Compressive sensing based data collection in wireless sensor networks

Alireza Masoum; Nirvana  Meratnia; Paul J.M. Havinga

doi:10.1109/MFI.2017.8170360

Compressive sensing based data collection in wireless sensor networks

Alireza Masoum, Nirvana Meratnia, Paul J.M. Havinga

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

2 Citations (Scopus)

3 Downloads (Pure)

Abstract

Compressive sensing originates in the field of signal processing and has recently become a topic of energy-efficient data gathering in wireless sensor networks. In this paper, we introduce a distributed compressive sensing approach, which utilizes spatial correlation among sensor nodes to group them into coalitions. The coalition formation method is represented by a block diagonal measurement matrix whose each diagonal entity corresponds to one of the coalitions. Then, a spatial-temporal correlation-based compressive sensing approach is used inside each coalition to schedule sensor nodes and encode their readings. Distributed data encoding over coalitions increases robustness and scalability of the approach. Simulation results verify that the proposed solution outperforms other compressive sensing approaches significantly in terms of data accuracy and energy efficiency.

Original language	English
Title of host publication	2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)
Place of Publication	Piscataway, NJ
Publisher	IEEE
Pages	442-447
ISBN (Electronic)	978-1-5090-6064-1, 978-1-5090-6063-4 (USB)
ISBN (Print)	978-1-5090-6065-8
DOIs	https://doi.org/10.1109/MFI.2017.8170360
Publication status	Published - 18 Nov 2017
Event	2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2017 - Daegu, Korea, Republic of Duration: 16 Nov 2017 → 18 Nov 2017

Conference

Conference	2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2017
Abbreviated title	MFI
Country/Territory	Korea, Republic of
City	Daegu
Period	16/11/17 → 18/11/17

Keywords

Compressive sensing
wireless sensor networks

Access to Document

10.1109/MFI.2017.8170360

Cite this

@inproceedings{e08aa8f146ea48be88bc207e42056787,

title = "Compressive sensing based data collection in wireless sensor networks",

abstract = "Compressive sensing originates in the field of signal processing and has recently become a topic of energy-efficient data gathering in wireless sensor networks. In this paper, we introduce a distributed compressive sensing approach, which utilizes spatial correlation among sensor nodes to group them into coalitions. The coalition formation method is represented by a block diagonal measurement matrix whose each diagonal entity corresponds to one of the coalitions. Then, a spatial-temporal correlation-based compressive sensing approach is used inside each coalition to schedule sensor nodes and encode their readings. Distributed data encoding over coalitions increases robustness and scalability of the approach. Simulation results verify that the proposed solution outperforms other compressive sensing approaches significantly in terms of data accuracy and energy efficiency.",

keywords = "Compressive sensing, wireless sensor networks",

author = "Alireza Masoum and Nirvana Meratnia and Havinga, {Paul J.M.}",

year = "2017",

month = nov,

day = "18",

doi = "10.1109/MFI.2017.8170360",

language = "English",

isbn = "978-1-5090-6065-8",

pages = "442--447",

booktitle = "2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)",

publisher = "IEEE",

address = "United States",

note = "2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2017, MFI ; Conference date: 16-11-2017 Through 18-11-2017",

}

Masoum, A, Meratnia, N & Havinga, PJM 2017, Compressive sensing based data collection in wireless sensor networks. in 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI). IEEE, Piscataway, NJ, pp. 442-447, 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2017, Daegu, Korea, Republic of, 16/11/17. https://doi.org/10.1109/MFI.2017.8170360

Compressive sensing based data collection in wireless sensor networks. / Masoum, Alireza; Meratnia, Nirvana ; Havinga, Paul J.M.
2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI). Piscataway, NJ: IEEE, 2017. p. 442-447.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Compressive sensing based data collection in wireless sensor networks

AU - Masoum, Alireza

AU - Meratnia, Nirvana

AU - Havinga, Paul J.M.

PY - 2017/11/18

Y1 - 2017/11/18

N2 - Compressive sensing originates in the field of signal processing and has recently become a topic of energy-efficient data gathering in wireless sensor networks. In this paper, we introduce a distributed compressive sensing approach, which utilizes spatial correlation among sensor nodes to group them into coalitions. The coalition formation method is represented by a block diagonal measurement matrix whose each diagonal entity corresponds to one of the coalitions. Then, a spatial-temporal correlation-based compressive sensing approach is used inside each coalition to schedule sensor nodes and encode their readings. Distributed data encoding over coalitions increases robustness and scalability of the approach. Simulation results verify that the proposed solution outperforms other compressive sensing approaches significantly in terms of data accuracy and energy efficiency.

AB - Compressive sensing originates in the field of signal processing and has recently become a topic of energy-efficient data gathering in wireless sensor networks. In this paper, we introduce a distributed compressive sensing approach, which utilizes spatial correlation among sensor nodes to group them into coalitions. The coalition formation method is represented by a block diagonal measurement matrix whose each diagonal entity corresponds to one of the coalitions. Then, a spatial-temporal correlation-based compressive sensing approach is used inside each coalition to schedule sensor nodes and encode their readings. Distributed data encoding over coalitions increases robustness and scalability of the approach. Simulation results verify that the proposed solution outperforms other compressive sensing approaches significantly in terms of data accuracy and energy efficiency.

KW - Compressive sensing

KW - wireless sensor networks

U2 - 10.1109/MFI.2017.8170360

DO - 10.1109/MFI.2017.8170360

M3 - Conference contribution

SN - 978-1-5090-6065-8

SP - 442

EP - 447

BT - 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)

PB - IEEE

CY - Piscataway, NJ

T2 - 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2017

Y2 - 16 November 2017 through 18 November 2017

ER -

Compressive sensing based data collection in wireless sensor networks

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Keywords

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