Mohammed Farrag

Wide-Band Cooperative Compressive Spectrum Sensing for Cognitive Radio Systems Using Distributed Sensing Matrix

In this paper, cooperative compressive spectrum sensing is considered to enable accurate sensing of the wide-band spectrum. The proposed algorithm is based on compressive sensing theory and aims to reduce the hardware complexity of the cognitive radio receiver by distributing the sensing work among groups of sensing nodes. The proposed algorithm classifies the cooperated sensing nodes into different sensing groups depending on the quality of the reporting channel between the sensing node and the fusion center (FC). To sense the wide- band analog signal and take a global decision about spectrum occupancy, each node uses its local sensing matrix, which is assigned to its sensing group and a part of a global sensing matrix at the FC. The size of the local sensing matrix of each sensing node,and consequently the contribution of this node in the overall measurement vector, depends on its sensing group. The FC classifies and rearranges the compressed data to formulate one global measurement vector which is used with a global sensing matrix to estimate the wide-band signal spectrum. The receiver operation characteristics (ROC) of the overall spectrum sensing system show that the proposed receiver provides more protection to primary users (higher detection probability) at the same secondary user throughput (probability of false alarm).

An energy consumption model for wireless sensor networks

Energy consumption and energy modeling are important issues in designing and implementing of Wireless Sensor Networks (WSNs), which help the designers to optimize the energy consumption in WSN nodes. Good knowledge of the sources of energy consumption in WSNs is the first step to reduce energy consumption. Therefore, an accurate energy model is required for the evaluation of communication protocols. In this paper, we provide an energy model for WSNs considering the physical layer and MAC layer parameters by determining the energy consumed per payload bit transferred without error over AWGN channel. We show how the transmission power must be chosen in order to achieve energy-efficient communications over AWGN channel. We also find that, for each modulation scheme, there are optimal transmission power at which the energy consumption is minimized. Moreover, we investigated the energy saving gained from optimizing the constellation size.

C44. Secure cooperative blindly-optimized compressive spectrum sensing for cognitive radio

In this paper, we propose a reduced complexity secure cooperative cognitive radio spectrum sensing algorithm for wideband systems. The proposed scheme aims to reduce the receiver hardware complexity by minimizing the required sampling rates. The proposed scheme is immune to spectrum sensing data falsification attacks. Each cognitive radio node samples the analog wideband signal from the primary users using an analog-to-information converter and sends the compressed signal to the fusion center that makes a global decision on the spectrum availability. Using the proposed authentication algorithm, the fusion center selects a limited number of trustful nodes and jointly reconstructs an estimate of the wideband signal spectrum. The proposed sensing algorithm blindly optimizes the compressive sensing sparsifying bases without prior knowledge about the sampled spectrum characteristics. Comparing the performance with previous sensing techniques shows that our proposed scheme has a better security and receiver operating characteristics at the same sampling rates.

Optimized bases compressive spectrum sensing for wideband cognitive radio

Designing efficient spectrum sensing techniques with low power consumption is crucial for the success of cognitive radio (CR). This is particularly challenging when sensing a wideband spectrum due to the high sampling rate required. Compressive sensing (CS) theory states that a signal can be measured at a rate significantly lower than the Nyquist rate and consequently reconstructed from the measurements using an optimization process. The minimum number of measurements required for reliable reconstruction of the sampled signal depends on the sparsity of the measured signal. To improve the performance of compressive spectrum sensing, we optimize the sparsifying bases used to represent the measured spectrum. Entropy-based best basis selection algorithm of Coifman and Wickerhauser (CW) is deployed to find the optimum basis. Our simulation results show that the proposed compressive sampling technique can improve the spectrum estimation accuracy and enhance the detection and false-alarm probabilities of the CR system at the same sampling rates.

C20. Energy consumption and lifetime analysis for Wireless Sensor Networks

Energy consumption and energy modeling are important issues in designing and implementing of Wireless Sensor Networks (WSNs), which help the designers to optimize the energy consumption in WSN nodes. Good knowledge of the sources of energy consumption in WSNs is the first step to reduce energy consumption. Therefore, an accurate energy model is required for the evaluation of communication protocols. In this paper, we present an energy analysis technique for WSNs considering the physical layer parameters by determining the energy consumed per payload bit transferred without error over AWGN channel. We show how the transmission power must be chosen in order to achieve energy-efficient communications over AWGN channel and provide a closed-form expression for optimum transmission power. We also find that, for each modulation scheme, there are optimal transmission power at which the energy consumption is minimized. The proposed model can be used to analyse the WSNs energy consumption, to evaluate communication protocols, and it can also use to estimate energy consumption and network lifetime which used for on-line energy accounting.

A blindly optimized compressive sensing receiver for cognitive radio

This paper proposes a spectrum sensing algorithm for cognitive radio transceivers to save in sampling rates and receiver hardware complexity. The proposed receiver blindly optimizes its compressed sensing sparsifying bases and has improved receiver-operating characteristics.

Compressed measurements based spectrum sensing for wideband cognitive radio systems

Spectrum sensing is the most important component in the cognitive radio (CR) technology. Spectrum sensing has considerable technical challenges, especially in wideband systems where higher sampling rates are required which increases the complexity and the power consumption of the hardware circuits. Compressive sensing (CS) is successfully deployed to solve this problem. Although CS solves the higher sampling rate problem, it does not reduce complexity to a large extent. Spectrum sensing via CS technique is performed in three steps: sensing compressed measurements, reconstructing the Nyquist rate signal, and performing spectrum sensing on the reconstructed signal. Compressed detectors perform spectrum sensing from the compressed measurements skipping the reconstruction step which is the most complex step in CS. In this paper, we propose a novel compressed detector using energy detection technique on compressed measurements sensed by the discrete cosine transform (DCT) matrix. The proposed algorithm not only reduces the computational complexity but also provides a better performance than the traditional energy detector and the traditional compressed detector in terms of the receiver operating characteristics. We also derive closed form expressions for the false alarm and detection probabilities. Numerical results show that the analytical expressions coincide with the exact probabilities obtained from simulations.

Wide-Band Secure Compressed Spectrum Sensing for Cognitive Radio Systems

Cooperative wide-band spectrum sensing has been considered to enable cognitive radio operation of wireless regional area networks (WRAN) in the UHF and VHF TV broadcasting bands. In this paper, cooperative compressed spectrum sensing is considered to enable fast sensing of the wide-band spectrum. The speed and accuracy of spectrum sensing are improved by further optimization of the compressed sensing receiver, which is done blindly without any prior knowledge of the sensed signal. Enhanced compressed spectrum sensing algorithms are proposed for the cases of individual spectrum sensing and cooperative spectrum sensing. The cooperative signal reconstruction process is modifled to optimally combine the received measurements at the fusion center. A low complexity authentication mechanism, which is inherent to cooperative compressed spectrum sensing, is proposed to make the cognitive radio system immune to adversary attacks.

Design and implementation of building energy monitoring and management system based on wireless sensor networks

Wireless sensor networks (WSNs) play a key role in extending the smart grid implementation towards residential premises and energy management applications. Efficient supply and demand balance, and consequently reducing the electricity expenses and carbon emissions, is an immediate benefit of implementing smart grids. In this paper, design and implementation of an energy management system (EMS) for efficient load management are proposed. The EMS reduces the consumption of the consumers at the peak load hours and thus reduces the carbon emissions of the household. The proposed system consists of two main parts. The first part is an Energy Management Unit (EMU) which has a graphical user interface for runtime monitoring and control. The second part is sensor nodes which measure the power consumption of the different loads and transfer it to the EMU via multi-hop network. The EMU is implemented using NI LABVIEW software and XBee-PRO ZigBee module to communicate with sensor nodes. Hardware model is implemented using Arduino Uno microcontroller, XBee-PRO ZigBee module and the ACS712 current sensor. The EMS is applied to building of Electrical Engineering Department at Assiut University as a case study.

Detection of primary user signal in wideband cognitive radio networks exploiting DCT as sensing matrix

Wideband spectrum sensing is a challenging task in wideband cognitive radio networks. It needs to be efficient, robust and fast. However, there are many challenges facing sensing in wideband spectrum. One of these challenges is Nyquist sampling rate bottleneck which required high speed DSP and large storage spaces. Compressive sensing is a pioneer solution for wideband spectrum sensing which have proved sampling below Nyquist criterion. In this paper, we provide mathematical expression that computes the number of measurements required for compressed detection exploiting DCT as sensing matrix. The simulation results show that the accuracy of detection for certain number measurements is similar to the results obtained by compressed measurements based spectrum sensing for wideband cognitive radio systems with small relative error. The comparison is based on term of probability of detection versus compression ratios.