Recovering missing values from corrupted spatio-temporal sensory data via robust low-rank tensor completion

Wenjie Ruan*, Peipei Xu, Quan Z. Sheng, Nickolas J. G. Falkner, Xue Li, Wei Emma Zhang

*Corresponding author for this work

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

6 Citations (Scopus)

Abstract

With the booming of the Internet of Things, tremendous amount of sensors have been installed in different geographic locations, generating massive sensory data with both time-stamps and geo-tags. Such type of data usually have shown complex spatio-temporal correlation and are easily missing in practice due to communication failure or data corruption. In this paper, we aim to tackle the challenge-how to accurately and efficiently recover the missing values for corrupted spatio-temporal sensory data. Specifically, we first formulate such sensor data as a high-dimensional tensor that can naturally preserve sensors’ both geographical and time information, thus we call spatio-temporal Tensor. Then we model the sensor data recovery as a low-rank robust tensor completion problem by exploiting its latent low-rank structure and sparse noise property. To solve this optimization problem, we design a highly efficient optimization method that combines the alternating direction method of multipliers and accelerated proximal gradient to minimize the tensor’s convex surrogate and noise’s ℓ1-norm. In addition to testing our method by a synthetic dataset, we also use passive RFID (radiofrequency identification) sensors to build a real-world sensor-array testbed, which generates overall 115,200 sensor readings for model evaluation. The experimental results demonstrate the accuracy and robustness of our approach.

Original languageEnglish
Title of host publicationDatabase Systems for Advanced Applications
Subtitle of host publication22nd International Conference, DASFAA 2017 Suzhou, China, March 27–30, 2017 Proceedings, Part I
EditorsSelçuk Candan, Lei Chen, Torben Bach Pedersen, Lijun Chang, Wen Hua
Place of PublicationSwitzerland
Pages607-622
Number of pages16
ISBN (Electronic)9783319557533
DOIs
Publication statusPublished - 2017
Event22nd International Conference on Database Systems for Advanced Applications, DASFAA 2017 - Suzhuo, China
Duration: 27 Mar 201730 Mar 2017

Publication series

NameLecture Notes in Computer Science
Volume10177
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference22nd International Conference on Database Systems for Advanced Applications, DASFAA 2017
CountryChina
CitySuzhuo
Period27/03/1730/03/17

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