Interpolating the Missing Values for Multi-Dimensional Spatial-Temporal Sensor Data: A Tensor SVD Approach

Xu, P, Ruan, W, Sheng, Q, Gu, T and Yao, L 2017, 'Interpolating the Missing Values for Multi-Dimensional Spatial-Temporal Sensor Data: A Tensor SVD Approach', in Proceedings of the 14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous 2017), Melbourne, Australia, 7-10 November 2017, pp. 442-451.


Document type: Conference Paper
Collection: Conference Papers

Title Interpolating the Missing Values for Multi-Dimensional Spatial-Temporal Sensor Data: A Tensor SVD Approach
Author(s) Xu, P
Ruan, W
Sheng, Q
Gu, T
Yao, L
Year 2017
Conference name MobiQuitous 2017
Conference location Melbourne, Australia
Conference dates 7-10 November 2017
Proceedings title Proceedings of the 14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous 2017)
Publisher Association for Computing Machinery
Place of publication New York, United States
Start page 442
End page 451
Total pages 10
Abstract With the booming of the Internet of Things, enormous number of smart devices/sensors have been deployed in the physical world to monitor our surroundings. Usually those devices generate highdimensional geo-tagged time-series data. However, these sensor readings are easily missing due to the hardware malfunction, connection errors or data corruption, which severely compromise the back-end data analysis. To solve this problem, in this paper we exploit tensor-based Singular Value Decomposition method to recover the missing sensor readings. The main novelty of this paper lies in that, i) our tensor-based recovery method can well capture the multi-dimensional spatial and temporal features by transforming the irregularly deployed sensors into a sensor-array and folding the periodic temporal patterns into multiple time dimensions, ii) it only requires to tune one key parameter in an unsupervised manner, and iii) Tensor Singular Value Decomposition structure is more efficient on representation of high-dimension sensor data than other tensor recovery methods based on tensor's vectorization or flattening. The experimental results in several real-world one-year air quality and meteorology datasets demonstrate the effectiveness and accuracy of our approach.
Subjects Networking and Communications
Ubiquitous Computing
Mobile Technologies
Keyword(s) Sensor Data Recovery
Tensor Completion
t-SVD
ADMM
DOI - identifier 10.1145/3144457.3144474
Copyright notice Copyright © 2017 ACM
ISBN 9781450353687
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