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Conference Paper: A descend-based evolutionary approach to enhance position estimation in wireless sensor networks

TitleA descend-based evolutionary approach to enhance position estimation in wireless sensor networks
Authors
Issue Date2006
Publisherthe IEEE Computer Society.
Citation
Proceedings - International Conference On Tools With Artificial Intelligence, Ictai, 2006, p. 568-571 How to Cite?
AbstractWireless sensor networks have wide applicability to many important applications including environmental monitoring and military applications. Typically with the absolute positions of only a small portion of sensors predetermined, localization works for the precise estimation of the remaining sensor positions on which most locationsensitive applications rely. Intrinsically, localization can be formulated as an unconstrained optimization problem based on various distance/path measures, for which most of the existing work focus on increasing its precision through different heuristic or mathematical techniques. In this paper, we propose to adapt an evolutionary approach, namely a micro-genetic algorithm (MGA), and its variant as postoptimizers to enhance the precision of existing localization methods including the Ad-hoc Positioning System. Our adapted MGA and its variants can easily be integrated into different localization methods. Besides, the prototypes of our evolutionary approach gained remarkable results on both uniform and anisotropic topologies of the simulation tests, thus prompting for many interesting directions for future investigation. © 2006 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/99619
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorTam, Ven_HK
dc.contributor.authorCheng, KYen_HK
dc.contributor.authorLui, KSen_HK
dc.date.accessioned2010-09-25T18:37:40Z-
dc.date.available2010-09-25T18:37:40Z-
dc.date.issued2006en_HK
dc.identifier.citationProceedings - International Conference On Tools With Artificial Intelligence, Ictai, 2006, p. 568-571en_HK
dc.identifier.issn1082-3409en_HK
dc.identifier.urihttp://hdl.handle.net/10722/99619-
dc.description.abstractWireless sensor networks have wide applicability to many important applications including environmental monitoring and military applications. Typically with the absolute positions of only a small portion of sensors predetermined, localization works for the precise estimation of the remaining sensor positions on which most locationsensitive applications rely. Intrinsically, localization can be formulated as an unconstrained optimization problem based on various distance/path measures, for which most of the existing work focus on increasing its precision through different heuristic or mathematical techniques. In this paper, we propose to adapt an evolutionary approach, namely a micro-genetic algorithm (MGA), and its variant as postoptimizers to enhance the precision of existing localization methods including the Ad-hoc Positioning System. Our adapted MGA and its variants can easily be integrated into different localization methods. Besides, the prototypes of our evolutionary approach gained remarkable results on both uniform and anisotropic topologies of the simulation tests, thus prompting for many interesting directions for future investigation. © 2006 IEEE.en_HK
dc.languageengen_HK
dc.publisherthe IEEE Computer Society.en_HK
dc.relation.ispartofProceedings - International Conference on Tools with Artificial Intelligence, ICTAIen_HK
dc.titleA descend-based evolutionary approach to enhance position estimation in wireless sensor networksen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailTam, V:vtam@eee.hku.hken_HK
dc.identifier.emailLui, KS:kslui@eee.hku.hken_HK
dc.identifier.authorityTam, V=rp00173en_HK
dc.identifier.authorityLui, KS=rp00188en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/ICTAI.2006.9en_HK
dc.identifier.scopuseid_2-s2.0-38949133416en_HK
dc.identifier.hkuros123909en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-38949133416&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage568en_HK
dc.identifier.epage571en_HK
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridTam, V=7005091988en_HK
dc.identifier.scopusauthoridCheng, KY=14631590500en_HK
dc.identifier.scopusauthoridLui, KS=7103390016en_HK

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