NDLI: A parallelization of orchard temperature predicting programs

Content Provider	IEEE Xplore Digital Library
Author	Mazurek, E. Fukuda, M.
Copyright Year	2011
Description	Author affiliation: Computing & Software Systems, University of Washington, Bothell, 18115 NE Campus Way, Bothell, WA 98011 (Mazurek, E.; Fukuda, M.)
Abstract	Frost protection is a significant concern among tree-fruit growers. In order to protect orchards from frost, temperature sensor networks have gained popularity to judge when and where to turn on wind generators and sprinklers. Currently these sensor networks only give growers a real-time alert, however such sensor networks would be more effective if they were integrated in a computation loop that uses past and current temperature data for predicting every overnight transition of orchard air temperature. Although a couple of algorithms using artificial neural network and empirically-formulated polynomials are available to the public, they need to be parallelized for useful on-the-fly prediction. To utilize a cluster of multi-core computing nodes (available through cloud services), we are developing MASS, a library for multi-agent spatial simulation, and parallelizing temperature prediction programs with MASS. This paper demonstrates the MASS library's suitability to parallelization of temperature prediction programs for on-the-fly sensor-data analysis by (1) porting the programs to MASS, (2) running them in a multi-core system, (3) feeding real-time sensor data to them, and (4) measuring their analyzing performance.
Starting Page	179
Ending Page	184
File Size	537481
Page Count	6
File Format	PDF
ISBN	9781457702525
ISSN	21545952
e-ISBN	9781457702532
e-ISBN	9781457702518
DOI	10.1109/PACRIM.2011.6032889
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-23
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Temperature sensors Temperature distribution Temperature measurement Real time systems Artificial neural networks Instruction sets Prediction algorithms
Content Type	Text
Resource Type	Article

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