Final published version
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Interpolating the missing values for multi-dimensional spatial-temporal sensor data
T2 - 14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, MobiQuitous 2017
AU - Xu, Peipei
AU - Ruan, Wenjie
AU - Sheng, Quan Z.
AU - Gu, Tao
AU - Yao, Lina
PY - 2017/11/7
Y1 - 2017/11/7
N2 - 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 high-dimensional 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.
AB - 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 high-dimensional 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.
KW - ADMM
KW - Sensor Data Recovery
KW - T-SVD
KW - Tensor Completion
U2 - 10.1145/3144457.3144474
DO - 10.1145/3144457.3144474
M3 - Conference contribution/Paper
AN - SCOPUS:85052517467
SN - 9781450353687
SP - 442
EP - 451
BT - 14th EAI International Conference on Mobile and Ubiquitous Systems
PB - Association for Computing Machinery (ACM)
Y2 - 7 November 2017 through 10 November 2017
ER -