| 基于支持向量机分类的WSN节点定位算法 |
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| 引用本文： | 徐小卜,王勇,陶晓玲.基于支持向量机分类的WSN节点定位算法[J].计算机工程,2010,36(24):90-92. |
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| 作者姓名： | 徐小卜 王勇 陶晓玲 |
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| 作者单位： | (桂林电子科技大学 a. 计算机与控制学院；b. 网络中心，广西 桂林 541004) |
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| 基金项目： | 2009年广西研究生教育创新计划基金资助项目，广西教育厅基金资助项目 |
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| 摘 要： | 在研究接收信号强度指示(RSSI)定位和支持向量机分类(SVC)的基础上，提出无线传感器网络(WSN)节点定位算法。将WSN室内定位问题看作以节点RSSI值为特征量的多分类问题，将节点RSSI值转化为节点位置，利用SVC良好的泛化能力，实现符号定位和物理定位，达到较高的定位精度。实验结果表明，该算法的符号定位效果较好，当锚节点密度为20%时，可使98.19%的节点正确定位。
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| 关 键 词： | 无线传感器网络 符号定位 物理定位 支持向量机分类 接收信号强度指示 |
WSN Node Positioning Algorithm Based on Support Vector Classification |
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| XU Xiao-bu,WANG Yong,TAO Xiao-ling.WSN Node Positioning Algorithm Based on Support Vector Classification[J].Computer Engineering,2010,36(24):90-92. |
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| Authors: | XU Xiao-bu WANG Yong TAO Xiao-ling |
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| Affiliation: | (a. College of Computer and Control; b. Network Center, Guilin University of Electronic Technology, Guilin 541004, China) |
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| Abstract: | This paper proposes a Wireless Sensor Network(WSN) node positioning algorithm based on studying Received Signal Strength Indicator(RSSI) and Support Vector Classification(SVC). It considers node RSSI value as a multi-classification problem of characteristic quantity, converts RSSI into node position directly by SVC which has good generalization ability to realize symbolic positioning and physical positioning, and achieves a higher positioning accuracy. Experimental results show that the symbolic positioning effect of the algorithm is good, when the anchor node density is 20%, 98.19% of the nodes can get correct position. |
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| Keywords: | Wireless Sensor Network(WSN) symbolic positioning physical positioning Support Vector Classification(SVC) Received Signal Strength Indicator(RSSI) |
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