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Conference Paper: An iteratively Reweighted Least Square algorithm for RSS-based sensor network localization

TitleAn iteratively Reweighted Least Square algorithm for RSS-based sensor network localization
Authors
KeywordsIRLS
localization
RSS
sensor network
signal-based maximum likelihood
SMACOF
SML
Issue Date2011
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001093
Citation
The 2011 IEEE International Conference on Mechatronics and Automation (ICMA 2011), Beijing, China, 7-10 August 2011. In Proceedings of ICMA, 2011, p. 1085-1092 How to Cite?
AbstractIn this article, we give a new algorithm for localization based on RSS measurement. There are many measurement methods for localizing the unknown nodes in a sensor network. RSS is the most popular one due to its simple and cheap hardware requirement. However, accurate algorithm based on RSS is needed to obtain the positions of unknown nodes. Recent algorithms such as MDS(Multi-Dimensional Scaling)-MAP, PDM (Proximity Distance Matrix) cannot give accurate results based on RSS as the RSS signals always have large variations. Besides, recent algorithms on sensor network localization ignore the received signal strength (RSS) and thus get a disappointing accuracy. This is because they are mostly focused on the difference between the estimated distance and the real distance. This paper introduces a target function - signal-based maximum likelihood (SML), which uses the maximum likelihood based on the directly measured RSS signal. Inspired by the SMACOF (Scaling by Majorizing A COmplicated Function) algorithm, an iteration surrogate algorithm named IRLS (Iteratively Reweighted Least Square) is introduced to solve the SML. From the simulation results, the IRLS algorithm can give accurate results for RSS positioning. When compared with other popular algorithms such as MDS-MAP, PDM, and SMACOF, the error (distance between the estimated position and the actual position) calculated by IRLS is less than all the other algorithms. In anisotropic network, IRLS also has good performance. © 2011 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/135859
ISBN
References

 

DC FieldValueLanguage
dc.contributor.authorQiao, Den_HK
dc.contributor.authorPang, GKHen_HK
dc.date.accessioned2011-07-27T01:49:42Z-
dc.date.available2011-07-27T01:49:42Z-
dc.date.issued2011en_HK
dc.identifier.citationThe 2011 IEEE International Conference on Mechatronics and Automation (ICMA 2011), Beijing, China, 7-10 August 2011. In Proceedings of ICMA, 2011, p. 1085-1092en_HK
dc.identifier.isbn978-1-4244-8115-6-
dc.identifier.urihttp://hdl.handle.net/10722/135859-
dc.description.abstractIn this article, we give a new algorithm for localization based on RSS measurement. There are many measurement methods for localizing the unknown nodes in a sensor network. RSS is the most popular one due to its simple and cheap hardware requirement. However, accurate algorithm based on RSS is needed to obtain the positions of unknown nodes. Recent algorithms such as MDS(Multi-Dimensional Scaling)-MAP, PDM (Proximity Distance Matrix) cannot give accurate results based on RSS as the RSS signals always have large variations. Besides, recent algorithms on sensor network localization ignore the received signal strength (RSS) and thus get a disappointing accuracy. This is because they are mostly focused on the difference between the estimated distance and the real distance. This paper introduces a target function - signal-based maximum likelihood (SML), which uses the maximum likelihood based on the directly measured RSS signal. Inspired by the SMACOF (Scaling by Majorizing A COmplicated Function) algorithm, an iteration surrogate algorithm named IRLS (Iteratively Reweighted Least Square) is introduced to solve the SML. From the simulation results, the IRLS algorithm can give accurate results for RSS positioning. When compared with other popular algorithms such as MDS-MAP, PDM, and SMACOF, the error (distance between the estimated position and the actual position) calculated by IRLS is less than all the other algorithms. In anisotropic network, IRLS also has good performance. © 2011 IEEE.en_HK
dc.languageengen_US
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001093-
dc.relation.ispartofProceedings of the IEEE International Conference on Mechatronics and Automation, ICMA 2011en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rightsProceedings of the IEEE International Conference on Mechatronics and Automation. Copyright © IEEE.-
dc.rights©2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.subjectIRLSen_HK
dc.subjectlocalizationen_HK
dc.subjectRSSen_HK
dc.subjectsensor networken_HK
dc.subjectsignal-based maximum likelihooden_HK
dc.subjectSMACOFen_HK
dc.subjectSMLen_HK
dc.titleAn iteratively Reweighted Least Square algorithm for RSS-based sensor network localizationen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailPang, GKH:gpang@eee.hku.hken_HK
dc.identifier.authorityPang, GKH=rp00162en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ICMA.2011.5985811en_HK
dc.identifier.scopuseid_2-s2.0-81055144409en_HK
dc.identifier.hkuros186799en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-81055144409&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage1085en_HK
dc.identifier.epage1092en_HK
dc.description.otherThe 2011 IEEE International Conference on Mechatronics and Automation (ICMA 2011), Beijing, China, 7-10 August 2011. In Proceedings of ICMA, 2011, p. 1085-1092-
dc.identifier.scopusauthoridQiao, D=25651913600en_HK
dc.identifier.scopusauthoridPang, GKH=7103393283en_HK

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