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 |
| Published: |
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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| Summary: | 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. |
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| ISSN: | 1550-1477 |