2.50
Hdl Handle:
http://hdl.handle.net/10547/276019
Title:
Mobile sensor networks for modelling environmental pollutant distribution
Authors:
Lu, Bowen; Oyekan, John; Gu, Dongbing; Hu, Huosheng; Nia, Hossein Farid Ghassem
Abstract:
This article proposes to deploy a group of mobile sensor agents to cover a polluted region so that they are able to retrieve the pollutant distribution. The deployed mobile sensor agents are capable of making point observation in the natural environment. There are two approaches to modelling the pollutant distribution proposed in this article. One is a model-based approach where the sensor agents sample environmental pollutant, build up an environmental pollutant model and move towards the region where high density pollutant exists. The modelling technique used is a distributed support vector regression and the motion control technique used is a distributed locational optimising algorithm (centroidal Voronoi tessellation). The other is a model-free approach where the sensor agents sample environmental pollutant and directly move towards the region where high density pollutant exists without building up a model. The motion control technique used is a bacteria chemotaxis behaviour. By combining this behaviour with a flocking behaviour, it is possible to form a spatial distribution matched to the underlying pollutant distribution. Both approaches are simulated and tested with a group of real robots.
Affiliation:
School of Computer Science and Electronic Engineering, University of Essex
Citation:
Lu, B., Oyekan, J., Gu, D., Hu, H. and Nia, H.F.G.(2011) 'Mobile sensor networks for modelling environmental pollutant distribution' International Journal of Systems Science 42 (9):1491-1505
Publisher:
Taylor and Francis
Journal:
International Journal of Systems Science
Issue Date:
2011
URI:
http://hdl.handle.net/10547/276019
DOI:
10.1080/00207721.2011.572198
Additional Links:
http://www.tandfonline.com/doi/abs/10.1080/00207721.2011.572198
Type:
Article
Language:
en
ISSN:
0020-7721; 1464-5319
Appears in Collections:
Centre for Research in Distributed Technologies (CREDIT)

Full metadata record

DC FieldValue Language
dc.contributor.authorLu, Bowenen_GB
dc.contributor.authorOyekan, Johnen_GB
dc.contributor.authorGu, Dongbingen_GB
dc.contributor.authorHu, Huoshengen_GB
dc.contributor.authorNia, Hossein Farid Ghassemen_GB
dc.date.accessioned2013-03-26T14:02:39Z-
dc.date.available2013-03-26T14:02:39Z-
dc.date.issued2011-
dc.identifier.citationLu, B., Oyekan, J., Gu, D., Hu, H. and Nia, H.F.G.(2011) 'Mobile sensor networks for modelling environmental pollutant distribution' International Journal of Systems Science 42 (9):1491-1505en_GB
dc.identifier.issn0020-7721-
dc.identifier.issn1464-5319-
dc.identifier.doi10.1080/00207721.2011.572198-
dc.identifier.urihttp://hdl.handle.net/10547/276019-
dc.description.abstractThis article proposes to deploy a group of mobile sensor agents to cover a polluted region so that they are able to retrieve the pollutant distribution. The deployed mobile sensor agents are capable of making point observation in the natural environment. There are two approaches to modelling the pollutant distribution proposed in this article. One is a model-based approach where the sensor agents sample environmental pollutant, build up an environmental pollutant model and move towards the region where high density pollutant exists. The modelling technique used is a distributed support vector regression and the motion control technique used is a distributed locational optimising algorithm (centroidal Voronoi tessellation). The other is a model-free approach where the sensor agents sample environmental pollutant and directly move towards the region where high density pollutant exists without building up a model. The motion control technique used is a bacteria chemotaxis behaviour. By combining this behaviour with a flocking behaviour, it is possible to form a spatial distribution matched to the underlying pollutant distribution. Both approaches are simulated and tested with a group of real robots.en_GB
dc.language.isoenen
dc.publisherTaylor and Francisen_GB
dc.relation.urlhttp://www.tandfonline.com/doi/abs/10.1080/00207721.2011.572198en_GB
dc.rightsArchived with thanks to International Journal of Systems Scienceen_GB
dc.titleMobile sensor networks for modelling environmental pollutant distributionen
dc.typeArticleen
dc.contributor.departmentSchool of Computer Science and Electronic Engineering, University of Essexen_GB
dc.identifier.journalInternational Journal of Systems Scienceen_GB
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