Compressive data gathering with low-rank constraints for Wireless Sensor networks

In this paper, a novel compressive data gathering with low-rank constraints is proposed for efficient data gathering and accurate recovery in wireless sensor networks. The proposed method utilizes both the low-rank feature of the data matrix by introducing the historical data and the sparsity featur...

Full description

Saved in:
Bibliographic Details
Published in:Signal processing 2017-02, Vol.131, p.73-76
Main Authors: He, Jingfei, Sun, Guiling, Li, Zhouzhou, Zhang, Ying
Format: Article
Language:English
Subjects:
Compressive sensing
Data gathering
Low-rank
Matrix completion
Wireless sensor networks
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this paper, a novel compressive data gathering with low-rank constraints is proposed for efficient data gathering and accurate recovery in wireless sensor networks. The proposed method utilizes both the low-rank feature of the data matrix by introducing the historical data and the sparsity feature based on compressive sensing. A reconstruction algorithm based on the alternating direction method of multipliers is described to efficiently solve the resultant optimization problem. Experimental results show the proposed method can significantly improve the recovery accuracy compared with the state-of-the-art methods. •A compressive data gathering method with low-rank constraints for WSNs is proposed.•The historical data are introduced to utilize the low-rank feature of the data.•The proposed method utilizes both the low-rank and sparsity feature.•A reconstruction algorithm based on the alternating direction method of multipliers is described.•The recovery accuracy of the proposed method is better than the state-of-the-art methods.
ISSN:0165-1684
1872-7557
DOI:10.1016/j.sigpro.2016.08.002