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

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Presentation on theme: "WATERSENSE: WATER FLOW DISAGGREGATION USING MOTION SENSORS Vijay Srinivasan, John Stankovic, Kamin Whitehouse Department of Computer Science University."— Presentation transcript:

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

2 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

3 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

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 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

6 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

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 Time in Hours Three Tier Approach

9 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

10 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

11 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

12 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

13 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

14 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

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 X10 motion sensor Zwave reed switch 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 86% classification accuracy Errors have reduced effect on consumption accuracy Water Usage Classification 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 You Feedback or Questions?


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