A Determination Algorithm for Probability Density about Uncertain Data Streams Based on GMM

Abstract

In research of uncertain data stream in sensor network, the probability density is usually used to describe the uncertainty of continuous uncertain object values. Current researches are mainly based on the simply assumption that the values of uncertain object meets some conventional distribution, such as Gaussian distribution. However, the statistical distribution of sensor data streams cannot be described accurately in many cases. In this paper, we propose an algorithm for probability density fitting about continuous uncertain sensor data streams, which based on GMM. Experiments show that this algorithm can meet the time requirement of actual sensing application and improve the fitting effect on the probability density of the value uncertain object.