Underwater acoustic localization is a crucial technique for most underwater applications. However, in highly dynamic marine environments, underwater acoustic localization faces many challenges, such as the stratification effect, the clock asynchronization, the node drift, and environmental noises. Concerning above problems, we propose a new underwater localization algorithm for mobile underwater acoustic sensor networks (UASNs). At first, the measurement biases are modeled as the combination of constant biases and random biases according to the physical mechanism of their generation and distribution characteristics in measured data. Then, an error-summation-incorporated Newton iteration (ESINI) algorithm is designed to compute the localization result along the direction of constant biases decrease, and a Taylor expansion is used to approach the actual localization result along the direction of random biases decrease. Subsequently, a simplified Kalman filter (SKF) fuses the two localization results and enhances the localization accuracy. In this way, the proposed algorithm effectively increases the accuracy of localization results without adding extra measurement. Finally, theoretical analyses, simulations, and lake experiments are provided to verify the proposed algorithm's effectiveness and noise resistance performance.

Online fountain codes (OFCs) have many advantages, such as low overhead, online feedback and optimal encoding, due to the feedback of the instantaneous decoding state. This paper analyzes the characteristics of underwater acoustic networks (UANs) as well as the issues of existing OFCs applied in UANs. Aiming at these issues, two optimization objectives of OFCs are put forward for UANs. In addition, a recursive OFC with limited feedback (ROFC-LF) is presented for UANs. The ROFC-LF reduces the consumption of bandwidth and energy caused by the transmission of useless coding packets. Through limited feedback, the problem of low channel utilization in UANs with half-duplex communication is solved. Furthermore, a data transmission mechanism based on the ROFC-LF for UANs is presented. The theoretical analysis and simulation results show that the proposed transmission mechanism based on the ROFC-LF scheme outperforms the existing OFC schemes in terms of overhead, computational complexity, coding efficiency and energy consumption. Consequently, the ROFC-LF is suitable for UANs with constrained resources.

As the most popular way of communication technology at the moment, wireless sensor networks have been widely concerned by academia and industry and plays an important role in military, agriculture, medicine, and other fields. Identity authentication offers the first line of defence to ensure the security communication of wireless sensor networks. Since the sensor nodes are resource-limited in the wireless networks, how to design an efficient and secure protocol is extremely significant. The current authentication protocols have the problem that the sensor nodes need to execute heavy calculation and communication consumption during the authentication process and cannot resist node capture attack, and the protocols also cannot provide perfect forward and backward security and cannot resist replay attack. Multifactor identity authentication protocols can provide a higher rank of security than single-factor and two-factor identity authentication protocols. The multigateway wireless sensor networks’ structure can provide a larger communication coverage area than the single-gateway network structure, so it has become the focus of recent studies. Therefore, we design a novel multifactor authentication protocol for multigateway wireless sensor networks, which only apply the lightweight hash function and are given biometric information to achieve a higher level of security and efficiency and a larger communication coverage area. We separately apply BAN logic, random oracle model, and AVISPA tool to validate the security of our authentication protocol in Case 1 and Case 2. We put forward sixteen evaluation criteria to comprehensively evaluate our authentication protocol. Compared with the related authentication protocols, our authentication protocol is able to achieve higher security and efficiency.

A generalized Sylvester equation is a special formulation containing the Sylvester equation, the Lyapunov equation and the Stein equation, which is often encountered in various fields. However, the time-variant generalized Sylvester equation (TVGSE) is rarely investigated in the existing literature. In this article, we propose a noise-suppressing recurrent neural network (NSRNN) model activated by saturation-allowed functions to solve the TVGSE. For comparison, the existing zeroing neural network (ZNN) models and some improved ZNN models are introduced. Additionally, theoretical analysis on the convergence and robustness of the NSRNN model is given. Furthermore, computer simulations on illustrative examples and applications to robots and acoustic source localization are carried out. Validation results synthesized by the NSRNN model and other ZNN models are provided to illustrate the ability in solving the TVGSE and dealing with noises of the NSRNN model, and the inaction of other ZNN models to noises.

In this paper, in order to analyze the existing repetitive motion planning (RMP) schemes for kinematic control of redundant robot manipulators, a generalized RMP scheme, which systematizes the existing RMP schemes, is presented. Then, the corresponding dynamic neural networks are derived, which leverage the gradient descent method with the velocity compensation with the feasibility proven theoretically. Given that the position errors of the end-effector should be tiny enough in the applications of redundant robot manipulators when executing a given task, especially for a precision instrument, the performance analyses on the control schemes are urgently desirable. In this paper, the upper bound of the position error on the existing RMP schemes is deduced theoretically and verified by computer simulations, with the relationship between the position error and the manipulability derived. In addition, dynamic neural networks are constructed to solve the generalized RMP schemes, with the joint velocity limits in RMP schemes extended to the nonconvex constraint. Finally, computer simulations based on different redundant robot manipulators and comparisons based on different controllers are conducted to verify the feasibility of the generalized RMP scheme and the proposed dynamic neural networks.