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Watersense : Water flow disaggregation using motion sensors

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 Household water conservation can save fresh water reserves

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Watersense : Water flow disaggregation using motion sensors

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  1. Watersense: Water flow disaggregation using motion sensors Vijay Srinivasan, John Stankovic, Kamin Whitehouse Department of Computer Science University of Virginia

  2. Water Monitoring • World’s 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 1000 gallons

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

  4. 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

  5. WaterSenseData 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

  6. WaterSenseData Fusion Approach • WaterSenseadvantages • Easy to install • Cheap ($5) • No Training Water Meter 5 gallons/min 1 minute 1 gallon/min .5 minutes 1 gallon/min .5 minutes

  7. Rest of the talk • WaterSense Design • WaterSense Evaluation • Conclusions

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

  9. 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 2 Flow event 1 0.75 kl/hr, 45 seconds 0.75 kl/hr, 35 seconds

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

  11. WaterSense Data Fusion Approach - Tier II Room Clustering • Temporal distance feature ambiguous? • Simultaneous activities • Missing activity Kitchen motion Bathroom1 motion Bathroom2 motion Water Flow rate in kl/hour Time in Hours Flow event 1 Flow event 2 0.3 kl/hr, 90 seconds 0.6 kl/hr, 40 seconds

  12. WaterSense Data Fusion Approach - Tier II Room Clustering • Temporal distance feature ambiguous? • Simultaneous activities • Missing activity • Cluster flow events by flow profile • Learn cluster to room likelihood Kitchen motion Bathroom1 motion Cluster 2 Bathroom2 motion Cluster 1 Water Flow rate in kl/hour Time in Hours Flow event 1 Flow event 2 Cluster 1 Cluster 2 0.3 kl/hr, 90 seconds 0.6 kl/hr, 40 seconds

  13. WaterSense Data Fusion Approach - Tier II Room Clustering Bayesnetto label each flow event Kitchen motion Flow cluster Room Bathroom1 motion Cluster 2 Hidden variables Bathroom2 motion Cluster 1 Evidence variables Flow rate, duration Temporal Distance Water Flow rate in kl/hour P(Room | Temporal Distance, Flow rate, Duration) Time in Hours • Use a binary temporal distance feature • Use quality threshold clustering for flow profiles • Maximum likelihood estimation Flow event 1 Flow event 2 Cluster 1 Cluster 2 0.3 kl/hr, 90 seconds 0.6 kl/hr, 40 seconds

  14. 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 Kitchen motion Bathroom1 motion Cluster 2 Bathroom2 motion Cluster 1 Water Flow rate in kl/hour Time in Hours Flow event 1 Flow event 2 Cluster 1 Cluster 2 0.3 kl/hr, 90 seconds 0.6 kl/hr, 40 seconds

  15. Rest of the talk • WaterSense Design • WaterSense Evaluation • Conclusions

  16. 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 Zwave reed switch sensor X10 motion sensor

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

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

  19. Rest of the talk • WaterSense Design • WaterSense Evaluation • Conclusions

  20. 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

  21. 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

  22. Thank YouFeedback or Questions?

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