Physics and Electronics

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

  • YANG Hai ,
  • LI Bing
Expand
  • 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

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.

Cite this article

YANG Hai , LI Bing . Node localization of wireless sensor networks based on the kernel matrix ISOMAP algorithm[J]. Journal of East China Normal University(Natural Science), 2019 , 2019(1) : 115 -123 . DOI: 10.3969/j.issn.1000-5641.2019.01.013

References

[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.
Outlines

/