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Type: Conference paper
Title: When sensor meets tensor: filling missing sensor values through a tensor approach
Author: Ruan, W.
Xu, P.
Sheng, Q.
Tran, N.
Falkner, N.
Li, X.
Zhang, W.
Citation: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, 2016 / vol.24-28-October-2016, pp.2025-2028
Publisher: ACM
Issue Date: 2016
ISBN: 9781450340731
Conference Name: 25th ACM International Conference on Information and Knowledge Management (CIKM) (24 Oct 2016 - 28 Oct 2016 : Indianapolis, IN)
Statement of
Wenjie Ruan, Peipei Xu, Quan Z. Sheng, Nguyen Khoi Tran, Xue Li, Wei Emma Zhang
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.
Rights: © 2016 ACM
RMID: 0030059268
DOI: 10.1145/2983323.2983900
Appears in Collections:Computer Science publications

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