Erratum to: Compressive Data Recovery in Wireless Sensor Networks—A Matrix Completion Approach

2016 ◽  
pp. E1-E1
Author(s):  
Maha A. Maged ◽  
Haitham M. Akah
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Matrix Completion ◽  
Data Recovery ◽  
Wireless Sensor

Data Recovery in Wireless Sensor Networks With Joint Matrix Completion and Sparsity Constraints

2015 ◽  
Vol 19 (12) ◽  
pp. 2230-2233 ◽  
Author(s):  
Jingfei He ◽  
Guiling Sun ◽  
Ying Zhang ◽  
Zhihong Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Matrix Completion ◽  
Data Recovery ◽  
Wireless Sensor ◽  
Sparsity Constraints

scholarly journals Data Recovery in Wireless Sensor Networks Based on Low Rank and Short-Term Stability

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 166796-166802
Author(s):  
Xiaochao Liu ◽  
Guiling Sun ◽  
Zhouzhou Li ◽  
Bowen Zheng
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Recovery ◽  
Low Rank ◽  
Wireless Sensor ◽  
Short Term ◽  
Term Stability

scholarly journals Low-Energy Data Collection in Wireless Sensor Networks Based on Matrix Completion

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 945 ◽  
Author(s):  
Yi Xu ◽  
Guiling Sun ◽  
Tianyu Geng ◽  
Jingfei He
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Collection ◽  
Sampling Method ◽  
Matrix Completion ◽  
Wireless Sensor ◽  
Sleep Mode ◽  
Real World Data ◽  
Recovery Algorithm ◽  
Sampling Ratio

Sparse sensing schemes based on matrix completion for data collection have been proposed to reduce the power consumption of data-sensing and transmission in wireless sensor networks (WSNs). While extensive efforts have been made to improve the recovery accuracy from the sparse samples, it is usually at the cost of running time. Moreover, most data-collection methods are difficult to implement with low sampling ratio because of the communication limit. In this paper, we design a novel data-collection method including a Rotating Random Sparse Sampling method and a Fast Singular Value Thresholding algorithm. With the proposed method, nodes are in the sleep mode most of the time, and the sampling ratio varies over time slots during the sampling process. From the samples, a corresponding algorithm with Nesterov technique is given to recover the original data accurately and fast. With two real-world data sets in WSNs, simulations verify that our scheme outperforms other schemes in terms of energy consumption, reconstruction accuracy, and rate. Moreover, the proposed sampling method enhances the recovery algorithm and prolongs the lifetime of WSNs.

Noise-Tolerant Wireless Sensor Networks Localization via Multinorms Regularized Matrix Completion

2018 ◽  
Vol 67 (3) ◽  
pp. 2409-2419 ◽  
Author(s):  
Fu Xiao ◽  
Wei Liu ◽  
Zhetao Li ◽  
Lei Chen ◽  
Ruchuan Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Matrix Completion ◽  
Wireless Sensor ◽  
Noise Tolerant
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