EWOk: Towards Efficient Multidimensional Compression of Indoor Positioning Datasets (Epub ahead of print)
Impacto
Scholar |
Otros documentos de la autoría: Klus, Lucie; Klus, Roman; Torres-Sospedra, Joaquín; Lohan, Elena Simona; Granell, Carlos; Nurmi, Jari
Metadatos
Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
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comunitat-uji-handle3:10234/8634
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INVESTIGACIONMetadatos
Título
EWOk: Towards Efficient Multidimensional Compression of Indoor Positioning Datasets (Epub ahead of print)Autoría
Fecha de publicación
2023-05-17Editor
Institute of Electrical and Electronics Engineers Inc.ISSN
1536-1233Cita bibliográfica
Klus, L., Klus, R., Torres-Sospedra, J., Lohan, E. S., Granell, C., & Nurmi, J. (2023). EWOk: Towards Efficient Multidimensional Compression of Indoor Positioning Datasets. IEEE Transactions on Mobile Computing.Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://ieeexplore.ieee.org/document/10128720Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
Indoor positioning performed directly at the end-user device ensures reliability in case the network connection fails but is limited by the size of the RSS radio map necessary to match the measured array to the device’s ... [+]
Indoor positioning performed directly at the end-user device ensures reliability in case the network connection fails but is limited by the size of the RSS radio map necessary to match the measured array to the device’s location. Reducing the size of the RSS database enables faster processing, and saves storage space and radio resources necessary for the database transfer, thus cutting implementation and operation costs, and increasing the quality of service. In this work, we propose EWOk, an Element-Wise cOmpression using k-means, which reduces the size of the individual radio measurements within the fingerprinting radio map while sustaining or boosting the dataset’s positioning capabilities. We show that the 7-bit representation of measurements is sufficient in positioning scenarios, and reducing the data size further using EWOk results in higher compression and faster data transfer and processing. To eliminate the inherent uncertainty of k-means we propose a data-dependent, non-random initiation scheme to ensure stability and limit variance. We further combine EWOk with principal component analysis to show its applicability in combination with other methods, and to demonstrate the efficiency of the resulting multidimensional compression. We evaluate EWOk on 25 RSS fingerprinting datasets and show that it positively impacts compression efficiency, and positioning performance. [-]
Publicado en
IEEE Transactions on Mobile Computing, 2023.Derechos de acceso
info:eu-repo/semantics/openAccess
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