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

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

Before you can conserve it, measure it first!

1000 gallons

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

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/min1 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 <flow + motion> profile

Water

Meter

5 gallons/min1 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/min1 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 HoursThree 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 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

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

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

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

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 – BathroomK – KitchenS – SinkF – Flush

86% classification accuracy Errors have reduced effect on

consumption accuracy

Water Usage Classification

B – BathroomK – KitchenS – SinkF – 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 YouFeedback or Questions?