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Wi-Fi signals database construction using Chebyshev wavelets for indoor positioning systems

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dc.FundingDetailsThis research has been supported by National Fundamental Research Grant Scheme FRGS of Malaysia, No.01-01-17-1921FR and Universiti Sains Islam Malaysia Internal Grant, No. 0510000-PPP-FST-15218-00.
dc.contributor.affiliationsFaculty of Science and Technology
dc.contributor.affiliationsUniversiti Sains Islam Malaysia (USIM)
dc.contributor.affiliationsNilai International University
dc.contributor.affiliationsUniversiti Putra Malaysia (UPM)
dc.contributor.authorNarzullaev A.en_US
dc.contributor.authorMuminov Z.en_US
dc.contributor.authorIbragimov G.en_US
dc.date.accessioned2024-05-28T08:24:32Z
dc.date.available2024-05-28T08:24:32Z
dc.date.issued2019
dc.description.abstractNowadays fast and accurate positioning of assets and people is as a crucial part of many businesses, such as, warehousing, manufacturing and logistics. Applications that offer different services based on mobile user location gaining more and more attention. Some of the most common applications include location-based advertising, directory assistance, point-to-point navigation, asset tracking, emergency and fleet management. While outdoors mostly covered by the Global Positioning System, there is no one versatile solution for indoor positioning. For the past decade Wi-Fi fingerprinting based indoor positioning systems gained a lot of attention by enterprises as an affordable and flexible solution to track their assets and resources more effectively. The concept behind Wi-Fi fingerprinting is to create signal strength database of the area prior to the actual positioning. This process is known as a calibration carried out manually and the indoor positioning system accuracy highly depends on a calibration intensity. Unfortunately, this procedure requires huge amount of time, manpower and effort, which makes extensive deployment of indoor positioning system a challenging task. approach of constructing signal strength database from a minimal number of measurements using Chebyshev wavelets approximation. The main objective of the research is to minimize the calibration workload while providing high positioning accuracy. The field tests as well as computer simulation results showed significant improvement in signal strength prediction accuracy compared to existing approximation algorithms. Furhtermore, the proposed algorithm can recover missing signal values with much smaller number of on-site measurements compared to conventional calibration algorithm.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.18517/ijaseit.9.1.7544
dc.identifier.epage241
dc.identifier.issn20885334
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85062416928
dc.identifier.spage236
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85062416928&doi=10.18517%2fijaseit.9.1.7544&partnerID=40&md5=9db96f6607be46dc41661934157b7a24
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/8510
dc.identifier.volume9
dc.languageEnglish
dc.language.isoen_USen_US
dc.publisherInsight Societyen_US
dc.relation.ispartofOpen Accessen_US
dc.relation.ispartofInternational Journal on Advanced Science, Engineering and Information Technology
dc.sourceScopus
dc.subjectChebyshev waveletsen_US
dc.subjectIndoor positioningen_US
dc.subjectReal-time locating systemsen_US
dc.subjectWi-Fi fingerprintingen_US
dc.titleWi-Fi signals database construction using Chebyshev wavelets for indoor positioning systemsen_US
dc.title.alternativeInt. J. Adv. Sci. Eng. Inf. Technol.en_US
dc.typeArticleen_US
dspace.entity.typePublication

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