Comparison of CART-based localization and SVMs-based localization in WSN.
ICNC（2012）
摘要
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 100mx100m area. © 2012 IEEE.
更多查看译文
关键词
localization,received signal strength,regression,support vector machines,wireless sensor network,support vector machine,decision tree,decision trees,base stations,data models,mathematical model,wireless sensor networks,accuracy,kernel,signal strength
AI 理解论文
溯源树
样例
生成溯源树，研究论文发展脉络
Chat Paper
正在生成论文摘要