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A. J. Onumanyi
Federal University of Technology
Computer Science & IT Electrical & Electronics Engineering

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Cooperative-hybrid detection of primary user emulators in cognitive radio networks S. A. Adebo; E. N. Onwuka; A. U. Usman; A. J. Onumanyi
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 3: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (972.223 KB) | DOI: 10.11591/ijece.v10i3.pp3116-3124

Abstract

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.
An adaptive wavelet transformation filtering algorithm for improving road anomaly detection and characterization in vehicular technology Habeeb Bello-Salau; A. J. Onumanyi; B. O. Sadiq; H. Ohize; A. T. Salawudeen; A. M. Aibinu
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 5: October 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (432.952 KB) | DOI: 10.11591/ijece.v9i5.pp3664-3670

Abstract

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.
Co-Authors A. M. Aibinu A. T. Salawudeen A. U. Usman B. O. Sadiq E. N. Onwuka H. Ohize Habeeb Bello-Salau S. A. Adebo