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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SUSTAINABLE CITIES AND COMMUNITIES
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2

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4

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1

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2

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1

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2022 - 20244
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Now showing 1 - 4 of 4
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    Conference Object
    Citation - Scopus: 6
    A Mini-Review on Radio Frequency Fingerprinting Localization in Outdoor Environments: Recent Advances and Challenges
    (Institute of Electrical and Electronics Engineers Inc., 2022) Dogan,D.; Dalveren,Y.; Kara,A.; Department of Electrical & Electronics Engineering; Mechatronics Engineering
    A considerable growth in demand for locating the source of emissions in outdoor environments has led to the rapid development of various localization methods. Among these, RF fingerprinting (RFF) localization has become one of the most promising method due to its unique advantages resulted from the recent developments in machine learning techniques. In this short review, it is aimed to assess the existing RFF methods in the literature for outdoor localization. For this purpose, firstly, the current state of RFF localization methods in outdoor environments are overviewed. Then, the main research challenges in the development of RFF localization are highlighted. This is followed by a brief discussion on the open issues in order to give future research directions. Furthermore, the research efforts currently undertaken by the authors are briefly addressed. © 2022 IEEE.
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    Master Thesis
    Iot Erişimli Akıllı Şehirlerde Radyo Frekansı Parmak İzi Tabanlı Yayıcı Konumlandırma
    (2023) Doğan, Deren; Dalveren, Yaser; Mechatronics Engineering; Department of Electrical & Electronics Engineering
    Kablosuz teknolojinin hızlı gelişimi, Nesnelerin İnterneti'nin (IoT) önemini artırdı. IoT uygulamaları, çeşitli sektörlerde maliyetleri azaltmak ve performansı yükseltmek için kullanılıyor. Akıllı şehirlerde bu tür uygulamalardan yararlanılarak konumlandırma tabanlı hizmetler de sunulmaktadır. Coğrafi bölgelerde konumlandırma talebi nedeniyle uzun yıllardır çeşitli konumlandırma prosedürleri kullanılmaktadır. Radyo frekansı parmak izi (RFF) konumlandırması, makine öğrenimi (ML) yöntemlerindeki son gelişmelerin sağladığı avantajlar dikkate alındığında en etkili yöntemlerden biri haline geldi. Makul fiyatlı ve yüksek performanslı bir IoT kablosuz teknolojisini uygulamak, konumlandırmada zorlu bir konudur. Bu bağlamda, IQRF teknolojisi yeni fırsatlar sunmaktadır. Bu nedenle, 868 MHz bandında çalışan IQRF sensör düğümlerini içeren bir sistemde bu tez, makine öğreniminde denetimli sınıflandırma yöntemlerini uygulayan bir alınan sinyal gücü göstergesi (RSSI) parmak izi tabanlı konumlandırma yöntemi önerir. Bu amaçla, Görüş Hattı (LoS) bağlantıları için yerel bir dış ortamda ölçümler yürütüldü. Elde edilen sonuçlar, 'Torbalı Ağaçlar', 'Ağırlıklı k-NN' ve 'Orta Gaussian SVM' yöntemlerinin son derece güçlü tahmin doğruluğunu gösterir. Tezin sonuçları, akıllı şehirlerde radyo frekansı parmak izine dayalı konumlandırma sistemlerinin ilerlemesine destek olma potansiyeline sahiptir.
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    Conference Object
    Lyapunov-Based Controller Design for Precise Monitoring, Speed Control and Trajectory Planning in Autonomous Tractors With Trailers
    (Ieee, 2024) Aydin, Gulsah Demirhan; Dogan, Deren; Turken, Yusuf Tugberk; Mechatronics Engineering
    Nowadays, modern technology-based agriculture operations are replacing traditional farming practices. Smart agricultural systems have gained popularity as a result of the demand for more productive and environmentally friendly farming methods. Consequently, the agricultural sector continues to be one of the driving forces behind automation, viewing technological advancements as a means of increasing productivity while lowering costs. Automation in agriculture ranges from tractors built with apparatus that can carry out complicated tasks on their own to cultivation surveillance. This study aims to optimize the speed of an autonomous tractor through a dynamic code based on Lyapunov control method as an innovative approach in smart agriculture. Beyond speed optimization, the research also addresses practical challenges encountered in real-world scenarios, including obstacles such as living entities. By evaluating the potential of Lyapunov control methodology in the effective management of agricultural machinery, this work offers an innovative perspective on smart agricultural technologies.
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    Article
    Citation - WoS: 3
    Citation - Scopus: 4
    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; Mechatronics Engineering; Department of Electrical & Electronics Engineering
    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.