WATERSENSE: WATER FLOW DISAGGREGATION USING MOTION SENSORS Vijay Srinivasan, John Stankovic, Kamin Whitehouse Department of Computer Science University of Virginia

Water Monitoring Worlds usable water supply decreasing Household water conservation can save fresh water reserves Before you can conserve it, measure it first! 1000 gallons 200 gallons 800 gallons

Water Monitoring Fixture level usage Change Behavior Change Fixtures Activity Recognition Water Meter Data Aggregate water consumption 1000 gallons 200 gallons 800 gallons Water Meter 3000 gallons Disaggregation problem

Background Flow Profiling Ambiguity with similar sinks, flushes Direct flow metering Expensive, In-line plumbing Accelerometers Sensors on all fixtures Single point water pressure sensor High training cost Water Meter 5 gallons/min 1 minute 1 gallon/min.5 minutes 1 gallon/min.5 minutes

WaterSense Data Fusion Approach Combine water meter with motion sensors Key Insight Fixtures with the same flow profile may have unique motion profiles Use profile Water Meter 5 gallons/min 1 minute 1 gallon/min.5 minutes 1 gallon/min.5 minutes

WaterSense Data Fusion Approach WaterSense advantages Easy to install Cheap ($5) No Training Water Meter 5 gallons/min 1 minute 1 gallon/min.5 minutes 1 gallon/min.5 minutes

Rest of the talk WaterSense Design WaterSense Evaluation Conclusions

WaterSense Data Fusion Approach Kitchen motion Bathroom1 motion Bathroom2 motion Water Flow rate in kl/hour Time in Hours Three Tier Approach

WaterSense Data Fusion Approach - Tier I Flow Event Detection Kitchen motion Bathroom1 motion Bathroom2 motion Water Flow rate in kl/hour Time in Hours Flow event 1 Flow event 2 Canny Edge Detection Rising and falling edges Bayesian matching Flow events 0.75 kl/hr, 35 seconds 0.75 kl/hr, 45 seconds

WaterSense Data Fusion Approach - Tier II Room Clustering Kitchen motion Bathroom1 motion Bathroom2 motion Water Flow rate in kl/hour Time in Hours Flow event 1 Flow event 2 Flow profile ambiguous Look at which motion sensors occur at the same time as the flow event Temporal distance feature for each room 0.75 kl/hr, 35 seconds 0.75 kl/hr, 45 seconds

Kitchen motion Bathroom1 motion Bathroom2 motion Water Flow rate in kl/hour Time in Hours Flow event 1Flow event kl/hr, 90 seconds 0.6 kl/hr, 40 seconds Temporal distance feature ambiguous? Simultaneous activities Missing activity WaterSense Data Fusion Approach - Tier II Room Clustering

Kitchen motion Bathroom1 motion Bathroom2 motion Water Flow rate in kl/hour Time in Hours Flow event 1Flow event kl/hr, 90 seconds 0.6 kl/hr, 40 seconds Temporal distance feature ambiguous? Simultaneous activities Missing activity Cluster flow events by flow profile Learn cluster to room likelihood WaterSense Data Fusion Approach - Tier II Room Clustering Cluster 1Cluster 2 Cluster 1 Cluster 2

Kitchen motion Bathroom1 motion Bathroom2 motion Water Flow rate in kl/hour Time in Hours Hidden variables Evidence variables Room Temporal Distance Flow rate, duration Flow cluster P(Room | Temporal Distance, Flow rate, Duration) Bayesnet to label each flow event Cluster 1 Cluster 2 Cluster 1Cluster 2 Flow event 1Flow event kl/hr, 90 seconds 0.6 kl/hr, 40 seconds WaterSense Data Fusion Approach - Tier II Room Clustering -Use a binary temporal distance feature -Use quality threshold clustering for flow profiles -Maximum likelihood estimation

Kitchen motion Bathroom1 motion Bathroom2 motion Water Flow rate in kl/hour Time in Hours Cluster 1 Cluster 2 Cluster 1Cluster 2 Flow event 1Flow event kl/hr, 90 seconds 0.6 kl/hr, 40 seconds WaterSense Data Fusion Approach - Tier III Fixture Identification Use simple flow profiling to identify fixture E.g.) Flush events different from sink events Tier III fixture type + Tier II room assignment results in a unique water fixture

Rest of the talk WaterSense Design WaterSense Evaluation Conclusions

Home Deployments Two homes for one week each Ultrasonic water flow meter (2 Hz) X10 motion sensor ($5) Ground Truth Zwave reed switch sensors Flow meter X10 motion sensor Zwave reed switch sensor

Water Consumption Accuracy 90% Water Consumption Accuracy Use Accurate feedback to improve water usage B – Bathroom K – Kitchen S – Sink F – Flush

86% classification accuracy Errors have reduced effect on consumption accuracy Water Usage Classification B – Bathroom K – Kitchen S – Sink F – Flush

Rest of the talk WaterSense Design WaterSense Evaluation Conclusions

Limitations and future work Current evaluation limited to simple fixtures Include all fixtures, including washing machines, sprinklers, and dishwashers, in future evaluation Extend evaluation period Current system uses binary motion data Explore joint clustering of infrared motion readings and water flow profiles

Conclusions WaterSense – Practical data fusion approach to water flow disaggregation Cheap Unsupervised Water consumption accuracy of 90% High Enough Classification accuracy for activity recognition applications

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