Neural Net Water Level Trend Prediction and Dynamic Water Level Sampling Frequency

Author

Sweeney, Steven P. ; Yoo, Sehwan ; Chi, Albert ; Lin, Frank ; Jeong, Taikyeong ; Hong, Sengphil ; Fernald, S.

Author_Institution

Dept. of Math. & Comput. Sci., Univ. of Maryland Eastern Shore, Princess Anne, MD

fYear

2008

fDate

6-8 Aug. 2008

Firstpage

29

Lastpage

37

Abstract

We have used neural network water level trend prediction (NNWLTP) in support of a water level sensing project. The NNWLTP approach allows dynamic change in water level sampling frequency, which will reduce power consumption and extend battery life in energy constrained devices. This paper deals primarily with the NNWLTP, which would allow sampling frequency change commands to be transmitted to the sensors when a transition or turning point was detected.

Keywords

geophysics computing; neural nets; water resources; battery life; dynamic water level sampling frequency; energy constrained device; neural network water level trend prediction; power consumption; water level sensing project; Artificial neural networks; Computer networks; Embedded computing; Frequency; Java; Neural networks; Sampling methods; Software engineering; Transducers; Wireless sensor networks; Artificial neural network; Trend prediction; Water level prediction;

fLanguage

English

Publisher

ieee

Conference_Titel

Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 2008. SNPD '08. Ninth ACIS International Conference on

Conference_Location

Phuket

Print_ISBN

978-0-7695-3263-9

Type

conf

DOI

10.1109/SNPD.2008.132

Filename

4617344