FixtureFinder: Discovering the Existence of Electrical and Water Fixtures

Vijay Srinivasan*, John Stankovic, Kamin Whitehouse

University of Virginia*(Currently affiliated to Samsung)

Motivation For Fixture Monitoring

CookingToileting

Home Healthcare Applications

7 KW hours 400 liters

Resource conservation applications

Fixture Monitoring Using Smart meters

Whole house

power orwater flow

Time

Power meter

Water meter

Bathroom Kitchen

Bedroom Livingroom

2000 W

100 W

100 W100 W

100 litres/hour

100 litres/hour

• Poor accuracy for low power or low water flow fixtures• False positive noise• Identical fixtures

Existing Fixture Monitoring Techniques

Direct metering on each fixture Indirect sensing + smart meter

Single-Point Infrastructure sensing

Images courtesy: HydroSense and Viridiscope (Ubicomp 2009)

Requires users to:• Identify each fixture, and for each fixture:• Install a sensor, or• Provide training data

FixtureFinder

Power meter

Water meter

Bathroom Kitchen

Bedroom Livingroom

• Automatically:– Identify fixtures– Infer usage times– Infer resource

consumption2 PM5 PM…

Single-Point Infrastructure sensing

Training data

7 KW hours

400 liters

Home security or automation sensors

Light and motion

+Lights, sinks and toilets

FixtureFinder Insights

Bathroom Kitchen

Bedroom Livingroom

Fixtures identical in meter data

Unique in (meter, sensor) data

100 W100 W, 30 lux

100 W, 50 lux

Light sensor

Power meter

Water meter

FixtureFinder Insights

Bathroom Kitchen

Bedroom Livingroom

100 W, 30 lux

100 W, 50 lux

Light sensor

False positive noise in meter

and sensor data

1. Eliminate noise events in one stream when no activity in other stream

2. Eliminate unmatched noise

Power meter

Water meter

ON-OFF pattern

Bedroom light sensor data

Power meter data

Outline

• FixtureFinder algorithm• Case studies• Experimental setup• Evaluation results• Conclusions

FixtureFinder Algorithm Inputs

Stream 1

Stream 2

Power meter

Water meter

Light or motion sensors

or

• Four step algorithm

Step 1 – Event Detection

Stream 1

Stream 2

Time

ON

OFF

ON

OFF

40

500 60

40

40

140

60200

Stream 1

Stream 2

False positives events:

True positive events:

40 lux100 Watts

For example:

Edge detection algorithms

Key challenge: Large number of false positives

100

40

100

Light sensor

Power meter

Step 2 – Data fusion

Stream 1

Stream 2

Time

ON

OFF

ON

OFF

40

100 500 60

40

40

140

100 60200

40

Stream 1

Stream 2

40 lux100 Watts

For example:

Light sensor

Power meter

Fixture use creates events in multiple streams simultaneously

Compute event pairs

Eliminate temporally isolated false positives

Step 3 – Matching

Stream 1

Stream 2

Time

ON

OFF

ON

OFF

40

100 500 60 40

140

100 60200

40

Stream 1

Stream 2

40 lux100 Watts

For example:

Light sensor

Power meter

Fixture use occurs in an ON-OFF pattern

Match ON event pairs to OFF event pairs

Eliminate unmatched false positives

High match probability

Step 3 – Matching

Stream 1

Stream 2

Time

ON

OFF

ON

OFF

40

100 60

100 60

40

Stream 1

Stream 2

40 lux100 Watts

For example:

Light sensor

Power meter

High match probabilityTwo ON-OFF event pairs:(40,100) or (40,60) ?

True event pairs are more likely than noisy event pairs

High pair probability

Use both match and pair probabilities to compute ON-OFF event pairs

Soft clustering and Min Cost Bipartite matching (Described in paper)

Low pair probabilityAll false positives eliminated in this example!

Step 4 – Fixture Discovery

Stream 1 (Light)

intensity

Stream 2 (Power) intensity

ON Time OFF Time

41 102 5 PM 6 PM

62 103 5:30 PM 6:15 PM

43 99 8 PM 10 PM

60 101 7 PM 8 PM

61 100 9 PM 10 PM

Step 3: Matching

ON-OFF events

Clustering

Clustering based on:(stream 1 intensity, stream 2 intensity)

40 lux,100 watts

60 lux,100 watts

Fixtures discovered

Outline

• FixtureFinder algorithm• Case studies• Experimental setup• Evaluation results• Conclusions

Light Fixture Discovery

Power meter

Water meter

Bathroom Kitchen

Bedroom Livingroom

Apply FixtureFinder algorithm on every (light sensor, power meter)

40 lumens, 100 watts

40 lumens, 150 watts

Unique fixture usage defined by:Light sensor locationLight intensityPower consumption

Light Fixture Discovery

Bedroom light sensor data

Bedroom light fixture ON-OFF events

Power meter data

Large number of false positives after step 1

False positives eliminated after steps 2 and 3

Water Fixture Discovery

Power meter

Water meter

Bathroom Kitchen

Bedroom Livingroom

Fused motion sensor stream

Apply FixtureFinder algorithm on (fused motion sensor, power meter)

Unique fixture usage defined by:Motion sensor signatureFlow rate

100 litres/hour

100 litres/hour300 litres/hour

Water Fixture Discovery

Two toilets with the same flow signature but different

motion signatures

Water Fixture Discovery

Two toilets with the same motion signature but

different flow signatures

Use event pair probability to pair simultaneous toilet events with correct rooms

Outline

• FixtureFinder algorithm• Case studies• Experimental setup• Evaluation results• Conclusions

In-Situ Sensor Deployments in Homes

Power meter(TED 5000)

Water meter(Shenitech)

X10 motion Custom light sensing mote

One per room in a central location(Except in 3 large rooms wheretwo sensors were used)

One per home

In-Situ Sensor Deployments in Homes

Smart switch Smart plug

Contact switches on water fixtures

Ground truth for light fixtures

Ground truth for water fixtures

All sensors deployed in 4 homes for 10 days(Except water meter deployed in 2 homes for 7 days)

Outline

• FixtureFinder algorithm• Case studies• Experimental setup• Evaluation results• Conclusions

Fixture Discovery Results

Discovered all sinks and toilets across 2 homes

Discovered 37 out of 41 light fixtures across 4 homes

Undiscovered lights:- All in large kitchens- Task lighting or under-cabinet lighting- Used rarely (1-3 times)- Low energy consumption

One false positive light with negligible energy consumption

Fixture Usage Inference Results

Recall: % of ground truth fixture events detected by Fixture Finder

Precision: % of detected fixture events that are supported by ground truth

Results shown for light fixtures

99% precision64% recall

True positive ON-OFF events from fixtures

Single-Point Infrastructure

sensing

Training data

High precision usage data

Fixture Usage Inference Results

Recall: % of ground truth fixture events detected by Fixture Finder

Results shown for light fixtures

92% precision82% recall

Balanced precision and recall

Home Activity Monitoring applications

Precision: % of detected fixture events that are supported by ground truth

Analysis of FixtureFinder Steps• Step 1: Event

Detection– ME: Meter event

detection– SE: Sensor event

detection• Step 3: Matching

– MM: Meter event matching

– SM: Sensor event matching

• Step 2: Data Fusion– SMF: Sensor

meter data fusion• FixtureFinder

Small reduction in recall

Significant increase in precision with steps 2, 3, and FixtureFinder

Results shown for light fixtures

Light Fixture Energy Estimation

• 91% average energy accuracy for top 90% energy consuming fixtures

Water Consumption Estimation

• 81.5% accuracy in Home 3• 89.9% accuracy in Home 4

Home 3 Home 4

B – BathroomK – KitchenS – SinkF – Flush

Outline

• FixtureFinder algorithm• Case studies• Experimental setup• Evaluation results• Conclusions

Conclusions

• FixtureFinder combines smart meters with existing home security sensors to automatically:– Identify fixtures– Infer usage times– Infer resource consumption

• Demonstrated for light and water fixtures• Complements other fixture monitoring

techniques by providing training data without manual effort

Future Improvements

• Expand scope to include:– Additional electrical appliances and water fixtures– Additional sensing modalities such as routers,

smart switches, infrastructure sensors• Extend algorithm to multi-state appliances– Not just two-state ON-OFF

• Explore temporal co-occurrence over multiple timescales

ThanksQuestions?

FixtureFinder Approach

Power meter

Water meter

Home security or automation sensors

+

• Automatically discover low power or low water flow fixtures– Lights, sinks,

and toilets

Bathroom Kitchen

Bedroom Livingroom

Light and motion

Step 3 – Bayesian Matching• Two matches possible

– (40,100) or (40,60)• Assumption: Edge pairs

from true fixtures are more frequent than noisy edge pairs– P(40,100) >> P(40,60)

Stream 1

Stream 2

Time

ON

OFF

ON

OFF

40

100 60

100 60

40

Hidden variables

Stream 1 cluster

Stream 1 edge Stream 2

edge

Stream 2 cluster

Observed variables

Step 3 – Bayesian Matching• Incorporate edge pair

probability into a match weight function

• Perform optimal bipartite matching based on match weight function

• Eliminate unlikely matches

Stream 1

Stream 2

Time

ON

OFF

ON

OFF

40

100 60

100 60

40