Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/80642
Title: MRCS : matrix recovery-based communication-efficient compressive sampling on temporal-spatial data of dynamic-scale sparsity in large-scale environmental IoT networks
Authors: Xu, Z
Zhang, L
Shen, J
Zhou, H
Liu, X
Cao, J 
Xing, K
Keywords: Data compression
Device data gathering
Matrix recovery
Issue Date: 2019
Publisher: Springer
Source: EURASIP journal on wireless communications and networking, 2019, v. 2019, no. 1, 18 How to cite?
Journal: EURASIP journal on wireless communications and networking 
Abstract: In the past few years, a large variety of IoT applications has been witnessed by fast proliferation of IoT devices (e.g., environment surveillance devices, wearable devices, city-wide NB-IoT devices). However, launching data collection from these mass IoT devices raises a challenge due to limited computation, storage, bandwidth, and energy support. Existing solutions either rely on traditional data gathering methods by relaying data from each node to the sink, which keep data unaltered but suffering from costly communication, or tackle the spacial data in a proper basis to compress effectively in order to reduce the magnitude of data to be collected, which implicitly assumes the sparsity of the data and inevitably may result in a poor data recovery on account of the risk of sparsity violation. Note that these data collection approaches focus on either the fidelity or the magnitude of data, which can solve either problem well but never both simultaneously. This paper presents a new attempt to tackle both problems at the same time from theoretical design to practical experiments and validate in real environmental datasets. Specifically, we exploit data correlation at both temporal and spatial domains, then provide a cross-domain basis to collect data and a low-rank matrix recovery design to recover the data. To evaluate our method, we conduct extensive experimental study with real datasets. The results indicate that the recovered data generally achieve SNR 10 times (10 db) better than compressive sensing method, while the communication cost is kept the same.
URI: http://hdl.handle.net/10397/80642
ISSN: 1687-1472
EISSN: 1687-1499
DOI: 10.1186/s13638-018-1312-1
Rights: © The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
The following publication Xu, Z., Zhang, L., Shen, J., Zhou, H., Liu, X., Cao, J., & Xing, K. (2019). MRCS: matrix recovery-based communication-efficient compressive sampling on temporal-spatial data of dynamic-scale sparsity in large-scale environmental IoT networks. EURASIP Journal on Wireless Communications and Networking, 2019(1), 18 is available at https://doi.org/10.1186/s13638-018-1312-1
Appears in Collections:Journal/Magazine Article

Files in This Item:
File Description SizeFormat 
Xu_matrix_recovery-based_communication.pdf3.46 MBAdobe PDFView/Open
Access
View full-text via PolyU eLinks SFX Query
Show full item record
PIRA download icon_1.1View/Download Contents

Page view(s)

26
Citations as of Jul 16, 2019

Download(s)

8
Citations as of Jul 16, 2019

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.