - Rusu-Casandra, Alexandru

AU - Burget, Radim

AU - Nurmi, Jari

AU - Ometov, Aleksandr

PY - 2024

Y1 - 2024

N2 - This short paper explores the usability of Long Range Wide Area Network (LoRaWAN) technology for localization within the context of modern Industry 5.0 wireless networks. Traditional localization methods have often fallen short in providing meaningful accuracy in this domain. Our research addresses this gap by investigating the potential of LoRaWAN for localization, synthesizing key findings and advancements. Two primary contributions are presented: the analysis of two underground LoRaWAN datasets, valuable resources for researchers and practitioners, and the proposal of two innovative 𝑘-nearest neighbors (𝑘-NN) algorithms designed to enhance position estimation accuracy through optimized nearest neighbor selection. By integrating preprocessing strategies with these algorithms, an improvement in accuracy of up to 17% is achieved.

AB - This short paper explores the usability of Long Range Wide Area Network (LoRaWAN) technology for localization within the context of modern Industry 5.0 wireless networks. Traditional localization methods have often fallen short in providing meaningful accuracy in this domain. Our research addresses this gap by investigating the potential of LoRaWAN for localization, synthesizing key findings and advancements. Two primary contributions are presented: the analysis of two underground LoRaWAN datasets, valuable resources for researchers and practitioners, and the proposal of two innovative 𝑘-nearest neighbors (𝑘-NN) algorithms designed to enhance position estimation accuracy through optimized nearest neighbor selection. By integrating preprocessing strategies with these algorithms, an improvement in accuracy of up to 17% is achieved.

KW - LoRaWAN

KW - localization

KW - accuracy

KW - machine learning (ML)

KW - dataset

M3 - Conference contribution

T3 - CEUR Workshop Proceedings

BT - Proceedings of Work-in-Progress in Hardware and Software for Location Computation (WIPHAL 2024)

A2 - Ometov, Aleksandr

A2 - Nurmi, Jari

A2 - Lohan, Elena Simona

A2 - Torres-Sospedra, Joaquín

A2 - Kuusniemi, Heidi

PB - CEUR-WS

Y2 - 25 June 2024 through 27 June 2024

ER -

Svertoka E, Rusu-Casandra A, Burget R, Nurmi J , Ometov A. k-NN Empowered LoRaWAN Localization for Surface and Underground Scenarios: Work-in-Progress Report. In Ometov A, Nurmi J, Lohan ES, Torres-Sospedra J, Kuusniemi H, editors, Proceedings of Work-in-Progress in Hardware and Software for Location Computation (WIPHAL 2024). CEUR-WS. 2024. (CEUR Workshop Proceedings).

k-NN Empowered LoRaWAN Localization for Surface and Underground Scenarios: Work-in-Progress Report

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Keywords

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