Online Drift Correction in Wireless Sensor Networks Using Spatio-Temporal Modeling

Takruri, MS; Rajasegarar, S; Challa, S; Leckie, C; Palaniswami, MS

Online Drift Correction in Wireless Sensor Networks Using Spatio-Temporal Modeling

Takruri, MS Rajasegarar, S Challa, S Leckie, C Palaniswami, MS

Permalink

Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings of the 11th International Conference on Information Fusion (Fusion 2008), 2008, pp. 1 - 8
Issue Date:: 2008-01

Closed Access

	Filename	Description	Size
	2009003667OK.pdf		947.82 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Takruri, MS	en_US
dc.contributor.author	Rajasegarar, S	en_US
dc.contributor.author	Challa, S	en_US
dc.contributor.author	Leckie, C	en_US
dc.contributor.author	Palaniswami, MS	en_US
dc.contributor.editor	Wolfgang Koch	en_US
dc.date	2008-06-30	en_US
dc.date.issued	2008-01	en_US
dc.identifier.citation	Proceedings of the 11th International Conference on Information Fusion (Fusion 2008), 2008, pp. 1 - 8	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16233
dc.description.abstract	Wireless sensor networks are deployed for the purpose of sensing and monitoring an area of interest. Sensors in the sensor network can suffer from both random and systematic bias problems. Even when the sensors are properly calibrated at the time of their deployment, they develop drift in their readings leading to erroneous inferences being made by the network. The drift in this context is defined as a slow, unidirectional, long-term change in the sensor measurements. In this paper we present a novel algorithm for detecting and correcting sensors drifts by utilising the spatio-temporal correlation between neigbouring sensors. Based on the assumption that neighbouring sensors have correlated measurements and that the instantiation of drift in a sensor is uncorrelated with other sensors, each sensor runs a support vector regression algorithm on its neigbourspsila corrected readings to obtain a predicted value for its measurements. It then uses this predicted data to self-assess its measurement and detect and correct its drift using a Kalman filter. The algorithm is run recursively and is totally decentralized. We demonstrate using real data obtained from the Intel Berkeley Laboratory that our algorithm successfully suppresses drifts developed in sensors and thereby prolongs the effective lifetime of the network.	en_US
dc.publisher	IEEE	en_US
dc.relation.ispartof	Proceedings of the 11th International Conference on Information Fusion (Fusion 2008)	en_US
dc.relation.ispartof	International Conference on Information Fusion	en_US
dc.rights	© 2008 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.title	Online Drift Correction in Wireless Sensor Networks Using Spatio-Temporal Modeling	en_US
dc.type	Conference Proceeding
utslib.location	USA	en_US
utslib.location.activity	Cologne, Germany	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
dc.location.activity	Cologne, Germany	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	closed_access
pubs.consider-herdc	false	en_US
pubs.place-of-publication	USA	en_US
pubs.start-date	2008-06-30	en_US

Abstract:

Wireless sensor networks are deployed for the purpose of sensing and monitoring an area of interest. Sensors in the sensor network can suffer from both random and systematic bias problems. Even when the sensors are properly calibrated at the time of their deployment, they develop drift in their readings leading to erroneous inferences being made by the network. The drift in this context is defined as a slow, unidirectional, long-term change in the sensor measurements. In this paper we present a novel algorithm for detecting and correcting sensors drifts by utilising the spatio-temporal correlation between neigbouring sensors. Based on the assumption that neighbouring sensors have correlated measurements and that the instantiation of drift in a sensor is uncorrelated with other sensors, each sensor runs a support vector regression algorithm on its neigbourspsila corrected readings to obtain a predicted value for its measurements. It then uses this predicted data to self-assess its measurement and detect and correct its drift using a Kalman filter. The algorithm is run recursively and is totally decentralized. We demonstrate using real data obtained from the Intel Berkeley Laboratory that our algorithm successfully suppresses drifts developed in sensors and thereby prolongs the effective lifetime of the network.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/16233