Transcript PPT - University of Virginia

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
Home Healthcare Applications
Cooking
Toileting
Resource conservation
applications
7 KW hours
400 liters
Fixture Monitoring Using Smart meters
Whole
house
power or
water
flow
Water
meter
Power
meter
Time
• Poor accuracy for low
power or low water
flow fixtures
• False positive noise
• Identical fixtures
100 litres/hour
Bathroom
100 W
Bedroom
100 W
2000 W
Kitchen
100 litres/hour
100 W
Livingroom
Existing Fixture Monitoring Techniques
Direct metering on each fixture
Single-Point Infrastructure sensing
Images courtesy: HydroSense and Viridiscope (Ubicomp 2009)
Indirect sensing + smart meter
Requires users to:
• Identify each fixture, and
for each fixture:
• Install a sensor, or
• Provide training data
FixtureFinder
• Automatically:
Light and motion
+
– Identify fixtures Lights, sinks and toilets
– Infer usage times
Home security or
automation sensors
– Infer resource
consumption
Water
meter
Power
meter
2 PM
5 PM
…
Training data
400 liters
Bathroom
Kitchen
Single-Point Infrastructure sensing
7 KW hours
Bedroom
Livingroom
FixtureFinder Insights
Fixtures
identical in
meter data
Unique in
(meter,
sensor)
data
Power
meter
Bathroom
Water
meter
Kitchen
Light sensor
Bedroom
100 W,
30 lux
100 W
100 W,
50 lux
Livingroom
FixtureFinder Insights
1. Eliminate noise
False positive
events in one
noise in meter
stream when no
and sensor
activity in other
data
stream
2. Eliminate
unmatched noise
Power
meter
Bathroom
Water
meter
Kitchen
Light sensor
Power meter data
Bedroom light sensor data
ON-OFF
pattern
Bedroom
100 W,
30 lux
100 W,
50 lux
Livingroom
Outline
•
•
•
•
•
FixtureFinder algorithm
Case studies
Experimental setup
Evaluation results
Conclusions
FixtureFinder Algorithm Inputs
Light or motion sensors
Stream 1
• Four step algorithm
or
Stream 2
Power
meter
Water
meter
Step 1 – Event Detection
For example:
40 lux
100 Watts
Stream 1
Light sensor
ON
40
40
Stream 1
Edge detection algorithms
Key challenge: Large
number of false positives
OFF
40
140
Time
ON
100
500
60
40
Stream 2
100
OFF
False positives events:
Stream 2
True positive events:
Power meter
200
60
Step 2 – Data fusion
For example:
40 lux
100 Watts
Stream 1
Light sensor
ON
40
40
Stream 1
Fixture use creates
events in multiple
streams simultaneously
Compute event pairs
OFF
40
140
Time
ON
100
500
60
40
Stream 2
100
OFF
Eliminate temporally
isolated false positives
Stream 2
Power meter
200
60
Step 3 – Matching
For example:
40 lux
100 Watts
Stream 1
Light sensor
ON
Fixture use occurs in
an ON-OFF pattern
Match ON event pairs
to OFF event pairs
High match probability
40
Stream 1
OFF
40
140
Time
ON
100
500
60
40
Stream 2
100
OFF
Eliminate unmatched
false positives
Stream 2
Power meter
200
60
Step 3 – Matching
For example:
40 lux
100 Watts
Two ON-OFF event pairs:
(40,100) or (40,60) ?
Stream 1
Light sensor
ON
High match probability
40
Stream 1
OFF
True event pairs are more
likely than noisy event pairs
Use both match and pair
probabilities to compute
ON-OFF event pairs
Soft clustering and
Min Cost Bipartite matching
(Described in paper)
40
All false
positives eliminated
Low pair
probability
in this example!
High pair probability
Time
ON
100
60
Stream 2
100
OFF
Stream 2
Power meter
60
Step 4 – Fixture Discovery
Step 3: Matching
ON-OFF events
Fixtures discovered
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
Clustering based on:
(stream 1 intensity, stream 2 intensity)
40 lux,
100 watts
Clustering
60 lux,
100 watts
Outline
•
•
•
•
•
FixtureFinder algorithm
Case studies
Experimental setup
Evaluation results
Conclusions
Light Fixture Discovery
Power
meter
Apply FixtureFinder
algorithm on every
(light sensor, power meter)
Unique fixture usage defined by:
Light sensor location
Light intensity
Power consumption
Water
meter
Bathroom
Kitchen
40 lumens,
100 watts
Bedroom
40 lumens,
150 watts
Livingroom
Light Fixture Discovery
False positives
eliminated after
steps 2 and 3
Bedroom light fixture ONOFF events
Large
number of
false
positives
after step 1
Bedroom light sensor data
Power meter data
Water Fixture Discovery
Power
meter
Apply FixtureFinder algorithm on
(fused motion sensor, power meter)
300 litres/hour
Fused
motion
sensor
stream
Water
meter
100 litres/hour
Bathroom
Kitchen
100 litres/hour
Unique fixture usage defined by:
Motion sensor signature
Flow rate
Bedroom
Livingroom
Water Fixture Discovery
Two toilets with the same
flow signature but different
motion signatures
Water Fixture Discovery
Use event pair probability
to pair simultaneous toilet
events with correct rooms
Two toilets with the same
motion signature but
different flow signatures
Outline
•
•
•
•
•
FixtureFinder algorithm
Case studies
Experimental setup
Evaluation results
Conclusions
In-Situ Sensor Deployments in Homes
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 home
Power meter
(TED 5000)
Water meter
(Shenitech)
In-Situ Sensor Deployments in Homes
Ground truth for light fixtures
Smart switch
Smart plug
Ground truth for water fixtures
Contact switches on 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
Results shown for light fixtures
True positive
ON-OFF events
from fixtures
Precision: % of
detected fixture
events that are
supported by
ground truth
Training data
99% precision
64% recall
High precision usage data
Recall: % of ground truth
fixture events detected by
Fixture Finder
Single-Point
Infrastructure
sensing
Fixture Usage Inference Results
Results shown for light fixtures
Home Activity Monitoring
applications
Precision: % of
detected fixture
events that are
supported by
ground truth
92% precision
82% recall
Balanced precision and recall
Recall: % of ground truth
fixture events detected by
Fixture Finder
Analysis of FixtureFinder Steps
• Step 1: Event
Detection
Results shown for
light fixtures
– ME: Meter event
detection
– SE: Sensor event
detection
Small
reduction
in recall
• Step 3: Matching
– MM: Meter
event matching
– SM: Sensor
event matching
• Step 2: Data
Fusion
– SMF: Sensor
meter data
fusion
• FixtureFinder
Significant
increase in
precision
with steps 2,
3, and
FixtureFinder
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
B – Bathroom
K – Kitchen
S – Sink
F – Flush
Home 4
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
Thanks
Questions?
FixtureFinder Approach
Light and motion
Home security or
automation sensors
• Automatically
discover low
power or low
water flow fixtures
– Lights, sinks,
and toilets
+
Power
meter
Bathroom
Bedroom
Water
meter
Kitchen
Livingroom
Step 3 – Bayesian Matching
• Two matches possible
Stream 1
– (40,100) or (40,60)
• Assumption: Edge pairs
from true fixtures are
more frequent than
noisy edge pairs
ON
40
OFF
40
– P(40,100) >> P(40,60)
Time
ON
Stream
1 cluster
Stream
2 cluster
100
60
100
OFF
Hidden variables
Stream 2
Stream 1
edge
Stream 2
edge
Observed variables
60
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
ON
40
OFF
40
Time
ON
100
60
100
OFF
Stream 2
60