Assessing the quality of WSAN topologies

Chaczko, Z; Resconi, G

Assessing the quality of WSAN topologies

Chaczko, Z

Resconi, G

Permalink

Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013, 8112 LNCS (PART 2), pp. 174 - 182
Issue Date:: 2013-12-01

Closed Access

	Filename	Description	Size
	2013002189OK.pdf		513.83 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Chaczko, Z https://orcid.org/0000-0002-2816-7510	en_US
dc.contributor.author	Resconi, G	en_US
dc.date.issued	2013-12-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013, 8112 LNCS (PART 2), pp. 174 - 182	en_US
dc.identifier.isbn	9783642538612	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/27156
dc.description.abstract	This paper describes novel approaches for assessing the quality of mote partitioning in Wireless Sensor and Actuators Networks (WSANs) that allows optimization of the topology of WSANs. The proposed solution aims to supports node placement and activation strategies both at the time of sensor deployment and during the network normal operation. A blend of statistical and unsupervised learning techniques is proposed to test the quality of the WSAN organisation. A formal review and interpretation of various metrics is provided. These metrics can be used to improve management of resources in WSAN infrastructures. © 2013 Springer-Verlag Berlin Heidelberg.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-642-53862-9-23	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Assessing the quality of WSAN topologies	en_US
dc.type	Conference Proceeding
utslib.citation.volume	PART 2	en_US
utslib.citation.volume	8112 LNCS	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0805 Distributed Computing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	closed_access
pubs.issue	PART 2	en_US
pubs.publication-status	Published	en_US
pubs.volume	8112 LNCS	en_US

Abstract:

This paper describes novel approaches for assessing the quality of mote partitioning in Wireless Sensor and Actuators Networks (WSANs) that allows optimization of the topology of WSANs. The proposed solution aims to supports node placement and activation strategies both at the time of sensor deployment and during the network normal operation. A blend of statistical and unsupervised learning techniques is proposed to test the quality of the WSAN organisation. A formal review and interpretation of various metrics is provided. These metrics can be used to improve management of resources in WSAN infrastructures. © 2013 Springer-Verlag Berlin Heidelberg.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/27156