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Real-time data management on a wireless sensor network.

Research output: Contribution to journalJournal article

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Real-time data management on a wireless sensor network. / Roadknight, Chris; Parrott, Laura; Boyd, Nathan; Marshall, Ian W.

In: International Journal of Distributed Sensor Networks, Vol. 1, No. 2, 01.04.2005, p. 215-225.

Research output: Contribution to journalJournal article

Harvard

Roadknight, C, Parrott, L, Boyd, N & Marshall, IW 2005, 'Real-time data management on a wireless sensor network.', International Journal of Distributed Sensor Networks, vol. 1, no. 2, pp. 215-225. https://doi.org/10.1080/15501320590966468

APA

Roadknight, C., Parrott, L., Boyd, N., & Marshall, I. W. (2005). Real-time data management on a wireless sensor network. International Journal of Distributed Sensor Networks, 1(2), 215-225. https://doi.org/10.1080/15501320590966468

Vancouver

Roadknight C, Parrott L, Boyd N, Marshall IW. Real-time data management on a wireless sensor network. International Journal of Distributed Sensor Networks. 2005 Apr 1;1(2):215-225. https://doi.org/10.1080/15501320590966468

Author

Roadknight, Chris ; Parrott, Laura ; Boyd, Nathan ; Marshall, Ian W. / Real-time data management on a wireless sensor network. In: International Journal of Distributed Sensor Networks. 2005 ; Vol. 1, No. 2. pp. 215-225.

Bibtex

@article{1fc09d6638e543568e7e2bd5141d6c2e,
title = "Real-time data management on a wireless sensor network.",
abstract = "A multi-layered algorithm is proposed that provides a scalable and adaptive method for handling data on a wireless sensor network. Statistical tests, local feedback, and global genetic style material exchange ensure limited resources such as battery and bandwidth which are used efficiently by manipulating data at the source and important features in the time series are not lost when compression needs to be made. The approach leads to a more 'hands off' implementation which is demonstrated by a real world oceanographic deployment of the system.",
keywords = "AI, sensor networks, oceanography",
author = "Chris Roadknight and Laura Parrott and Nathan Boyd and Marshall, {Ian W.}",
note = "This paper reports the progress made on instantiating and experimentally testing self-management in the SECOAS project (DTI, led by Marshall). SECOAS was the first sensor network project to recognize and address network management as a key issue for users. The solutions developed here have formed the basis of the successor projects Prosen (EPSRC WINES, led by Marshall), DIAS (EPSRC WINES), Tramsnod (EPSRC) and Neptune (EPSRC strategic partnership with ABB Yorkshire Water and United Utilities), and also of collaboration with two groups in Australia (funded by a Gledden fellowship and an ARC network). RAE_import_type : Journal article RAE_uoa_type : Computer Science and Informatics",
year = "2005",
month = apr
day = "1",
doi = "10.1080/15501320590966468",
language = "English",
volume = "1",
pages = "215--225",
journal = "International Journal of Distributed Sensor Networks",
issn = "1550-1329",
publisher = "Hindawi Publishing Corporation",
number = "2",

}

RIS

TY - JOUR

T1 - Real-time data management on a wireless sensor network.

AU - Roadknight, Chris

AU - Parrott, Laura

AU - Boyd, Nathan

AU - Marshall, Ian W.

N1 - This paper reports the progress made on instantiating and experimentally testing self-management in the SECOAS project (DTI, led by Marshall). SECOAS was the first sensor network project to recognize and address network management as a key issue for users. The solutions developed here have formed the basis of the successor projects Prosen (EPSRC WINES, led by Marshall), DIAS (EPSRC WINES), Tramsnod (EPSRC) and Neptune (EPSRC strategic partnership with ABB Yorkshire Water and United Utilities), and also of collaboration with two groups in Australia (funded by a Gledden fellowship and an ARC network). RAE_import_type : Journal article RAE_uoa_type : Computer Science and Informatics

PY - 2005/4/1

Y1 - 2005/4/1

N2 - A multi-layered algorithm is proposed that provides a scalable and adaptive method for handling data on a wireless sensor network. Statistical tests, local feedback, and global genetic style material exchange ensure limited resources such as battery and bandwidth which are used efficiently by manipulating data at the source and important features in the time series are not lost when compression needs to be made. The approach leads to a more 'hands off' implementation which is demonstrated by a real world oceanographic deployment of the system.

AB - A multi-layered algorithm is proposed that provides a scalable and adaptive method for handling data on a wireless sensor network. Statistical tests, local feedback, and global genetic style material exchange ensure limited resources such as battery and bandwidth which are used efficiently by manipulating data at the source and important features in the time series are not lost when compression needs to be made. The approach leads to a more 'hands off' implementation which is demonstrated by a real world oceanographic deployment of the system.

KW - AI

KW - sensor networks

KW - oceanography

U2 - 10.1080/15501320590966468

DO - 10.1080/15501320590966468

M3 - Journal article

VL - 1

SP - 215

EP - 225

JO - International Journal of Distributed Sensor Networks

JF - International Journal of Distributed Sensor Networks

SN - 1550-1329

IS - 2

ER -