华东师范大学学报（自然科学版） >

2019 , Vol. 2019 >Issue 1: 115 - 123

DOI: https://doi.org/10.3969/j.issn.1000-5641.2019.01.013

物理学与电子学

基于核矩阵等距映射的无线传感网络节点定位算法

  • 杨海 ,
  • 李兵
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  • 1. 湖南机电职业技术学院, 长沙 410151;
    2. 合肥工业大学 电气与自动化工程学院, 合肥 230009
杨海,男,副教授,研究方向为无线传感网络.E-mail:yanghai@foxmail.com.

收稿日期: 2017-09-19

  网络出版日期: 2019-01-24

基金资助

国家自然科学基金（51777050）；湖南省自然科学基金（2017JJ2080）；湖南省教育厅科学研究项目（13C260）；安徽省自然科学基金（1508085MF112）；中央高校基本科研业务费（JDK16TD017）

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Node localization of wireless sensor networks based on the kernel matrix ISOMAP algorithm

  • YANG Hai ,
  • LI Bing
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  • 1. College of Hunan Mechanical and Electrical Polytechnic, Changsha 410151, China;
    2. School of Electrical and Automation Engineering, Hefei University of Technology, Hefei 230009, China

Received date: 2017-09-19

  Online published: 2019-01-24

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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.

关键词： 无线传感网络; 节点定位; 核矩阵; 等距映射

本文引用格式

杨海 , 李兵 . 基于核矩阵等距映射的无线传感网络节点定位算法[J]. 华东师范大学学报（自然科学版）, 2019 , 2019(1) : 115 -123 . DOI: 10.3969/j.issn.1000-5641.2019.01.013

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.

Key words： wireless sensor network; node localization; kernel matrix; Isometric Feature Mapping (ISOMAP)

参考文献

[1] 丁英, 孙雨耕, 李婷雪. 基于多维校正的无线传感器网络多维标度定位算法[J]. 仪器仪表学报, 2009, 30(5):1002-1011.
[2] KUMAR S, KUMAR R, RAJAWAT K. Cooperative localization of mobile networks via velocity-assisted multidimensional scaling[J]. IEEE Transactions on Signal Processing, 2016, 64(7):1744-1758.
[3] 郝志凯, 王硕, 谭民. 基于优化策略的混合定位算法[J]. 自动化学报, 2010(5):711-719.
[4] 叶飞虎, 白光伟, 沈航. 无线传感器网络距离自调整的MDS定位算法[J]. 计算机科学, 2012(5):40-43.
[5] CUI W, WU C D, MENG Wi, et al. Dynamic multidimensional scaling algorithm for 3-D mobile localization[J]. IEEE Transactions on Instrument and Measurement, 2016, 65(12):2853-2865.
[6] MANDANAS F D, KOTROPOULOS C L. robust multidimensional scaling using a maximum correntropy criterion[J]. IEEE Transactions on Signal Processing, 2017, 65(4):919-932.
[7] ZHAO Y, CHENG H W, YI D Y, et al. Initial state estimation for boost phase object based on linear least square estimation[J]. Journal of Electronics and Information Technology, 2010, 32(12):2884-2889.
[8] TENENBAUM J B, DE SILVA V, LANGFORD J C. A global geometric framework for nonlinear dimensionality reduction[J]. Science, 2000, 290:2319-2323.
[9] ROWEIS S T, SAUL L K. Nonlinear dimensionality reduction by locally linear embedding[J]. Science, 2000, 290:2323-2326.
[10] LAW M H C, JAIN A K. Incremental nonlinear dimensionality reduction by manifold learning[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(3):377-391.
[11] 邓文莲. 无线传感器网络节点定位的仿真研究[J]. 计算机仿真, 2012(5):56-73.
[12] 陈璋鑫, 宋玉梅, 万群. LAD准则下的无线传感器网络节点定位方法[J].电子科技大学学报, 2009, 38(1):43-46.
[13] CHOI H, KATAKE A, CHOI S, et al. Alpha-integration of multiple evidence[C]//2010 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2010:2210-2213.
[14] LI B, HE Y G, GUO F M, et al. A novel localization algorithm based on isomap and partial least squares for wireless sensor networks[J]. IEEE Transactions on Instrument and Measurement, 2013, 62(2):304-314.
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