Wireless sensor network (WSN) is one of the most important components of the Internet of Things (IoT). IoT on the WSN layer, measures different parameters by different sensors embedded in the multi-sensor nodes. The limitation of energy sources in the sensor nodes is one of the most important challenges in exploiting WSNs. Routing and data aggregation are two basic methods to reduce the energy consumption in the WSNs. Compressive Sensing ((CS)) is one of the most effective techniques for data aggregation in WSNs. The most studies related to the use of (CS) techniques to reduce communication cost in the network are based on the single-sensor node WSNs. So, in this paper, we show that how (CS) techniques can be applied to the multi-sensor IoT-based WSNs. Given that the sparsity of the environmental data read by multi-sensor nodes is an important parameter for using the (CS) techniques in WSNs, various different scenarios have been analyzed from the point of view of data sparsity in this study, as well as transmission methods, and data aggregation techniques in a multi-sensor WSN. To evaluate the performance of the (CS) techniques in a multi-sensor WSN, all investigated scenarios are evaluated for two important techniques of (CS) named Compressive data gathering (CDG) and hybrid Compressive Sensing (H(CS)), and in order to show the efficiency of the system in using of the (CS), these techniques have been compared to the conventional Non-(CS) method. We show that the use of H(CS) technique has a considerable effect on reducing communication costs in a multi-sensor IoT-based WSN.

In this paper, a new method is proposed to estimate the direction of arrival (DOA) using non-uniform linear array structure and modeling the measurement matrix as a DFT matrix. In order to estimate the DOA using Compressive Sensing ((CS)), continuous angle space should be divided into a discrete set using small steps. This division, leads to the increment of mutual coherence between columns of the measurement matrix and performance of the sparse recovery algorithms is degraded. To solve this problem, we propose a new method in which DFT matrix with mutual coherence of zero is used as the measurement matrix. In order to increase the accuracy of estimation, the size of DFT matrix or the number of antennas should be increased. Implementation of an array with large number of antennas is complex and expensive. A solution to decrease the number of antennas is using a non-uniform linear array and constructing a virtual uniform linear array. A virtual uniform linear array can be constructed by vectorizing the correlation matrix of the received signal of a non-uniform linear array. Increasing the number of antennas in the virtual array will increase the size of DFT matrix. Therefore, the accuracy of DOA estimation will be increased. Simulation results show that DOA estimation using Compressive Sensing, based on DFT measurement matrix, has a good performance in terms of mean square error of estimation.

Stepped frequency radars attract many attentions due to their high range resolution. Conventional processing technique in these radars is based on the IDFT. In detecting moving targets, the IDFT has the problems of range shifting and range spreading, which result in degrading target amplitude, loss of range accuracy and range resolution. To overcome these problems, the velocity compensation method is used. Since the target velocity is unknown, one should compensate the received signal with all possible velocities and choose the one with the highest and sharpest IDFT output. In this paper, by using the Compressive Sensing based algorithms, a new method for determining the range and the velocity of moving targets in the stepped frequency radar is proposed. The results show that the proposed method does not have any problem with range shifting and range spreading, and has a better performance compared with other methods in literature. Coherent processing concept is also investigated. Simulations show that using the coherent processing, the performance of Compressive Sensing based radar detectors is considerably improved.

Compressive sampling ((CS)) is a new technique for simultaneous sampling and compression of signals in which the sampling rate can be very small under certain conditions. Due to the limited number of samples, image reconstruction based on (CS) samples is a challenging task. Most of the existing (CS) image reconstruction methods have a high computational complexity as they are applied on the entire image. To reduce this complexity, block-based (CS) (B(CS)) image reconstruction algorithms have been developed in which the image sampling and reconstruction processes are applied on a block by block basis. In almost all the existing B(CS) methods, a fixed transform is used to achieve a sparse representation of the image. however such fixed transforms usually do not achieve very sparse representations, thereby degrading the reconstruction quality. To remedy this problem, we propose an adaptive block-based transform, which exploits the correlation and similarity of neighboring blocks to achieve sparser transform coefficients. We also propose an adaptive soft-thresholding operator to process the transform coefficients to reduce any potential noise and perturbations that may be produced during the reconstruction process, and also impose sparsity. Experimental results indicate that the proposed method outperforms several prominent existing methods using four different popular image quality assessment metri(CS).

Background and Objectives: Compressive Sensing ((CS)) theory has been widely used in various fields, such as wireless communications. One of the main issues in the wireless communication field in recent years is how to identify block-sparse systems. We can follow this issue, by using (CS) theory and block-sparse signal recovery algorithms. Methods: This paper presents a new block-sparse signal recovery algorithm for the adaptive block-sparse system identification scenario, named stochastic block normalized iterative hard thresholding (SBNIHT) algorithm. The proposed algorithm is a new block version of the SSR normalized iterative hard thresholding (NIHT) algorithm with an adaptive filter framework. It uses a search method to identify the blocks of the impulse response of the unknown block-sparse system that we wish to estimate. In addition, the necessary condition to guarantee the convergence for this algorithm is derived in this paper. Results: Simulation results show that the proposed SBNIHT algorithm has a better performance than other algorithms in the literature with respect to the convergence and tracking capability. Conclusion: In this study, one new greedy algorithm is suggested for the block-sparse system identification scenario. Although the proposed SBNIHT algorithm is more complex than other competing algorithms but has better convergence and tracking capability performance.

Massive multiple-input multiple-output is a promising technology in future communication networks where a large number of antennas are used. It provides huge advantages to the future communication systems in data rate, the quality of services, energy efficiency, and spectral efficiency. Linear detection algorithms can achieve a near-optimal performance in large-scale MIMO systems, due to the asymptotic orthogonal channel property. But, the performance of linear MIMO detectors degrades when the number of transmit antennas is close to the number of receive antennas (loaded scenario). Therefore, this paper proposes a series of detectors for large MIMO systems, which is capable of achieving promising performance in loaded scenarios. The main idea is to improve the performance of the detector by finding the hidden sparsity in the residual error of the received signal. At the first step, the conventional MIMO model is converted into the sparse model via the symbol error vector obtained from a linear detector. With the aid of the Compressive Sensing methods, the incorrectly detected symbols are recovered and performance improvement in the detector output is obtained. Different sparse recovery algorithms have been considered to reconstruct the sparse error signal. This study reveals that error recovery by imposing sparse constraint would decrease the bit error rate of the MIMO detector. Simulation results show that the iteratively reweighted least squares method achieves the best performance among other sparse recovery methods.

Nowadays, wireless sensor networks (WSNs) have found many applications in a variety of topi(CS). The main purpose of these networks is to measure environmental phenomena and to send read data in multi-hop paths to the sink to be exploited by users. The most important challenge in WSNs is to minimize energy consumption in sensor batteries and increase network lifetime. One of the most important techniques for reducing energy consumption in WSNs is the Compressive Sensing ((CS)) technique. (CS) reduces network energy consumption by reducing data transmission in the network and increasing the network lifetime. The use of (CS) technique in a WSN results in the production of different models of (CS) signals. These models are based on spatial, temporal and spatio-temporal sensors readings. On the other hand, in order to overcome the challenge of energy consumption, the exact recognition of energy resources in the network is essential. Energy consumption in a sensor node can be divided into two parts: (a) the energy used for computing, and (b) the energy consumed by the communication. The energy used for the computing consists of three components: 1. sensor energy consumption (data reading), 2. background energy consumption, and 3. energy consumption for processing. The power consumption of the communication includes the following: 1. energy consumption for data transmission, 2. energy consumption for data receiving, 3. energy consumption for sending messages, and 4. energy consumption for receiving messages. Hence, the existence of a model for analyzing energy consumption in a (CS)-based WSN is necessary. Several models have been developed to analyze energy consumption in a WSN, but there is not a complete model for analyzing energy consumption in a (CS)-based WSN. In this paper, we study all energy consumption components mentioned above in a (CS)-based WSN and present a complete model for energy consumption analysis. This model can optimize the design of (CS)-based WSNs energy efficiency improvement approach. To evaluate the proposed model, we use this model to analyze energy consumption in the Compressive data gathering technique which is a (CS)-based data aggregation method. Using this model can optimize the design of (CS)-based WSNs.