Scalable and Efficient Clustering for Fingerprint-Based Positioning
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Otros documentos de la autoría: Torres-Sospedra, Joaquín; Quezada Gaibor, Darwin; Nurmi, Jari; Koucheryavy, Yevgeni; Lohan, Elena Simona; Huerta, Joaquin
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Título
Scalable and Efficient Clustering for Fingerprint-Based PositioningAutoría
Fecha de publicación
2023Editor
IEEECita bibliográfica
J. Torres-Sospedra, D. P. Quezada Gaibor, J. Nurmi, Y. Koucheryavy, E. S. Lohan and J. Huerta, "Scalable and Efficient Clustering for Fingerprint-Based Positioning," in IEEE Internet of Things Journal, vol. 10, no. 4, pp. 3484-3499, 15 Feb.15, 2023, doi: 10.1109/JIOT.2022.3230913.Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://ieeexplore.ieee.org/abstract/document/9993735Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
Abstract:
Indoor positioning based on IEEE 802.11 wireless LAN (Wi-Fi) fingerprinting needs a reference data set, also known as a radio map, in order to match the incoming fingerprint in the operational phase with ... [+]
Abstract:
Indoor positioning based on IEEE 802.11 wireless LAN (Wi-Fi) fingerprinting needs a reference data set, also known as a radio map, in order to match the incoming fingerprint in the operational phase with the most similar fingerprint in the data set and then estimate the device position indoors. Scalability problems may arise when the radio map is large, e.g., providing positioning in large geographical areas or involving crowdsourced data collection. Some researchers divide the radio map into smaller independent clusters, such that the search area is reduced to less dense groups than the initial database with similar features. Thus, the computational load in the operational stage is reduced both at the user devices and on servers. Nevertheless, the clustering models are machine-learning algorithms without specific domain knowledge on indoor positioning or signal propagation. This work proposes several clustering variants to optimize the coarse and fine-grained search and evaluates them over different clustering models and data sets. Moreover, we provide guidelines to obtain efficient and accurate positioning depending on the data set features. Finally, we show that the proposed new clustering variants reduce the execution time by half and the positioning error by ≈7 % with respect to fingerprinting with the traditional clustering models. [-]
Publicado en
IEEE Internet of Things Journal ( Volume: 10, Issue: 4, 15 February 2023)Entidad financiadora
European Commission
Código del proyecto o subvención
813278 | 101023072
Derechos de acceso
info:eu-repo/semantics/openAccess
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