Real time probability density function estimation in sensor networks

The real time probability density function (PDF) estimation of any environmental function from sensor network measurement is addressed. The sensor measurement data is modeled using Gaussian mixture PDFs and an algorithm is proposed to estimate the parameters by maximizing the log likelihood function of the sensor data. Here the real time probability density function (PDF) estimation of environmental function over a geographical space where the sensors are placed has been considered. This algorithm for real time parameter estimation of any environmental function have been validated using some simulated data.