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

dc.authorscopusid 57824405000
dc.authorscopusid 51763497600
dc.authorscopusid 7102824862
dc.authorscopusid 35408917600
dc.authorwosid Kara, Ali/R-8038-2019
dc.contributor.author Dogan, Deren
dc.contributor.author Dalveren, Yaser
dc.contributor.author Kara, Ali
dc.contributor.author Derawi, Mohammad
dc.contributor.other Mechatronics Engineering
dc.date.accessioned 2024-12-05T20:48:48Z
dc.date.available 2024-12-05T20:48:48Z
dc.date.issued 2024
dc.department Atılım University en_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, Norway en_US
dc.description.abstract The 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.woscitationindex Science Citation Index Expanded
dc.identifier.citationcount 0
dc.identifier.doi 10.1109/ACCESS.2024.3491977
dc.identifier.endpage 163763 en_US
dc.identifier.issn 2169-3536
dc.identifier.scopus 2-s2.0-85208678995
dc.identifier.scopusquality Q1
dc.identifier.startpage 163752 en_US
dc.identifier.uri https://doi.org/10.1109/ACCESS.2024.3491977
dc.identifier.uri https://hdl.handle.net/20.500.14411/10280
dc.identifier.volume 12 en_US
dc.identifier.wos WOS:001354628600001
dc.identifier.wosquality Q2
dc.institutionauthor Doğan, Deren
dc.language.iso en en_US
dc.publisher Ieee-inst Electrical Electronics Engineers inc en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.scopus.citedbyCount 4
dc.subject Location awareness en_US
dc.subject Fingerprint recognition en_US
dc.subject Internet of Things en_US
dc.subject Smart cities en_US
dc.subject Accuracy en_US
dc.subject Logic gates en_US
dc.subject Performance evaluation en_US
dc.subject LoRaWAN en_US
dc.subject Training en_US
dc.subject Low-power wide area networks en_US
dc.subject Machine learning en_US
dc.subject Fingerprinting en_US
dc.subject IQRF en_US
dc.subject localization en_US
dc.subject machine learning en_US
dc.subject RSSI en_US
dc.subject smart city en_US
dc.title A Simplified Method Based on Rssi Fingerprinting for Iot Device Localization in Smart Cities en_US
dc.type Article en_US
dc.wos.citedbyCount 3
dspace.entity.type Publication
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