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PROJECT TITLE :

A Novel Statistical Model for Distributed Estimation in Wireless Sensor Networks

ABSTRACT:

During this paper, we tend to consider the problem of distributed parameter estimation in imperfect environments for wireless sensor networks (WSNs). By imperfect environments, we discuss with distortions that may be caused by sensor noise, quantization noise and channel effect. A completely unique statistical model is proposed to quantify these errors in WSNs. The first and second order statistics are derived analytically. The estimator is then chance density perform unaware. An analytical bound of the mean sq. error (MSE) performance at the fusion center is also derived. We have a tendency to additional apply the proposed methodology to the ability scheduling problem of WSNs. By formulating it as a convex optimization problem, an analytical answer is obtained. Simulation results show that the proposed approach outperforms the traditional distributed estimation ways. For the power scheduling application, the proposed method is shown to have an improved power saving compared to a classic technique within the literature.


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