Real-Time Data Recovery in Wireless Sensor Networks Using Spatiotemporal Correlation Based on Sparse Representation
Joint Authors
Source
Wireless Communications and Mobile Computing
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-05-20
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Information Technology and Computer Science
Abstract EN
Due to data loss and sparse sampling methods utilized in WSNs to reduce energy consumption, reconstructing the raw sensed data from partial data is an indispensable operation.
In this paper, a real-time data recovery method is proposed using the spatiotemporal correlation among WSN data.
Specifically, by introducing the historical data, joint low-rank constraint and temporal stability are utilized to further exploit the data spatiotemporal correlation.
Furthermore, an algorithm based on the alternating direction method of multipliers is described to solve the resultant optimization problem efficiently.
The simulation results show that the proposed method outperforms the state-of-the-art methods for different types of signal in the network.
American Psychological Association (APA)
He, Jingfei& Zhou, Yatong. 2019. Real-Time Data Recovery in Wireless Sensor Networks Using Spatiotemporal Correlation Based on Sparse Representation. Wireless Communications and Mobile Computing،Vol. 2019, no. 2019, pp.1-7.
https://search.emarefa.net/detail/BIM-1212027
Modern Language Association (MLA)
He, Jingfei& Zhou, Yatong. Real-Time Data Recovery in Wireless Sensor Networks Using Spatiotemporal Correlation Based on Sparse Representation. Wireless Communications and Mobile Computing No. 2019 (2019), pp.1-7.
https://search.emarefa.net/detail/BIM-1212027
American Medical Association (AMA)
He, Jingfei& Zhou, Yatong. Real-Time Data Recovery in Wireless Sensor Networks Using Spatiotemporal Correlation Based on Sparse Representation. Wireless Communications and Mobile Computing. 2019. Vol. 2019, no. 2019, pp.1-7.
https://search.emarefa.net/detail/BIM-1212027
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1212027