Residential Water Meters as Edge Computing Nodes: Disaggregating End Uses and Creating Actionable Information at the...

Area of Science:

• Environmental Engineering
• Computer Science
• Water Resource Management

Background:

• Traditional residential water meters lack advanced data collection and analysis capabilities.
• High temporal resolution water use data is crucial for effective water management and conservation.
• Existing systems often require significant infrastructure for data transmission and centralized processing.

Purpose of the Study:

• To introduce a novel, open-source datalogger for high temporal resolution residential water use data collection and analysis.
• To enable edge computing directly on analog water meters, transforming them into smart devices.
• To demonstrate the feasibility and benefits of on-site data processing for water management.

Main Methods:

• Coupling an Arduino microcontroller for data acquisition with a Raspberry Pi computer for computation.
• Developing and calibrating a computational node at the Utah Water Research Laboratory.
• Field deployment and testing on a residential water meter in Providence City, UT.

Main Results:

• The datalogger accurately collects water use data and performs on-site computations.
• Edge computing on the meter minimizes data transmission, storage, and processing requirements.
• Demonstrated functionality, accuracy, power efficiency, and communication capabilities in field tests.

Conclusions:

• The open-source datalogger effectively transforms existing water meters into intelligent, edge computing devices.
• On-meter computation significantly enhances efficiency and reduces latency in water use data analysis.
• The system's open-source nature allows for adaptation and reuse in various research and application contexts.