In this article, we study an energy sustainable Internet-of-Underwater Things (IoUT) network with tidal energy harvesting. Specifically, an analytical model is first developed to analyze the performance of the IoUT network, characterizing the stochastic nature of energy harvesting and traffic demands of IoUT nodes, and the salient features of acoustic communication channels. It is found that the spatial uncertainty resulting from underwater acoustic communication may cause a severe fairness issue. As such, an optimization problem is formulated to maximize the network throughput under fairness constraints, by tuning the random access parameters of each node. Given the global network information, including the number of nodes, energy harvesting rates, communication distances, etc., the optimization problem can be efficiently solved with the Branch and Bound (BnB) method. Considering a realistic network where the network information may not be available at the IoUT nodes, we further propose a multiagent reinforcement learning approach for each node to autonomously adapt the random access parameter based on the interactions with the dynamic network environment. The numerical results show that the proposed learning algorithm greatly improves the throughput performance compared with the existing solutions, and approaches the derived theoretical bound.

Enabling Sustainable Underwater IoT Networks With Energy Harvesting: A Decentralized Reinforcement Learning Approach

The complexity and variability of wireless channels makes reliable mobile multiuser communications challenging. As a consequence, research on mobile multiuser communication networks has increased significantly in recent years. The outage probability (OP) is commonly employed to evaluate the performance of these networks. In this paper, exact closed-form OP expressions are derived and an OP prediction algorithm is presented. Monte-Carlo simulation is used to evaluate the OP performance and verify the analysis. Then, a grey wolf optimization back-propagation (GWO-BP) neural network based OP performance prediction algorithm is proposed. Theoretical results are used to generate training data. We also examine the extreme learning machine (ELM), locally weighted linear regression (LWLR), support vector machine (SVM), BP neural network, and wavelet neural network methods. Compared to the wavelet neural network, LWLR, SVM, BP, and ELM methods, the results obtained show that the GWO-BP method provides the best OP performance prediction.

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GWO-BP Neural Network Based OP Performance Prediction for Mobile Multiuser Communication Networks

Most existing nonblind denoising approaches assumed the noise to be homogeneous white Gaussian distribution with known intensity. However, it is difficult to know beforehand or model accurately real‐world noises with complex hybrid distribution and noise intensity. In this paper, active joint prior learning (JPL) is proposed for real‐world ISAR image blind denoising. (1) To explore strong model hierarchy and components relationship automatically, a novel graphical Dirichlet mixture process (GDMP) model is developed, where the latent representations and component hyperparameters are jointly learned from each other. (2) A multiscale joint learning strategy (MJLS) is proposed to take advantage of both the optimization‐ and discriminative learning‐based capabilities. The external noiseless, internal noisy image information and their relationships are jointly explored simultaneously. (3) Low‐rank weighted sparse learning (LWSL) is proposed to learn sparse discriminative correlation components for robust prior learning, and latent low‐rank embedding for GDMP patterns self‐adaptive inference. Extensive experimental results on ISAR image datasets demonstrate the effectiveness of the proposed model for both synthesis and real‐world noisy ISAR images, and the proposed method outperforms the state‐of‐the‐art denoising methods.

Dynamic coarse-to-fine ISAR image blind denoising using active joint prior learning

Contention-basedmedium access control (MAC) protocols for underwater acoustic sensor networks are designed to handle packet collisions that are caused by long propagation delays. However, existing protocols are known to suffer from relatively high collisions, which decrease system performance. To enhance system performance, we propose a contention-based MAC protocol that employs a widely-popular machine learning technique, namely, Q-learning. Using Q-learning, the proposed protocol allows the sensor nodes to intelligently select the back-off slots and accordingly schedule the transmission of data packets such that collisions are minimized at the receiver. Unlike in existing protocols, the sensor nodes are not required to exchange scheduling information, which implies that the proposed protocol has low complexity and overhead. Under varying traffic loads and node numbers, the proposed protocol is compared with the state-of-the-art ALOHA-Q for underwater environment (UW-ALOHA-Q), multiple access collision avoidance for underwater (MACA-U) and exponential increase exponential decrease (EIED) protocols. Results demonstrate the effectiveness of the proposed protocol in terms of energy efficiency, channel utilization, and latency.

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A Time-Slotted Data Gathering Medium Access Control Protocol Using Q-Learning for Underwater Acoustic Sensor Networks

Underwater acoustic signal denoising model based on secondary variational mode decomposition

Gaussian/non-Gaussian impulsive noises in underwater acoustic (UWA) channel seriously impact the quality of underwater acoustic communication. The common denoising algorithms are based on Gaussian noise model and are difficult to apply to the coexistence of Gaussian/non-Gaussian impulsive noises. Therefore, a new UWA noise model is described in this paper by combining the symmetric $\alpha$ -stable (S $\alpha$ S) distribution and normal distribution. Furthermore, a novel underwater acoustic signal denoising algorithm called AWMF+GDES is proposed. First, the non-Gaussian impulsive noise is adaptively suppressed by the adaptive window median filter (AWMF). Second, an enhanced wavelet threshold optimization algorithm with a new threshold function is proposed to suppress the Gaussian noise. The optimal threshold parameters are obtained based on good point set and dynamic elite group guidance combined simulated annealing selection artificial bee colony (GDES-ABC) algorithm. The numerical simulations demonstrate that the convergence speed and the convergence precision of the proposed GDES-ABC algorithm can be increased by 25% $\sim$ 66% and 21% $\sim$ 73%, respectively, compared with the existing algorithms. Finally, the experimental results verify the effectiveness of the proposed underwater acoustic signal denoising algorithm and demonstrate that both the proposed wavelet threshold optimization method based on GDES-ABC and the AWMF+GDES algorithm can obtain higher output signal-to-noise ratio (SNR), noise suppression ratio (NSR), and smaller root mean square error (RMSE) compared with the other algorithms.

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A Novel Underwater Acoustic Signal Denoising Algorithm for Gaussian/Non-Gaussian Impulsive Noise

In underwater wireless sensor networks (UWSNs), media access control (MAC) is important because it may have a significant impact on network performance; however, the complex and changeable underwater communication environment is a great challenge for the MAC protocol. In flowing water, the network nodes are constantly moving, and the number of competitors in the network also varies. The existing hybrid MAC protocol neither can adapt to the dynamic network load nor can switch the access control protocols with changing network loads, which may result in poor network performance. In order to solve the above problems, this paper proposes a load-based time slot allocation (LBTSA) protocol. The LBTSA selects the slot allocation scheme, from a set of possible schemes, according to the instantaneous network load. Then, based on the relative priority of the nodes and the optimal number of backoff stages, the host node selects the optimal access control protocol. This not only adapts well to changing network loads but also maximizes network throughput. By assuming that the number of competitors obeys a universal Poisson distribution, the LBSTA protocol and the HCR (a hybrid MAC protocol using channel reservation) protocol are compared. As the results show, the throughput of the LBTSA is higher than that of the HCR protocol, and the end-to-end delay of the LBTSA is lower than that of the HCR protocol.

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A Load-Based Hybrid MAC Protocol for Underwater Wireless Sensor Networks

The invention discloses a beam-forming method capable of suppressing multiple non-stable sub-Gaussian interference, belonging to a beam forming technology in the field of adaptive array signal processing. The invention first transforms the interference suppression problem into an optimization problem with constraints, and then designs a solution algorithm to obtain an optimal weight vector of a beam-former based on the optimization theory. By using the beam-forming method capable of suppressing the multiple non-stable sub-Gaussian interference, the situation that multiple interference sourcesexist in an underwater acoustic communication environment can be effectively solved; in addition, when the position of the interference source fluctuates and the characteristic is the sub-Gaussian interference, the beam-forming method capable of suppressing the multiple non-stable sub-Gaussian interference can obtain higher output SINR performance; and the beam-forming method disclosed by the invention has faster convergence and stronger robustness.

Beam-forming method capable of suppressing multiple non-stable sub-Gaussian interference

The invention discloses an underwater acoustic signal denoising method based on self-adaptive window filtering and wavelet threshold optimization. The method comprises the steps: firstly, Gaussian/non-Gaussian pulse noise in an underwater acoustic channel is described by combining S[alpha]S distribution and a normal distribution model; a median filtering method based on a self-adaptive window is designed, the size of the filtering window is corrected according to the number of noise points in the window, and non-Gaussian pulse noise is restrained; then, based on an improved artificial bee colony method GDES-ABC, threshold parameters of a wavelet threshold denoising method are optimized, and the Gaussian noise suppression capacity is improved. According to the method, Gaussian/non-Gaussianpulse noise in a complex underwater acoustic environment can be effectively suppressed, the receiving capability of underwater acoustic communication signals such as 2FSK, QPSK and 16QAM is improved,and a relatively high output signal-to-noise ratio and a relatively high noise suppression ratio are obtained.

Underwater acoustic signal denoising method based on self-adaptive window filtering and wavelet threshold optimization

The invention discloses a parameter wavelet threshold signal denoising method based on an improved artificial bee colony algorithm. The parameter wavelet threshold signal denoising method comprises the steps: firstly obtaining a to-be-denoised signal, carrying out wavelet transformation, and obtaining a wavelet coefficient; designing a new threshold function on the basis of a traditional thresholdfunction, proving the property of the new threshold function through mathematical derivation, and determining threshold parameters to be optimized; improving an original artificial bee colony algorithm; taking a mean square error between the to-be-denoised signal and the denoised signal as a fitness function of the improved artificial bee colony algorithm in S3, and obtaining an optimal thresholdparameter under the condition of obtaining a minimum mean square error; and applying the optimal threshold parameter obtained in the step S4 to the new threshold function in the step S2, performing shrinkage processing on the wavelet coefficient to obtain a new wavelet coefficient, and performing inverse wavelet transform to obtain a denoised signal. According to the parameter wavelet threshold signal denoising method, a smaller mean square error, a higher output signal-to-noise ratio and a larger noise rejection ratio can be obtained.

Guo Ying

Papers

A Novel Underwater Acoustic Signal Denoising Algorithm for Gaussian/Non-Gaussian Impulsive Noise

A Load-Based Hybrid MAC Protocol for Underwater Wireless Sensor Networks

Beam-forming method capable of suppressing multiple non-stable sub-Gaussian interference

Underwater acoustic signal denoising method based on self-adaptive window filtering and wavelet threshold optimization

Parameter wavelet threshold signal denoising method based on improved artificial bee colony algorithm