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Article: Evolutionary approach on connectivity-based sensor network localization

TitleEvolutionary approach on connectivity-based sensor network localization
Authors
Issue Date2014
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/asoc
Citation
Applied Soft Computing, 2014, v. 22, p. 36-46 How to Cite?
AbstractThe sensor network localization based on connectivity can be modeled as a non-convex optimization problem. It can be argued that the actual problem should be represented as an optimization problem with both convex and non-convex constraints. A two-objective evolutionary algorithm is proposed which utilizes the result of all convex constraints to provide a starting point on the location of the unknown nodes and then searches for a solution to satisfy all the convex and non-convex constraints of the problem. The final solution can reach the most suitable configuration of the unknown nodes because all the information on the constraints (convex and non-convex) related to connectivity have been used. Compared with current models that only consider the nodes that have connections, this method considers not only the connection constraints, but also the disconnection constraints. As a MOEA (Multi-Objective Evolution Algorithm), PAES (Pareto Archived Evolution Strategy) is used to solve the problem. Simulation results have shown that better solution can be obtained through the use of this method when compared with those produced by other methods. © 2014 Elsevier B.V.
Persistent Identifierhttp://hdl.handle.net/10722/202819
ISSN
2015 Impact Factor: 2.857
2015 SCImago Journal Rankings: 1.763

 

DC FieldValueLanguage
dc.contributor.authorQiao, D-
dc.contributor.authorPang, GKH-
dc.date.accessioned2014-09-19T10:08:01Z-
dc.date.available2014-09-19T10:08:01Z-
dc.date.issued2014-
dc.identifier.citationApplied Soft Computing, 2014, v. 22, p. 36-46-
dc.identifier.issn1568-4946-
dc.identifier.urihttp://hdl.handle.net/10722/202819-
dc.description.abstractThe sensor network localization based on connectivity can be modeled as a non-convex optimization problem. It can be argued that the actual problem should be represented as an optimization problem with both convex and non-convex constraints. A two-objective evolutionary algorithm is proposed which utilizes the result of all convex constraints to provide a starting point on the location of the unknown nodes and then searches for a solution to satisfy all the convex and non-convex constraints of the problem. The final solution can reach the most suitable configuration of the unknown nodes because all the information on the constraints (convex and non-convex) related to connectivity have been used. Compared with current models that only consider the nodes that have connections, this method considers not only the connection constraints, but also the disconnection constraints. As a MOEA (Multi-Objective Evolution Algorithm), PAES (Pareto Archived Evolution Strategy) is used to solve the problem. Simulation results have shown that better solution can be obtained through the use of this method when compared with those produced by other methods. © 2014 Elsevier B.V.-
dc.languageeng-
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/asoc-
dc.relation.ispartofApplied Soft Computing-
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Applied Soft Computing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Applied Soft Computing, 2014, v. 22, p. 36-46. DOI: 10.1016/j.asoc.2014.04.019-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleEvolutionary approach on connectivity-based sensor network localization-
dc.typeArticle-
dc.identifier.emailPang, GKH: gpang@eee.hku.hk-
dc.identifier.authorityPang, GKH=rp00162-
dc.description.naturepostprint-
dc.identifier.doi10.1016/j.asoc.2014.04.019-
dc.identifier.scopuseid_2-s2.0-84901394054-
dc.identifier.hkuros236049-
dc.identifier.volume22-
dc.identifier.spage36-
dc.identifier.epage46-
dc.publisher.placeNetherlands-

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