7 results
Search Results
Now showing 1 - 7 of 7
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: 7Citation - Scopus: 8Flexible and Lightweight Mitigation Framework for Distributed Denial-Of Attacks in Container-Based Edge Networks Using Kubernetes(Ieee-inst Electrical Electronics Engineers inc, 2024) Koksal, Sarp; Catak, Ferhat Ozgur; Dalveren, YaserMobile Edge Computing (MEC) has a significant potential to become more prevalent in Fifth Generation (5G) networks, requiring resource management that is lightweight, agile, and dynamic. Container-based virtualization platforms, such as Kubernetes, have emerged as key enablers for MEC environments. However, network security and data privacy remain significant concerns, particularly due to Distributed Denial-of-Service (DDoS) attacks that threaten the massive connectivity of end-devices. This study proposes a defense mechanism to mitigate DDoS attacks in container-based MEC networks using Kubernetes. The mechanism dynamically scales Containerized Network Functions (CNFs) with auto-scaling through an Intrusion Detection and Prevention System (IDPS). The architecture of the proposed mechanism leverages distributed edge clusters and Kubernetes to manage resources and balance the load of IDPS CNFs. Experiments conducted in a real MEC environment using OpenShift and Telco-grade MEC profiles demonstrate the effectiveness of the proposed mechanism against Domain Name System (DNS) flood and Yo-Yo attacks. Results also verify that Kubernetes efficiently meets the lightweight, agile, and dynamic resource management requirements of MEC networks.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.Article Citation - WoS: 9Citation - Scopus: 12A Simplified Model for Characterizing the Effects of Scattering Objects and Human Body Blocking Indoor Links at 28 Ghz(Ieee-inst Electrical Electronics Engineers inc, 2019) Dalveren, Yaser; Alabish, Ahmed H.; Kara, AliThis paper presents a simple approach to characterize the effects of scattering objects around indoor links at 28 GHz while the link is fully blocked by a human body. The effects of scattering objects nearby the link were studied by conducting measurements with a metallic reflector and the human body. Here, the basic mechanisms of wave propagation, such as reflection and diffraction, were accounted for each scattering object. To predict the attenuation caused by the metallic reflector, a specular reflection model was employed in reflection modeling. In diffraction modeling, on the other hand, the double knife-edge diffraction (DKED) model was exploited to predict the attenuation by the human body. Simulations were then compared with measurements to evaluate the prediction accuracy of the models. Results indicate that the presented simple models work well for indoor links. Therefore, the results of this paper could be extended to model multiple human bodies near the indoor links of fifth generation (5G) systems.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: 11Citation - Scopus: 14Performance Assessment of Transient Signal Detection Methods and Superiority of Energy Criterion (ec) Method(Ieee-inst Electrical Electronics Engineers inc, 2020) Mohamed, Ismail S.; Dalveren, Yaser; Kara, AliRadio frequency fingerprinting (RFF) based on RF transients is one of the most effective techniques for improving wireless security. For an efficient RFF development, RF transients need to be accurately detected. However, the detection of the transient starting point remains a main challenge due to the channel noise. In the literature, several methods have been presented to detect the starting point of the transient signals. As an alternative to these methods, this study proposes a method that utilizes Energy Criterion (EC) technique for the first time. In order to test its performance, firstly, an extensive dataset consisting of Wi-Fi signals recorded under realistic Signal-to-Noise Ratio (SNR) conditions is created. Using the provided dataset, the proposed method as well as common transient detection methods are employed for transient start detection. Then, the effect of SNR on the performance of transient start detection is evaluated. Moreover, a performance comparison between the methods is provided based on their respective computational speed and complexity. The results prove the feasibility and efficiency of the proposed method to detect the transient starting point for RFF of Wi-Fi device identification. As to the knowledge of the authors, this study is the first report that comparatively assesses the transient detection methods by using extensive data under realistic noise conditions.Article Radar Emitter Localization Based on Multipath Exploitation Using Machine Learning(Ieee-inst Electrical Electronics Engineers inc, 2024) Catak, Ferhat Ozgur; Al Imran, Md Abdullah; Dalveren, Yaser; Yildiz, Beytullah; Kara, AliIn this study, a Machine Learning (ML)-based approach is proposed to enhance the computational efficiency of a particular method that was previously proposed by the authors for passive localization of radar emitters based on multipath exploitation with a single receiver in Electronic Support Measures (ESM) systems. The idea is to utilize a ML model on a dataset consisting of useful features obtained from the priori-known operational environment. To verify the applicability and computational efficiency of the proposed approach, simulations are performed on the pseudo-realistic scenes to create the datasets. Well-known regression ML models are trained and tested on the created datasets. The performance of the proposed approach is then evaluated in terms of localization accuracy and computational speed. Based on the results, it is verified that the proposed approach is computationally efficient and implementable in radar detection applications on the condition that the operational environment is known prior to implementation.
