As the Internet of Things (IoT) continues to expand and reshape our world, new vertical application scenarios have emerged, such as underwater communications, leading to increased interest in academia and industries. The multiple-input–multiple-output (MIMO) technology plays a critical role in enhancing channel capacity for underwater acoustic (UWA) communications, where accurate channel prediction is essential for system performance. In this article, we propose a novel efficient channel impulse response (CIR) prediction model for the UWA MIMO communications with a small adaptive bidirectional gated recurrent unit (ABiGRU) network. The proposed model can capture the channel information without additional knowledge of the internal properties of the channel itself. Moreover, it first utilizes preceding short-term CIR data from the channel estimation for online training, and then exploits the trained model for the CIR prediction, which tracks time-varying UWA channels. To verify the effectiveness of the predicted CIRs, we design a scheme combining a space-time block coding (STBC) and minimum mean square error (MMSE) pre-equalization for the UWA MIMO system. Our proposed STBC-MMSE pre-equalization scheme has demonstrated practical feasibility and low-bit-error rate (BER) in numerical simulations. In addition, we evaluate the prediction error performance of the proposed ABiGRU network through comparison with the widely used MMSE algorithm and two common recurrent neural networks (RNNs) predictors, i.e., the gated recurrent unit and long short term memory (LSTM) network. Finally, we conduct realistic in-field UWA MIMO experiments to demonstrate and justify the superiority of the proposed ABiGRU network, which can lay the solid foundation for cost-effective UWA MIMO communications for building promising underwater IoT sensor networks.

The process of gathering of scientific data plays an important role in telemonitoring and communications technologies of underwater information. However, to obtain such a huge data in underwater-wireless-sensor-networks (UWSNs), conventional methods often fail in energy efficiency. Compressive sampling (CS) provides a new perspective to solve the problem. Unfortunately, the underwater acoustic signal is non-sparse in the time domain and the current CS methods cannot be used directly. This study adopts the discrete cosine transform (DCT) based dictionary-matrix for sparse representation. In addition, the measurement matrix is optimized via the steepest descent method for more efficient sampling. Then we introduce an approach based on an approximated l0 (AL0) norm at the receiving terminal, to search the sparse solution via the steepest descent method and projections. Combing with the measurement matrix and dictionary-matrix, the sparse estimation is used for reconstruction. Experimental results confirm the superior performances of the strategies of the proposed compressive sampling and reconstruction than those of the traditional measurement matrix and reconstruction methods including matching pursuit (MP) and orthogonal matching pursuit (OMP) methods.

Telemonitoring of diaphragmatic electromyogram (EMGdi) signal in wireless sensor networks (WSNs) and Internet of Things (IoT) holds the promise to be an evolving direction in personalized medicine. The WSNs and IoT enable EMGdi information telemonitoring and communications technologies play important roles in the process of personal medical care, especially for the respiratory diseases. However, while designing such a system, one should consider the required functionality, miniaturization, energy efficiency, etc., to make fewer resources required in WSNs and IoT. Conventional methods of data acquisition cannot energy-effectively compress data with reduced device costs. Different from the traditional compression methods, compressed sensing (CS) takes promising steps toward these challenges. Unfortunately, EMGdi is not sparse in time domain. Hence, current CS algorithms are extremely difficult to use directly for recovering EMGdi. In order to satisfy the requirements of applications of personal medical care in WSNs and IoT, this study proposes an approximated ι ₀ norm based method to search the solution via the gradient descent method, then projects the searched solution to the reconstruction feasible set. Meanwhile, this study adopts a new wavelet threshold based method to denoise the electrocardiographic interference. Experimental results are provided to testify the performance of the proposed methods.

The measurements of delay and Doppler (DD) spreading in underwater acoustic channels (UACs) have multiple applications, including communications as well as the development of a dynamic UAC simulator. However, these measurements suffer from the difficulties of fast time variations and large data sets. This paper addresses an efficient compressed sensing (CS) method to solve these problems. First, the DD spreading in UACs is studied by using a doubly spread model; second, the least-square criterion is implemented and its limit is analyzed. Subsequently, the matching pursuit (MP) method is applied to the problem by exploiting the sparsity of the DD model-based UACs. Although the MP method improves the performance of the LS method, it has unavoidable deficiencies, e.g., the redundant selections of bases that lead to a limited measurement of DD spreading. Thus, this paper proposes an improved version by inserting a projection matrix. The projected MP (PMP) method adopts QR decomposition for an efficient computation. Finally, at-sea data-based comparisons among the above mentioned three methods are conducted to verify the superiority of the PMP method.

The underwater-wireless-sensor-networks (UWSNs) enable the telemonitoring and communications technologies of underwater information play important roles in the process of gathering of scientific data in collaborative monitoring missions. However, to design such a system, several aspects must be considered to make fewer resources required, such as energy efficiency, miniaturization and the required functionality etc. Conventional methods of data compression and reconstruction fail in energy efficient. Different with the conventional compression methods, compressive sampling (CS) provides a new perspective to compress huge data with low energy consumption. Unfortunately, the recordings of underwater acoustic signal (UAS) is non-sparse in time domain. Hence, the current CS methods cannot be used directly for compression and reconstruction of UAS. This study adopts the wavelet-transform based dictionary-matrix to build a framework for sparse representation, then introduces an approach based on structured approximation l0 (SAL0) norm, which is designed by exploring and exploiting the correlation structure of UAS. The proposed method searches the optimal sparse solution via steepest descent method and then projects the solution to its feasible set. Combing with the compression-matrix and dictionary-matrix, the estimation of SAL0 method is used for reconstructing non-sparse UAS. Experimental results confirm its recovery quality for practical use, and a clear improvement than the traditional methods including l1 (L1) and orthogonal matching pursuit (OMP) methods.