An Environmentally Aware, Intelligently Controlled System for Power Efficient Wireless Sensor Networks

Author

Podpora, Jody ; Reznik, Leon

Author_Institution

Rochester Inst. of Technol., Rochester

fYear

2007

fDate

28-31 Oct. 2007

Firstpage

147

Lastpage

150

Abstract

This paper examines the advantages of applying an ANN (artificial neural network) and other machine learning techniques to a battery-powered WSN (wireless sensor network) with the goal of extending network deployment lifetime. Preliminary experimental results have shown that through the use of these techniques that it is possible to achieve network lifetime extensions of several orders-of-magnitude versus always-on systems. (All experimental results were obtained by observing the power consumption of a "live" sensor network deployed using MotelV TelosB sensors in a laboratory environment.).

Keywords

intelligent control; learning (artificial intelligence); neural nets; power supplies to apparatus; telecommunication computing; telecommunication control; wireless sensor networks; artificial neural network; battery-powered WSN; environmentally aware intelligently controlled system; machine learning; network deployment lifetime; power efficient wireless sensor networks; Artificial intelligence; Artificial neural networks; Control systems; Intelligent networks; Intelligent sensors; Intelligent systems; Learning systems; Machine learning; Sensor systems; Wireless sensor networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Sensors, 2007 IEEE

Conference_Location

Atlanta, GA

ISSN

1930-0395

Print_ISBN

978-1-4244-1261-7

Electronic_ISBN

1930-0395

Type

conf

DOI

10.1109/ICSENS.2007.4388357

Filename

4388357