A Graph Embedding Method Based on Sparse Representation for Wireless Sensor Network Localization
In accordance with the problem that the traditional trilateral or multilateral estimation localization method is highly dependent on the proportion of beacon nodes and the measurement accuracy, an algorithm based on kernel sparse preserve projection (KSPP) is proposed in this dissertation. The Gauss...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-07-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2014/607943 |
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| _version_ | 1832555276243304448 |
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| author | Xiaoyong Yan Aiguo Song Hao Yan |
| author_facet | Xiaoyong Yan Aiguo Song Hao Yan |
| author_sort | Xiaoyong Yan |
| collection | DOAJ |
| description | In accordance with the problem that the traditional trilateral or multilateral estimation localization method is highly dependent on the proportion of beacon nodes and the measurement accuracy, an algorithm based on kernel sparse preserve projection (KSPP) is proposed in this dissertation. The Gaussian kernel function is used to evaluate the similarity between nodes, and the location of the unknown nodes will be commonly decided by all the nodes within communication radius through selection of sparse preserve projection self-adaptation and maintaining of the topological structure between adjacent nodes. Therefore, the algorithm can effectively solve the nonlinear problem while ranging, and it becomes less affected by the measuring error and beacon nodes quantity. |
| format | Article |
| id | doaj-art-780d3c2e65334355912c2951cf2c2834 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2014-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-780d3c2e65334355912c2951cf2c28342025-02-03T05:48:36ZengWileyInternational Journal of Distributed Sensor Networks1550-14772014-07-011010.1155/2014/607943607943A Graph Embedding Method Based on Sparse Representation for Wireless Sensor Network LocalizationXiaoyong Yan0Aiguo Song1Hao Yan2 School of Intelligence Science and Control, Jinling Institute of Technology, Nanjing 211169, China Remote Measurement and Control Key Lab of Jiangsu Province, Nanjing 210096, China School of Software Engineering, Jinling Institute of Technology, Nanjing 211169, ChinaIn accordance with the problem that the traditional trilateral or multilateral estimation localization method is highly dependent on the proportion of beacon nodes and the measurement accuracy, an algorithm based on kernel sparse preserve projection (KSPP) is proposed in this dissertation. The Gaussian kernel function is used to evaluate the similarity between nodes, and the location of the unknown nodes will be commonly decided by all the nodes within communication radius through selection of sparse preserve projection self-adaptation and maintaining of the topological structure between adjacent nodes. Therefore, the algorithm can effectively solve the nonlinear problem while ranging, and it becomes less affected by the measuring error and beacon nodes quantity.https://doi.org/10.1155/2014/607943 |
| spellingShingle | Xiaoyong Yan Aiguo Song Hao Yan A Graph Embedding Method Based on Sparse Representation for Wireless Sensor Network Localization International Journal of Distributed Sensor Networks |
| title | A Graph Embedding Method Based on Sparse Representation for Wireless Sensor Network Localization |
| title_full | A Graph Embedding Method Based on Sparse Representation for Wireless Sensor Network Localization |
| title_fullStr | A Graph Embedding Method Based on Sparse Representation for Wireless Sensor Network Localization |
| title_full_unstemmed | A Graph Embedding Method Based on Sparse Representation for Wireless Sensor Network Localization |
| title_short | A Graph Embedding Method Based on Sparse Representation for Wireless Sensor Network Localization |
| title_sort | graph embedding method based on sparse representation for wireless sensor network localization |
| url | https://doi.org/10.1155/2014/607943 |
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