In-Network Distributed Solar Current Prediction
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Rus_In-network.pdf
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1.34 MB
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Adobe PDF
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Author(s)
Basha, Elizabeth
Jurdak, Raja
Rus, Daniela L.
Date Issued
December 2014
Journal
ACM Transactions on Sensor Networks
Publisher
Association for Computing Machinery (ACM)
Citation
Elizabeth Basha, Raja Jurdak, and Daniela Rus. 2014. In-Network Distributed Solar Current Prediction. ACM Trans. Sen. Netw. 11, 2, Article 23 (December 2014), 28 pages.
Version
Original manuscript
Abstract
Long-term sensor network deployments demand careful power management. While managing power requires understanding the amount of energy harvestable from the local environment, current solar prediction methods rely only on recent local history, which makes them susceptible to high variability. In this article, we present a model and algorithms for distributed solar current prediction based on multiple linear regression to predict future solar current based on local, in situ climatic and solar measurements. These algorithms leverage spatial information from neighbors and adapt to the changing local conditions not captured by global climatic information. We implement these algorithms on our Fleck platform and run a 7-week-long experiment validating our work. In analyzing our results from this experiment, we determined that computing our model requires an increased energy expenditure of 4.5mJ over simpler models (on the order of 10[superscript -7%] of the harvested energy) to gain a prediction improvement of 39.7%.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/2629593
