Fingerprint Database Enhancement by Applying Interpolation and Regression Techniques for IoT-based Indoor Localization

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dc.contributor.authorSuroso, Dwi Joko
dc.contributor.authorAdiyatma, Farid Yuli Martin
dc.contributor.authorCherntanomwong, Panarat
dc.contributor.authorSooraksa, Pitikhate
dc.date.accessioned2026-08-06T10:26:37Z
dc.date.available2026-08-06T10:26:37Z
dc.date.issued2020-01-01
dc.description.abstractMost applied indoor localization is based on distance and fingerprint techniques. The distance-based technique converts specific parameters to a distance, while the fingerprint technique stores parameters as the fingerprint database. The widely used Internet of Things (IoT) technologies, e.g., Wi-Fi and ZigBee, provide the localization parameters, i.e., received signal strength indicator (RSSI). The fingerprint technique advantages over the distance-based method as it straightforwardly uses the parameter and has better accuracy. However, the burden in database reconstruction in terms of complexity and cost is the disadvantage of this technique. Some solutions, i.e., interpolation, image-based method, machine learning (ML)-based, have been proposed to enhance the fingerprint methods. The limitations are complex and evaluated only in a single environment or simulation. This paper proposes applying classical interpolation and regression to create the synthetic fingerprint database using only a relatively sparse RSSI dataset. We use bilinear and polynomial interpolation and polynomial regression techniques to create the synthetic database and apply our methods to the 2D and 3D environments. We obtain an accuracy improvement of 0.2m for 2D and 0.13m for 3D by applying the synthetic database. Adding the synthetic database can tackle the sparsity issues, and the offline fingerprint database construction will be less burden.
dc.identifier.citationEmerging Science Journal, 4(Special issue), 167-189, 2020
dc.identifier.doi10.28991/esj-2021-SP1-012
dc.identifier.issn26109182
dc.identifier.other2-s2.0-85127799476
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/10427
dc.sourceEmerging Science Journal
dc.subjectFingerprint Database
dc.subjectFingerprint Technique
dc.subjectIndoor Localization
dc.subjectInternet of Things
dc.subjectInterpolation
dc.subjectPolynomial
dc.subjectRegression
dc.subjectZigbee
dc.titleFingerprint Database Enhancement by Applying Interpolation and Regression Techniques for IoT-based Indoor Localization
dc.typeArticle

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