RSS Fingerprinting Dataset Size Reduction Using Feature-Wise Adaptive k-Means Clustering

Lucie Klus; Darwin Quezada-Gaibor; Joaquin Torres-Sospedra; Elena Simona Lohan; Carlos Granell; Jari Nurmi

doi:10.1109/ICUMT51630.2020.9222458

RSS Fingerprinting Dataset Size Reduction Using Feature-Wise Adaptive k-Means Clustering

Lucie Klus, Darwin Quezada-Gaibor, Joaquin Torres-Sospedra, Elena Simona Lohan, Carlos Granell, Jari Nurmi

Sähkötekniikka

Tutkimustuotos: Konferenssiartikkeli › Tieteellinen › vertaisarvioitu

20 Sitaatiot (Scopus)

19 Lataukset (Pure)

Abstrakti

Modern IoT devices, that include smartphones and wearables, usually have limited resources. They require efficient methods to optimize the use of internal storage, provide computational efficiency, and reduce energy consumption. Device resources should be used appropriately, especially when employed for time-consuming and energy-intensive computations such as positioning or localization. However, reducing computational costs usually degrades the positioning methods. Therefore, the goal of this article is to propose and compare compression mechanisms of the fingerprinting datasets for energy-saving without losing relevant information, by using adaptive k-means clustering. As a result, we achieved a compression ratio of up to 15.97 with a small decrease (1%) in position error.

Alkuperäiskieli	Englanti
Otsikko	2020 12th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops, ICUMT 2020
Kustantaja	IEEE
Sivut	195-200
Sivumäärä	6
ISBN (elektroninen)	9781728192819
DOI - pysyväislinkit	https://doi.org/10.1109/ICUMT51630.2020.9222458
Tila	Julkaistu - lokak. 2020
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	International Congress on Ultra Modern Telecommunications and Control Systems and Workshops - Brno, Tshekki Kesto: 5 lokak. 2020 → 7 lokak. 2020

Julkaisusarja

Nimi	International Congress on Ultra Modern Telecommunications and Control Systems and Workshops
Vuosikerta	2020-October
ISSN (painettu)	2157-0221
ISSN (elektroninen)	2157-023X

Conference

Conference	International Congress on Ultra Modern Telecommunications and Control Systems and Workshops
Maa/Alue	Tshekki
Kaupunki	Brno
Ajanjakso	5/10/20 → 7/10/20

Julkaisufoorumi-taso

Jufo-taso 1

!!ASJC Scopus subject areas

Computer Networks and Communications
Control and Systems Engineering

YK:n kestävän kehityksen tavoitteet

Tämä tuotos edistää seuraavia kestävän kehityksen tavoitteita:

Pääsy asiakirjaan

10.1109/ICUMT51630.2020.9222458

RSS Fingerprinting Dataset Size Reduction 2020
This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited.
Accepted author manuscript, 286 KB

http://urn.fi/URN:NBN:fi:tuni-202011308316

Supplementary materials for "RSS fingerprinting dataset size reduction using feature-wise adaptive k-means clustering"
Klus, L. (Creator), Quezada Gaibor, D. (Creator) & Torres-Sospedra, J. (Creator), Zenodo, 1 syysk. 2020
DOI - pysyväislinkki: 10.5281/zenodo.4026370
Tietoaineisto: Dataset

Siteeraa tätä

Klus, L., Quezada-Gaibor, D., Torres-Sospedra, J., Lohan, E. S., Granell, C., & Nurmi, J. (2020). RSS Fingerprinting Dataset Size Reduction Using Feature-Wise Adaptive k-Means Clustering. teoksessa 2020 12th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops, ICUMT 2020 (Sivut 195-200). (International Congress on Ultra Modern Telecommunications and Control Systems and Workshops; Vuosikerta 2020-October). IEEE. https://doi.org/10.1109/ICUMT51630.2020.9222458

Klus, Lucie ; Quezada-Gaibor, Darwin ; Torres-Sospedra, Joaquin et al. / RSS Fingerprinting Dataset Size Reduction Using Feature-Wise Adaptive k-Means Clustering. 2020 12th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops, ICUMT 2020. IEEE, 2020. Sivut 195-200 (International Congress on Ultra Modern Telecommunications and Control Systems and Workshops).

@inproceedings{91660cf8ac3f495f9867a7b0e70f3c34,

title = "RSS Fingerprinting Dataset Size Reduction Using Feature-Wise Adaptive k-Means Clustering",

abstract = "Modern IoT devices, that include smartphones and wearables, usually have limited resources. They require efficient methods to optimize the use of internal storage, provide computational efficiency, and reduce energy consumption. Device resources should be used appropriately, especially when employed for time-consuming and energy-intensive computations such as positioning or localization. However, reducing computational costs usually degrades the positioning methods. Therefore, the goal of this article is to propose and compare compression mechanisms of the fingerprinting datasets for energy-saving without losing relevant information, by using adaptive k-means clustering. As a result, we achieved a compression ratio of up to 15.97 with a small decrease (1%) in position error. ",

keywords = "clustering, compression ratio, data compression, fingerprinting, indoor positioning, k-means, k-nearest neighbors",

author = "Lucie Klus and Darwin Quezada-Gaibor and Joaquin Torres-Sospedra and Lohan, {Elena Simona} and Carlos Granell and Jari Nurmi",

note = "Funding Information: Corresponding Author: Lucie Klus ([email protected]) The authors gratefully acknowledge funding from European Union{\textquoteright}s Horizon 2020 Research and Innovation programme under the Marie Sklodowska Curie grant agreement No. 813278 (A-WEAR: A network for dynamic wearable applications with privacy constraints, http://www.a-wear.eu/). Publisher Copyright: {\textcopyright} 2020 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved. jufoid=72315; International Congress on Ultra Modern Telecommunications and Control Systems and Workshops ; Conference date: 05-10-2020 Through 07-10-2020",

year = "2020",

month = oct,

doi = "10.1109/ICUMT51630.2020.9222458",

language = "English",

series = "International Congress on Ultra Modern Telecommunications and Control Systems and Workshops",

publisher = "IEEE",

pages = "195--200",

booktitle = "2020 12th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops, ICUMT 2020",

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Klus, L, Quezada-Gaibor, D, Torres-Sospedra, J, Lohan, ES, Granell, C & Nurmi, J 2020, RSS Fingerprinting Dataset Size Reduction Using Feature-Wise Adaptive k-Means Clustering. julkaisussa 2020 12th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops, ICUMT 2020. International Congress on Ultra Modern Telecommunications and Control Systems and Workshops, Vuosikerta 2020-October, IEEE, Sivut 195-200, International Congress on Ultra Modern Telecommunications and Control Systems and Workshops, Brno, Tshekki, 5/10/20. https://doi.org/10.1109/ICUMT51630.2020.9222458

RSS Fingerprinting Dataset Size Reduction Using Feature-Wise Adaptive k-Means Clustering. / Klus, Lucie; Quezada-Gaibor, Darwin; Torres-Sospedra, Joaquin et al.
2020 12th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops, ICUMT 2020. IEEE, 2020. s. 195-200 (International Congress on Ultra Modern Telecommunications and Control Systems and Workshops; Vuosikerta 2020-October).

Tutkimustuotos: Konferenssiartikkeli › Tieteellinen › vertaisarvioitu

TY - GEN

T1 - RSS Fingerprinting Dataset Size Reduction Using Feature-Wise Adaptive k-Means Clustering

AU - Klus, Lucie

AU - Quezada-Gaibor, Darwin

AU - Torres-Sospedra, Joaquin

AU - Lohan, Elena Simona

AU - Granell, Carlos

AU - Nurmi, Jari

N1 - Funding Information: Corresponding Author: Lucie Klus ([email protected]) The authors gratefully acknowledge funding from European Union’s Horizon 2020 Research and Innovation programme under the Marie Sklodowska Curie grant agreement No. 813278 (A-WEAR: A network for dynamic wearable applications with privacy constraints, http://www.a-wear.eu/). Publisher Copyright: © 2020 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved. jufoid=72315

PY - 2020/10

Y1 - 2020/10

N2 - Modern IoT devices, that include smartphones and wearables, usually have limited resources. They require efficient methods to optimize the use of internal storage, provide computational efficiency, and reduce energy consumption. Device resources should be used appropriately, especially when employed for time-consuming and energy-intensive computations such as positioning or localization. However, reducing computational costs usually degrades the positioning methods. Therefore, the goal of this article is to propose and compare compression mechanisms of the fingerprinting datasets for energy-saving without losing relevant information, by using adaptive k-means clustering. As a result, we achieved a compression ratio of up to 15.97 with a small decrease (1%) in position error.

AB - Modern IoT devices, that include smartphones and wearables, usually have limited resources. They require efficient methods to optimize the use of internal storage, provide computational efficiency, and reduce energy consumption. Device resources should be used appropriately, especially when employed for time-consuming and energy-intensive computations such as positioning or localization. However, reducing computational costs usually degrades the positioning methods. Therefore, the goal of this article is to propose and compare compression mechanisms of the fingerprinting datasets for energy-saving without losing relevant information, by using adaptive k-means clustering. As a result, we achieved a compression ratio of up to 15.97 with a small decrease (1%) in position error.

KW - clustering

KW - compression ratio

KW - data compression

KW - fingerprinting

KW - indoor positioning

KW - k-means

KW - k-nearest neighbors

U2 - 10.1109/ICUMT51630.2020.9222458

DO - 10.1109/ICUMT51630.2020.9222458

M3 - Conference contribution

AN - SCOPUS:85094872061

T3 - International Congress on Ultra Modern Telecommunications and Control Systems and Workshops

SP - 195

EP - 200

BT - 2020 12th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops, ICUMT 2020

PB - IEEE

T2 - International Congress on Ultra Modern Telecommunications and Control Systems and Workshops

Y2 - 5 October 2020 through 7 October 2020

ER -

Klus L, Quezada-Gaibor D, Torres-Sospedra J, Lohan ES, Granell C, Nurmi J. RSS Fingerprinting Dataset Size Reduction Using Feature-Wise Adaptive k-Means Clustering. julkaisussa 2020 12th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops, ICUMT 2020. IEEE. 2020. s. 195-200. (International Congress on Ultra Modern Telecommunications and Control Systems and Workshops). doi: 10.1109/ICUMT51630.2020.9222458

RSS Fingerprinting Dataset Size Reduction Using Feature-Wise Adaptive k-Means Clustering

Abstrakti

Julkaisusarja

Conference

Julkaisufoorumi-taso

!!ASJC Scopus subject areas

YK:n kestävän kehityksen tavoitteet

Pääsy asiakirjaan

Sormenjälki

Tietoaineistot

Supplementary materials for "RSS fingerprinting dataset size reduction using feature-wise adaptive k-means clustering"

Siteeraa tätä