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Genetic machine learning algorithms in the optimization of communication efficiency in wireless sensor networks

Title
Genetic machine learning algorithms in the optimization of communication efficiency in wireless sensor networks
Type
Article in International Conference Proceedings Book
Year
2009
Authors
A. R. Pinto
(Author)
Other
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Marcos Camada
(Author)
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M. A. R. Dantas
(Author)
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Carlos Montez
(Author)
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Conference proceedings International
Pages: 2306-2311
35th Annual Conference of the IEEE-Industrial-Electronics-Society (IECON 2009)
Porto, PORTUGAL, NOV 03-05, 2009
Other information
Authenticus ID: P-003-QFF
Abstract (EN): Wireless Sensor Networks (WSN) can be used to monitor hazardous and inaccessible areas. In these situations, the power supply (e g battery) in each node can not be easily replaced One solution is to deploy a large number of sensor nodes, since the lifetime and dependability of the network can he increased through cooperation among nodes In addition to energy consumption, applications for WSN may also have other concerns, such as, meeting deadlines and maximizing the quality of information In this paper, we present a Genetic Machine Learning algorithm aimed at applications that make use of trade-offs between different metrics Simulations were performed on random topologies assuming different levels of faults Our approach showed a significant improvement when compared with the use of IEEE 802.15 4 protocol
Language: English
Type (Professor's evaluation): Scientific
Contact: arpinto@fe.up.pt; mcamada@das.ufsc.br; mario@inf.ufsc.br; montez@das.ufsc.br; pportugal@fe.up.pt; vasques@fe.up.pt
No. of pages: 6
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