Latest revision as of 18:53, 28 January 2021

Abstract

lawn irrigation is estimated to consume 7 billion gallons of scarce fresh water each day in North America alone, lawn irrigation systems are a high priority for improvements in efficiency. To this end, recent work has introduced several key advancements in irrigation control. Distributed actuation systems allow the irrigation system to apply water completely independently across the field allowing flexibility of control, and the use of fluid flow modeling and optimization allows more efficient schedules to be computed automatically, significantly improving the irrigation quality of service as well. However, the proposed systems are designed with centralized architectures that introduce single points of failure, computational bottlenecks in data processing, and significant network energy for data forwarding used by the centralized data-driven modeling strategies. In response to these challenges, we propose and demonstrate WISDOM, whose novel and flexible hardware and processing pipelines enable the use of a distributed system for the management of irrigation systems of any scale, with energy independence by way of energy harvesting. Across 4 weeks of live system deployment, we find that the WISDOM system can save up to 32.9% of water in comparison to industry-best, while maintaining a perfect quality of service to the plant. Furthermore, with substantial analysis in simulation we find that in addition to practical system improvements, the use of the proposed distributed system within typical operating conditions will provide all of the efficiency and quality-of-service benefits of the globally-modeled, centrally controlled systems, while allowing the robust control of irrigation systems of any size.

Original document

The different versions of the original document can be found in:

• https://dl.acm.org/doi/pdf/10.1145/3356250.3360023?download=true

• https://dblp.uni-trier.de/db/conf/sensys/sensys2019.html#WinklerC19,

https://dl.acm.org/doi/pdf/10.1145/3356250.3360023,

http://doi.org/10.1145/3356250.3360023,

https://doi.org/10.1145/3356250.3360023,

https://academic.microsoft.com/#/detail/2982891032

• https://dl.acm.org/doi/pdf/10.1145/3356250.3360023,

http://dx.doi.org/10.1145/3356250.3360023 under the license http://www.acm.org/publications/policies/copyright_policy#Background