Genetic machine learning approach for data fusion applications in dense wireless sensor networks

• Type: Article in International Conference Proceedings Book
• Date: 2008

Authors

A. R. Pinto (Author) Other; Benedito Bitencort (Author) Other; M. A. R. Dantas (Author) Other; Carlos B. Montez (Author) Other; Francisco Vasques (Author) FEUP

Conference proceedings International

Title: 2008 IEEE INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION, PROCEEDINGS

Pages: 1177-1180

13th IEEE International Conference on Emerging Technologies and Factory Automation

Hamburg, GERMANY, SEP 15-18, 2008

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Scientific classification

CORDIS: Technological sciences > Engineering

Other information

Authenticus ID: P-007-NYY

DOI: 10.1109/etfa.2008.4638549

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

Notes: Genetic machine learning approach for data fusion applications in dense Wireless Sensor Networks Pinto, A.R. ; Bitencort, B. ; Dantas, M.A.R. ; Montez, C.B. ; Vasques, F. Emerging Technologies and Factory Automation, 2008. ETFA 2008. IEEE International Conference on

No. of pages: 4