Node localization of wireless sensor networks based on the kernel matrix ISOMAP algorithm
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摘要: 针对无线传感网络（Wireless Sensor Network，WSN）中节点位置信息呈现非线性的问题，基于偏最小二乘法（Partial Least Squares，PLS）稳健的多元线性回归特点，结合流形学习中的非线性降维方法，提出了一种基于PLS的核矩阵等距映射（Isometric Feature Mapping，ISOMAP）节点定位算法.通过节点间测地距离表征节点非相似性，利用样本点贡献率找寻和剔除邻域中的"短路"边，经质心变换和核变换后映射至高维特征区间，采用PLS方法求得节点位置.仿真结果表明，相比ISOMAP和多维尺度（Multidimensional Scale Method，MDS）算法，该算法具有良好的拓扑稳定性、泛化能力、稳健性和定位精度，降低了计算复杂度.Abstract: Position information is nonlinear in the node localization of wireless sensor networks (WSN). Based on the robust ability of multivariate linear regression of partial least squares (PLS), and in combination with nonlinear data dimension reduction of manifold learning, a novel kernel matrix ISOMAP (Isometric Feature Mapping) algorithm is proposed. Geodesic distances between nodes are used as a measure of dissimilarity, and the contribution rate is then used to find and delete the "short circuit" edge. The matrix constructed by a double-centered transformation and the kernel transformation trick is mapped to a high dimensional feature space; finally, the relative position is obtained by PLS. Compared with the traditional ISOMAP algorithm and the multidimensional scale method (MDS), simulation results indicate that the proposed algorithm has good topology stability, generalization properties, robustness, positioning accuracy, and lower computational complexity.
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