An Adaptive and Autonomous Sensor Sampling Frequency Control Scheme for Energy-Efficient Data Acquisition in Wireless Sensor Networks

Supriyo Chatterjea, Paul J.M. Havinga

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

20 Citations (Scopus)

Abstract

Wireless sensor networks are increasingly being used in environmental monitoring applications. Collecting raw data from these networks can lead to excessive energy consumption. This is especially true when the application requires specialized sensors that have very high energy consumption, e.g. hydrological sensors for monitoring marine environments. We describe an adaptive sensor sampling scheme where nodes change their sampling frequencies autonomously based on the variability of the measured parameters. The sampling scheme also meets the user’s sensing coverage requirements by using information provided by the underlying MAC protocol. This allows the scheme to automatically adapt to topology changes. Our results based on real and synthetic data sets, indicate a reduction in sensor sampling by up to 93%, reduction in message transmissions by up to 99% and overall energy savings of up to 87%. We also show that generally more than 90% of the collected readings fall within the user-defined error threshold.
Original languageUndefined
Title of host publication4th IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS)
EditorsS.E. Nikoletseas, B.S. Chlebus, D. Johnson, B. Krishnamachari
Place of PublicationBerlin
PublisherSpringer
Pages60-78
Number of pages19
ISBN (Print)978-3-540-69169-3
DOIs
Publication statusPublished - 7 Jun 2008
Event4th IEEE International Conference on Distributed Computing in Sensor Systems, DCOSS 2008 - Santorini, Greece
Duration: 11 Jun 200814 Jun 2008
Conference number: 4
http://www.dcoss.org/dcoss08/index.php

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Verlag
Number08332
Volume5067
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference4th IEEE International Conference on Distributed Computing in Sensor Systems, DCOSS 2008
Abbreviated titleDCOSS 2008
CountryGreece
CitySantorini
Period11/06/0814/06/08
Internet address

Keywords

  • IR-62556
  • EWI-14161
  • METIS-252118

Cite this

Chatterjea, S., & Havinga, P. J. M. (2008). An Adaptive and Autonomous Sensor Sampling Frequency Control Scheme for Energy-Efficient Data Acquisition in Wireless Sensor Networks. In S. E. Nikoletseas, B. S. Chlebus, D. Johnson, & B. Krishnamachari (Eds.), 4th IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS) (pp. 60-78). [10.1007/978-3-540-69170-9] (Lecture Notes in Computer Science; Vol. 5067, No. 08332). Berlin: Springer. https://doi.org/10.1007/978-3-540-69170-9
Chatterjea, Supriyo ; Havinga, Paul J.M. / An Adaptive and Autonomous Sensor Sampling Frequency Control Scheme for Energy-Efficient Data Acquisition in Wireless Sensor Networks. 4th IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS). editor / S.E. Nikoletseas ; B.S. Chlebus ; D. Johnson ; B. Krishnamachari. Berlin : Springer, 2008. pp. 60-78 (Lecture Notes in Computer Science; 08332).
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title = "An Adaptive and Autonomous Sensor Sampling Frequency Control Scheme for Energy-Efficient Data Acquisition in Wireless Sensor Networks",
abstract = "Wireless sensor networks are increasingly being used in environmental monitoring applications. Collecting raw data from these networks can lead to excessive energy consumption. This is especially true when the application requires specialized sensors that have very high energy consumption, e.g. hydrological sensors for monitoring marine environments. We describe an adaptive sensor sampling scheme where nodes change their sampling frequencies autonomously based on the variability of the measured parameters. The sampling scheme also meets the user’s sensing coverage requirements by using information provided by the underlying MAC protocol. This allows the scheme to automatically adapt to topology changes. Our results based on real and synthetic data sets, indicate a reduction in sensor sampling by up to 93{\%}, reduction in message transmissions by up to 99{\%} and overall energy savings of up to 87{\%}. We also show that generally more than 90{\%} of the collected readings fall within the user-defined error threshold.",
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Chatterjea, S & Havinga, PJM 2008, An Adaptive and Autonomous Sensor Sampling Frequency Control Scheme for Energy-Efficient Data Acquisition in Wireless Sensor Networks. in SE Nikoletseas, BS Chlebus, D Johnson & B Krishnamachari (eds), 4th IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS)., 10.1007/978-3-540-69170-9, Lecture Notes in Computer Science, no. 08332, vol. 5067, Springer, Berlin, pp. 60-78, 4th IEEE International Conference on Distributed Computing in Sensor Systems, DCOSS 2008, Santorini, Greece, 11/06/08. https://doi.org/10.1007/978-3-540-69170-9

An Adaptive and Autonomous Sensor Sampling Frequency Control Scheme for Energy-Efficient Data Acquisition in Wireless Sensor Networks. / Chatterjea, Supriyo; Havinga, Paul J.M.

4th IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS). ed. / S.E. Nikoletseas; B.S. Chlebus; D. Johnson; B. Krishnamachari. Berlin : Springer, 2008. p. 60-78 10.1007/978-3-540-69170-9 (Lecture Notes in Computer Science; Vol. 5067, No. 08332).

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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T1 - An Adaptive and Autonomous Sensor Sampling Frequency Control Scheme for Energy-Efficient Data Acquisition in Wireless Sensor Networks

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AU - Havinga, Paul J.M.

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N2 - Wireless sensor networks are increasingly being used in environmental monitoring applications. Collecting raw data from these networks can lead to excessive energy consumption. This is especially true when the application requires specialized sensors that have very high energy consumption, e.g. hydrological sensors for monitoring marine environments. We describe an adaptive sensor sampling scheme where nodes change their sampling frequencies autonomously based on the variability of the measured parameters. The sampling scheme also meets the user’s sensing coverage requirements by using information provided by the underlying MAC protocol. This allows the scheme to automatically adapt to topology changes. Our results based on real and synthetic data sets, indicate a reduction in sensor sampling by up to 93%, reduction in message transmissions by up to 99% and overall energy savings of up to 87%. We also show that generally more than 90% of the collected readings fall within the user-defined error threshold.

AB - Wireless sensor networks are increasingly being used in environmental monitoring applications. Collecting raw data from these networks can lead to excessive energy consumption. This is especially true when the application requires specialized sensors that have very high energy consumption, e.g. hydrological sensors for monitoring marine environments. We describe an adaptive sensor sampling scheme where nodes change their sampling frequencies autonomously based on the variability of the measured parameters. The sampling scheme also meets the user’s sensing coverage requirements by using information provided by the underlying MAC protocol. This allows the scheme to automatically adapt to topology changes. Our results based on real and synthetic data sets, indicate a reduction in sensor sampling by up to 93%, reduction in message transmissions by up to 99% and overall energy savings of up to 87%. We also show that generally more than 90% of the collected readings fall within the user-defined error threshold.

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SN - 978-3-540-69169-3

T3 - Lecture Notes in Computer Science

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BT - 4th IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS)

A2 - Nikoletseas, S.E.

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PB - Springer

CY - Berlin

ER -

Chatterjea S, Havinga PJM. An Adaptive and Autonomous Sensor Sampling Frequency Control Scheme for Energy-Efficient Data Acquisition in Wireless Sensor Networks. In Nikoletseas SE, Chlebus BS, Johnson D, Krishnamachari B, editors, 4th IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS). Berlin: Springer. 2008. p. 60-78. 10.1007/978-3-540-69170-9. (Lecture Notes in Computer Science; 08332). https://doi.org/10.1007/978-3-540-69170-9