Exploring a WSN design space using genetic algorithms.

M. Nabi; M. Blagojevic; T. Basten; M.C.W. Geilen; T. Hendriks

Exploring a WSN design space using genetic algorithms.

M. Nabi, M. Blagojevic, T. Basten, M.C.W. Geilen, T. Hendriks

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

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Abstract

Configuring wireless sensor networks (WSNs) plays an important role for the performance and quality of service of the running application. The huge number of sensor nodes and the possibility of configuring each of them in several ways cause an extremely large design space. Multi-objective genetic algorithms (GAs) are well suited to explore the trade offs in a WSN design space. A GA has many parameters in itself. So properly configuring a GA for design space exploration, given a specification of the WSN to be configured and a time budget available for analysis can lead us to better configurations for the WSN.

Original language	English
Title of host publication	Proceedings of 5th International Summer School on Advanced Computer Architecture and Compilation for Embedded Systems, ACACES'09. 12-15 July 2009, Ghent, Belgium
Place of Publication	Ghent
Publisher	HiPEAC
Pages	327-328
ISBN (Print)	978-90-382-1467-2
Publication status	Published - 2009

Cite this

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title = "Exploring a WSN design space using genetic algorithms.",

abstract = "Configuring wireless sensor networks (WSNs) plays an important role for the performance and quality of service of the running application. The huge number of sensor nodes and the possibility of configuring each of them in several ways cause an extremely large design space. Multi-objective genetic algorithms (GAs) are well suited to explore the trade offs in a WSN design space. A GA has many parameters in itself. So properly configuring a GA for design space exploration, given a specification of the WSN to be configured and a time budget available for analysis can lead us to better configurations for the WSN.",

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Exploring a WSN design space using genetic algorithms. / Nabi, M.; Blagojevic, M.; Basten, T. et al.
Proceedings of 5th International Summer School on Advanced Computer Architecture and Compilation for Embedded Systems, ACACES'09. 12-15 July 2009, Ghent, Belgium. Ghent: HiPEAC, 2009. p. 327-328.

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

TY - GEN

T1 - Exploring a WSN design space using genetic algorithms.

AU - Nabi, M.

AU - Blagojevic, M.

AU - Basten, T.

AU - Geilen, M.C.W.

AU - Hendriks, T.

PY - 2009

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N2 - Configuring wireless sensor networks (WSNs) plays an important role for the performance and quality of service of the running application. The huge number of sensor nodes and the possibility of configuring each of them in several ways cause an extremely large design space. Multi-objective genetic algorithms (GAs) are well suited to explore the trade offs in a WSN design space. A GA has many parameters in itself. So properly configuring a GA for design space exploration, given a specification of the WSN to be configured and a time budget available for analysis can lead us to better configurations for the WSN.

AB - Configuring wireless sensor networks (WSNs) plays an important role for the performance and quality of service of the running application. The huge number of sensor nodes and the possibility of configuring each of them in several ways cause an extremely large design space. Multi-objective genetic algorithms (GAs) are well suited to explore the trade offs in a WSN design space. A GA has many parameters in itself. So properly configuring a GA for design space exploration, given a specification of the WSN to be configured and a time budget available for analysis can lead us to better configurations for the WSN.

M3 - Conference contribution

SN - 978-90-382-1467-2

SP - 327

EP - 328

BT - Proceedings of 5th International Summer School on Advanced Computer Architecture and Compilation for Embedded Systems, ACACES'09. 12-15 July 2009, Ghent, Belgium

PB - HiPEAC

CY - Ghent

ER -

Exploring a WSN design space using genetic algorithms.

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