WATERSENSE: WATER FLOW DISAGGREGATION USING MOTION SENSORS Vijay Srinivasan, John Stankovic, Kamin...
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Transcript of WATERSENSE: WATER FLOW DISAGGREGATION USING MOTION SENSORS Vijay Srinivasan, John Stankovic, Kamin...
![Page 1: WATERSENSE: WATER FLOW DISAGGREGATION USING MOTION SENSORS Vijay Srinivasan, John Stankovic, Kamin Whitehouse Department of Computer Science University.](https://reader035.fdocuments.in/reader035/viewer/2022062619/5517b14855034645368b5f85/html5/thumbnails/1.jpg)
WATERSENSE: WATER FLOW DISAGGREGATION USING MOTION SENSORS
Vijay Srinivasan, John Stankovic, Kamin Whitehouse
Department of Computer Science
University of Virginia
![Page 2: WATERSENSE: WATER FLOW DISAGGREGATION USING MOTION SENSORS Vijay Srinivasan, John Stankovic, Kamin Whitehouse Department of Computer Science University.](https://reader035.fdocuments.in/reader035/viewer/2022062619/5517b14855034645368b5f85/html5/thumbnails/2.jpg)
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
1000 gallons
200 gallons
800 gallons
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Water Monitoring
Fixture level usage Change Behavior Change Fixtures Activity
Recognition
Water Meter Data Aggregate water
consumption
1000 gallons
1000 gallons
200 gallons
800 gallons
Water
Meter
3000 gallons
Disaggregation problem
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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/min1 minute
1 gallon/min.5 minutes
1 gallon/min.5 minutes
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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/min1 minute
1 gallon/min.5 minutes
1 gallon/min.5 minutes
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WaterSense Data Fusion Approach WaterSense
advantages Easy to install Cheap ($5) No Training
Water
Meter
5 gallons/min1 minute
1 gallon/min.5 minutes
1 gallon/min.5 minutes
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Rest of the talk
WaterSense Design WaterSense Evaluation Conclusions
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WaterSense Data Fusion Approach
Kitchen motion
Bathroom1 motion
Bathroom2 motion
Water Flow rate in kl/hour
Time in HoursThree Tier Approach
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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
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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
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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
Temporal distance feature ambiguous? Simultaneous
activities Missing activity
WaterSense Data Fusion Approach - Tier II Room Clustering
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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
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 1 Cluster 2
Cluster 1
Cluster 2
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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 1 Cluster 2
Flow event 1
Flow event 2
0.3 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
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Kitchen motion
Bathroom1 motion
Bathroom2 motion
Water Flow rate in kl/hour
Time in Hours
Cluster 1
Cluster 2
Cluster 1 Cluster 2
Flow event 1
Flow event 2
0.3 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
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Rest of the talk
WaterSense Design WaterSense Evaluation Conclusions
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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
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Water Consumption Accuracy 90% Water Consumption Accuracy Use Accurate feedback to improve water
usage
B – BathroomK – KitchenS – SinkF – Flush
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86% classification accuracy Errors have reduced effect on
consumption accuracy
Water Usage Classification
B – BathroomK – KitchenS – SinkF – Flush
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Rest of the talk
WaterSense Design WaterSense Evaluation Conclusions
![Page 20: WATERSENSE: WATER FLOW DISAGGREGATION USING MOTION SENSORS Vijay Srinivasan, John Stankovic, Kamin Whitehouse Department of Computer Science University.](https://reader035.fdocuments.in/reader035/viewer/2022062619/5517b14855034645368b5f85/html5/thumbnails/20.jpg)
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
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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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Thank YouFeedback or Questions?