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This article investigates the extended Kalman filtering problem for a class of stochastic nonlinear systems with quantization effects and Round-Robin (RR) communication protocols. The uniform quantization is considered and the resulting quantization error is characterized as an additive white noise sequence obeying the uniform distribution over certain intervals. For the sake of reducing communication traffic of the network as well as alleviating data collisions, the RR mechanism is introduced to schedule the data transmission from the sensors to the filter. By combining the periodic property of the RR protocol and the zero-order holder strategy, the input signal of the filter is modeled by a sequence of delayed quantized measurements. The main purpose of this article is to design an extended Kalman filter for the stochastic nonlinear systems, in the simultaneous presence of quantization errors, stochastic nonlinearities, and stochastic noises, such that an optimized upper bound for the filtering error covariance is derived. By solving two coupled Riccati-like difference equations, the filter gain matrix is explicitly formulated. An RR protocol-based recursive filtering algorithm is developed for the online implementation. Furthermore, a sufficient condition is established to ensure the uniform boundedness of the filtering error in the mean-square sense. Finally, a simulation example is given to demonstrate the practical validity of the designed filter algorithm.

Most of the existing node depth-adjustment deployment algorithms for underwater wireless sensor networks (UWSNs) just consider how to optimize network coverage and connectivity rate. However, these literatures don’t discuss full network connectivity, while optimization of network energy efficiency and network reliability are vital topics for UWSN deployment. Therefore, in this study, a depth-adjustment deployment algorithm based on two-dimensional (2D) convex hull and spanning tree (NDACS) for UWSNs is proposed. First, the proposed algorithm uses the geometric characteristics of a 2D convex hull and empty circle to find the optimal location of a sleep node and activate it, minimizes the network coverage overlaps of the 2D plane, and then increases the coverage rate until the first layer coverage threshold is reached. Second, the sink node acts as a root node of all active nodes on the 2D convex hull and then forms a small spanning tree gradually. Finally, the depth-adjustment strategy based on time marker is used to achieve the three-dimensional overall network deployment. Compared with existing depth-adjustment deployment algorithms, the simulation results show that the NDACS algorithm can maintain full network connectivity with high network coverage rate, as well as improved network average node degree, thus increasing network reliability.

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A five-value memristor model is proposed, it is proved that the model has a typical hysteresis loop by analyzing the relationship between voltage and current. Then, based on the classical Liu-Chen system, a new memristor-based fourdimensional (4D) chaotic system is designed by using the five-value memristor. The trajectory phase diagram, Poincare mapping, bifurcation diagram, and Lyapunov exponent spectrum are drawn by numerical simulation. It is found that, in addition to the general chaos characteristics, the system has some special phenomena, such as hidden homogenous multistabilities, hidden heterogeneous multistabilities, and hidden super-multistabilities. Finally, according to the dimensionless equation of the system, the circuit model of the system is built and simulated. The results are consistent with the numerical simulation results, which proves the physical realizability of the five-value memristor-based chaotic system proposed in this paper.

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