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Article Citation - WoS: 14Citation - Scopus: 26Deep Learning-Based Vehicle Classification for Low Quality Images(Mdpi, 2022) Tas, Sumeyra; Sari, Ozgen; Dalveren, Yaser; Pazar, Senol; Kara, Ali; Derawi, MohammadThis study proposes a simple convolutional neural network (CNN)-based model for vehicle classification in low resolution surveillance images collected by a standard security camera installed distant from a traffic scene. In order to evaluate its effectiveness, the proposed model is tested on a new dataset containing tiny (100 x 100 pixels) and low resolution (96 dpi) vehicle images. The proposed model is then compared with well-known VGG16-based CNN models in terms of accuracy and complexity. Results indicate that although the well-known models provide higher accuracy, the proposed method offers an acceptable accuracy (92.9%) as well as a simple and lightweight solution for vehicle classification in low quality images. Thus, it is believed that this study might provide useful perception and understanding for further research on the use of standard low-cost cameras to enhance the ability of the intelligent systems such as intelligent transportation system applications.Article From Street Canyons To Corridors: Adapting Urban Propagation Models for an Indoor IQRF Network(MDPI, 2025) Doyan, Talip Eren; Yalcinkaya, Bengisu; Dogan, Deren; Dalveren, Yaser; Derawi, MohammadAmong wireless communication technologies underlying Internet of Things (IoT)-based smart buildings, IQRF (Intelligent Connectivity Using Radio Frequency) technology is a promising candidate due to its low power consumption, cost-effectiveness, and wide coverage. However, effectively modeling the propagation characteristics of IQRF in complex indoor environments for simple and accurate network deployment remains challenging, as architectural elements like walls and corners cause substantial signal attenuation and unpredictable propagation behavior. This study investigates the applicability of a site-specific modeling approach, originally developed for urban street canyons, to characterize peer-to-peer (P2P) IQRF links operating at 868 MHz in typical indoor scenarios, including line-of-sight (LoS), one-turn, and two-turn non-line-of-sight (NLoS) configurations. The received signal powers are compared with well-known empirical models, including international telecommunication union radio communication sector (ITU-R) P.1238-9 and WINNER II, and ray-tracing simulations. The results show that while ITU-R P.1238-9 achieves lower prediction error under LoS conditions with a root mean square error (RMSE) of 5.694 dB, the site-specific approach achieves substantially higher accuracy in NLoS scenarios, maintaining RMSE values below 3.9 dB for one- and two-turn links. Furthermore, ray-tracing simulations exhibited notably larger deviations, with RMSE values ranging from 7.522 dB to 16.267 dB and lower correlation with measurements. These results demonstrate the potential of site-specific modeling to provide practical, computationally efficient, and accurate insights for IQRF network deployment planning in smart building environments.Article Citation - WoS: 8Citation - Scopus: 9Propagation Measurements for Iqrf Network in an Urban Environment(Mdpi, 2022) Bouzidi, Mohammed; Dalveren, Yaser; Mohamed, Marshed; Dalveren, Yaser; Moldsvor, Arild; Cheikh, Faouzi Alaya; Derawi, Mohammad; Dalveren, Yaser; Department of Electrical & Electronics Engineering; Department of Electrical & Electronics EngineeringRecently, IQRF has emerged as a promising technology for the Internet of Things (IoT), owing to its ability to support short- and medium-range low-power communications. However, real world deployment of IQRF-based wireless sensor networks (WSNs) requires accurate path loss modelling to estimate network coverage and other performances. In the existing literature, extensive research on propagation modelling for IQRF network deployment in urban environments has not been provided yet. Therefore, this study proposes an empirical path loss model for the deployment of IQRF networks in a peer-to-peer configured system where the IQRF sensor nodes operate in the 868 MHz band. For this purpose, extensive measurement campaigns are conducted outdoor in an urban environment for Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) links. Furthermore, in order to evaluate the prediction accuracy of well-known empirical path loss models for urban environments, the measurements are compared with the predicted path loss values. The results show that the COST-231 Walfisch-Ikegami model has higher prediction accuracy and can be used for IQRF network planning in LoS links, while the COST-231 Hata model has better accuracy in NLoS links. On the other hand, the effects of antennas on the performance of IQRF transceivers (TRs) for LoS and NLoS links are also scrutinized. The use of IQRF TRs with a Straight-Line Dipole Antenna (SLDA) antenna is found to offer more stable results when compared to IQRF (TRs) with Meander Line Antenna (MLA) antenna. Therefore, it is believed that the findings presented in this article could offer useful insights for researchers interested in the development of IoT-based smart city applications.Article Citation - WoS: 10Citation - Scopus: 20Covid-19 and E-Learning: an Exploratory Analysis of Research Topics and Interests in E-Learning During the Pandemic(Ieee-inst Electrical Electronics Engineers inc, 2022) Gurcan, Fatih; Dalveren, Gonca Gokce Menekse; Derawi, MohammadE-learning has gained further importance and the amount of e-learning research and applications has increased exponentially during the COVID-19 pandemic. Therefore, it is critical to examine trends and interests in e-learning research and applications during the pandemic period. This paper aims to identify trends and research interests in e-learning articles related to COVID-19 pandemic. Consistent with this aim, a semantic content analysis was conducted on 3562 peer-reviewed journal articles published since the beginning of the COVID-19 pandemic, using the N-gram model and Latent Dirichlet Allocation (LDA) topic modeling approach. Findings of the study revealed the high-frequency bigrams such as "online learn ", "online education ", "online teach " and "distance learn ", as well as trigrams such as "higher education institution ", "emergency remote teach ", "education online learn " and "online teach learn ". Moreover, the LDA topic modeling analysis revealed 42 topics. The topics of "Learning Needs ", "Higher Education " and "Social Impact " respectively were the most focused topics. These topics also revealed concepts, dimensions, methods, tools, technologies, applications, measurement and evaluation models, which are the focal points of e-learning field during the pandemic. The findings of the study are expected to provide insights to researchers and future studies.Article Citation - WoS: 6Citation - Scopus: 10Lpwan Cyber Security Risk Analysis: Building a Secure Iqrf Solution(Mdpi, 2023) Bouzidi, Mohammed; Amro, Ahmed; Dalveren, Yaser; Cheikh, Faouzi Alaya; Derawi, MohammadLow-power wide area network (LPWAN) technologies such as IQRF are becoming increasingly popular for a variety of Internet of Things (IoT) applications, including smart cities, industrial control, and home automation. However, LPWANs are vulnerable to cyber attacks that can disrupt the normal operation of the network or compromise sensitive information. Therefore, analyzing cybersecurity risks before deploying an LPWAN is essential, as it helps identify potential vulnerabilities and threats as well as allowing for proactive measures to be taken to secure the network and protect against potential attacks. In this paper, a security risk analysis of IQRF technology is conducted utilizing the failure mode effects analysis (FMEA) method. The results of this study indicate that the highest risk corresponds to four failure modes, namely compromised end nodes, a compromised coordinator, a compromised gateway and a compromised communication between nodes. Moreover, through this methodology, a qualitative risk evaluation is performed to identify potential security threats in the IQRF network and propose countermeasures to mitigate the risk of cyber attacks on IQRF networks.Article Citation - WoS: 17Citation - Scopus: 24Use of the Iqrf Technology in Internet-Of Smart Cities(Ieee-inst Electrical Electronics Engineers inc, 2020) Bouzidi, Mohammed; Dalveren, Yaser; Cheikh, Faouzi Alaya; Derawi, MohammadIn recent years, there has been a growing interest in building smart cities based on the Internet of Things (IoT) technology. However, selecting a low-cost IoT wireless technology that enables low-power connectivity remains one of the key challenges in integrating IoT to smart cities. In this context, the IQRF technology offers promising opportunities to provide cost-effective solutions. Yet, in the literature, there are limited studies on utilizing IQRF technology for smart city applications. Therefore, this study is aimed at increasing the awareness about the use of IQRF technology in IoT-based smart city development. For this purpose, a review of smart city architectures along with challenges/requirements in adopting IoT for smart cities is provided. Then, some of the common cost-effective IoT wireless technologies that enable low-power consumption are briefly presented. Next, the benefits of IQRF technology over other technologies are discussed by making theoretical comparisons based on technical documentations and reports. Moreover, the research efforts currently being undertaken by the authors as a part of ongoing project on the development of IoT-based smart city system in Gj & x00F8;vik Municipality, Norway, are conceptually introduced. Finally, the future research directions are addressed.Article Citation - WoS: 6Citation - Scopus: 7A 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, MohammadThe 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.Article Citation - WoS: 2Citation - Scopus: 2Advancing Mmwave Altimetry for Unmanned Aerial Systems: a Signal Processing Framework for Optimized Waveform Design(Mdpi, 2024) Awan, Maaz Ali; Dalveren, Yaser; Kara, Ali; Derawi, MohammadThis research advances millimeter-wave (mmWave) altimetry for unmanned aerial systems (UASs) by optimizing performance metrics within the constraints of inexpensive automotive radars. Leveraging the software-defined architecture, this study encompasses the intricacies of frequency modulated continuous waveform (FMCW) design for three distinct stages of UAS flight: cruise, landing approach, and touchdown within a signal processing framework. Angle of arrival (AoA) estimation, traditionally employed in terrain mapping applications, is largely unexplored for UAS radar altimeters (RAs). Time-division multiplexing multiple input-multiple output (TDM-MIMO) is an efficient method for enhancing angular resolution without compromising the size, weight, and power (SWaP) characteristics. Accordingly, this work argues the potential of AoA estimation using TDM-MIMO to augment situational awareness in challenging landing scenarios. To this end, two corner cases comprising landing a small-sized drone on a platform in the middle of a water body are included. Likewise, for the touchdown stage, an improvised rendition of zoom fast Fourier transform (ZFFT) is investigated to achieve millimeter (mm)-level range accuracy. Aptly, it is proposed that a mm-level accurate RA may be exploited as a software redundancy for the critical weight-on-wheels (WoW) system in fixed-wing commercial UASs. Each stage is simulated as a radar scenario using the specifications of automotive radar operating in the 77-81 GHz band to optimize waveform design, setting the stage for field verification. This article addresses challenges arising from radial velocity due to UAS descent rates and terrain variation through theoretical and mathematical approaches for characterization and mandatory compensation. While constant false alarm rate (CFAR) algorithms have been reported for ground detection, a comparison of their variants within the scope UAS altimetry is limited. This study appraises popular CFAR variants to achieve optimized ground detection performance. The authors advocate for dedicated minimum operational performance standards (MOPS) for UAS RAs. Lastly, this body of work identifies potential challenges, proposes solutions, and outlines future research directions.Article Citation - WoS: 7Citation - Scopus: 7Towards Mmwave Altimetry for Uas: Exploring the Potential of 77 Ghz Automotive Radars(Mdpi, 2024) Awan, Maaz Ali; Dalveren, Yaser; Kara, Ali; Derawi, MohammadPrecise altitude data are indispensable for flight navigation, particularly during the autonomous landing of unmanned aerial systems (UASs). Conventional light and barometric sensors employed for altitude estimation are limited by poor visibility and temperature conditions, respectively, whilst global positioning system (GPS) receivers provide the altitude from the mean sea level (MSL) marred with a slow update rate. To cater to the landing safety requirements, UASs necessitate precise altitude information above ground level (AGL) impervious to environmental conditions. Radar altimeters, a mainstay in commercial aviation for at least half a century, realize these requirements through minimum operational performance standards (MOPSs). More recently, the proliferation of 5G technology and interference with the universally allocated band for radar altimeters from 4.2 to 4.4 GHz underscores the necessity to explore novel avenues. Notably, there is no dedicated MOPS tailored for radar altimeters of UASs. To gauge the performance of a radar altimeter offering for UASs, existing MOPSs are the de facto choice. Historically, frequency-modulated continuous wave (FMCW) radars have been extensively used in a broad spectrum of ranging applications including radar altimeters. Modern monolithic millimeter wave (mmWave) automotive radars, albeit designed for automotive applications, also employ FMCW for precise ranging with a cost-effective and compact footprint. Given the technology maturation with excellent size, weight, and power (SWaP) metrics, there is a growing trend in industry and academia to explore their efficacy beyond the realm of the automotive industry. To this end, their feasibility for UAS altimetry remains largely untapped. While the literature on theoretical discourse is prevalent, a specific focus on mmWave radar altimetry is lacking. Moreover, clutter estimation with hardware specifications of a pure look-down mmWave radar is unreported. This article argues the applicability of MOPSs for commercial aviation for adaptation to a UAS use case. The theme of the work is a tutorial based on a simplified mathematical and theoretical discussion on the understanding of performance metrics and inherent intricacies. A systems engineering approach for deriving waveform specifications from operational requirements of a UAS is offered. Lastly, proposed future research directions and insights are included.
