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Abstract Wireless sensor networks (WSN) are used for many delay-sensitive applications, e.g. military surveillance, emergency response, city management, etc. Therefore, modeling of delay is very important. This paper presents a study of the impact of sensors' sensing delay, process delay and transmission delay in the estimation of the desired unknown parameter in the WSN. Recently proposed wireless sensor networks, in the literature, assume perfect nodes and links, in view of delay. It means that no consideration has been made about the delay in the sensing, processing and, transmission procedures. The proposed method in this paper analyzes the behavior of the distributed incremental estimation algorithm in the presence of delay in wireless sensor networks. Weighted spatio-temporal energy conservation method is used to evaluate the transient and steady state behavior of the wireless sensor networks with delay without putting any restriction on regressor's distribution. The equations that illustrate mean square deviation (MSD), excess mean square error (EMSE) and mean square error (MSE) behavior of individual nodes, are driven. Also, simulations show that overall delay could be calculated to turn off nodes in some iterations without affecting the performance of the distributed estimation algorithm or adding extra latency to the network, which can improve power management strategies by modifying sleep-wake scheduling protocols. Eventually, it is shown that simulation results have a good match with derived theoretical expressions. [ABSTRACT FROM AUTHOR]

The wireless sensor networks (WSNs) is the result of evolution of wireless and networking technologies, micro-electromechanical systems, and micro-services. One important task which can be performed by nodes in WSNs is to find a common solution to a problem by using distributed processing. In this paper, we study the problem of distributed estimation, where a group of nodes are required to collectively estimate a sparse parameter vector of interest, and we solve it by an estimation algorithm based on the set membership (SM) and affine projection (AP) methods. At each iteration of the algorithm, in addition to the current data, the proposed algorithm uses data obtained from previous measurements to attain faster convergence rate. A method is also proposed to select the constraint bound for set membership such that the computational load is uniformly distributed among the nodes of network. Then the distributed estimation algorithm is analyzed and a closed form expression for the steady state mean square deviation (MSD) is developed. The performance of proposed method is assessed via computer simulations. The simulation results show that the proposed algorithm provides faster convergence rate and smaller steady state MSD value when compared to conventional methods such as diffusion least mean squares (LMS), distributed set membership normalized LMS (DSM-NLMS), and distributed APA. Moreover, it achieves lower computational load compared to the AP method. These advantages make the proposed method useful in sparse parameter vector estimation whenever the nodes have sufficient memory size. [ABSTRACT FROM AUTHOR]

There have been significant advances in fabrication of high-density microelectrode arrays using silicon micromachining technology in neural signal recording systems. The interface between microelectrodes and chemical environment is of great interest to researchers, working on extracellular stimulation. This interface is quite complex and must be modeled carefully to match experimental results. Computer simulation is a method to increase the knowledge about these arrays and to this end the finite element method (FEM) provides a strong environment for investigation of relative changes of the electrical field extension surrounding an electrode positioned in chemical environment. In this paper FEM simulation environment is used for modeling the metal–chemical interface, which provides helpful information about noise, impedance, and bandwidth for circuit designers to design the front-end electronics of these systems, more efficiently and reliable.

Compressive sensing (CS) has been widely used in wireless sensor networks (WSNs). In WSNs, the sensors are battery-powered and hence their communication and processing powers are limited. One of the dominant features of the CS is its complex recovery phase. Thus, great care should be taken into account when designing the CS recovery algorithm for WSNs. In this paper, we propose a distributed and cooperative recovery algorithm for two different cooperation modes of sensor networks including incremental and diffusion. The theoretical performance analysis of the proposed algorithms in both exact and noisy measurements is investigated. The obtained results show the superiority of the proposed method in terms of convergence rate and steady-state error compared with the non-cooperative scenario and the well-known distributed least absolute shrinkage and selection operator (D-LASSO) approach. Furthermore, the proposed structure requires much fewer measurements for exact recovery. [ABSTRACT FROM AUTHOR]