Cooperative-hybrid detection of primary user emulators in cognitive radio networks

Primary user emulator (PUE) attack occurs in Cognitive Radio Networks (CRNs) when a malicious secondary user (SU) poses as a primary user (PU) in order to deprive other legitimate SUs the right to free spectral access for opportunistic communication. In most cases, these legitimate SUs are unable to effectively detect PUEs because the quality of the signals received from a PUE may be severely attenuated by channel fading and/or shadowing. Consequently, in this paper, we have investigated the use of cooperative spectrum sensing (CSS) to improve PUE detection based on a hybrid localization scheme. We considered different pairs of secondary users (SUs) over different received signal strength (RSS) values to evaluate the energy efficiency, accuracy, and speed of the new cooperative scheme. Based on computer simulations, our findings suggest that a PUE can be effectively detected by a pair of SUs with a low Root Mean Square Error rate of 0.0047 even though these SUs may have close RSS values within the same cluster. Furthermore, our scheme performs better in terms of speed, accuracy and low energy consumption rates when compared with other PUE detection schemes. Thus, it is a viable proposition to better detect PUEs in CRNs

A real valued neural network based autoregressive energy detector for cognitive radio application

A real valued neural network (RVNN) based energy detector (ED) is proposed and analyzed for cognitive radio (CR) application. This was developed using a known two-layered RVNN model to estimate the model coefficients of an autoregressive (AR) system. By using appropriate modules and a well-designed detector, the power spectral density (PSD) of the AR system transfer function was estimated and subsequent receiver operating characteristic (ROC) curves of the detector generated and analyzed. A high detection performance with low false alarm rate was observed for varying signal to noise ratio (SNR), sample number, and model order conditions. The proposed RVNN based ED was then compared to the simple periodogram (SP), Welch periodogram (WP), multitaper (MT), Yule-Walker (YW), Burg (BG), and covariance (CV) based ED techniques. The proposed detector showed better performance than the SP, WP, and MT while providing better false alarm performance than the YW, BG, and CV. Data provided here support the effectiveness of the proposed RVNN based ED for CR application

A real valued neural network based autoregressive energy detector for cognitive radio application

A real valued neural network (RVNN) based energy detector (ED) is proposed and analyzed for cognitive radio (CR) application. This was developed using a known two-layered RVNN model to estimate the model coefficients of an autoregressive (AR) system. By using appropriate modules and a well-designed detector, the power spectral density (PSD) of the AR system transfer function was estimated and subsequent receiver operating characteristic (ROC) curves of the detector generated and analyzed. A high detection performance with low false alarm rate was observed for varying signal to noise ratio (SNR), sample number, and model order conditions. The proposed RVNN based ED was then compared to the simple periodogram (SP), Welch periodogram (WP), multitaper (MT), Yule-Walker (YW), Burg (BG), and covariance (CV) based ED techniques. The proposed detector showed better performance than the SP, WP, and MT while providing better false alarm performance than the YW, BG, and CV. Data provided here support the effectiveness of the proposed RVNN based ED for CR application

An adaptive wavelet transformation filtering algorithm for improving road anomaly detection and characterization in vehicular technology

Accelerometers are widely used in modern vehicular technologies to automatically detect and characterize road anomalies such as potholes and bumps. However, measurements from an accelerometer are usually plagued by high noise levels, which typically increase the false alarm and misdetection rates of an anomaly detection system. To address this problem, we have developed in this paper an adaptive threshold estimation technique to filter accelerometer measurements effectively to improve road anomaly detection and characterization in vehicular technologies. Our algorithm decomposes the output signal of an accelerometer into multiple scales using wavelet transformation (WT). Then, it correlates the wavelet coefficients across adjacent scales and classifies them using a newly proposed adaptive threshold technique. Furthermore, our algorithm uses a spatial filter to smoothen further the correlated coefficients before using these coefficients to detect road anomalies. Our algorithm then characterizes the detected road anomalies using two unique features obtained from the filtered wavelet coefficients to differentiate potholes from bumps. The findings from several comparative tests suggest that our algorithm successfully detects and characterizes road anomalies with high levels of accuracy, precision and low false alarm rates as compared to other known methods

A Generalization of Otsu's Method and Minimum Error Thresholding

We present Generalized Histogram Thresholding (GHT), a simple, fast, and effective technique for histogram-based image thresholding. GHT works by performing approximate maximum a posteriori estimation of a mixture of Gaussians with appropriate priors. We demonstrate that GHT subsumes three classic thresholding techniques as special cases: Otsu's method, Minimum Error Thresholding (MET), and weighted percentile thresholding. GHT thereby enables the continuous interpolation between those three algorithms, which allows thresholding accuracy to be improved significantly. GHT also provides a clarifying interpretation of the common practice of coarsening a histogram's bin width during thresholding. We show that GHT outperforms or matches the performance of all algorithms on a recent challenge for handwritten document image binarization (including deep neural networks trained to produce per-pixel binarizations), and can be implemented in a dozen lines of code or as a trivial modification to Otsu's method or MET.Comment: ECCV 202

Spectral Data for Evaluating and Validating Threshold Estimators in Cognitive Radio

We provide a collection of different spectral measurements characterizing different communication bands. These bands were measured and the numerical values obtained are provided for evaluating and validating threshold estimators and detectors in Cognitive Radio. Each sheet in the Microsoft Excel Work book contains a different set of spectral numerals. It is expected that a common set of measurements, like these provided here, will greatly enhance comparative evaluation and analysis of detectors across the CR domain

Spectral Data for Evaluating and Validating Threshold Estimators in Cognitive Radio

Different collections of spectral measurements characterizing different bands are provided. Bands were measured and the numerical values obtained are provided for evaluating and validating threshold estimators and detectors in Cognitive Radio. Each sheet in the Microsoft Excel Work book contains a different set of spectral numerals. It is expected that a common set of measurements, like these provided here, will greatly enhance comparative evaluation and analysis of detectors across the CR domain

Dataset for testing the performance of the Nonparametric Amplitude Quantization Method

In a forthcoming research paper, a new method is proposed termed the Nonparametric Amplitude Quantization Method (NPAQM) for threshold estimation in Cognitive Radio (CR). In this regard, we provide the dataset used to test the NPAQM and other state-of-the-art nonparametric methods. The dataset provided will motivate future validation exercises, serve as a benchmark for future comparative exercises, and bolster global research collaboration in the field

Dataset for testing the performance of a Cuckoo Search Optimization based Forward Consecutive Mean Excision model for threshold adaptation in Cognitive Radio

We present the dataset used to evaluate our newly proposed Cuckoo Search Optimization (CSO) based Forward Consecutive Mean Excision (FCME) model for threshold adaptation in Cognitive Radio (CR). Our CSO-FCME model is designed to effectively auto-tune the parameters of the FCME algorithm. Thus, instead of manually computing the parameter values of the FCME algorithm, which can be often error-prone, our model provides an automatic means to achieve this. Our model also achieves fully blind spectrum sensing in CR, which is highly desirable in the development of CR technologies. The dataset are provided in MATLAB format for future validation purposes and to spur global collaboration amongst researchers working on same or similar subject matter

Frequency Domain Data for Analyzing the Performance of Adaptive Threshold Estimators in Cognitive Radio

The data provided here contains MATLAB files of different frequency domain samples simulated to describe different sensing conditions in Cognitive Radio (CR). The sensing conditions considered in these datasets refer to Frequency Modulated Signals, OFDM signals, Narrow and Wideband Signals, and Real-life TV signals. These datasets are intended to be used to analyze the performance of adaptive threshold estimation algorithms typically deployed for use in the Energy Detector front end of a CR system. Researchers can thus use these datasets as a common platform for evaluating their algorithms