A Simplified Method Based on Rssi Fingerprinting for Iot Device Localization in Smart Cities

dc.authorscopusid57824405000
dc.authorscopusid51763497600
dc.authorscopusid7102824862
dc.authorscopusid35408917600
dc.authorwosidKara, Ali/R-8038-2019
dc.contributor.authorDogan, Deren
dc.contributor.authorDalveren, Yaser
dc.contributor.authorKara, Ali
dc.contributor.authorDerawi, Mohammad
dc.date.accessioned2024-12-05T20:48:48Z
dc.date.available2024-12-05T20:48:48Z
dc.date.issued2024
dc.departmentAtılım Universityen_US
dc.department-temp[Dogan, Deren] Atilim Univ, Dept Mechatron Engn, TR-06830 Ankara, Turkiye; [Dalveren, Yaser] Izmir Bakircay Univ, Dept Elect & Elect Engn, TR-35665 Izmir, Turkiye; [Kara, Ali] Gazi Univ, Dept Elect & Elect Engn, TR-06570 Ankara, Turkiye; [Derawi, Mohammad] Norwegian Univ Sci & Technol, Dept Elect Syst, N-2815 Gjovik, Norwayen_US
dc.description.abstractThe Internet of Things (IoT) has significantly improved location-based services in smart cities, such as automated public transportation and traffic management. Estimating the location of connected devices is a critical problem. Low Power Wide Area Network (LPWAN) technologies are used for localization due to their low power consumption and long communication range. Recent advances in Machine Learning have made Received Signal Strength Indicator (RSSI) fingerprinting with LPWAN technologies effective. However, this requires a connection between devices and gateways or base stations, which can increase network deployment, maintenance, and installation costs. This study proposes a cost-effective RSSI fingerprinting solution using IQRF technology for IoT device localization. The region of interest is divided into grids to provide training locations, and measurements are conducted to create a training dataset containing RSSI fingerprints. Pattern matching is performed to localize the device by comparing the fingerprint of the end device with the fingerprints in the created database. To evaluate the efficiency of the proposed solution, measurements were conducted in a short-range local area ( $80\times 30$ m) at 868 MHz. In the measurements, four IQRF nodes were utilized to receive the RSSIs from a transmitting IQRF node. The performances of well-known ML classifiers on the created dataset are then comparatively assessed in terms of test accuracy, prediction speed, and training time. According to the results, the Bagged Trees classifier demonstrated the highest accuracy with 96.87%. However, with an accuracy of 95.69%, the Weighted k-NN could also be a reasonable option for real-world implementations due to its faster prediction speed (37615 obs/s) and lower training time (28.1 s). To the best of the authors' knowledge, this is the first attempt to explore the feasibility of the IQRF networks to develop a RSSI fingerprinting-based IoT device localization in the literature. The promising results suggest that the proposed method could be used as a low-cost alternative for IoT device localization in short-range location-based smart city applications.en_US
dc.description.woscitationindexScience Citation Index Expanded
dc.identifier.citationcount0
dc.identifier.doi10.1109/ACCESS.2024.3491977
dc.identifier.endpage163763en_US
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-85208678995
dc.identifier.scopusqualityQ1
dc.identifier.startpage163752en_US
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2024.3491977
dc.identifier.urihttps://hdl.handle.net/20.500.14411/10280
dc.identifier.volume12en_US
dc.identifier.wosWOS:001354628600001
dc.identifier.wosqualityQ2
dc.language.isoenen_US
dc.publisherIeee-inst Electrical Electronics Engineers incen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.scopus.citedbyCount0
dc.subjectLocation awarenessen_US
dc.subjectFingerprint recognitionen_US
dc.subjectInternet of Thingsen_US
dc.subjectSmart citiesen_US
dc.subjectAccuracyen_US
dc.subjectLogic gatesen_US
dc.subjectPerformance evaluationen_US
dc.subjectLoRaWANen_US
dc.subjectTrainingen_US
dc.subjectLow-power wide area networksen_US
dc.subjectMachine learningen_US
dc.subjectFingerprintingen_US
dc.subjectIQRFen_US
dc.subjectlocalizationen_US
dc.subjectmachine learningen_US
dc.subjectRSSIen_US
dc.subjectsmart cityen_US
dc.titleA Simplified Method Based on Rssi Fingerprinting for Iot Device Localization in Smart Citiesen_US
dc.typeArticleen_US
dc.wos.citedbyCount0
dspace.entity.typePublication

Files

Collections