Recursive bayesian approaches for auto calibration in drift aware wireless sensor networks

Takruri, MS; Challa, S; Chakravorty, R

Recursive bayesian approaches for auto calibration in drift aware wireless sensor networks

Takruri, MS Challa, S Chakravorty, R

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Publisher:: Academy Publisher
Publication Type:: Journal Article
Citation:: Journal of Networks, 2010, 5 (7), pp. 823 - 832
Issue Date:: 2010-01

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Field	Value	Language
dc.contributor.author	Takruri, MS	en_US
dc.contributor.author	Challa, S	en_US
dc.contributor.author	Chakravorty, R	en_US
dc.date.issued	2010-01	en_US
dc.identifier.citation	Journal of Networks, 2010, 5 (7), pp. 823 - 832	en_US
dc.identifier.issn	1796-2056	en_US
dc.identifier.uri	http://hdl.handle.net/10453/15437
dc.description.abstract	The purpose for wireless sensor networks is to deploy low cost sensors with sufficient computing and communication capabilities to support networked sensing applications. Even when the sensors are properly calibrated at the time of their deployment, they develop drift in their readings leading to biased sensor measurements. Noting that a physical phenomenon in a certain area follows some spatio-temporal correlation, we assume that the sensors readings in that area are correlated. We also assume that the instantiations of drifts are uncorrelated. Based on these assumptions, and inspired by the resemblance of registration problem in radar target tracking with the bias error problem in wireless sensor networks, we follow a Bayesian framework to solve the Drift/Bias problem in wireless sensor networks. We present two methods for solving the drift problem in a densely deployed sensor network, one for smooth drifts and the other for unsmooth drifts. We also show that both methods successfully detect and correct sensor errors and extend the effective life time of the sensor network.	en_US
dc.publisher	Academy Publisher	en_US
dc.relation.ispartof	Journal of Networks	en_US
dc.title	Recursive bayesian approaches for auto calibration in drift aware wireless sensor networks	en_US
dc.type	Journal Article
utslib.citation.volume	7	en_US
utslib.citation.volume	5	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	1602 Criminology	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	open_access
pubs.consider-herdc	true	en_US
pubs.issue	7	en_US
pubs.volume	5	en_US

Abstract:

The purpose for wireless sensor networks is to deploy low cost sensors with sufficient computing and communication capabilities to support networked sensing applications. Even when the sensors are properly calibrated at the time of their deployment, they develop drift in their readings leading to biased sensor measurements. Noting that a physical phenomenon in a certain area follows some spatio-temporal correlation, we assume that the sensors readings in that area are correlated. We also assume that the instantiations of drifts are uncorrelated. Based on these assumptions, and inspired by the resemblance of registration problem in radar target tracking with the bias error problem in wireless sensor networks, we follow a Bayesian framework to solve the Drift/Bias problem in wireless sensor networks. We present two methods for solving the drift problem in a densely deployed sensor network, one for smooth drifts and the other for unsmooth drifts. We also show that both methods successfully detect and correct sensor errors and extend the effective life time of the sensor network.

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http://hdl.handle.net/10453/15437