Collaborative Spectrum Sensing from Sparse Observations Using Matrix Completion for Cognitive Radio Networks

In cognitive radio, spectrum sensing is a key component to detect spectrum holes (i.e., channels not used by any primary users). Collaborative spectrum sensing among the cognitive radio nodes is expected to improve the ability of checking complete spectrum usage states. Unfortunately, due to power limitation and channel fading, available channel sensing information is far from being sufficient to tell the unoccupied channels directly. Aiming at breaking this bottleneck, we apply recent matrix completion techniques to greatly reduce the sensing information needed. We formulate the collaborative sensing problem as a matrix completion subproblem and a joint-sparsity reconstruction subproblem. Results of numerical simulations that validated the effectiveness and robustness of the proposed approach are presented. In particular, in noiseless cases, when number of primary user is small, exact detection was obtained with no more than 8% of the complete sensing information, whilst as number of primary user increases, to achieve a detection rate of 95.55%, the required information percentage was merely 16.8%

Compressed Sensing based Dynamic PSD Map Construction in Cognitive Radio Networks

In the context of spectrum sensing in cognitive radio networks, collaborative spectrum sensing has been proposed as a way to overcome multipath and shadowing, and hence increasing the reliability of the sensing. Due to the high amount of information to be transmitted, a dynamic compressive sensing approach is proposed to map the PSD estimate to a sparse domain which is then transmitted to the fusion center. In this regard, CRs send a compressed version of their estimated PSD to the fusion center, whose job is to reconstruct the PSD estimates of the CRs, fuse them, and make a global decision on the availability of the spectrum in space and frequency domains at a given time. The proposed compressive sensing based method considers the dynamic nature of the PSD map, and uses this dynamicity in order to decrease the amount of data needed to be transmitted between CR sensors’ and the fusion center. By using the proposed method, an acceptable PSD map for cognitive radio purposes can be achieved by only 20 % of full data transmission between sensors and master node. Also, simulation results show the robustness of the proposed method against the channel variations, diverse compression ratios and processing times in comparison with static methods

Framework for Security and Privacy in RFID based Telematics

Telematics by its nature requires the capture of sensor data, storage and exchange of data to obtain remote servicesMost of the commercial antivirus software fail to detect unknown and new malicious code. Proliferation of malicious code (viruses, worms, Trojans, root kits, spyware, crime ware, phishing attacks, and other malware designed to infiltrate or damage a system without user's consent) in recent years has presented a serious threat to Internet, individual users, and enterprises alike. In addition malware once confined to wired networks has now found a new breeding ground in mobile devices, automatic identification and collection (AIDC) technologies, and radio frequency identification devices (RFID) that use wireless networks to communicate and connect to the Internet. RFID systems encountered a number of threats and privacy issues. In order to stay ahead and be proactive in an asymmetric race against malicious code writers, developers of anti-malware technologies have to rely on automatic malware analysis tools. In this paper, we introduce a method of functionally classifying malware and malicious code by using well-known computational intelligent techniques. MEDiC (Malware Examiner using Disassembled Code) is our answer to a more accurate malware detection method. This work is an also attempt to address the information security issues chiefly the attacks through the databases that these RFID tags called iCLASS, which are of the active type. After a particular malicious code has been first identified, it can be analyzed to extract the signature, which provides a basis for detecting variants and mutants of the same malware in the future

Collaborative Spectrum Sensing from Sparse Observations in Cognitive Radio Networks

Spectrum sensing, which aims at detecting spectrum holes, is the precondition for the implementation of cognitive radio (CR). Collaborative spectrum sensing among the cognitive radio nodes is expected to improve the ability of checking complete spectrum usage. Due to hardware limitations, each cognitive radio node can only sense a relatively narrow band of radio spectrum. Consequently, the available channel sensing information is far from being sufficient for precisely recognizing the wide range of unoccupied channels. Aiming at breaking this bottleneck, we propose to apply matrix completion and joint sparsity recovery to reduce sensing and transmitting requirements and improve sensing results. Specifically, equipped with a frequency selective filter, each cognitive radio node senses linear combinations of multiple channel information and reports them to the fusion center, where occupied channels are then decoded from the reports by using novel matrix completion and joint sparsity recovery algorithms. As a result, the number of reports sent from the CRs to the fusion center is significantly reduced. We propose two decoding approaches, one based on matrix completion and the other based on joint sparsity recovery, both of which allow exact recovery from incomplete reports. The numerical results validate the effectiveness and robustness of our approaches. In particular, in small-scale networks, the matrix completion approach achieves exact channel detection with a number of samples no more than 50% of the number of channels in the network, while joint sparsity recovery achieves similar performance in large-scale networks.Comment: 12 pages, 11 figure

Accelerated High-Performance Compressive Sensing using the Graphics Processing Unit

This thesis demonstrates the advantages of new practical implementations of compressive sensing (CS) algorithms tailored for the graphics processing unit (CPU) using a software platform called Jacket. There exist many applications which utilize CS including medical imaging, signal processing and data acquisition which have benefited from advancements in CS. However, as problems become larger not only do they become more difficult to solve but also more computationally expensive. In light of tins, existing CS algorithms are augmented for practical use on the CPU, reaping performance gains from the highly parallel architecture of the GPU. I discuss the issues associated with this transition and analyze the effects of such a movement, as well as provide results exhibiting advantages of using CPU-based methods

Recurrent Neural Networks and Matrix Methods for Cognitive Radio Spectrum Prediction and Security

In this work, machine learning tools, including recurrent neural networks (RNNs), matrix completion, and non-negative matrix factorization (NMF), are used for cognitive radio problems. Specifically addressed are a missing data problem and a blind signal separation problem. A specialized RNN called Cellular Simultaneous Recurrent Network (CSRN), typically used in image processing applications, has been modified. The CRSN performs well for spatial spectrum prediction of radio signals with missing data. An algorithm called soft-impute for matrix completion used together with an RNN performs well for missing data problems in the radio spectrum time-frequency domain. Estimating missing spectrum data can improve cognitive radio efficiency. An NMF method called tuning pruning is used for blind source separation of radio signals in simulation. An NMF optimization technique using a geometric constraint is proposed to limit the solution space of blind signal separation. Both NMF methods are promising in addressing a security problem known as spectrum sensing data falsification attack