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

• Type: Article in International Conference Proceedings Book
• Date: 2009

Authors

A. R. Pinto (Author) Other; Marcos Camada (Author) Other; M. A. R. Dantas (Author) Other; Carlos Montez (Author) Other; Paulo Portugal (Author) FEUP; Francisco Vasques (Author) FEUP

Conference proceedings International

Title: IECON: 2009 35TH ANNUAL CONFERENCE OF IEEE INDUSTRIAL ELECTRONICS, VOLS 1-6

Pages: 2306-2311

35th Annual Conference of the IEEE-Industrial-Electronics-Society (IECON 2009)

Porto, PORTUGAL, NOV 03-05, 2009

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 10 Citations

INSPEC

Other information

Authenticus ID: P-003-QFF

DOI: 10.1109/iecon.2009.5415438

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

No. of pages: 6