Real time probability density function estimation in sensor networks

Author

Mukherjee, Arpita ; Datta, Uma

Author_Institution

Electron. & Instrum. Lab., Central Mech. Eng. Res. Inst. (CSIR), Durgapur, India

fYear

2010

fDate

15-19 Dec. 2010

Firstpage

1

Lastpage

4

Abstract

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.

Keywords

probability; wireless sensor networks; Gaussian mixture PDF; parameter estimation; probability density function estimation; wireless sensor network; Clustering algorithms; Data models; Estimation; Probability density function; Real time systems; Signal processing algorithms; Wireless sensor networks; Gaussian Mixture models; log likelihood function; sensor network;

fLanguage

English

Publisher

ieee

Conference_Titel

Wireless Communication and Sensor Networks (WCSN), 2010 Sixth International Conference on

Conference_Location

Allahabad

Print_ISBN

978-1-4244-9731-7

Type

conf

DOI

10.1109/WCSN.2010.5712301

Filename

5712301