Location Without GPS
John KrummMicrosoft Research
Redmond, Washington, USA
Seattle, Washington, USA
Kyoto, Japan
Location
Importance of Location• Find your way• Find nearby things• Invoke location-based
services– Electronic graffiti, e.g. “There is
a better Mexican restaurant 0.2 km north of here.”
– List of nearby events
• Part of context– In lecture hall → cell phone off– At home → use home network
IWMMS & Location“Study of Structuring and Recalling Life Log Experience Using Location Information”, Y. Aihara, R. Ueoka, K. Hirota and M. Hirose
-- Already using location for activity inference
“Active Wearable Vision Sensor: Recognition of Human Activities”, K. Sumi, M. Toda, S. Tsukizawa and T. Matsuyama
“Cooperative Dialogue Planning with User and Situation Models via Example-based Training”, I. R. Lane, S. Ueno and T. Kawahara
-- Inferring context of user – location is part of context
“A Hybrid Dynamical System for Event Segmentation, Learning, and Recognition”, H. Kawashima, K. Tsutsumi and T. Matsuyama
“Time-Series Human-Motion Analysis with Kernels derived from Learned Switching Linear Dynamics”, T. Mori, M. Shimosaka, T. Harada and T. Sato
-- Apply HDS/SLDS to infer location & mode of transportation & destination?
Why Not Use GPS?
• Does not work indoors• Needs view of satellites
Location Sensing
Hazas, Scott, Krumm, “Location-Aware Computing”, IEEE Computer Magazine, February 2004.
Outline
• Introduction
• LOCADIO – Wi-Fi triangulation
• NearMe – Wi-Fi proximity
• RightSPOT – FM radio triangulation
• TempIO – Inside/outside from temperature
Location from 802.11 with LOCADIO*
• Mobile device measures signal strengths from Wi-Fi access points• Computes its own location
Wi-Fi (802.11) access point
* Location from Radio
with Eric Horvitz
LOCADIO – Radio Survey
Radio survey to get signal strength as a function of position
LOCADIO - Constraints
No passing through walls No speeding
200 400 600 800 1000 1200
still
moving
still
moving
still
moving
A Posteriori Probability of Move
time (seconds)
actual
unsmoothed
smoothed
We know when you move
Make the client as smart as possible to reduce calibration effort
LOCADIO - Results
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 160
0.1
0.2
0.3
error in meters
rela
tive
fre
qu
ency
Error Histogram
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 160
0.5
1
error in meters
rela
tive
fre
qu
ency
Cumulative Error Distribution
Hidden Markov model gives median error of 1.53 meters
Outline
• Introduction
• LOCADIO – Wi-Fi triangulation
• NearMe – Wi-Fi proximity
• RightSPOT – FM radio triangulation
• TempIO – Inside/outside from temperature
NearMe
Person
conferencerooms
printers
bathroom reception desk
people
Find people and things nearby
Download from http://research.microsoft.com/~jckrumm/NearMe.htm
with Ken Hinckley
The Basic Idea802.11 Wi-Fi access point
NearMe Proximity Server
Download from http://research.microsoft.com/~jckrumm/NearMe.htm
Location vs. Proximity
s1 = measured signals
s2 = measured signals
x1 = (x,y) location
x2 = (x,y) location
d12 = f(x1, x2)
d12 = g(s1, s2)
Download from http://research.microsoft.com/~jckrumm/NearMe.htm
NearMe Client
Windows XP PocketPC 2003
Requirements:• Windows XP• WWW access• Microsoft .NET Framework
Download from http://research.microsoft.com/~jckrumm/NearMe.htm
NearMe Client – Test Connections
Download from http://research.microsoft.com/~jckrumm/NearMe.htm
NearMe Client – Register
Register with:• Name• Email (optional)• URL (optional)• Expiration interval
Download from http://research.microsoft.com/~jckrumm/NearMe.htm
NearMe Client – Report Wi-Fi
• List of detectable Wi-Fi access points• Access points used only as beacons
• Periodic reports for mobility
Download from http://research.microsoft.com/~jckrumm/NearMe.htm
NearMe Client -- Query
Adjustable “Look back” time to filter outdated reports
Download from http://research.microsoft.com/~jckrumm/NearMe.htm
Register as thing
NearMe Client – Nearby Things
person elevator kitchen bathroom
conference room stairs mail room stitchable device
printer cafeteria reception desk demo person
Report signal strengths Query for things
Download from http://research.microsoft.com/~jckrumm/NearMe.htm
Simple Distance Function
1
2
3
4
5 -1 -0.75 -0.5 -0.25 0.0 0.25 0.5 0.75 1.0
5
10
15
20
25
Number of Access Points in Common, n∩
Spearman Correlation, ρs
d = -2.53∙n∩ – 2.90∙ρs - 22.31
rms error = 14.04 meters
ρs = 0.39
Download from http://research.microsoft.com/~jckrumm/NearMe.htm
Access Point Layout
21
3
AF
B
D
C
E
F
A B C D E
• Access point topology in database• Recomputed every hour
Download from http://research.microsoft.com/~jckrumm/NearMe.htm
NearMe Demo
Outline
• Introduction
• LOCADIO – Wi-Fi triangulation
• NearMe – Wi-Fi proximity
• RightSPOT – FM radio triangulation
• TempIO – Inside/outside from temperature
SPOT Watch Location
weather traffic
dining movies
Commercial FM: transmit new data every ~2 minutes
Filter on watch to take what it wants
Watch displays “personalized” data
with Adel Youssef, Ed Miller, Gerry Cermak, Eric Horvitz
Location-Sensitive Features
Nice to have
• Local traffic• Nearby movie times• Nearby restaurants
Need to know location of device …
Use FM Radio Signal StrengthsKPLU 88.5KAOS 89.3KASB 89.3KNHC 89.5KWFJ 89.7KGHP 89.9KGRG 89.9KUPS 90.1KEXP 90.3KSER 90.7KVTI 90.9KBCS 91.3KBTC 91.7KLSY 92.5KUBE 93.3KMPS 94.1KRXY 94.5KUOW 94.9KJR 95.7KYPT 96.5KBSG 97.3KING 98.1KWJZ 98.9KISW 99.9KQBZ 100.7KPLZ 101.5KZOK 102.5KMTT 103.7KMIH 104.5KFNK 104.9KCMS 105.3KBKS 106.1KRWM 106.9KNDD 107.7
Scan signal strengths of 32 FM radio stations at 1 Hz
0 100 200 300 400 500 600 700560
570
580
590
600
610
620
630
640
650
660
Sample Number
RS
SI
RSSI Values in One Suburb
raw rssimedian filtered
Ranking Approach
• Each watch scales signal strengths differently• Impractical to calibrate every watch
Input Power
Mea
sure
d P
ower
A B C
Redmond: KPLU < KMTT < KMPSBellevue: KMTT < KPLU < KMPSIssaquah: KMTT < KMPS < KPLU…
Any monotonically increasing function of signal strength preserves ranking
N radio stations → N! possible rankings
1. A B C2. A C B3. B A C4. B C A5. C A B6. C B A
Test
RedmondWoodinville
KirklandBellevue
IssaquahSammamish
01
23
45
0
0.2
0.4
0.6
0.8
1
LocationPermutation Hash Code
Six suburbs and six radio stations
81.7% correct from 8 radio stations
Avoid Manual Training
Seattle KMPS 94.1 MHz KSER 90.7 MHz
Classify Into Grid Cell
• Find location in grid
• Use predicted signal strengths to avoid manual training
≈ 8 kilometers average error
Summer intern Adel Youssef, U. Maryland
Outline
• Introduction
• LOCADIO – Wi-Fi triangulation
• NearMe – Wi-Fi proximity
• RightSPOT – FM radio triangulation
• TempIO – Inside/outside from temperature
TempIO – Inside/Outside ClassificationSuunto X9 – GPS, altimeter, thermometer
Suunto N3 – SPOT watch, knows outside temperature, location
Are you inside or outside?
• Turn off GPS if inside to save batteries• Metadata for digital photos • Higher-level context reasoning
Inside/Outside Outside Temperature Inside Temperature
Measured Temperature
Outside
Inside
Measured
Bayes Net
with Ramaswamy Hariharan
World Weather Stations
6509 weather stations → http://weather.noaa.gov/weather/metar.shtml → our web service
Inside/Outside from Temperature
Inside/Outside Accuracy
00.10.20.30.40.50.60.70.80.9
1
Seattle, WA Barrow, AK Key West,FL
San Diego,CA
Atlantic City,NJ
Fra
ctio
n C
orr
ect
in 2
003
• From hourly temperature data in five US cities, 2003• Average correct 81%
Kyoto
The End