Weighted mixed regression localization method based on three-dimensional Voronoi diagram division
With the development of the wireless communication technology and sensing technology, various technologies based on wireless sensor networks are applied.These technologies are widely used in the fields of intelligent agriculture, intelligent transportation, fire rescue and so on.Node localization te...
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China InfoCom Media Group
2022-06-01
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| Series: | 物联网学报 |
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| Online Access: | http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2022.00273/ |
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| author | Fenfang LI Xiaochao DANG Zhanjun HAO |
| author_facet | Fenfang LI Xiaochao DANG Zhanjun HAO |
| author_sort | Fenfang LI |
| collection | DOAJ |
| description | With the development of the wireless communication technology and sensing technology, various technologies based on wireless sensor networks are applied.These technologies are widely used in the fields of intelligent agriculture, intelligent transportation, fire rescue and so on.Node localization technology is one of the basic technologies of wireless sensor networks.Location information is a part of the sensing data, which determines the specific measures to be taken in the next step.Due to the complexity of the three-dimensional (3D) space localization environment, the application of the plane positioning method in 3D space will have some limitations.Aiming at above problems, the weighted hybrid regression location algorithm WMR-SKR based on a 3D Voronoi diagram was studied.The localization algorithm was divided into two stages: offline training and online testing.The 3D space was divided into Voronoi diagrams according to the anchor nodes in the network.In the offline training stage, the sequence composed of the coordinates of the anchor nodes and Voronoi cell vertices was used as the training set for training.In the online test stage, the coordinates of unknown nodes in the network were predicted through the trained localization model.Simulation results show that the WMR-SKR algorithm can effectively reduce the node localization error and improve the node localization speed in 3D space. |
| format | Article |
| id | doaj-art-a92e494632304fac9b79137349bb38d1 |
| institution | Kabale University |
| issn | 2096-3750 |
| language | zho |
| publishDate | 2022-06-01 |
| publisher | China InfoCom Media Group |
| record_format | Article |
| series | 物联网学报 |
| spelling | doaj-art-a92e494632304fac9b79137349bb38d12025-01-15T02:53:27ZzhoChina InfoCom Media Group物联网学报2096-37502022-06-01610611659648648Weighted mixed regression localization method based on three-dimensional Voronoi diagram divisionFenfang LIXiaochao DANGZhanjun HAOWith the development of the wireless communication technology and sensing technology, various technologies based on wireless sensor networks are applied.These technologies are widely used in the fields of intelligent agriculture, intelligent transportation, fire rescue and so on.Node localization technology is one of the basic technologies of wireless sensor networks.Location information is a part of the sensing data, which determines the specific measures to be taken in the next step.Due to the complexity of the three-dimensional (3D) space localization environment, the application of the plane positioning method in 3D space will have some limitations.Aiming at above problems, the weighted hybrid regression location algorithm WMR-SKR based on a 3D Voronoi diagram was studied.The localization algorithm was divided into two stages: offline training and online testing.The 3D space was divided into Voronoi diagrams according to the anchor nodes in the network.In the offline training stage, the sequence composed of the coordinates of the anchor nodes and Voronoi cell vertices was used as the training set for training.In the online test stage, the coordinates of unknown nodes in the network were predicted through the trained localization model.Simulation results show that the WMR-SKR algorithm can effectively reduce the node localization error and improve the node localization speed in 3D space.http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2022.00273/node localizationVoronoi diagramweighted mixed regressionWMR-SKR |
| spellingShingle | Fenfang LI Xiaochao DANG Zhanjun HAO Weighted mixed regression localization method based on three-dimensional Voronoi diagram division 物联网学报 node localization Voronoi diagram weighted mixed regression WMR-SKR |
| title | Weighted mixed regression localization method based on three-dimensional Voronoi diagram division |
| title_full | Weighted mixed regression localization method based on three-dimensional Voronoi diagram division |
| title_fullStr | Weighted mixed regression localization method based on three-dimensional Voronoi diagram division |
| title_full_unstemmed | Weighted mixed regression localization method based on three-dimensional Voronoi diagram division |
| title_short | Weighted mixed regression localization method based on three-dimensional Voronoi diagram division |
| title_sort | weighted mixed regression localization method based on three dimensional voronoi diagram division |
| topic | node localization Voronoi diagram weighted mixed regression WMR-SKR |
| url | http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2022.00273/ |
| work_keys_str_mv | AT fenfangli weightedmixedregressionlocalizationmethodbasedonthreedimensionalvoronoidiagramdivision AT xiaochaodang weightedmixedregressionlocalizationmethodbasedonthreedimensionalvoronoidiagramdivision AT zhanjunhao weightedmixedregressionlocalizationmethodbasedonthreedimensionalvoronoidiagramdivision |