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Lightweight Wi-Fi Fingerprinting with a Novel RSS Clustering Algorithm
dc.contributor.author | Quezada Gaibor, Darwin | |
dc.contributor.author | Torres-Sospedra, Joaquín | |
dc.contributor.author | Nurmi, Jari | |
dc.contributor.author | Koucheryavy, Yevgeni | |
dc.contributor.author | Huerta, Joaquin | |
dc.date.accessioned | 2022-04-13T07:24:05Z | |
dc.date.available | 2022-04-13T07:24:05Z | |
dc.date.issued | 2021-11-29 | |
dc.identifier.citation | QUEZADA-GAIBOR, Darwin, et al. Lightweight Wi-Fi Fingerprinting with a Novel RSS Clustering Algorithm. En 2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN). IEEE, 2021. p. 1-8. | ca_CA |
dc.identifier.uri | http://hdl.handle.net/10234/197332 | |
dc.description | Ponencia presentada en la 2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN), 29 Nov.-2 Dec. 2021, Lloret de Mar (Spain) | ca_CA |
dc.description.abstract | Nowadays, several indoor positioning solutions sup-port Wi-Fi and use this technology to estimate the user position. It is characterized by its low cost, availability in indoor and outdoor environments, and a wide variety of devices support Wi-Fi technology. However, this technique suffers from scalability problems when the radio map has a large number of reference fingerprints because this might increase the time response in the operational phase. In order to minimize the time response, many solutions have been proposed along the time. The most common solution is to divide the data set into clusters. Thus, the incoming fingerprint will be compared with a specific number of samples grouped by, for instance similarity (clusters). Many of the current studies have proposed a variety of solutions based on the modification of traditional clustering algorithms in order to provide a better distribution of samples and reduce the computational load. This work proposes a new clustering method based on the maximum Received Signal Strength (RSS) values to join similar fingerprints. As a result, the proposed fingerprinting clustering method outperforms three of the most well-known clustering algorithms in terms of processing time at the operational phase of fingerprinting. | ca_CA |
dc.format.extent | 8 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | IEEE | ca_CA |
dc.relation.isPartOf | 2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN) | ca_CA |
dc.rights | © Copyright 2022 IEEE - All rights reserved. | ca_CA |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | ca_CA |
dc.subject | indoor positioning | ca_CA |
dc.subject | Wi-Fi fingerprinting | ca_CA |
dc.subject | clustering | ca_CA |
dc.subject | computing efficiency | ca_CA |
dc.title | Lightweight Wi-Fi Fingerprinting with a Novel RSS Clustering Algorithm | ca_CA |
dc.type | info:eu-repo/semantics/conferenceObject | ca_CA |
dc.identifier.doi | 10.1109/IPIN51156.2021.9662612 | |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
dc.type.version | info:eu-repo/semantics/submittedVersion | ca_CA |
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