EWOk: Towards Efficient Multidimensional Compression of Indoor Positioning Datasets (Epub ahead of print)
Impacte
Scholar |
Altres documents de l'autoria: Klus, Lucie; Klus, Roman; Torres-Sospedra, Joaquín; Lohan, Elena Simona; Granell, Carlos; Nurmi, Jari
Metadades
Mostra el registre complet de l'elementcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7038
comunitat-uji-handle3:10234/8634
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INVESTIGACIONMetadades
Títol
EWOk: Towards Efficient Multidimensional Compression of Indoor Positioning Datasets (Epub ahead of print)Autoria
Data de publicació
2023-05-17Editor
Institute of Electrical and Electronics Engineers Inc.ISSN
1536-1233Cita bibliogràfica
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.Tipus de document
info:eu-repo/semantics/articleVersió de l'editorial
https://ieeexplore.ieee.org/document/10128720Versió
info:eu-repo/semantics/publishedVersionParaules clau / Matèries
Resum
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. [-]
Publicat a
IEEE Transactions on Mobile Computing, 2023.Drets d'accés
info:eu-repo/semantics/openAccess
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