Articles | Volume 9, issue 2
https://doi.org/10.5194/amt-9-347-2016
https://doi.org/10.5194/amt-9-347-2016
Research article
 | 
04 Feb 2016
Research article |  | 04 Feb 2016

Mobile sensor network noise reduction and recalibration using a Bayesian network

Y. Xiang, Y. Tang, and W. Zhu

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Cited articles

Arshak, K., Moore, E., Lyons, G. M., Harris, J., and Clifford, S.: A review of gas sensors employed in electronic nose applications, Sensor Rev., 24, 181–198, 2004.
Bayes toolbox: Bayes Net Toolbox for Matlab, https://code.google.com/p/bnt/, last access date: 19 October 2007.
Bettencourt, L. M., Hagberg, A., and Larkey, L.: Separating the Wheat from the Chaff: Practical Anomaly Detection Schemes in Ecological Applications of Distributed Sensor Networks, Lect. Notes Comput. Sc., 4549, 223–239, 2007.
Bychkovskiy, V., Megerian, S., Estrin, D., and Potkonjak, M.: A collaborative approach to in-place sensor calibration, Lect. Notes Comput. Sc., 2634, 301–316, 2003.
Chan, H. and Darwiche, A.: On the revision of probabilistic beliefs using uncertain evidence, Artif. Intell., 163, 67–90, 2005.
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Short summary
Motivated by unreliable sensor readings and the difficulties in calibrating sensors, we developed a Bayesian-network-based method to remove the abnormal readings and re-calibrate the sensors.