Multi-Attribute Data Recovery in Wireless Sensor Networks With Joint Sparsity and Low-Rank Constraints Based on Tensor Completion

• Published in: IEEE access Vol. 7; pp. 135220 - 135230
• Main Authors: He, Jingfei, Zhou, Yatong, Sun, Guiling, Geng, Tianyu
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Constraints; Correlation; Data loss; Data models; Data recovery; Electron tubes; low-rank tensors; Mathematical analysis; Matrix decomposition; Optimization; Sensors; Sparsity; sparsity constraints; Spatiotemporal phenomena; tensor singular value decomposition; Tensors; Wireless networks; Wireless sensor networks
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2942195

In wireless sensor networks (WSNs), data recovery is an indispensable operation for data loss or energy constrained WSNs using sparse sampling. However, the recovery accuracy is not satisfying for WSNs with various sensor types due to the neglect of the correlation among multi-attribute data. In this paper, we propose a novel data recovery method with joint sparsity and low-rank constraints based on tensor completion for multi-attribute data in WSNs. The proposed method represents the high-dimensional data as low-rank tensors to effectively exploit the correlation that exists in the multi-attribute data. The utilization of the spatiotemporal sparsity in the signal is emphasized by sparsity constraints. Furthermore, an algorithm based on the alternating direction method of multipliers is developed to solve the resultant optimization problem efficiently. Experimental results demonstrate that the proposed method significantly outperforms existing solutions in terms of recovery accuracy in WSNs.