Title :
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
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;
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
DOI :
10.1109/SNPD.2008.132
