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Article Performance Evaluation of Empirical Path Loss Models for a Linear Wireless Sensor Network Deployment in Suburban and Rural Environments(2020) Dalveren, Yaser; Kara, AliThis article presents a preliminary propagation study on the accuracy of empirical path loss models for efficient planning and deployment of a linear wireless sensor network (LWSN) based on long range (LoRa) enabled sensor nodes in suburban and rural environments. Real-world deployment of such network requires accurate path loss modelling to estimate the network coverage and performance. Although several models have been studied in the literature to predict the path loss for LoRa links, the assessment of empirical path loss models within the context of low-height peer to peer configured system has not been provided yet. Therefore, this study aims at providing a performance evaluation of well-known empirical path loss models including the Log-distance, Okumura, Hata, and COST-231 Hata model in a peer to peer configured system where the sensor nodes are deployed at the same low heights. To this end, firstly, measurement campaigns are carried out in suburban and rural environments by utilizing LoRa enabled sensor nodes operating at 868 MHz band. The measured received signal strength values are then compared with the predicted values to assess the prediction accuracy of the models. The results achieved from this study show that the Okumura model has higher accuracyArticle Citation - WoS: 9Citation - Scopus: 12On the Performance of Energy Criterion Method in Wi-Fi Transient Signal Detection(Mdpi, 2022) Mohamed, Ismail; Dalveren, Yaser; Catak, Ferhat Ozgur; Kara, AliIn the development of radiofrequency fingerprinting (RFF), one of the major challenges is to extract subtle and robust features from transmitted signals of wireless devices to be used in accurate identification of possible threats to the wireless network. To overcome this challenge, the use of the transient region of the transmitted signals could be one of the best options. For an efficient transient-based RFF, it is also necessary to accurately and precisely estimate the transient region of the signal. Here, the most important difficulty can be attributed to the detection of the transient starting point. Thus, several methods have been developed to detect transient start in the literature. Among them, the energy criterion method based on the instantaneous amplitude characteristics (EC-a) was shown to be superior in a recent study. The study reported the performance of the EC- a method for a set of Wi-Fi signals captured from a particular Wi-Fi device brand. However, since the transient pattern varies according to the type of wireless device, the device diversity needs to be increased to achieve more reliable results. Therefore, this study is aimed at assessing the efficiency of the EC-a method across a large set ofWi-Fi signals captured from variousWi-Fi devices for the first time. To this end, Wi-Fi signals are first captured from smartphones of five brands, for a wide range of signalto-noise ratio (SNR) values defined as low (3 to 5 dB), medium (5 to 15 dB), and high (15 to 30 dB). Then, the performance of the EC-a method and well-known methods was comparatively assessed, and the efficiency of the EC-a method was verified in terms of detection accuracy.Article Citation - WoS: 9Multipath Exploitation in Emitter Localization for Irregular Terrains(Spolecnost Pro Radioelektronicke inzenyrstvi, 2019) Dalveren, Yaser; Kara, AliElectronic Support Measures (ESM) systems have many operational challenges while locating radar emitter's position around irregular terrains such as islands due to multipath scattering. To overcome these challenges, this paper addresses exploiting multipath scattering in passive localization of radar emitters around irregular terrains. The idea is based on the use of multipath scattered signals as virtual sensor through Geographical Information System (GIS). In this way, it is presented that single receiver (ESM receiver) passive localization can be achieved for radar emitters. The study is initiated with estimating candidate multipath scattering centers over irregular terrain. To do this, ESM receivers' Angle of Arrival (AOA) and Time of Arrival (TOA) information are required for directly received radar pulses along with multipath scattered pulses. The problem then turns out to be multiple-sensor localization problem for which Time Difference of Arrival (TDOA)-based techniques can easily be applied. However, there is high degree of uncertainty in location of candidate multipath scattering centers as the multipath scattering involves diffuse components over irregular terrain. Apparently, this causes large localization errors in TDOA. To reduce this error, a reliability based weighting method is proposed. Simulation results regarding with a simplified 3D model are also presented.Article Citation - WoS: 29Citation - Scopus: 33On the Performance of Variational Mode Decomposition-Based Radio Frequency Fingerprinting of Bluetooth Devices(Mdpi, 2020) Aghnaiya, Alghannai; Dalveren, Yaser; Kara, AliRadio frequency fingerprinting (RFF) is one of the communication network's security techniques based on the identification of the unique features of RF transient signals. However, extracting these features could be burdensome, due to the nonstationary nature of transient signals. This may then adversely affect the accuracy of the identification of devices. Recently, it has been shown that the use of variational mode decomposition (VMD) in extracting features from Bluetooth (BT) transient signals offers an efficient way to improve the classification accuracy. To do this, VMD has been used to decompose transient signals into a series of band-limited modes, and higher order statistical (HOS) features are extracted from reconstructed transient signals. In this study, the performance bounds of VMD in RFF implementation are scrutinized. Firstly, HOS features are extracted from the band-limited modes, and then from the reconstructed transient signals directly. Performance comparison due to both HOS feature sets is presented. Moreover, the lower SNR bound within which the VMD can achieve acceptable accuracy in the classification of BT devices is determined. The approach has been tested experimentally with BT devices by employing a Linear Support Vector Machine (LSVM) classifier. According to the classification results, a higher classification performance is achieved (similar to 4% higher) at lower SNR levels (-5-5 dB) when HOS features are extracted from band-limited modes in the implementation of VMD in RFF of BT devices.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: 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: 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: 5Citation - Scopus: 8Optimal Operation Mode Selection for Energy-Efficient Light-Weight Multi-Hop Time Synchronization in Linear Wireless Sensor Networks(Springer, 2020) Al Imran, Md Abdullah; Dalveren, Yaser; Tavli, Bulent; Kara, AliWe explored the joint effect of synchronization window and offset/drift mode selection on the time synchronization of linear wireless sensor networks (LWSNs). Recent advances in the field along with the availability of capable hardware led to adoption of LWSNs in diverse areas like monitoring of roads, pipelines, and tunnels. The linear topology applications are susceptible to single point of failure; therefore, energy efficient operation of LWSNs is even more important than the traditional WSNs. To address the challenge, we investigate the time synchronization mode selection for the optimum operation of a multi-hop and low-overhead LWSN. We investigate two modes of synchronization: synchronization by using only offset and synchronization by using offset in addition to the clock drift. Furthermore, we investigate the effects of synchronization window size. Our experimental results reveal that computation of offset alone for smaller window sizes and resynchronization periods is sufficient in achieving acceptable degree of synchronization.Data Paper Citation - WoS: 42Citation - Scopus: 62A Database for the Radio Frequency Fingerprinting of Bluetooth Devices(Mdpi, 2020) Uzundurukan, Emre; Dalveren, Yaser; Kara, AliRadio frequency fingerprinting (RFF) is a promising physical layer protection technique which can be used to defend wireless networks from malicious attacks. It is based on the use of the distinctive features of the physical waveforms (signals) transmitted from wireless devices in order to classify authorized users. The most important requirement to develop an RFF method is the existence of a precise, robust, and extensive database of the emitted signals. In this context, this paper introduces a database consisting of Bluetooth (BT) signals collected at different sampling rates from 27 different smartphones (six manufacturers with several models for each). Firstly, the data acquisition system to create the database is described in detail. Then, the two well-known methods based on transient BT signals are experimentally tested by using the provided data to check their solidity. The results show that the created database may be useful for many researchers working on the development of the RFF of BT devices.Article Citation - WoS: 41Citation - Scopus: 44Variational Mode Decomposition-Based Threat Classification for Fiber Optic Distributed Acoustic Sensing(Ieee-inst Electrical Electronics Engineers inc, 2020) Abufana, Saleh A.; Dalveren, Yaser; Aghnaiya, Alghannai; Kara, AliIn this study, a novel method is proposed to detect and classify the threats for fiber optic distributed acoustic sensing (DAS) systems. In the study, phase-sensitive optical time-domain reflectometry (phase-OTDR) is realized for the sensing system. The proposed method is consisted of three main stages. In the first stage, Wavelet denoising method is applied for noise reduction in the measured signal, and difference in time domain approach is used to perform high-pass filtering. Autocorrelation is then used for comparing the signal with itself over time in each bin to remove uncorrelated signals. Next, the power of the correlated signals at each bin is calculated and sorted where maximum valued bins are considered as the event signal. In the second stage, Variational Mode Decomposition (VMD) technique is used to decompose the detected event signals into a series of band-limited modes from which the event signals are reconstructed. From the reconstructed event signals, higher order statistical (HOS) features including variance, skewness, and kurtosis are extracted. In the last stage, the threats are discriminated by implementing Linear Support Vector Machine (LSVM)-based classification approach to the extracted features. In order to evaluate the effects of proposed method on the classification performance, different types of activities such as digging with hammer, pickaxe, and shovel collected from various points of a buried fiber optic cable have been used under different Signal-to-Noise Ratio (SNR) levels (& x2212;4 to & x2212;18 dB). It has observed that the classification accuracy at high/moderate (& x2212;4 to & x2212;8 dB) and low (& x2212;8 to & x2212;18 dB) SNR levels are 79.5 & x0025; and 75.2 & x0025;, respectively. To the best of authors & x2019; knowledge, this research study is the first report to use VMD technique for threat classification in phase-OTDR-based DAS systems.
