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8/10/2019 110124 Location
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Mobile Location
Technologies
Jeff HightowerIntel Labs
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A smartphone without location
sensing is like a laptop without WiFi.
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Why?
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Maps and Way Finding
Where am I?
Map View, Nearby stuff
How do I get to X?
Directions & Navigation
What is around here?
Nearest Starbucks
Local search
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Social-Mobile Services
Who is around here?
Where do my friends go?
What is a good exercise route?
How far did I walk today?
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IT Management & Asset Tracking
Inventory Tracking
Finding lost and stolen devices
Virus breakout tracking Location as tool in computer virus epidemiology
Controlling wireless network access
e.g. Access denied beyond 20m of building
Monitoring device usage
Measure mobility, usage by device class
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Location Sensing Technologies
Technology Application Domain
Satellite Positioning Systems Outdoor navigation
Manual User-entry Location-based web services
Cell-tower Triangulation Web and Fee-based locationtransactions
802.11 Fingerprinting Process management
e.g. HospitalsBeacon-based Location Indoor and fast TTFF mobile
computing
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Long Range Navigation (LORAN)
Coverage Outdoors, high seas
Accuracy 200-400 meters
Infrastructure cost High
Per-client cost LowPrivacy High
Application Domain Aircraft & Vessels
Simplified LORAN TD lines
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Global Navigation Satellite Systems (GNSS)
(e.g. GPS, GLONASS, Galileo, Compass)
Coverage Outdoors (line of sight)
Accuracy 10m
Infrastructure cost High
Per-client cost MediumPrivacy High
Application Domain Outdoor navigation
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GPS Variants
WAAS (LAAS) Improve accuracy to 3 meters (LAAS to 10cm)
Assisted GPS (A-GPS) Uses data network, faster lock times, comparable coverage
Relaxed GPS Loosen the GPS algorithm requirements improve coverage
at the cost of some accuracy
Can work indoors, but with >50m error
Soft-GPS GPS antenna + A/D + CPU
Slight improvement in coverage, time to lock
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Manual Entry
Coverage Populated Areas
Accuracy 10m-50km
Infrastructure cost Low
Per-client cost LowPrivacy High
Application Domain Location-based web services
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Cell-Tower Triangulation
Coverage Populated Areas
Accuracy 50-150m
Infrastructure cost Low
Per-client cost MediumPrivacy Low
Application Domain Web services and Fee-basedlocation transactions
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Radio Beacon Location
Coverage Populated Areas + Outdoors
Accuracy 5-150M
Infrastructure cost Low
Per-client cost LowPrivacy Low-High
Application Domain Mobile computing, fast TTFF
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Signal Strength is a Mediocre Indicator of Distance
802.11 signal
strength by
distance
GSM signal
strength by
distance
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 50 100 150 200 250 300 350 400 450 500
Meters from GSM Cell Tower
SignalStrength
Line shows median signal strength
Bars denote 25th to 75th percentile values
-100
-95
-90
-85
-80
-75
0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180
Meters from 802.11 Access Point
SignalStrength(dB)
Line shows median signal strength
Bars denote 25th to 75th percentile values
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GSM Response Rate By Distance
0%
10%
20%
30%
40%
50%
60%
50 100 150 200 250 300 350 400 450 500
Distance from Cell Tower (meters)
ChanceofAssociatingwithC
ell
802.11 Response Rate By Distance
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
5 15 25 35 45 55 65 75 85 95 105
115
125
135
145
155
165
175
Distance from 802.11 Access Point (meters)
ChanceofReceivingBeaconFrame
Response Rate Another Indicator of Distance
802.11
response rate
by distance
GSM response
rate by
distance
Response rate = 1 - loss rate of beacon frames
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Self-Mapping Radio Beacons
Known beacon Inferred beacon
- Grows beacon database using everyday radio traces
- Accuracy and coverage improve over time
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One Users Experience with Self-Mapping
Self-mapping with sporadic GPS for one volunteer
Day
Accuracy(m)
Cove
rage
0%
20%40%
60%
80%
100%
0 5 10 15 20 25 30
Coverage of self-mapping
0
20
40
60
80
100
0 5 10 15 20 25 30
Accuracy of self-mapping
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Location in Todays Smartphones
A-GPS
Accurate navigation
and tracking
Cell-ID Lookup
Highly availablefallback
Beacon
LocationFast TTFF
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Hot Topics in Mobile Location
Computer vision and Indoor Location
Mobile Augmented Reality
Discovering the Places people go Mobile [Push] Advertising
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Computer Vision Location
Accuracy of 30cm and
10o, 80% of the time
4 fps with GPU
Google starting major3D indoor mapping
effort, startup out of
Cambridge
Bundle
adjustment
algorithm
Camera image sequenceMetrically accurate 3D map
3D mapping
3D localization
http://../Documents/work/past/cse590b%20lecture%20Jan%202011/mapping.wmvhttp://../Documents/work/past/cse590b%20lecture%20Jan%202011/mapping.wmv8/10/2019 110124 Location
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Use your mobile phones camera to
estimate location/orientation and find web
content about where you are right now!
Mobile Augmented Reality
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Knowing types and sequences of
places we go is valuable
Predict likely destinations
Build personal quick-lists
Develop behavior models and
detect changes
Discovering the Places People Go
Manual Check-ins to places is the
commercial state-of-the-art approach
Research underway to augment
check-ins with automatic methods for
place detection and recognition
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Place Learning Two Approaches
Geometry-based Fingerprint-based
Input
Location coordinates
(e.g., GPS, WiFi/Cell tower
triangulation)
Radio environment
(e.g., currently visible cell towers,
WiFi access points)
ProsTightly coupled with the
geographical location of the place
Does not depend on the
underlying positioning systems
accuracy (especially indoors)
Cons
Depends on the underlying
positioning systems accuracy andavailability
Radio environment may changeover time (affecting recognition,
not necessarily detection)
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Sample Trace of WiFi APs encountered
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Sample Trace of WiFi APs encountered
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Results from a 4 week place learning experiment
Fingerprint-based techniques outperform geographic techniques due to the
challenge of accurately clustering coordinates
Many indoor places merged
as a single visit