Localization in wireless sensor networks based on support vector machines
Localization in wireless sensor networks based on support vector machines
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DOI：
10.1109/tpds.2007.70800
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发表时间：
2008-07-01
影响因子：
5.3
通讯作者：
Nguyen, Thinh
中科院分区：
文献类型：
--
作者：
Tran, Duc A.;Nguyen, Thinh
我们考虑的问题，估计在无线传感器网络中的节点的地理位置，大多数传感器是没有一个有效的自我定位功能。我们提出了最小二乘支持向量机-一种新的解决方案，具有以下优点。首先，LSVM仅基于连接性信息（即，仅跳数）来定位网络，并且因此是简单的，并且不像大多数现有技术那样需要专门的测距硬件或辅助移动的设备。其次，LSVM是基于支持向量机（SVM）学习。虽然支持向量机是一种分类方法，我们展示了它的适用性的本地化问题，并证明了本地化误差的上限可以由任何小的阈值给定一个适当的训练数据的大小。第三，LSVM有效地解决了边界和覆盖空洞问题。最后但并非最不重要的是，LSVM以分布式方式提供快速定位，有效利用处理和通信资源。我们还提出了一个修改版本的质量弹簧优化，以进一步提高LSVM的位置估计。仿真结果表明，LSVM具有良好的性能。
We consider the problem of estimating the geographic locations of nodes in a wireless sensor network where most sensors are without an effective self-positioning functionality. We propose LSVM-a novel solution with the following merits. First, LSVM localizes the network based on mere connectivity information ( that is, hop counts only) and therefore is simple and does not require specialized ranging hardware or assisting mobile devices as in most existing techniques. Second, LSVM is based on Support Vector Machine (SVM) learning. Although SVM is a classification method, we show its applicability to the localization problem and prove that the localization error can be upper bounded by any small threshold given an appropriate training data size. Third, LSVM addresses the border and coverage-hole problems effectively. Last but not least, LSVM offers fast localization in a distributed manner with efficient use of processing and communication resources. We also propose a modified version of mass-spring optimization to further improve the location estimation in LSVM. The promising performance of LSVM is exhibited by our simulation study.