Please use this identifier to cite or link to this item: https://hdl.handle.net/10216/69390
Author(s): A. R. Pinto
Benedito Bitencort
M. A. R. Dantas
Carlos B. Montez
Francisco Vasques
Title: Genetic machine learning approach for data fusion applications in dense wireless sensor networks
Issue Date: 2008
Abstract: Wireless Sensor Networks (WSN) are being targeted for use in applications like security, resources monitoring and factory automation. However, the reduced available resources raise a lot of technical challenges. Self organization in WSN is a desirable characteristic that can be achieved by means of data fusion techniques when delivering reliable data to users. In this paper it is proposed a genetic machine learning algorithm (GMLA) approach that makes a trade-off between quality of information and communication efficiency. GMLA is based on genetic algorithms and it can adapt itself dynamically to environment modifications. The main target of the proposed approach is to achieve set(organization in a WSN application with data fusion. Simulations demonstrate that the proposed approach can optimize communication efficiency in a dense WSN.
Subject: Engenharia
Engineering
URI: https://repositorio-aberto.up.pt/handle/10216/69390
Source: 2008 IEEE INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION, PROCEEDINGS
Document Type: Artigo em Livro de Atas de Conferência Internacional
Rights: openAccess
License: https://creativecommons.org/licenses/by-nc/4.0/
Appears in Collections:FEUP - Artigo em Livro de Atas de Conferência Internacional

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