Data Cleansing for Indoor Positioning Wi-Fi Fingerprinting Datasets
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Scholar |
Otros documentos de la autoría: Quezada Gaibor, Darwin; Klus, Lucie; Torres-Sospedra, Joaquín; Lohan, Elena Simona; Nurmi, Jari; Granell, Carlos; Huerta, Joaquin
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https://doi.org/10.1109/MDM55031.2022.00079 |
Metadatos
Título
Data Cleansing for Indoor Positioning Wi-Fi Fingerprinting DatasetsAutoría
Fecha de publicación
2022Editor
IEEEISBN
9781665451765Cita bibliográfica
D. Quezada-Gaibor et al., "Data Cleansing for Indoor Positioning Wi-Fi Fingerprinting Datasets," 2022 23rd IEEE International Conference on Mobile Data Management (MDM), 2022, pp. 349-354, doi: 10.1109/MDM55031.2022.00079.Tipo de documento
info:eu-repo/semantics/conferenceObjectVersión de la editorial
https://ieeexplore.ieee.org/document/9861169Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
Wearable and IoT devices requiring positioning and
localisation services grow in number exponentially every year.
This rapid growth also produces millions of data entries that
need to be pre-processed prior to being ... [+]
Wearable and IoT devices requiring positioning and
localisation services grow in number exponentially every year.
This rapid growth also produces millions of data entries that
need to be pre-processed prior to being used in any indoor
positioning system to ensure the data quality and provide a
high Quality of Service (QoS) to the end-user. In this paper,
we offer a novel and straightforward data cleansing algorithm
for WLAN fingerprinting radio maps. This algorithm is based
on the correlation among fingerprints using the Received Signal
Strength (RSS) values and the Access Points (APs)’s identifier.
We use those to compute the correlation among all samples in
the dataset and remove fingerprints with low level of correlation
from the dataset. We evaluated the proposed method on 14
independent publicly-available datasets. As a result, an average
of 14% of fingerprints were removed from the datasets. The 2D
positioning error was reduced by 2.7% and 3D positioning error
by 5.3% with a slight increase in the floor hit rate by 1.2% on
average. Consequently, the average speed of position prediction
was also increased by 14%. [-]
Descripción
Ponència presentada en el 2022 23rd IEEE International Conference on Mobile Data Management (MDM)
Publicado en
Proceedings 2022 23rd IEEE International Conference on Mobile Data Management MDM 2022Entidad financiadora
European Comission
Código del proyecto o subvención
info:eu-repo/grantAgreement/EC/H2020/813278 | info:eu-repo/grantAgreement/EC/H2020/101023072
Título del proyecto o subvención
A-WEAR: A network for dynamic wearable applications with privacy constraints, http://www.a-wear.eu/ | ORIENTATE: Low-cost Reliable Indoor Positioning in Smart Factories, http://orientate.dsi.uminho.pt
Derechos de acceso
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