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

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

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 prim...

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Hauptverfasser: Sweeney, S.P., Sehwan Yoo, Chi, A., Lin, F., Taikyeong Jeong, Sengphil Hong, Fernald, S.
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Artificial neural network
Artificial neural networks
Digital filters
Java
Sensors
Time series analysis
Trend prediction
Turning
Water level prediction
Wireless sensor networks
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Zusammenfassung: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.
DOI:10.1109/SNPD.2008.132