Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Conference Object Testbed Sdr Implementation Approach for Millimetre Wave Iot Applications(IEEE, 2022) Glazkov, Roman; Özbek, Berna; Pyattaev, Alexander; Musavian, Leila; Koucheryavy, YevgeniMillimetre wave (mmWave) communication is a promising technology which can fulfil the growing demands for spectrum for future wireless networks. One of the key areas for the development of the mmWave networks is the Internet of Things (IoT) communications within fifth generation (5G) and beyond 5G networks. For significant analysis and development of the compliant IoT systems through testbed implementation, current mmWave spectrum transceivers are too expensive when substantial number of the nodes is required by the IoT applications. Considering all the above, it is suggested to use Software Defined Radio (SDR) transceivers with a lower frequency band and with an increased distance between the nodes. The idea is to scale observation time and distance to emulate mmWave radio without actual mmWave hardware. Using scaling factors for the certain system parameters to keep the signal characteristics in accordance with the mmWave band makes it possible. This approach allows to develop mmWave IoT testbeds with significant improvement in the system scalability and cost-effectiveness without the need to transmit and receive the signal in the mmWave band. In this paper, the concept of SDR-based Hardware-in-the-loop (HIL) system combined with the observation time and distance scaling approach is proposed. As an example, a testbed with a simple Wireless Physical Network Coding scheme is implemented and demonstrated. © 2022 IEEE.Conference Object Citation - Scopus: 3A Novel Countermeasure for Selective Forwarding Attacks in Iot Networks(IEEE, 2022) Yaman, Okan; Sokat, Barış; Ayav, Tolga; Erten, Yusuf MuratAs the Internet of Things (IoT) devices become more widespread there are rising public concerns about whether or not IoT devices and their services are secure. One of the major threats they face is selective forwarding attacks performed by malicious nodes. Although packets can be lost inherently due to network conditions, malicious nodes, such as those performing blackhole attacks, may deliberately drop some, but not all of them. Therefore, distinguishing these nodes from legitimate ones is not so easy. This study has proposed a lightweight countermeasure to deal with this kind of attack in IoT networks, using the standard IPv6 Routing Protocol for Low Power and Lossy Networks (RPL). The mechanism is based on Mobile Trusted Nodes (MTNs). For the given threat model, we showed that our model has robust detection accuracy and brings no additional overhead to the network. © 2022 IEEE.Article Citation - WoS: 10Citation - Scopus: 13User Selection and Codebook Design for Noma-Based High Altitude Platform Station (haps) Communications(IEEE, 2022) Cumalı, İrem; Özbek, Berna; Karabulut Kurt, Güneş; Yanıkömeroğlu, HalimHigh altitude platform station (HAPS) communications have made a tremendous impact on recent research into sixth-generation (6G) and beyond wireless networks. The large coverage area and significant computational capability of HAPS systems enable many areas of utilization in 6G and beyond applications, including Internet of Things (IoT) services, augmented reality, and connected autonomous vehicles. In addition, non-orthogonal multiple access (NOMA) is a cutting-edge technology that can be utilized to enhance spectral efficiency in HAPS systems. In this paper, we exploit NOMA-based HAPS communications and multiple antennas to meet the connectivity, reliability, and high-data-rate requirements of 6G and beyond applications. We propose a user selection and correlation-based user pairing algorithm for a NOMA-based multi-user HAPS system. Moreover, we investigate the codebook design for HAPS communication and adapt the polar-cap codebook (PCC) to the HAPS channel which shows Rician fading propagation characteristics dominated by the line-of-sight (LOS) component. Performance evaluations show that the proposed user selection algorithm is perfectly suited to the HAPS channel and that the PCC provides a remarkable spectral efficiency.Article Citation - WoS: 8Citation - Scopus: 14An End-To Trainable Feature Selection-Forecasting Architecture Targeted at the Internet of Things(IEEE, 2021) Nakıp, Mert; Karakayalı, Kubilay; Güzeliş, Cüneyt; Rodoplu, VolkanWe develop a novel end-to-end trainable feature selection-forecasting (FSF) architecture for predictive networks targeted at the Internet of Things (IoT). In contrast with the existing filter-based, wrapper-based and embedded feature selection methods, our architecture enables the automatic selection of features dynamically based on feature importance score calculation and gamma-gated feature selection units that are trained jointly and end-to-end with the forecaster. We compare the performance of our FSF architecture on the problem of forecasting IoT device traffic against the following existing (feature selection, forecasting) technique pairs: Autocorrelation Function (ACF), Analysis of Variance (ANOVA), Recurrent Feature Elimination (RFE) and Ridge Regression methods for feature selection, and Linear Regression, Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM), 1 Dimensional Convolutional Neural Network (1D CNN), Autoregressive Integrated Moving Average (ARIMA), and Logistic Regression for forecasting. We show that our FSF architecture achieves either the best or close to the best performance among all of the competing techniques by virtue of its dynamic, automatic feature selection capability. In addition, we demonstrate that both the training time and the execution time of FSF are reasonable for IoT applications. This work represents a milestone for the development of predictive networks for IoT in smart cities of the near future.