Water Fixtures Vijay Srinivasan John Stankovic Kamin Whitehouse University of Virginia Currently affiliated to Samsung Motivation For Fixture Monitoring Cooking Toileting Home Healthcare Applications ID: 284640
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Slide1
FixtureFinder: Discovering the Existence of Electrical and Water Fixtures
Vijay
Srinivasan*,
John Stankovic,
Kamin
Whitehouse
University of
Virginia
*(Currently affiliated to Samsung)Slide2
Motivation For Fixture Monitoring
Cooking
Toileting
Home Healthcare Applications
7 KW hours
400 liters
Resource conservation applicationsSlide3
Fixture Monitoring Using Smart meters
Whole house
power or
water flow
Time
Power meter
Water meter
Bathroom
Kitchen
Bedroom
Livingroom
2000 W
100 W
100 W
100 W
100
litres
/hour
100
litres
/hour
Poor accuracy for low power or low water flow fixtures
False positive noise
Identical
fixturesSlide4
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 dataSlide5
FixtureFinder
Power meter
Water meter
Bathroom
Kitchen
Bedroom
Livingroom
Automatically:
Identify fixtures
Infer usage times
Infer resource consumption
2 PM
5 PM
…
Single-Point Infrastructure sensing
Training data
7 KW hours
400 liters
Home security or automation sensors
Light and motion
+
Lights, sinks and toiletsSlide6
FixtureFinder Insights
Bathroom
Kitchen
Bedroom
Livingroom
Fixtures identical in meter data
Unique in (meter, sensor) data
100 W
100 W,
30
lux
100 W,
50
lux
Light sensor
Power meter
Water meterSlide7
FixtureFinder Insights
Bathroom
Kitchen
Bedroom
Livingroom
100 W,
30
lux
100 W,
50
lux
Light sensor
False positive noise
in meter and sensor data
Eliminate noise events in one stream when no activity in other stream
Eliminate unmatched noise
Power meter
Water meter
ON-OFF pattern
Bedroom light sensor data
Power meter dataSlide8
OutlineFixtureFinder algorithm
Case studies
Experimental setup
Evaluation results
ConclusionsSlide9
FixtureFinder Algorithm Inputs
Stream 1
Stream 2
Power meter
Water meter
Light or motion sensors
or
Four step algorithmSlide10
Step 1 – Event Detection
Stream 1
Stream 2
Time
ON
OFF
ON
OFF
40
500
60
40
40
140
60
200
Stream 1
Stream 2
False positives events:
True positive events:
40
lux
100 Watts
For example:
Edge detection algorithms
Key challenge: Large number of false positives
100
40
100
Light sensor
Power meterSlide11
Step 2 – Data fusion
Stream 1
Stream 2
Time
ON
OFF
ON
OFF
40
100
500
60
40
40
140
100
60
200
40
Stream 1
Stream 2
40
lux
100 Watts
For example:
Light sensor
Power meter
Fixture use creates events in multiple streams
simultaneously
Compute
event pairs
Eliminate temporally isolated false positivesSlide12
Step 3 – Matching
Stream 1
Stream 2
Time
ON
OFF
ON
OFF
40
100
500
60
40
140
100
60
200
40
Stream 1
Stream 2
40
lux
100 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 probabilitySlide13
Step 3 – Matching
Stream 1
Stream 2
Time
ON
OFF
ON
OFF
40
100
60
100
60
40
Stream 1
Stream 2
40
lux
100 Watts
For example:
Light sensor
Power meter
High match probability
Two 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 probability
All false positives eliminated in this example!Slide14
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 discoveredSlide15
OutlineFixtureFinder algorithm
Case studies
Experimental setup
Evaluation results
ConclusionsSlide16
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 location
Light intensity
Power consumptionSlide17
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 3Slide18
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 signature
Flow rate
100
litres
/hour
100
litres
/hour
300
litres
/hourSlide19
Water Fixture Discovery
Two toilets with the same flow signature but different motion signaturesSlide20
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 roomsSlide21
OutlineFixtureFinder algorithmCase studies
Experimental setup
Evaluation results
ConclusionsSlide22
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 where
two sensors were used)
One per homeSlide23
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)Slide24
OutlineFixtureFinder algorithmCase studies
Experimental setup
Evaluation results
ConclusionsSlide25
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 consumptionSlide26
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% precision
64% recall
True positive ON-OFF events from fixtures
Single-Point Infrastructure sensing
Training data
High precision usage dataSlide27
Fixture Usage Inference Results
Recall:
% of ground truth fixture events detected by Fixture Finder
Results shown for light fixtures
92% precision
82% recall
Balanced precision and recall
Home Activity Monitoring applications
Precision:
% of detected fixture events that are supported by ground truthSlide28
Analysis of FixtureFinder Steps
Step 1: Event Detection
ME: Meter event detection
SE: Sensor event detection
Step 3: MatchingMM: Meter event matchingSM: Sensor event matchingStep 2: Data Fusion
SMF: Sensor meter data fusionFixtureFinder
Small reduction in recall
Significant increase in precision with steps 2, 3, and FixtureFinder
Results shown for light fixturesSlide29
Light Fixture Energy Estimation91% average energy accuracy for top 90% energy consuming fixturesSlide30
Water Consumption Estimation81.5% accuracy in Home 3
89.9% accuracy in Home 4
Home 3
Home 4
B – Bathroom
K – Kitchen
S – Sink
F – FlushSlide31
OutlineFixtureFinder algorithmCase studies
Experimental setup
Evaluation results
ConclusionsSlide32
ConclusionsFixtureFinder combines smart meters with existing home security sensors to automatically:
Identify fixtures
Infer usage times
Infer resource consumption
Demonstrated for light and water fixturesComplements other fixture monitoring techniques by providing training data without manual effortSlide33
Future ImprovementsExpand scope to include:
Additional
electrical appliances
and
water fixturesAdditional sensing modalities such as routers, smart switches, infrastructure sensorsExtend algorithm to multi-state appliancesNot just two-state ON-OFF
Explore temporal co-occurrence over multiple timescalesSlide34
ThanksQuestions?Slide35
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 motionSlide36
Step 3 – Bayesian Matching
Two matches possible
(40,100) or
(40,60)
Assumption: Edge pairs from true fixtures are more frequent than noisy edge pairsP(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 variablesSlide37
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