Auto Calibration in Drift Aware Wireless Sensor Networks using The Interacting Multiple Model Algorithm

Takruri, MS; Challa, S; Chakravorty, R

Auto Calibration in Drift Aware Wireless Sensor Networks using The Interacting Multiple Model Algorithm

Takruri, MS Challa, S Chakravorty, R

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings of Mosharaka International Conference on Communications, Computers and Applications, 2008. MIC-CCA 2008, 2008, pp. 98 - 103
Issue Date:: 2008-01

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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.contributor.editor	Jaser Mahasneh	en_US
dc.date	2008-08-08	en_US
dc.date.issued	2008-01	en_US
dc.identifier.citation	Proceedings of Mosharaka International Conference on Communications, Computers and Applications, 2008. MIC-CCA 2008, 2008, pp. 98 - 103	en_US
dc.identifier.isbn	978-9957-486-03-7	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16487
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. The emphasis on lower cost led to sensors that are less accurate and less reliable than their wired sensor counterparts. Sensors usually 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 biased sensor measurements. The drift in this context is defined as a unidirectional long-term change in the sensor measurement. Assuming that neighboring sensors have correlated measurements and noting that the instantiation of drift in a sensor is uncorrelated with other sensors, we present the methodology for detecting and correcting sensors smooth and steep drifts. The methodology improves the reliability and the effective life of the network.	en_US
dc.publisher	IEEE	en_US
dc.relation.ispartof	Proceedings of Mosharaka International Conference on Communications, Computers and Applications, 2008. MIC-CCA 2008	en_US
dc.relation.ispartof	Mosharaka International Conference on Communications, Computers and Applications	en_US
dc.relation.isbasedon	10.1109/MICCCA.2008.4669857	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	Auto Calibration in Drift Aware Wireless Sensor Networks using The Interacting Multiple Model Algorithm	en_US
dc.type	Conference Proceeding
utslib.location	Piscataway, USA	en_US
utslib.location.activity	Amman, Jordan	en_US
utslib.for	1005 Communications Technologies	en_US
dc.location.activity	Amman, Jordan	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	Piscataway, USA	en_US
pubs.start-date	2008-08-08	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. The emphasis on lower cost led to sensors that are less accurate and less reliable than their wired sensor counterparts. Sensors usually 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 biased sensor measurements. The drift in this context is defined as a unidirectional long-term change in the sensor measurement. Assuming that neighboring sensors have correlated measurements and noting that the instantiation of drift in a sensor is uncorrelated with other sensors, we present the methodology for detecting and correcting sensors smooth and steep drifts. The methodology improves the reliability and the effective life of the network.

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http://hdl.handle.net/10453/16487