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ASample: Adaptive spatial sampling in Wireless Sensor Networks

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

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ASample : Adaptive spatial sampling in Wireless Sensor Networks. / Szczytowski, P.; Khelil, A.; Suri, Neeraj.

2010 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing. IEEE, 2010. p. 35-42.

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

Harvard

Szczytowski, P, Khelil, A & Suri, N 2010, ASample: Adaptive spatial sampling in Wireless Sensor Networks. in 2010 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing. IEEE, pp. 35-42. https://doi.org/10.1109/SUTC.2010.37

APA

Szczytowski, P., Khelil, A., & Suri, N. (2010). ASample: Adaptive spatial sampling in Wireless Sensor Networks. In 2010 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (pp. 35-42). IEEE. https://doi.org/10.1109/SUTC.2010.37

Vancouver

Szczytowski P, Khelil A, Suri N. ASample: Adaptive spatial sampling in Wireless Sensor Networks. In 2010 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing. IEEE. 2010. p. 35-42 https://doi.org/10.1109/SUTC.2010.37

Author

Szczytowski, P. ; Khelil, A. ; Suri, Neeraj. / ASample : Adaptive spatial sampling in Wireless Sensor Networks. 2010 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing. IEEE, 2010. pp. 35-42

Bibtex

@inproceedings{1bb8a880ccb64ab594bcf9bf0d639753,
title = "ASample: Adaptive spatial sampling in Wireless Sensor Networks",
abstract = "A prominent application of Wireless Sensor Networks is the monitoring of physical phenomena. The value of the monitored attributes naturally depends on the accuracy of the spatial sampling achieved by the deployed sensors. The monitored phenomena often tend to have unknown spatial distributions at pre-deployment stage, which also change over time. This can detrimentally affect the overall achievable accuracy of monitoring. Consequently, reaching an optimal (accuracy driven) static sensor node deployment is generally not possible, resulting in either under- or over-sampling of signals in space. Our goal is to provide for adaptive spatial sampling. The key challenges consist in identifying the regions of over- or under-sampling and in suggesting the appropriate countermeasures. In this paper, we propose a Voronoi based adaptive spatial sampling (ASample) solution. Our approach removes unnecessary samples from regions of over-sampling and generates additional new sampling locations in the under-sampling regions to fulfill specified accuracy requirements. Simulation results show that ASample significantly and efficiently reduces the mean square error of the achieved measurement accuracy. {\textcopyright} 2010 IEEE.",
keywords = "Adaptive spatial sampling, Reconfiguration, Wireless sensor networks, Measurement accuracy, Over sampling, Physical phenomena, Sampling location, Signals in spaces, Simulation result, Spatial distribution, Spatial sampling, Static sensor nodes, Under-sampling, Voronoi, Wireless sensor, Mobile computing, Monitoring, Sensor networks, Sensor nodes, Technical presentations, Telecommunication equipment, Ubiquitous computing",
author = "P. Szczytowski and A. Khelil and Neeraj Suri",
year = "2010",
month = jun,
day = "7",
doi = "10.1109/SUTC.2010.37",
language = "English",
isbn = "97814244-0877",
pages = "35--42",
booktitle = "2010 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - ASample

T2 - Adaptive spatial sampling in Wireless Sensor Networks

AU - Szczytowski, P.

AU - Khelil, A.

AU - Suri, Neeraj

PY - 2010/6/7

Y1 - 2010/6/7

N2 - A prominent application of Wireless Sensor Networks is the monitoring of physical phenomena. The value of the monitored attributes naturally depends on the accuracy of the spatial sampling achieved by the deployed sensors. The monitored phenomena often tend to have unknown spatial distributions at pre-deployment stage, which also change over time. This can detrimentally affect the overall achievable accuracy of monitoring. Consequently, reaching an optimal (accuracy driven) static sensor node deployment is generally not possible, resulting in either under- or over-sampling of signals in space. Our goal is to provide for adaptive spatial sampling. The key challenges consist in identifying the regions of over- or under-sampling and in suggesting the appropriate countermeasures. In this paper, we propose a Voronoi based adaptive spatial sampling (ASample) solution. Our approach removes unnecessary samples from regions of over-sampling and generates additional new sampling locations in the under-sampling regions to fulfill specified accuracy requirements. Simulation results show that ASample significantly and efficiently reduces the mean square error of the achieved measurement accuracy. © 2010 IEEE.

AB - A prominent application of Wireless Sensor Networks is the monitoring of physical phenomena. The value of the monitored attributes naturally depends on the accuracy of the spatial sampling achieved by the deployed sensors. The monitored phenomena often tend to have unknown spatial distributions at pre-deployment stage, which also change over time. This can detrimentally affect the overall achievable accuracy of monitoring. Consequently, reaching an optimal (accuracy driven) static sensor node deployment is generally not possible, resulting in either under- or over-sampling of signals in space. Our goal is to provide for adaptive spatial sampling. The key challenges consist in identifying the regions of over- or under-sampling and in suggesting the appropriate countermeasures. In this paper, we propose a Voronoi based adaptive spatial sampling (ASample) solution. Our approach removes unnecessary samples from regions of over-sampling and generates additional new sampling locations in the under-sampling regions to fulfill specified accuracy requirements. Simulation results show that ASample significantly and efficiently reduces the mean square error of the achieved measurement accuracy. © 2010 IEEE.

KW - Adaptive spatial sampling

KW - Reconfiguration

KW - Wireless sensor networks

KW - Measurement accuracy

KW - Over sampling

KW - Physical phenomena

KW - Sampling location

KW - Signals in spaces

KW - Simulation result

KW - Spatial distribution

KW - Spatial sampling

KW - Static sensor nodes

KW - Under-sampling

KW - Voronoi

KW - Wireless sensor

KW - Mobile computing

KW - Monitoring

KW - Sensor networks

KW - Sensor nodes

KW - Technical presentations

KW - Telecommunication equipment

KW - Ubiquitous computing

U2 - 10.1109/SUTC.2010.37

DO - 10.1109/SUTC.2010.37

M3 - Conference contribution/Paper

SN - 97814244-0877

SP - 35

EP - 42

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

PB - IEEE

ER -