A Decentralized Quality Aware Adaptive Sampling Strategy in Wireless Sensor Networks

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

doi:10.1109/UIC-ATC.2012.156

A Decentralized Quality Aware Adaptive Sampling Strategy 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

10 Citations (Scopus)

161 Downloads (Pure)

Abstract

Since WSNs suffer from sever resource constraints, in terms of energy, memory and processing, temporal, spatial and spatio-temporal correlation among sensor data can be exploited by adaptive sampling approaches to find out an optimal sampling strategy, which reduces the number of sampling nodes and/or sampling rates while maintaining high data quality. In this paper, a quality aware decentralized adaptive sampling strategy is proposed which benefit from the data correlation for predicting future samples. In this algorithm, sensor nodes adjust their sampling rates, based on environmental conditions and user defined data range. Simulation results show that proposed approach provides 90 percentage event detection accuracy level while consumes lesser energy rather than existing adaptive sampling approach.

Original language	Undefined
Title of host publication	9th IEEE International Conference on Ubiquitous Intelligence and Computing, UIC 2012
Place of Publication	USA
Publisher	IEEE
Pages	298-305
Number of pages	8
ISBN (Print)	978-1-4673-3084-8
DOIs	https://doi.org/10.1109/UIC-ATC.2012.156
Publication status	Published - Sept 2012
Event	IEEE 9th International Conference on Ubiquitous Intelligence and Computing, UIC 2012 - Fukuoka, Japan Duration: 4 Sept 2012 → 7 Sept 2012 Conference number: 9

Publication series

Name
Publisher	IEEE Computer Society

Conference

Conference	IEEE 9th International Conference on Ubiquitous Intelligence and Computing, UIC 2012
Abbreviated title	UIC
Country/Territory	Japan
City	Fukuoka
Period	4/09/12 → 7/09/12

Keywords

EWI-22543
IR-83420
METIS-293201

Access to Document

10.1109/UIC-ATC.2012.156

06332012

Cite this

@inproceedings{b92403fc3c554a4690a7848199542168,

title = "A Decentralized Quality Aware Adaptive Sampling Strategy in Wireless Sensor Networks",

abstract = "Since WSNs suffer from sever resource constraints, in terms of energy, memory and processing, temporal, spatial and spatio-temporal correlation among sensor data can be exploited by adaptive sampling approaches to find out an optimal sampling strategy, which reduces the number of sampling nodes and/or sampling rates while maintaining high data quality. In this paper, a quality aware decentralized adaptive sampling strategy is proposed which benefit from the data correlation for predicting future samples. In this algorithm, sensor nodes adjust their sampling rates, based on environmental conditions and user defined data range. Simulation results show that proposed approach provides 90 percentage event detection accuracy level while consumes lesser energy rather than existing adaptive sampling approach.",

keywords = "EWI-22543, IR-83420, METIS-293201",

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

note = "10.1109/UIC-ATC.2012.156 ; IEEE 9th International Conference on Ubiquitous Intelligence and Computing, UIC 2012 ; Conference date: 04-09-2012 Through 07-09-2012",

year = "2012",

month = sep,

doi = "10.1109/UIC-ATC.2012.156",

language = "Undefined",

isbn = "978-1-4673-3084-8",

publisher = "IEEE",

pages = "298--305",

booktitle = "9th IEEE International Conference on Ubiquitous Intelligence and Computing, UIC 2012",

address = "United States",

}

Masoum, A, Meratnia, N & Havinga, PJM 2012, A Decentralized Quality Aware Adaptive Sampling Strategy in Wireless Sensor Networks. in 9th IEEE International Conference on Ubiquitous Intelligence and Computing, UIC 2012. IEEE, USA, pp. 298-305, IEEE 9th International Conference on Ubiquitous Intelligence and Computing, UIC 2012, Fukuoka, Japan, 4/09/12. https://doi.org/10.1109/UIC-ATC.2012.156

A Decentralized Quality Aware Adaptive Sampling Strategy in Wireless Sensor Networks. / Masoum, Alireza; Meratnia, Nirvana; Havinga, Paul J.M.
9th IEEE International Conference on Ubiquitous Intelligence and Computing, UIC 2012. USA: IEEE, 2012. p. 298-305.

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

TY - GEN

T1 - A Decentralized Quality Aware Adaptive Sampling Strategy in Wireless Sensor Networks

AU - Masoum, Alireza

AU - Meratnia, Nirvana

AU - Havinga, Paul J.M.

N1 - Conference code: 9

PY - 2012/9

Y1 - 2012/9

N2 - Since WSNs suffer from sever resource constraints, in terms of energy, memory and processing, temporal, spatial and spatio-temporal correlation among sensor data can be exploited by adaptive sampling approaches to find out an optimal sampling strategy, which reduces the number of sampling nodes and/or sampling rates while maintaining high data quality. In this paper, a quality aware decentralized adaptive sampling strategy is proposed which benefit from the data correlation for predicting future samples. In this algorithm, sensor nodes adjust their sampling rates, based on environmental conditions and user defined data range. Simulation results show that proposed approach provides 90 percentage event detection accuracy level while consumes lesser energy rather than existing adaptive sampling approach.

AB - Since WSNs suffer from sever resource constraints, in terms of energy, memory and processing, temporal, spatial and spatio-temporal correlation among sensor data can be exploited by adaptive sampling approaches to find out an optimal sampling strategy, which reduces the number of sampling nodes and/or sampling rates while maintaining high data quality. In this paper, a quality aware decentralized adaptive sampling strategy is proposed which benefit from the data correlation for predicting future samples. In this algorithm, sensor nodes adjust their sampling rates, based on environmental conditions and user defined data range. Simulation results show that proposed approach provides 90 percentage event detection accuracy level while consumes lesser energy rather than existing adaptive sampling approach.

KW - EWI-22543

KW - IR-83420

KW - METIS-293201

U2 - 10.1109/UIC-ATC.2012.156

DO - 10.1109/UIC-ATC.2012.156

M3 - Conference contribution

SN - 978-1-4673-3084-8

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EP - 305

BT - 9th IEEE International Conference on Ubiquitous Intelligence and Computing, UIC 2012

PB - IEEE

CY - USA

T2 - IEEE 9th International Conference on Ubiquitous Intelligence and Computing, UIC 2012

Y2 - 4 September 2012 through 7 September 2012

ER -