Fingerprint Database Enhancement using Spatial Interpolation for IoT-based Indoor Localization

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dc.contributor.authorMartin Adiyatma, Farid Yuli
dc.contributor.authorJoko Suroso, Dwi
dc.contributor.authorCherntanomwong, Panarat
dc.date.accessioned2026-08-06T10:34:20Z
dc.date.available2026-08-06T10:34:20Z
dc.date.issued2022-01-01
dc.description.abstractThe widespread adoption of the internet of things (IoT) drives indoor location-based service (ILBS) applications forward. The core parameter of ILBS is indoor localization. Generally, indoor localization is divided into two techniques, distance-based, i.e., triangulation, and distance-free, i.e., fingerprint technique. This paper discusses the fingerprint technique because of some advantages, i.e., higher accuracy performance compared to the distance-based technique. However, the fingerprint technique has drawbacks in offline database construction: extraordinarily time-consuming and labor-intensive, which hinders its application in the real world. Furthermore, the fingerprint database needs to be updated regularly in a dynamic environment. Therefore, we propose fingerprint database enhancement based on various spatial interpolations to tackle the issues of fingerprint database construction. We apply Inverse Distance Weighted (IDW), Quadratic Spline, Cubic Spline, and Ordinary Kriging Interpolation methods to generate the synthetic database. We have conducted a measurement campaign to obtain Received Signal Strength Indicator (RSSI) as the fingerprint-based localization parameter. From our results, the interpolation methods show that the generated synthetic RSSI can provide a lower prediction error. Our proposed methods can have similar accuracy performance compared to manual fingerprints using actual data. Moreover, the synthetic RSSI data has a 0 dBm error for the best prediction and not more than 6 dBm for the worst prediction. Thus, we conclude that our proposed methods can enhance the fingerprint database and have proven to increase localization performance.
dc.identifier.citationIcsec 2022 International Computer Science and Engineering Conference 2022, 192-197, 2022
dc.identifier.doi10.1109/ICSEC56337.2022.10049367
dc.identifier.other2-s2.0-85149705346
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/12494
dc.sourceIcsec 2022 International Computer Science and Engineering Conference 2022
dc.subjectdatabase enhancement
dc.subjectfingerprint technique
dc.subjectindoor localization
dc.subjectIoT
dc.subjectRSSI
dc.subjectspatial interpolation
dc.titleFingerprint Database Enhancement using Spatial Interpolation for IoT-based Indoor Localization
dc.typeConference Paper

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