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Reordering for better compressibility: Efficient spatial sampling in Wireless Sensor Networks

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Reordering for better compressibility: Efficient spatial sampling in Wireless Sensor Networks. / Mahmudimanesh, M.; Khelil, A.; Suri, Neeraj.

2010 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing. IEEE, 2010. p. 50-57.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Mahmudimanesh, M, Khelil, A & Suri, N 2010, Reordering for better compressibility: Efficient spatial sampling in Wireless Sensor Networks. in 2010 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing. IEEE, pp. 50-57. https://doi.org/10.1109/SUTC.2010.30

APA

Mahmudimanesh, M., Khelil, A., & Suri, N. (2010). Reordering for better compressibility: Efficient spatial sampling in Wireless Sensor Networks. In 2010 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (pp. 50-57). IEEE. https://doi.org/10.1109/SUTC.2010.30

Vancouver

Mahmudimanesh M, Khelil A, Suri N. Reordering for better compressibility: Efficient spatial sampling in Wireless Sensor Networks. In 2010 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing. IEEE. 2010. p. 50-57 https://doi.org/10.1109/SUTC.2010.30

Author

Mahmudimanesh, M. ; Khelil, A. ; Suri, Neeraj. / Reordering for better compressibility: Efficient spatial sampling in Wireless Sensor Networks. 2010 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing. IEEE, 2010. pp. 50-57

Bibtex

@inproceedings{97504cede4254ad7863b347f4425f0a8,
title = "Reordering for better compressibility: Efficient spatial sampling in Wireless Sensor Networks",
abstract = "Compressed Sensing (CS) is a novel sampling paradigm that tries to take data-compression concepts down to the sampling layer of a sensory system. It states that discrete compressible signals are recoverable from sub-sampled data, when the data vector is acquired by a special linear transform of the original discrete signal vector. Distributed sampling problems especially in Wireless Sensor Networks (WSN) are good candidates to apply CS and increase sensing efficiency without sacrificing accuracy. In this paper, we discuss how to reorder the samples of a discrete spatial signal vector by defining an alternative permutation of the sensor nodes (SN). Accordingly, we propose a method to enhance CS in WSN through improving signal compressibility by finding a sub-optimal permutation of the SNs. Permutation doesn't involve physical relocation of the SNs. It is a reordering function computed at the sink to gain a more compressible view of the spatial signal. We show that sub-optimal reordering stably maintains a more compressible view of the signal until the state of the environment changes so that another up-to-date reordering has to be computed. Our method can increase signal reconstruction accuracy at the same spatial sampling rate, or recover the state of the operational environment with the same quality at lower spatial sampling rate. Sub-sampling takes place during the interval that our reordered version of the spatial signal remains more compressible than the original signal. {\textcopyright} 2010 IEEE.",
keywords = "Compressed sensing, Compressibility, Compressive wireless sensing, Permutation, Reordering, Spatial sampling, Wireless sensing, Data compression, Metadata, Mobile computing, Optimization, Sensor networks, Sensor nodes, Signal reconstruction, Technical presentations, Telecommunication equipment, Ubiquitous computing, Wireless sensor networks",
author = "M. Mahmudimanesh and A. Khelil and Neeraj Suri",
year = "2010",
month = jun,
day = "7",
doi = "10.1109/SUTC.2010.30",
language = "English",
isbn = "9781424470877",
pages = "50--57",
booktitle = "2010 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Reordering for better compressibility: Efficient spatial sampling in Wireless Sensor Networks

AU - Mahmudimanesh, M.

AU - Khelil, A.

AU - Suri, Neeraj

PY - 2010/6/7

Y1 - 2010/6/7

N2 - Compressed Sensing (CS) is a novel sampling paradigm that tries to take data-compression concepts down to the sampling layer of a sensory system. It states that discrete compressible signals are recoverable from sub-sampled data, when the data vector is acquired by a special linear transform of the original discrete signal vector. Distributed sampling problems especially in Wireless Sensor Networks (WSN) are good candidates to apply CS and increase sensing efficiency without sacrificing accuracy. In this paper, we discuss how to reorder the samples of a discrete spatial signal vector by defining an alternative permutation of the sensor nodes (SN). Accordingly, we propose a method to enhance CS in WSN through improving signal compressibility by finding a sub-optimal permutation of the SNs. Permutation doesn't involve physical relocation of the SNs. It is a reordering function computed at the sink to gain a more compressible view of the spatial signal. We show that sub-optimal reordering stably maintains a more compressible view of the signal until the state of the environment changes so that another up-to-date reordering has to be computed. Our method can increase signal reconstruction accuracy at the same spatial sampling rate, or recover the state of the operational environment with the same quality at lower spatial sampling rate. Sub-sampling takes place during the interval that our reordered version of the spatial signal remains more compressible than the original signal. © 2010 IEEE.

AB - Compressed Sensing (CS) is a novel sampling paradigm that tries to take data-compression concepts down to the sampling layer of a sensory system. It states that discrete compressible signals are recoverable from sub-sampled data, when the data vector is acquired by a special linear transform of the original discrete signal vector. Distributed sampling problems especially in Wireless Sensor Networks (WSN) are good candidates to apply CS and increase sensing efficiency without sacrificing accuracy. In this paper, we discuss how to reorder the samples of a discrete spatial signal vector by defining an alternative permutation of the sensor nodes (SN). Accordingly, we propose a method to enhance CS in WSN through improving signal compressibility by finding a sub-optimal permutation of the SNs. Permutation doesn't involve physical relocation of the SNs. It is a reordering function computed at the sink to gain a more compressible view of the spatial signal. We show that sub-optimal reordering stably maintains a more compressible view of the signal until the state of the environment changes so that another up-to-date reordering has to be computed. Our method can increase signal reconstruction accuracy at the same spatial sampling rate, or recover the state of the operational environment with the same quality at lower spatial sampling rate. Sub-sampling takes place during the interval that our reordered version of the spatial signal remains more compressible than the original signal. © 2010 IEEE.

KW - Compressed sensing

KW - Compressibility

KW - Compressive wireless sensing

KW - Permutation

KW - Reordering

KW - Spatial sampling

KW - Wireless sensing

KW - Data compression

KW - Metadata

KW - Mobile computing

KW - Optimization

KW - Sensor networks

KW - Sensor nodes

KW - Signal reconstruction

KW - Technical presentations

KW - Telecommunication equipment

KW - Ubiquitous computing

KW - Wireless sensor networks

U2 - 10.1109/SUTC.2010.30

DO - 10.1109/SUTC.2010.30

M3 - Conference contribution/Paper

SN - 9781424470877

SP - 50

EP - 57

BT - 2010 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing

PB - IEEE

ER -