When sensor meets tensor

filling missing sensor values through a tensor approach

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

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

4 Citations (Scopus)

Abstract

In the era of the Internet of Things, enormous number of sensors have been deployed in different locations, generating massive time-series sensory data with geo-tags. However, such sensory readings are easily missing due to various reasons such as the hardware malfunction, connection errors, and data corruption. This paper focuses on this challenge-how to accurately yet efficiently recover the missing values for corrupted time-series sensor data with geo-stamps. In this paper, we formulate the time-series sensor data as a 3-order tensor that naturally preserves sensors' temporal and spatial dependencies. Then we exploit its low-rank and sparse-noise structures by drawing upon recent advances in Robust Principal Component Analysis (RPCA) and tensor completion theory. The main novelty of this paper lies in that, we design a highly efficient optimization method that combines the alternating direction method of multipliers and accelerated proximal gradient to recover the data tensor. Besides testing our method using the synthetic data, we also design a real-world testbed by passive RFID (Radio-Frequency IDentification) sensors. The results demonstrate the effectiveness and accuracy of our approach.

Original languageEnglish
Title of host publicationCIKM 2016
Subtitle of host publicationProceedings of the 25th ACM International on Conference on Information and Knowledge Management
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery
Pages2025-2028
Number of pages4
ISBN (Electronic)9781450340731
DOIs
Publication statusPublished - 2016
Externally publishedYes
Event25th ACM International Conference on Information and Knowledge Management, CIKM 2016 - Indianapolis, United States
Duration: 24 Oct 201628 Oct 2016

Other

Other25th ACM International Conference on Information and Knowledge Management, CIKM 2016
CountryUnited States
CityIndianapolis
Period24/10/1628/10/16

Fingerprint Dive into the research topics of 'When sensor meets tensor: filling missing sensor values through a tensor approach'. Together they form a unique fingerprint.

  • Cite this

    Ruan, W., Xu, P., Sheng, Q. Z., Tran, N. K., Falkner, N. J. G., Li, X., & Zhang, W. E. (2016). When sensor meets tensor: filling missing sensor values through a tensor approach. In CIKM 2016: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management (pp. 2025-2028). New York, NY: Association for Computing Machinery. https://doi.org/10.1145/2983323.2983900