Water flow disaggregation using motion sensors
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WATERSENSE: WATER FLOW DISAGGREGATION USING MOTION SENSORS Vijay Srinivasan, John Stankovic, Kamin Whitehouse Department of Computer Science University of Virginia Water Monitoring World’s usable water supply decreasing 1000 gallons 800 gallons Household water conservation can save fresh water reserves Before you can conserve it, measure it first! 1000 gallons 200 gallons Water Monitoring Fixture level usage Behavior Change Fixtures Activity Recognition Water Meter Change 1000 gallons 800 gallons 1000 gallons Water Meter Data water consumption 3000 gallons Aggregate Disaggregation problem 200 gallons Background Flow Profiling Expensive, In-line plumbing Accelerometers 5 gallons/min 1 minute Direct flow metering Ambiguity with similar sinks, flushes Water Meter Sensors on all fixtures Single point water pressure sensor High training cost 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 <flow + motion> profile Water Meter 5 gallons/min 1 minute 1 gallon/min .5 minutes 1 gallon/min .5 minutes WaterSense Data Fusion Approach WaterSense advantages to install Cheap ($5) No Training Water Meter 5 gallons/min 1 minute Easy 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 Three Tier Approach Time in Hours WaterSense Data Fusion Approach Tier I Flow Event Detection Canny Edge Detection Rising and falling edges Bayesian matching Flow events Kitchen motion Bathroom1 motion Bathroom2 motion Water Flow rate in kl/hour Time in Hours Flow event 1 0.75 kl/hr, 35 seconds Flow event 2 0.75 kl/hr, 45 seconds WaterSense Data Fusion Approach Tier II Room Clustering Flow profile ambiguous Kitchen motion Look at which motion sensors occur at the same time as the flow event Temporal distance feature for each room Bathroom1 motion Bathroom2 motion Water Flow rate in kl/hour Time in Hours Flow event 1 0.75 kl/hr, 35 seconds Flow event 2 0.75 kl/hr, 45 seconds WaterSense Data Fusion Approach Tier II Room Clustering Temporal distance feature ambiguous? Kitchen motion Simultaneous activities Missing activity Bathroom1 motion Bathroom2 motion Water Flow rate in kl/hour Time in Hours Flow event 1 0.3 kl/hr, 90 seconds Flow event 2 0.6 kl/hr, 40 seconds WaterSense Data Fusion Approach Tier II Room Clustering Temporal distance feature ambiguous? Simultaneous activities Missing activity Kitchen motion Cluster 2 Bathroom1 motion Cluster 1 Bathroom2 motion Cluster flow events by flow profile Learn cluster to room likelihood Water Flow rate in kl/hour Time in Hours Flow event 1 Cluster 1 0.3 kl/hr, 90 seconds Flow event 2 0.6 kl/hr, 40 seconds Cluster 2 WaterSense Data Fusion Approach Tier II Room Clustering Bayesnet to label each flow event Kitchen motion Room Flow cluster Cluster 2 Bathroom1 motion Cluster 1 Bathroom2 motion Hidden variables Evidence variables Temporal Distance Flow rate, duration Water Flow rate in kl/hour P(Room | Temporal Distance, Flow rate, Duration) - Use a binary temporal distance feature - Use quality threshold clustering for flow profiles - Maximum likelihood estimation Time in Hours Flow event 1 Cluster 1 0.3 kl/hr, 90 seconds Flow event 2 0.6 kl/hr, 40 seconds Cluster 2 WaterSense Data Fusion Approach Tier III Fixture Identification Use simple flow profiling to identify fixture E.g.) Flush events different from sink events Kitchen motion Cluster 2 Bathroom1 motion Cluster 1 Bathroom2 motion Water Flow rate in kl/hour Tier III fixture type + Tier II room assignment results in a unique water fixture Time in Hours Flow event 1 Cluster 1 0.3 kl/hr, 90 seconds Flow event 2 0.6 kl/hr, 40 seconds Cluster 2 Rest of the talk WaterSense Design WaterSense Evaluation Conclusions Home Deployments Two homes for one week each Ultrasonic water flow meter (2 Hz) Flow meter X10 motion sensor ($5) Ground Truth Zwave reed switch sensors 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 Water Usage Classification 86% classification accuracy Errors have reduced effect on consumption accuracy 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 Thank You Feedback or Questions?
