Data Fusion Techniques for Auto Calibration inWireless Sensor Networks

Takruri, MS; Challa, S; Yunis, R

Data Fusion Techniques for Auto Calibration inWireless Sensor Networks

Takruri, MS Challa, S Yunis, R

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 12th International Conference on Information Fusion, Proceedings, 2009, pp. 132 - 139
Issue Date:: 2009-01

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Field	Value	Language
dc.contributor.author	Takruri, MS	en_US
dc.contributor.author	Challa, S	en_US
dc.contributor.author	Yunis, R	en_US
dc.contributor.editor	IEEE	en_US
dc.date	2009-07-06	en_US
dc.date.issued	2009-01	en_US
dc.identifier.citation	12th International Conference on Information Fusion, Proceedings, 2009, pp. 132 - 139	en_US
dc.identifier.isbn	978-0-9824438-0-4	en_US
dc.identifier.uri	http://hdl.handle.net/10453/11219
dc.description.abstract	Wireless sensor networks are deployed for the purpose of sensing and monitoring an area of interest. Sensor measurements in sensor networks usually suffer from both random errors (noise) and systematic errors (drift and bias). Even when the sensors are properly calibrated at the time of deployment, they develop errors in their readings leading to erroneous inferences to be made by the network. In this paper we present a novel algorithm for detecting and correcting sensor measurement errors by utilising the spatio-temporal correlation among the neighbouring sensors. The algorithm is designed for sparsely deployed wireless sensor networks. It can follow and correct both slowly and suddenly changing sensor measurements. As a result, the algorithm can adapt for under sampling the sensor measurements. Therefore, it allows for reducing the communication between sensors to maintain the calibration which leads to reducing the energy consumed from the batteries. The algorithm runs recursively and is totally decentralized. We demonstrate using real data obtained from the Intel Berkeley Laboratory that our algorithm successfully suppresses errors developed in sensors and thereby prolongs the effective lifetime of the network.	en_US
dc.publisher	IEEE	en_US
dc.relation.ispartof	12th International Conference on Information Fusion, Proceedings	en_US
dc.relation.ispartof	International Conference on Information Fusion	en_US
dc.rights	© 2009 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	Data Fusion Techniques for Auto Calibration inWireless Sensor Networks	en_US
dc.type	Conference Proceeding
utslib.location	USA	en_US
utslib.location.activity	Seattle, WA, USA	en_US
utslib.for	110906 Sensory Systems	en_US
dc.location.activity	Seattle, WA, USA	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	true	en_US
pubs.place-of-publication	USA	en_US
pubs.start-date	2009-07-06	en_US

Abstract:

Wireless sensor networks are deployed for the purpose of sensing and monitoring an area of interest. Sensor measurements in sensor networks usually suffer from both random errors (noise) and systematic errors (drift and bias). Even when the sensors are properly calibrated at the time of deployment, they develop errors in their readings leading to erroneous inferences to be made by the network. In this paper we present a novel algorithm for detecting and correcting sensor measurement errors by utilising the spatio-temporal correlation among the neighbouring sensors. The algorithm is designed for sparsely deployed wireless sensor networks. It can follow and correct both slowly and suddenly changing sensor measurements. As a result, the algorithm can adapt for under sampling the sensor measurements. Therefore, it allows for reducing the communication between sensors to maintain the calibration which leads to reducing the energy consumed from the batteries. The algorithm runs recursively and is totally decentralized. We demonstrate using real data obtained from the Intel Berkeley Laboratory that our algorithm successfully suppresses errors developed in sensors and thereby prolongs the effective lifetime of the network.

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http://hdl.handle.net/10453/11219