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Article: Evolutionary approach on connectivity-based sensor network localization
Title | Evolutionary approach on connectivity-based sensor network localization |
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Authors | |
Keywords | Connectivity Evolutionary algorithm Genetic algorithm Localization Non-convex constraints Wireless sensor network |
Issue Date | 2014 |
Publisher | Elsevier 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? |
Abstract | The 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 Identifier | http://hdl.handle.net/10722/202819 |
ISSN | 2023 Impact Factor: 7.2 2023 SCImago Journal Rankings: 1.843 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Qiao, D | - |
dc.contributor.author | Pang, GKH | - |
dc.date.accessioned | 2014-09-19T10:08:01Z | - |
dc.date.available | 2014-09-19T10:08:01Z | - |
dc.date.issued | 2014 | - |
dc.identifier.citation | Applied Soft Computing, 2014, v. 22, p. 36-46 | - |
dc.identifier.issn | 1568-4946 | - |
dc.identifier.uri | http://hdl.handle.net/10722/202819 | - |
dc.description.abstract | The 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.language | eng | - |
dc.publisher | Elsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/asoc | - |
dc.relation.ispartof | Applied Soft Computing | - |
dc.rights | NOTICE: 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.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | Connectivity | - |
dc.subject | Evolutionary algorithm | - |
dc.subject | Genetic algorithm | - |
dc.subject | Localization | - |
dc.subject | Non-convex constraints | - |
dc.subject | Wireless sensor network | - |
dc.title | Evolutionary approach on connectivity-based sensor network localization | - |
dc.type | Article | - |
dc.identifier.email | Pang, GKH: gpang@eee.hku.hk | - |
dc.identifier.authority | Pang, GKH=rp00162 | - |
dc.description.nature | postprint | - |
dc.identifier.doi | 10.1016/j.asoc.2014.04.019 | - |
dc.identifier.scopus | eid_2-s2.0-84901394054 | - |
dc.identifier.hkuros | 236049 | - |
dc.identifier.volume | 22 | - |
dc.identifier.spage | 36 | - |
dc.identifier.epage | 46 | - |
dc.identifier.isi | WOS:000338706600004 | - |
dc.publisher.place | Netherlands | - |
dc.identifier.issnl | 1568-4946 | - |