{"title":"基于cart的WSN定位与基于svm的WSN定位比较","authors":"W. Zhou, Chunhua Liu, Hongbing Liu","doi":"10.1109/ICNC.2012.6234509","DOIUrl":null,"url":null,"abstract":"Localization of sensor nodes is essential for wireless sensor network when it is applied to the special applications. We formed two models to estimate the location of sensor nodes, CART-based localization and SVMs-based Localization. During the training process, the received signal strength of the reference nodes is selected as the input of two models and the location information is regarded as the output of two models. During the localization process, the decision trees of CART and support vector machines are used to estimate the location of blindfolded nodes. We demonstrate the practicality and feasibility of the two models through simulations in the 100m×100m area.","PeriodicalId":404981,"journal":{"name":"2012 8th International Conference on Natural Computation","volume":"50 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Comparison of CART-based localization and SVMs-based localization in WSN\",\"authors\":\"W. Zhou, Chunhua Liu, Hongbing Liu\",\"doi\":\"10.1109/ICNC.2012.6234509\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Localization of sensor nodes is essential for wireless sensor network when it is applied to the special applications. We formed two models to estimate the location of sensor nodes, CART-based localization and SVMs-based Localization. During the training process, the received signal strength of the reference nodes is selected as the input of two models and the location information is regarded as the output of two models. During the localization process, the decision trees of CART and support vector machines are used to estimate the location of blindfolded nodes. We demonstrate the practicality and feasibility of the two models through simulations in the 100m×100m area.\",\"PeriodicalId\":404981,\"journal\":{\"name\":\"2012 8th International Conference on Natural Computation\",\"volume\":\"50 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-05-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 8th International Conference on Natural Computation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICNC.2012.6234509\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 8th International Conference on Natural Computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNC.2012.6234509","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Comparison of CART-based localization and SVMs-based localization in WSN
Localization of sensor nodes is essential for wireless sensor network when it is applied to the special applications. We formed two models to estimate the location of sensor nodes, CART-based localization and SVMs-based Localization. During the training process, the received signal strength of the reference nodes is selected as the input of two models and the location information is regarded as the output of two models. During the localization process, the decision trees of CART and support vector machines are used to estimate the location of blindfolded nodes. We demonstrate the practicality and feasibility of the two models through simulations in the 100m×100m area.
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