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Sensors 2015, 15(8), 19443-19465; doi:10.3390/s150819443

An Efficient Data Compression Model Based on Spatial Clustering and Principal Component Analysis in Wireless Sensor Networks

1
School of Data Science and Computer, Sun Yat-Sen University, Guangzhou 510006, China
2
Collaborative Innovation Center of High Performance Computing,National University of Defense Technology, Changsha 410073, China
*
Author to whom correspondence should be addressed.
Academic Editor: Leonhard M. Reindl
Received: 7 June 2015 / Revised: 17 July 2015 / Accepted: 4 August 2015 / Published: 7 August 2015
(This article belongs to the Section Sensor Networks)
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Abstract

Wireless sensor networks (WSNs) have been widely used to monitor the environment, and sensors in WSNs are usually power constrained. Because inner-node communication consumes most of the power, efficient data compression schemes are needed to reduce the data transmission to prolong the lifetime of WSNs. In this paper, we propose an efficient data compression model to aggregate data, which is based on spatial clustering and principal component analysis (PCA). First, sensors with a strong temporal-spatial correlation are grouped into one cluster for further processing with a novel similarity measure metric. Next, sensor data in one cluster are aggregated in the cluster head sensor node, and an efficient adaptive strategy is proposed for the selection of the cluster head to conserve energy. Finally, the proposed model applies principal component analysis with an error bound guarantee to compress the data and retain the definite variance at the same time. Computer simulations show that the proposed model can greatly reduce communication and obtain a lower mean square error than other PCA-based algorithms. View Full-Text
Keywords: data compression; principal component analysis; cluster; wireless sensor network data compression; principal component analysis; cluster; wireless sensor network
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Yin, Y.; Liu, F.; Zhou, X.; Li, Q. An Efficient Data Compression Model Based on Spatial Clustering and Principal Component Analysis in Wireless Sensor Networks. Sensors 2015, 15, 19443-19465.

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