Doğan, Deren

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https://hdl.handle.net/20.500.14411/6969
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Doğan, Deren
Dogan, Deren
D.,Doğan
D., Dogan
D.,Deren
D.,Dogan
Doğan,D.
D., Deren
Deren, Dogan
Deren, Doğan
Doğan D.
D., Doğan
Dogan,D.
Job Title
Araştırma Görevlisi
Email Address
deren.dogan@atilim.edu.tr
Main Affiliation
Mechatronics Engineering
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Scholarly Output

5

Articles

2

Views / Downloads

5/0

Supervised MSc Theses

1

Supervised PhD Theses

0

WoS Citation Count

7

Scopus Citation Count

15

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0

WoS Citations per Publication

1.40

Scopus Citations per Publication

3.00

Open Access Source

2

Supervised Theses

1

JournalCount
14th International Conference on Communications, COMM 2022 - Proceedings -- 14th International Conference on Communications, COMM 2022 -- 16 June 2022 through 18 June 2022 -- Bucharest -- 1808961
32nd IEEE Signal Processing and Communications Applications Conference (SIU) -- MAY 15-18, 2024 -- Tarsus Univ Campus, Mersin, TURKEY1
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Author: Dogan, DerenSubject: machine learning

Scholarly Output Search Results

Now showing 1 - 1 of 1
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    Article
    Citation - WoS: 7
    Citation - Scopus: 8
    A Simplified Method Based on Rssi Fingerprinting for Iot Device Localization in Smart Cities
    (Ieee-inst Electrical Electronics Engineers inc, 2024) Dogan, Deren; Dalveren, Yaser; Kara, Ali; Derawi, Mohammad
    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.