Strategies for principal component analysis in wireless sensor networks

被引：0

作者：

Ghadban, Nisrine ^{[1
,2
]}

Honeine, Paul ^{[2
]}

Francis, Clovis ^{[1
]}

Mourad-Chehade, Farah ^{[2
]}

Farah, Joumana ^{[3
]}

机构：

[1] Univ Libanaise, Fac Genie, Beirut, Lebanon

[2] Univ Technol Troyes, CNRS, Inst Charles Delaunay, Troyes, France

[3] Holy Spirit Univ Kaslik, Fac Engn, Telecommun Dept, Beirut, Lebanon

来源：

2014 IEEE 8TH SENSOR ARRAY AND MULTICHANNEL SIGNAL PROCESSING WORKSHOP (SAM) | 2014年

关键词：

Principal component analysis; wireless sensor network; adaptive learning; distributed processing; ADAPTATION;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper deals with the issue of monitoring physical phenomena using wireless sensor networks. It provides principal component analysis for the time series of sensors' measurements. Without the need to compute the sample covariance matrix, we derive several in-network strategies to estimate the principal axis, including noncooperative and diffusion strategies. The performance of the proposed strategies is illustrated in the issue of monitoring gas diffusion.

引用

页码：233 / 236

页数：4

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