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.

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.