Node Localization Algorithm in Wireless Sensor Networks Based on SVM
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摘要: 机器学习是利用经验来改善自身性能的一种学习方法，而支持向量机(support vector machine, SVM)作为机器学习中的一种新模式，在解决小样本、非线性及高维模式识别等方面有着其特有的优势.基于支持向量机的节点定位算法利用机器学习算法的特性，实现无线传感网络节点定位.其基本思路是将网络区域划分为若干个等分的小格，每一小格代表机器学习算法中一个确定的类别，机器学习算法在学习了已知的信标节点对应的类别后，对未知节点所处位置进行分类，从而进一步确定未知节点的位置坐标.仿真实验表明，“一对一”节点定位算法有较高的定位精度，对测距误差的容忍性较好，同时对信标节点的比例要求并不高，比较适合用于信标节点稀疏的网络环境中；而“决策树”节点定位算法受覆盖空洞的影响并不大，比较适合应用于节点分布不均匀或者存在覆盖空洞的网络环境中.Abstract: Machine learning, as a learning method, uses the experience to improve its performance. The support vector machine (SVM), as a new model of the machine learning, is good at dealing with the condition of small sample size, nonlinear and high dimensional pattern recognition. The node localization algorithm based on SVM can locate the nodes in wireless sensor networks, WSN depending on the characteristics of machine learning algorithms. The basic idea is dividing the network area into several small aliquots of grids and each represents a certain class of machine learning algorithm. And when the machine learning algorithm learns the classes corresponding to the known beacon nodes, it will classify the unknown nodes’ localization and then further determine the position coordinates of the unknown nodes. For the SVM “one against one” location algorithm, the simulation results show that it has higher location accuracy and better tolerance of the ranging error, which is suitable for the network environment where the beacon nodes are sparse as it doesn’t require a high beacon node ratio. For the SVM decision tree location algorithm, the results show that it is not affected seriously by the coverage holes, which is applicable for the network environment where nodes distribution is uneven or the coverage holes exist.
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