Lightweight Hybrid CNN-ELM Model for Multi-building and Multi-floor Classification

Darwin Quezada-Gaibor; Joaquín Torres-Sospedra; Jari Nurmi; Yevgeni Koucheryavy; Joaquín Huerta

doi:10.1109/ICL-GNSS54081.2022.9797021

Lightweight Hybrid CNN-ELM Model for Multi-building and Multi-floor Classification

Darwin Quezada-Gaibor, Joaquín Torres-Sospedra, Jari Nurmi, Yevgeni Koucheryavy, Joaquín Huerta

Sähkötekniikka

Tutkimustuotos: Konferenssiartikkeli › Scientific › vertaisarvioitu

2 Sitaatiot (Scopus)

28 Lataukset (Pure)

Abstrakti

Machine learning models have become an essential tool in current indoor positioning solutions, given their high capa-bilities to extract meaningful information from the environment. Convolutional neural networks (CNNs) are one of the most used neural networks (NNs) due to that they are capable of learning complex patterns from the input data. Another model used in indoor positioning solutions is the Extreme Learning Machine (ELM), which provides an acceptable generalization performance as well as a fast speed of learning. In this paper, we offer a lightweight combination of CNN and ELM, which provides a quick and accurate classification of building and floor, suitable for power and resource-constrained devices. As a result, the proposed model is 58% faster than the benchmark, with a slight improvement in the classification accuracy (by less than 1 %).

Alkuperäiskieli	Englanti
Otsikko	2022 International Conference on Localization and GNSS, ICL-GNSS 2022 - Proceedings
Toimittajat	Jari Nurmi, Elena-Simona Lohan, Joaquin Torres Sospedra, Heidi Kuusniemi, Aleksandr Ometov
Kustantaja	IEEE
Sivumäärä	6
ISBN (elektroninen)	9781665405751
ISBN (painettu)	9781665405768
DOI - pysyväislinkit	https://doi.org/10.1109/ICL-GNSS54081.2022.9797021
Tila	Julkaistu - 2022
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	International Conference on Localization and GNSS - Tampere, Suomi Kesto: 7 kesäk. 2022 → 9 kesäk. 2022

Julkaisusarja

Nimi	International Conference on Localization and GNSS
ISSN (painettu)	2325-0747
ISSN (elektroninen)	2325-0771

Conference

Conference	International Conference on Localization and GNSS
Maa/Alue	Suomi
Kaupunki	Tampere
Ajanjakso	7/06/22 → 9/06/22

Julkaisufoorumi-taso

Jufo-taso 1

!!ASJC Scopus subject areas

Computer Networks and Communications
Aerospace Engineering

Pääsy asiakirjaan

10.1109/ICL-GNSS54081.2022.9797021

Lightweight Hybrid CNN-ELM Model
This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited.
Accepted author manuscript, 227 KBLisenssi: Unspecified

https://urn.fi/URN:NBN:fi:tuni-202212139135Lisenssi: Unspecified

Supplementary material "Lightweight Hybrid CNN-ELM Model for Multi-building and Multi-floor Classification"
Quezada Gaibor, D. (Creator), Torres-Sospedra, J. (Contributor), Nurmi, J. (Contributor), Koucheryavy, Y. (Contributor) & Huerta, J. (Contributor), Zenodo, 11 maalisk. 2022
DOI - pysyväislinkki: 10.5281/zenodo.6347465
Tietoaineisto: Dataset

Siteeraa tätä

Quezada-Gaibor, D., Torres-Sospedra, J., Nurmi, J., Koucheryavy, Y., & Huerta, J. (2022). Lightweight Hybrid CNN-ELM Model for Multi-building and Multi-floor Classification. teoksessa J. Nurmi, E.-S. Lohan, J. T. Sospedra, H. Kuusniemi, & A. Ometov (Toimittajat), 2022 International Conference on Localization and GNSS, ICL-GNSS 2022 - Proceedings (International Conference on Localization and GNSS). IEEE. https://doi.org/10.1109/ICL-GNSS54081.2022.9797021

Quezada-Gaibor, Darwin ; Torres-Sospedra, Joaquín ; Nurmi, Jari et al. / Lightweight Hybrid CNN-ELM Model for Multi-building and Multi-floor Classification. 2022 International Conference on Localization and GNSS, ICL-GNSS 2022 - Proceedings. Toimittaja / Jari Nurmi ; Elena-Simona Lohan ; Joaquin Torres Sospedra ; Heidi Kuusniemi ; Aleksandr Ometov. IEEE, 2022. (International Conference on Localization and GNSS).

@inproceedings{c0db1d5cf0e24e2eafa068f72074cba9,

title = "Lightweight Hybrid CNN-ELM Model for Multi-building and Multi-floor Classification",

abstract = "Machine learning models have become an essential tool in current indoor positioning solutions, given their high capa-bilities to extract meaningful information from the environment. Convolutional neural networks (CNNs) are one of the most used neural networks (NNs) due to that they are capable of learning complex patterns from the input data. Another model used in indoor positioning solutions is the Extreme Learning Machine (ELM), which provides an acceptable generalization performance as well as a fast speed of learning. In this paper, we offer a lightweight combination of CNN and ELM, which provides a quick and accurate classification of building and floor, suitable for power and resource-constrained devices. As a result, the proposed model is 58% faster than the benchmark, with a slight improvement in the classification accuracy (by less than 1 %).",

keywords = "deep learning, extreme learning machine, Indoor Localisation, Wi-Fi fingerprinting",

author = "Darwin Quezada-Gaibor and Joaqu{\'i}n Torres-Sospedra and Jari Nurmi and Yevgeni Koucheryavy and Joaqu{\'i}n Huerta",

note = "Funding Information: Corresponding Author: D. Quezada Gaibor ([email protected]) The authors gratefully acknowledge funding from European Union{\textquoteright}s Horizon 2020 Research and Innovation programme under the Marie Sk{\l}odowska Curie grant agreements No. 813278 (A-WEAR: A network for dynamic wearable applications with privacy constraints, http://www.a-wear.eu/) and No. 101023072 (ORIENTATE: Low-cost Reliable Indoor Positioning in Smart Factories, http://orientate.dsi.uminho.pt). Publisher Copyright: {\textcopyright} 2022 IEEE. JUFOID=72237; International Conference on Localization and GNSS ; Conference date: 07-06-2022 Through 09-06-2022",

year = "2022",

doi = "10.1109/ICL-GNSS54081.2022.9797021",

language = "English",

isbn = "9781665405768",

series = "International Conference on Localization and GNSS",

publisher = "IEEE",

editor = "Jari Nurmi and Elena-Simona Lohan and Sospedra, {Joaquin Torres} and Heidi Kuusniemi and Aleksandr Ometov",

booktitle = "2022 International Conference on Localization and GNSS, ICL-GNSS 2022 - Proceedings",

}

Quezada-Gaibor, D, Torres-Sospedra, J, Nurmi, J, Koucheryavy, Y & Huerta, J 2022, Lightweight Hybrid CNN-ELM Model for Multi-building and Multi-floor Classification. julkaisussa J Nurmi, E-S Lohan, JT Sospedra, H Kuusniemi & A Ometov (toim), 2022 International Conference on Localization and GNSS, ICL-GNSS 2022 - Proceedings. International Conference on Localization and GNSS, IEEE, International Conference on Localization and GNSS, Tampere, Suomi, 7/06/22. https://doi.org/10.1109/ICL-GNSS54081.2022.9797021

Lightweight Hybrid CNN-ELM Model for Multi-building and Multi-floor Classification. / Quezada-Gaibor, Darwin; Torres-Sospedra, Joaquín; Nurmi, Jari et al.
2022 International Conference on Localization and GNSS, ICL-GNSS 2022 - Proceedings. toim. / Jari Nurmi; Elena-Simona Lohan; Joaquin Torres Sospedra; Heidi Kuusniemi; Aleksandr Ometov. IEEE, 2022. (International Conference on Localization and GNSS).

Tutkimustuotos: Konferenssiartikkeli › Scientific › vertaisarvioitu

TY - GEN

T1 - Lightweight Hybrid CNN-ELM Model for Multi-building and Multi-floor Classification

AU - Quezada-Gaibor, Darwin

AU - Torres-Sospedra, Joaquín

AU - Nurmi, Jari

AU - Koucheryavy, Yevgeni

AU - Huerta, Joaquín

N1 - Funding Information: Corresponding Author: D. Quezada Gaibor ([email protected]) The authors gratefully acknowledge funding from European Union’s Horizon 2020 Research and Innovation programme under the Marie Skłodowska Curie grant agreements No. 813278 (A-WEAR: A network for dynamic wearable applications with privacy constraints, http://www.a-wear.eu/) and No. 101023072 (ORIENTATE: Low-cost Reliable Indoor Positioning in Smart Factories, http://orientate.dsi.uminho.pt). Publisher Copyright: © 2022 IEEE. JUFOID=72237

PY - 2022

Y1 - 2022

N2 - Machine learning models have become an essential tool in current indoor positioning solutions, given their high capa-bilities to extract meaningful information from the environment. Convolutional neural networks (CNNs) are one of the most used neural networks (NNs) due to that they are capable of learning complex patterns from the input data. Another model used in indoor positioning solutions is the Extreme Learning Machine (ELM), which provides an acceptable generalization performance as well as a fast speed of learning. In this paper, we offer a lightweight combination of CNN and ELM, which provides a quick and accurate classification of building and floor, suitable for power and resource-constrained devices. As a result, the proposed model is 58% faster than the benchmark, with a slight improvement in the classification accuracy (by less than 1 %).

AB - Machine learning models have become an essential tool in current indoor positioning solutions, given their high capa-bilities to extract meaningful information from the environment. Convolutional neural networks (CNNs) are one of the most used neural networks (NNs) due to that they are capable of learning complex patterns from the input data. Another model used in indoor positioning solutions is the Extreme Learning Machine (ELM), which provides an acceptable generalization performance as well as a fast speed of learning. In this paper, we offer a lightweight combination of CNN and ELM, which provides a quick and accurate classification of building and floor, suitable for power and resource-constrained devices. As a result, the proposed model is 58% faster than the benchmark, with a slight improvement in the classification accuracy (by less than 1 %).

KW - deep learning

KW - extreme learning machine

KW - Indoor Localisation

KW - Wi-Fi fingerprinting

U2 - 10.1109/ICL-GNSS54081.2022.9797021

DO - 10.1109/ICL-GNSS54081.2022.9797021

M3 - Conference contribution

AN - SCOPUS:85134559155

SN - 9781665405768

T3 - International Conference on Localization and GNSS

BT - 2022 International Conference on Localization and GNSS, ICL-GNSS 2022 - Proceedings

A2 - Nurmi, Jari

A2 - Lohan, Elena-Simona

A2 - Sospedra, Joaquin Torres

A2 - Kuusniemi, Heidi

A2 - Ometov, Aleksandr

PB - IEEE

T2 - International Conference on Localization and GNSS

Y2 - 7 June 2022 through 9 June 2022

ER -

Quezada-Gaibor D, Torres-Sospedra J, Nurmi J, Koucheryavy Y, Huerta J. Lightweight Hybrid CNN-ELM Model for Multi-building and Multi-floor Classification. julkaisussa Nurmi J, Lohan ES, Sospedra JT, Kuusniemi H, Ometov A, toimittajat, 2022 International Conference on Localization and GNSS, ICL-GNSS 2022 - Proceedings. IEEE. 2022. (International Conference on Localization and GNSS). doi: 10.1109/ICL-GNSS54081.2022.9797021

Lightweight Hybrid CNN-ELM Model for Multi-building and Multi-floor Classification

Abstrakti

Julkaisusarja

Conference

Julkaisufoorumi-taso

!!ASJC Scopus subject areas

Pääsy asiakirjaan

Sormenjälki

Tietoaineistot

Supplementary material "Lightweight Hybrid CNN-ELM Model for Multi-building and Multi-floor Classification"

Siteeraa tätä