Online Bayesian Data Fusion in Environment Monitoring Sensor Networks

Abstract

Assuring reliable data collection in environment monitoring sensor network is a major design challenge. This paper gives a novel Bayesian model to reliably monitor physical phenomenon. We briefly review the errors on the data transfer channel between the sensor quantifying the physical phenomenon and the fusion node, and a discrete K-ary input and K-ary output channel is presented to model the data transfer channel, where K is the number of quantification levels at the sensor. Then, discrete time series models are used to estimate the mean value of the physical phenomenon, and the estimation error is modeled as a Gaussian process. Finally, based on the transition probability of the proposed data transfer channel and the probability of the estimated value transited to specific quantification levels, the level with the maximum posterior probability is decided to be the current value of the physical phenomenon. Evaluations based on real sensor data show that significant gain can be achieved by the proposed algorithms in environment monitoring sensor networks compared with channel-unaware algorithms.