Interpolating the missing values for multi-dimensional spatial-temporal sensor data

a tensor SVD approach

Peipei Xu, Wenjie Ruan, Quan Z. Sheng, Tao Gu, Lina Yao

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

2 Citations (Scopus)

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 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.

Original languageEnglish
Title of host publicationProceedings of the 14th EAI International Conference on Mobile and Ubiquitous Systems
Subtitle of host publicationComputing, Networking and Services, MobiQuitous 2017
PublisherAssociation for Computing Machinery
Pages442-451
Number of pages10
ISBN (Print)9781450353687
DOIs
Publication statusPublished - 7 Nov 2017
Event14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, MobiQuitous 2017 - Melbourne, Australia
Duration: 7 Nov 201710 Nov 2017

Conference

Conference14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, MobiQuitous 2017
CountryAustralia
CityMelbourne
Period7/11/1710/11/17

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Keywords

  • ADMM
  • Sensor Data Recovery
  • T-SVD
  • Tensor Completion

Cite this

Xu, P., Ruan, W., Sheng, Q. Z., Gu, T., & 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 (pp. 442-451). Association for Computing Machinery. https://doi.org/10.1145/3144457.3144474