A Comprehensive and Reproducible Comparison of Clustering and Optimization Rules in Wi-Fi Fingerprinting2020
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Otros documentos de la autoría: Torres-Sospedra, Joaquín; Richter, Philipp; Moreira, Adriano; Mendoza-Silva, Germán Martín; Lohan, Elena Simona; Trilles, Sergio; Matey-Sanz, Miguel; Huerta, Joaquin
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INVESTIGACIONMetadatos
Título
A Comprehensive and Reproducible Comparison of Clustering and Optimization Rules in Wi-Fi Fingerprinting2020Autoría
Fecha de publicación
2020-08-17Editor
IEEEISSN
1536-1233Cita bibliográfica
Lucie Klus, Darwin Quezada-Gaibor, Joaquín Torres-Sospedra, Elena Simona Lohan, Carlos Granell, Jari Nurmi, "RSS Fingerprinting Dataset Size Reduction Using Feature-Wise Adaptive k-Means Clustering", Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT) 2020 12th International Congress on, pp. 195-200, 2020Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://ieeexplore.ieee.org/document/9169843Versión
info:eu-repo/semantics/acceptedVersionPalabras clave / Materias
Resumen
Wi-Fi fingerprinting is a well-known technique used for indoor positioning. It relies on a pattern recognition method that compares the captured operational fingerprint with a set of previously collected reference ... [+]
Wi-Fi fingerprinting is a well-known technique used for indoor positioning. It relies on a pattern recognition method that compares the captured operational fingerprint with a set of previously collected reference samples (radio map) using a similarity function. The matching algorithms suffer from a scalability problem in large deployments with a huge density of fingerprints, where the number of reference samples in the training dataset is prohibitively large. This paper presents a comprehensive comparative study of existing methods to reduce the complexity and size of the radio map used at the operational stage. Our empirical results show that most of the methods reduce the computational burden at the expense of failing to provide a competitive accuracy. Among the studied methods, only k-means, affinity propagation, and the rules based on the strongest Access Point properly balance the positioning accuracy and computational time. In addition to the comparative results, this paper also introduces a new evaluation framework with multiple datasets, aiming at getting more general results and contributing to a better reproducibility of new proposed solutions in the future. [-]
Proyecto de investigación
Programa Torres-Quevedo, PTQ2018-009981 ; PRISMA project (#313039) ; : UIDB/00319/2020 ; UJI’s research programme PREDOC/2016/55 and POSDOC-B/2018/12Derechos de acceso
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