Plug-and-play irrigation control at scale

• Published in: 2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) pp. 1 - 12
• Main Authors: Winkler, Daniel A., Carreira-Perpiñán, Miguel Á., Cerpa, Alberto E.
• Format: Conference Proceeding
• Language: English
• Published: Piscataway, NJ, USA IEEE Press 11-04-2018; IEEE
• Series: ACM Conferences
• Subjects: Adaptation models; Biological system modeling; Decentralized Control; Irrigation; Meteorology; Moisture; Monitoring; Soil; Soil measurements
• ISBN: 9781538652985; 1538652986
• DOI: 10.1109/IPSN.2018.00008

Lawns, also known as turf, cover an estimated 128,000km2 [9] in North America alone, with landscape requirements representing 30% of freshwater consumed in the residential domain [27]. 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 don't revise the strategy over time in response to collected data. In this work, we introduce PICS, 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 PICS 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, PICS 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.