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dc.contributor.authorKhandker, Syed
dc.contributor.authorTorres-Sospedra, Joaquín
dc.contributor.authorRistaniemi, Tapani
dc.date.accessioned2020-06-25T06:11:03Z
dc.date.available2020-06-25T06:11:03Z
dc.date.issued2020
dc.identifier.citationKhandker, S., Torres-Sospedra, J., & Ristaniemi, T. (2020). Analysis of Received Signal Strength Quantization in Fingerprinting Localization. <i>Sensors</i>, <i>20</i>(11), Article 3203. <a href="https://doi.org/10.3390/s20113203" target="_blank">https://doi.org/10.3390/s20113203</a>
dc.identifier.otherCONVID_35917736
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/70909
dc.description.abstractIn recent times, Received Signal Strength (RSS)-based Wi-Fi fingerprinting localization has become one of the most promising techniques for indoor localization. The primary aim of RSS is to check the quality of the signal to determine the coverage and the quality of service. Therefore, fine-resolution RSS is needed, which is generally expressed by 1-dBm granularity. However, we found that, for fingerprinting localization, fine-granular RSS is unnecessary. A coarse-granular RSS can yield the same positioning accuracy. In this paper, we propose quantization for only the effective portion of the signal strength for fingerprinting localization. We found that, if a quantized RSS fingerprint can carry the major characteristics of a radio environment, it is sufficient for localization. Five publicly open fingerprinting databases with four different quantization strategies were used to evaluate the study. The proposed method can help to simplify the hardware configuration, enhance security, and save approximately 40–60% storage space and data traffic.en
dc.format.mimetypeapplication/pdf
dc.languageeng
dc.language.isoeng
dc.publisherMDPI
dc.relation.ispartofseriesSensors
dc.rightsCC BY 4.0
dc.subject.otherfingerprinting
dc.subject.otherquantization
dc.subject.otherindoor positioning
dc.titleAnalysis of Received Signal Strength Quantization in Fingerprinting Localization
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202006255099
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMathematical Information Technologyen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.description.reviewstatuspeerReviewed
dc.relation.issn1424-8220
dc.relation.numberinseries11
dc.relation.volume20
dc.type.versionpublishedVersion
dc.rights.copyright© 2020 by the authors. Licensee MDPI, Basel, Switzerland.
dc.rights.accesslevelopenAccessfi
dc.subject.ysosisätilapaikannus
dc.subject.ysolangattomat lähiverkot
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p27620
jyx.subject.urihttp://www.yso.fi/onto/yso/p2118
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.3390/s20113203
jyx.fundinginformationSyed Khandker expresses his warm thanks to the Riitta and Jorma J. Takanen foundation for its financial support. Joaquín Torres-Sospedra is funded by the Torres-Quevedo Programme of the Spanish government, Grant No. PTQ2018-009981 (Project INSIGNIA).


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