research-article

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

Authors:

Lina YaoAuthors Info & Claims

MobiQuitous 2017: Proceedings of the 14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

Pages 442 - 451

https://doi.org/10.1145/3144457.3144474

Published: 07 November 2017 Publication History

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.

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Cited By

Yu ZWu DHe Y(2022)A Robust Latent Factor Analysis Model for Incomplete Data Recovery in Wireless Sensor Networks2022 IEEE International Conference on Edge Computing and Communications (EDGE)10.1109/EDGE55608.2022.00033(178-183)Online publication date: Jul-2022
https://doi.org/10.1109/EDGE55608.2022.00033
Osawa RSuekane KNakamura RInagaki ATakagi TMunemasa I(2021)Predicting Human Behavior with Transformer Considering the Mutual Relationship between Categories and Regions2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR)10.1109/MIPR51284.2021.00029(144-150)Online publication date: Sep-2021
https://doi.org/10.1109/MIPR51284.2021.00029
Rashid KLouis J(2020)Automated Activity Identification for Construction Equipment Using Motion Data From Articulated MembersFrontiers in Built Environment10.3389/fbuil.2019.001445Online publication date: 9-Jan-2020
https://doi.org/10.3389/fbuil.2019.00144
Show More Cited By

Index Terms

Interpolating the Missing Values for Multi-Dimensional Spatial-Temporal Sensor Data: A Tensor SVD Approach
1. Hardware
  1. Communication hardware, interfaces and storage
    1. Sensor applications and deployments
    2. Signal processing systems

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MobiQuitous 2017: Proceedings of the 14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

November 2017

555 pages

ISBN:9781450353687

DOI:10.1145/3144457

General Chairs:
Tao Gu
RMIT University, Australia
,
Ramamohanarao Kotagiri
University of Melbourne, Australia
,
Publications Chair:
Huai Liu
Victoria University, Australia

Copyright © 2017 ACM.

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EAI: The European Alliance for Innovation
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Published: 07 November 2017

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MobiQuitous 2017

MobiQuitous 2017: Computing, Networking and Services

November 7 - 10, 2017

VIC, Melbourne, Australia

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Cited By

Yu ZWu DHe Y(2022)A Robust Latent Factor Analysis Model for Incomplete Data Recovery in Wireless Sensor Networks2022 IEEE International Conference on Edge Computing and Communications (EDGE)10.1109/EDGE55608.2022.00033(178-183)Online publication date: Jul-2022
https://doi.org/10.1109/EDGE55608.2022.00033
Osawa RSuekane KNakamura RInagaki ATakagi TMunemasa I(2021)Predicting Human Behavior with Transformer Considering the Mutual Relationship between Categories and Regions2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR)10.1109/MIPR51284.2021.00029(144-150)Online publication date: Sep-2021
https://doi.org/10.1109/MIPR51284.2021.00029
Rashid KLouis J(2020)Automated Activity Identification for Construction Equipment Using Motion Data From Articulated MembersFrontiers in Built Environment10.3389/fbuil.2019.001445Online publication date: 9-Jan-2020
https://doi.org/10.3389/fbuil.2019.00144
Teh HKempa-Liehr AWang K(2020)Sensor data quality: a systematic reviewJournal of Big Data10.1186/s40537-020-0285-17:1Online publication date: 11-Feb-2020
https://doi.org/10.1186/s40537-020-0285-1
Gao CChen YWang ZXia HLv N(2020)Anomaly detection frameworks for outlier and pattern anomaly of time series in wireless sensor networks2020 International Conference on Networking and Network Applications (NaNA)10.1109/NaNA51271.2020.00046(229-232)Online publication date: Dec-2020
https://doi.org/10.1109/NaNA51271.2020.00046

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