A Simplified Method Based on RSSI Fingerprinting for IoT Device Localization in Smart Cities

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dc.authoridDalveren, Yaser/0000-0002-9459-0042
dc.authoridKara, Ali/0000-0002-9739-7619
dc.contributor.authorDogan, Deren
dc.contributor.authorDalveren, Yaser
dc.contributor.authorKara, Ali
dc.contributor.authorDerawi, Mohammad
dc.date.accessioned2025-03-20T09:50:48Z
dc.date.available2025-03-20T09:50:48Z
dc.date.issued2024
dc.departmentİzmir Bakırçay Üniversitesi
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.
dc.identifier.doi10.1109/ACCESS.2024.3491977
dc.identifier.endpage163763
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-85208678995
dc.identifier.scopusqualityQ1
dc.identifier.startpage163752
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2024.3491977
dc.identifier.urihttps://hdl.handle.net/20.500.14034/2324
dc.identifier.volume12
dc.identifier.wosWOS:001354628600001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIeee-Inst Electrical Electronics Engineers Inc
dc.relation.ispartofIeee Access
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250319
dc.subjectLocation awareness
dc.subjectFingerprint recognition
dc.subjectInternet of Things
dc.subjectSmart cities
dc.subjectAccuracy
dc.subjectLogic gates
dc.subjectPerformance evaluation
dc.subjectLoRaWAN
dc.subjectTraining
dc.subjectLow-power wide area networks
dc.subjectMachine learning
dc.subjectFingerprinting
dc.subjectIQRF
dc.subjectlocalization
dc.subjectmachine learning
dc.subjectRSSI
dc.subjectsmart city
dc.titleA Simplified Method Based on RSSI Fingerprinting for IoT Device Localization in Smart Cities
dc.typeArticle

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