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Sensors 2014, 14(12), 23137-23158; doi:10.3390/s141223137

Correlated Spatio-Temporal Data Collection in Wireless Sensor Networks Based on Low Rank Matrix Approximation and Optimized Node Sampling

1
Beijing Key Laboratory of Multimedia and Intelligent Software Technology, College of Metropolitan Transportation, Beijing University of Technology, Pingleyuan 100, Chaoyang District, Beijing 100124, China
2
School of Computing and Mathematics, Charles Sturt University, Bathurst, NSW 2795, Australia
*
Author to whom correspondence should be addressed.
Received: 16 September 2014 / Revised: 21 November 2014 / Accepted: 26 November 2014 / Published: 5 December 2014
(This article belongs to the Section Sensor Networks)
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Abstract

The emerging low rank matrix approximation (LRMA) method provides an energy efficient scheme for data collection in wireless sensor networks (WSNs) by randomly sampling a subset of sensor nodes for data sensing. However, the existing LRMA based methods generally underutilize the spatial or temporal correlation of the sensing data, resulting in uneven energy consumption and thus shortening the network lifetime. In this paper, we propose a correlated spatio-temporal data collection method for WSNs based on LRMA. In the proposed method, both the temporal consistence and the spatial correlation of the sensing data are simultaneously integrated under a new LRMA model. Moreover, the network energy consumption issue is considered in the node sampling procedure. We use Gini index to measure both the spatial distribution of the selected nodes and the evenness of the network energy status, then formulate and resolve an optimization problem to achieve optimized node sampling. The proposed method is evaluated on both the simulated and real wireless networks and compared with state-of-the-art methods. The experimental results show the proposed method efficiently reduces the energy consumption of network and prolongs the network lifetime with high data recovery accuracy and good stability. View Full-Text
Keywords: wireless sensor networks; data collection; low rank matrix approximation. wireless sensor networks; data collection; low rank matrix approximation.
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

Piao, X.; Hu, Y.; Sun, Y.; Yin, B.; Gao, J. Correlated Spatio-Temporal Data Collection in Wireless Sensor Networks Based on Low Rank Matrix Approximation and Optimized Node Sampling. Sensors 2014, 14, 23137-23158.

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