New Cluster Selection and Fine-grained Search for k-Means Clustering and Wi-Fi Fingerprinting
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Otros documentos de la autoría: Torres-Sospedra, Joaquín; Quezada Gaibor, Darwin; Mendoza-Silva, Germán Martín; Nurmi, Jari; Koucheryavy, Yevgeni; Huerta, Joaquin
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INVESTIGACIONMetadatos
Título
New Cluster Selection and Fine-grained Search for k-Means Clustering and Wi-Fi FingerprintingAutoría
Fecha de publicación
2020-06-02Editor
IEEECita bibliográfica
J. Torres-Sospedra, D. Quezada-Gaibor, G. M. Mendoza-Silva, J. Nurmi, Y. Koucheryavy and J. Huerta, "New Cluster Selection and Fine-grained Search for k-Means Clustering and Wi-Fi Fingerprinting," 2020 International Conference on Localization and GNSS (ICL-GNSS), 2020, pp. 1-6, doi: 10.1109/ICL-GNSS49876.2020.9115419.Tipo de documento
info:eu-repo/semantics/conferenceObjectVersión de la editorial
NURMI, Jari, et al. 2020 International Conference on Localization and GNSS-Conference Proceedings. IEEE, 2020.Versión
info:eu-repo/semantics/acceptedVersionPalabras clave / Materias
Resumen
Wi-Fi fingerprinting is a popular technique for Indoor Positioning Systems (IPSs) thanks to its low complexity and the ubiquity of WLAN infrastructures. However, this technique may present scalability issues when the ... [+]
Wi-Fi fingerprinting is a popular technique for Indoor Positioning Systems (IPSs) thanks to its low complexity and the ubiquity of WLAN infrastructures. However, this technique may present scalability issues when the reference dataset (radio map) is very large. To reduce the computational costs, k-Means Clustering has been successfully applied in the past. However, it is a general-purpose algorithm for unsupervised classification. This paper introduces three variants that apply heuristics based on radio propagation knowledge in the coarse and fine-grained searches. Due to the heterogeneity either in the IPS side (including radio map generation) and in the network infrastructure, we used an evaluation framework composed of 16 datasets. In terms of general positioning accuracy and computational costs, the best proposed k-means variant provided better general positioning accuracy and a significantly better computational cost –around 40% lower– than the original k-means. [-]
Descripción
Ponencia presentada en 2020 International Conference on Localization and GNSS (ICL-GNSS), 02-04 June 2020, Tampere, Finland
Entidad financiadora
Ministerio de Ciencia, Innovación y Universidades (Spain) | European Union’s ´ H2020 Research and Innovation programme under the Marie SkłodowskaCurie grant agreement | Universitat Jaume I
Código del proyecto o subvención
INSIGNIA, PTQ2018-009981 | 813278 | PREDOC/2016/55
Título del proyecto o subvención
A-WEAR: A network for dynamic wearable applications with privacy constraints
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