RSS Fingerprinting Dataset Size Reduction Using Feature-Wise Adaptive k-Means Clustering
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Scholar |
Otros documentos de la autoría: Klus, Lucie; Quezada Gaibor, Darwin; Torres-Sospedra, Joaquín; Lohan, Elena Simona; Granell, Carlos; Nurmi, Jari
Metadatos
Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/43662
comunitat-uji-handle3:10234/159451
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INVESTIGACIONMetadatos
Título
RSS Fingerprinting Dataset Size Reduction Using Feature-Wise Adaptive k-Means ClusteringAutoría
Fecha de publicación
2020-10Editor
IEEEISSN
2157-023XCita bibliográfica
L. Klus, D. Quezada-Gaibor, J. Torres-Sospedra, E. S. Lohan, C. Granell and J. Nurmi, "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), Brno, Czech Republic, 2020, pp. 195-200, doi: 10.1109/ICUMT51630.2020.9222458Tipo de documento
info:eu-repo/semantics/conferenceObjectVersión de la editorial
https://ieeexplore.ieee.org/document/9222458Versión
info:eu-repo/semantics/acceptedVersionPalabras clave / Materias
Resumen
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 ... [+]
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. [-]
Descripción
Ponència presentada a 12th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT 2020) , celebrat a Brno del 5 al 7 d'octubre de 2020