Please use this identifier to cite or link to this item: http://repositorio.inesctec.pt/handle/123456789/3498
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dc.contributor.authorPinto,ARen
dc.contributor.authorMontez,Cen
dc.contributor.authorAraujo,Gen
dc.contributor.authorFrancisco Vasquesen
dc.contributor.authorPaulo Portugalen
dc.date.accessioned2017-11-20T10:32:55Z-
dc.date.available2017-11-20T10:32:55Z-
dc.date.issued2014en
dc.identifier.urihttp://repositorio.inesctec.pt/handle/123456789/3498-
dc.identifier.urihttp://dx.doi.org/10.1016/j.inffus.2013.05.003en
dc.description.abstractWireless Sensor Networks (WSNs) can be used to monitor hazardous and inaccessible areas. In these situations, the power supply (e.g. battery) of each node cannot be easily replaced. One solution to deal with the limited capacity of current power supplies is to deploy a large number of sensor nodes, since the lifetime and dependability of the network will increase through cooperation among nodes. Applications on WSN may also have other concerns, such as meeting temporal deadlines on message transmissions and maximizing the quality of information. Data fusion is a well-known technique that can be useful for the enhancement of data quality and for the maximization of WSN lifetime. In this paper, we propose an approach that allows the implementation of parallel data fusion techniques in IEEE 802.15.4 networks. One of the main advantages of the proposed approach is that it enables a trade-off between different user-defined metrics through the use of a genetic machine learning algorithm. Simulations and field experiments performed in different communication scenarios highlight significant improvements when compared with, for instance, the Gur Game approach or the implementation of conventional periodic communication techniques over IEEE 802.15.4 networks.en
dc.languageengen
dc.relation5903en
dc.relation6446en
dc.rightsinfo:eu-repo/semantics/openAccessen
dc.titleAn approach to implement data fusion techniques in wireless sensor networks using genetic machine learning algorithmsen
dc.typearticleen
dc.typePublicationen
Appears in Collections:CPES - Indexed Articles in Journals
CTM - Indexed Articles in Journals

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