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Mobile Sensor Network Noise Reduction and Re-calibration Using Bayesian Network : Volume 8, Issue 8 (31/08/2015)

By Xiang, Y.

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Book Id: WPLBN0004000577
Format Type: PDF Article :
File Size: Pages 38
Reproduction Date: 2015

Title: Mobile Sensor Network Noise Reduction and Re-calibration Using Bayesian Network : Volume 8, Issue 8 (31/08/2015)  
Author: Xiang, Y.
Volume: Vol. 8, Issue 8
Language: English
Subject: Science, Atmospheric, Measurement
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


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Zhu, W., Tang, Y., & Xiang, Y. (2015). Mobile Sensor Network Noise Reduction and Re-calibration Using Bayesian Network : Volume 8, Issue 8 (31/08/2015). Retrieved from

Description: College of Information Engineering, Zhejiang University of Technology, Hangzhou, China. People are becoming increasingly interested in mobile air quality sensor network applications. By eliminating the inaccuracies caused by spatial and temporal heterogeneity of pollutant distributions, this method shows great potentials in atmosphere researches. However, such system usually suffers from the problem of sensor noises and drift. For the sensing systems to operate stably and reliably in the real-world applications, those problems must be addressed. In this work, we exploit the correlation of different types of sensors caused by cross sensitivity to help identify and correct the outlier readings. By employing a Bayesian network based system, we are able to recover the erroneous readings and re-calibrate the drifted sensors simultaneously. Specifically, we have (1) designed a Bayesian belief network based system to detect and recover the abnormal readings; (2) developed methods to update the sensor calibration functions in-field without requirement of ground truth; and (3) deployed a real-world mobile sensor network using the custom-built M-Pods to verify our assumptions and technique. Compared with the existing Bayesian belief network technique, the experiment results on the real-world data demonstrate that our system can reduce error by 34.1 % and recover 4 times more data on average.

Mobile sensor network noise reduction and re-calibration using Bayesian network

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