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OPTICS OPTimizing Irrigation Control at Scale
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DOI：10.1145/3372024.png)
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Abstract
En 中文
Lawns, also known as turf, cover an estimated 128,000 km(2) in North America alone, with landscape requirements representing 30% of freshwater consumed in the residential domain. With this consumption comes a large amount of environmental, economic, and social incentive to make turf irrigation systems as efficient as possible. Recent work introduced the concept of distributed control in irrigation systems, but existing control strategies either do not take advantage of the distributed control, or do not revise the strategy over time in response to collected data. In this work, we introduce OPTICS, a data-driven control strategy that self-improves over time, adapts to the local specific conditions and weather changes, and requires virtually no human input in both setup and maintenance providing a plug-and-play system that requires minimal pre-deployment efforts. In addition to substantial improvements in ease-of-use, we find across 4 weeks of large-scale irrigation system deployment that OPTICS improves system efficiency by 12.0% in comparison to industry best and 3.3% in comparison to academic state of the art. Despite using less water, OPTICS also was found to improve quality of service by a factor of 4.0x compared to industry best and 2.5x compared to academic state of the art.
Keywords:
Irrigation
monitoring
decentralized control
experimentation
measurement
performance
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