Research Article
Time Series Prediction for Energy-Efficient Wireless Sensors: Applications to Environmental Monitoring and Video Games
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@INPROCEEDINGS{10.1007/978-3-642-32778-0_5, author={Yann-A\`{\i}l Borgne and Gianluca Bontempi}, title={Time Series Prediction for Energy-Efficient Wireless Sensors: Applications to Environmental Monitoring and Video Games}, proceedings={Sensor Systems and Software. Third International ICST Conference, S-Cube 2012, Lisbon, Portugal, June 4-5, 2012, Revised Selected Papers}, proceedings_a={S-CUBE}, year={2012}, month={10}, keywords={Wireless sensors energy-efficiency machine learning time series prediction exponential smoothing}, doi={10.1007/978-3-642-32778-0_5} }- Yann-Aël Borgne
Gianluca Bontempi
Year: 2012
Time Series Prediction for Energy-Efficient Wireless Sensors: Applications to Environmental Monitoring and Video Games
S-CUBE
Springer
DOI: 10.1007/978-3-642-32778-0_5
Abstract
Time series prediction techniques have been shown to significantly reduce the radio use and energy consumption of wireless sensor nodes performing periodic data collection tasks. In this paper, we propose an implementation of exponential smoothing, a standard time series prediction technique, for wireless sensors. We rely on a framework called (AMS), specifically designed for running time series prediction techniques on resource-constrained wireless sensors. We showcase our implementation with two demos, related to environmental monitoring and video games. The demos are implemented with TinyOS, a reference operating system for low-power embedded systems, and TMote Sky and TMote Invent wireless sensors.
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