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Location Without GPS. John Krumm Microsoft Research Redmond, Washington, USA. Seattle, Washington, USA. Kyoto, Japan. Location. Importance of Location. Find your way Find nearby things Invoke location-based services

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location without gps

Location Without GPS

John KrummMicrosoft ResearchRedmond, Washington, USA

importance of 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
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
Why Not Use GPS?
  • Does not work indoors
  • Needs view of satellites
location sensing
Location Sensing

Hazas, Scott, Krumm, “Location-Aware Computing”, IEEE Computer Magazine, February 2004.

outline
Outline
  • Introduction
  • LOCADIO – Wi-Fi triangulation
  • NearMe – Wi-Fi proximity
  • RightSPOT – FM radio triangulation
  • TempIO – Inside/outside from temperature
location from 802 11 with l ocadio
Location from 802.11 with LOCADIO*

with Eric Horvitz

Wi-Fi (802.11) access point

  • Mobile device measures signal strengths from Wi-Fi access points
  • Computes its own location

*Location from Radio

l ocadio radio survey
LOCADIO – Radio Survey

Radio survey to get signal strength as a function of position

l ocadio constraints
LOCADIO - Constraints

Make the client as smart as possible to reduce calibration effort

No passing through walls

No speeding

We know when you move

l ocadio results
LOCADIO - Results

Hidden Markov model gives median error of 1.53 meters

outline12
Outline
  • Introduction
  • LOCADIO – Wi-Fi triangulation
  • NearMe – Wi-Fi proximity
  • RightSPOT – FM radio triangulation
  • TempIO – Inside/outside from temperature
nearme

Download from http://research.microsoft.com/~jckrumm/NearMe.htm

NearMe

with Ken Hinckley

Find people and things nearby

printers

people

reception desk

bathroom

conferencerooms

the basic idea

Download from http://research.microsoft.com/~jckrumm/NearMe.htm

The Basic Idea

802.11 Wi-Fi access point

NearMe Proximity Server

location vs proximity

Download from http://research.microsoft.com/~jckrumm/NearMe.htm

d12 = g(s1, s2)

Location vs. Proximity

x1 = (x,y) location

x2 = (x,y) location

d12 = f(x1, x2)

s1 = measured signals

s2 = measured signals

nearme client

Download from http://research.microsoft.com/~jckrumm/NearMe.htm

NearMe Client

PocketPC 2003

Windows XP

  • Requirements:
  • Windows XP
  • WWW access
  • Microsoft .NET Framework
nearme client test connections

Download from http://research.microsoft.com/~jckrumm/NearMe.htm

NearMe Client – Test Connections
nearme client register

Download from http://research.microsoft.com/~jckrumm/NearMe.htm

NearMe Client – Register
  • Register with:
  • Name
  • Email (optional)
  • URL (optional)
  • Expiration interval
nearme client report wi fi

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
nearme client query

Download from http://research.microsoft.com/~jckrumm/NearMe.htm

NearMe Client -- Query

Adjustable “Look back” time to filter outdated reports

nearme client nearby things

Download from http://research.microsoft.com/~jckrumm/NearMe.htm

Register as thing

Report signal strengths

Query for things

NearMe Client – Nearby Things
simple distance function

Download from http://research.microsoft.com/~jckrumm/NearMe.htm

Simple Distance Function

d = -2.53∙n∩ – 2.90∙ρs - 22.31

rms error = 14.04 meters

ρs = 0.39

access point layout

Download from http://research.microsoft.com/~jckrumm/NearMe.htm

D

E

B

C

B

E

A

D

C

  • Access point topology in database
  • Recomputed every hour

F

Access Point Layout

1

2

A

F

3

outline25
Outline
  • Introduction
  • LOCADIO – Wi-Fi triangulation
  • NearMe – Wi-Fi proximity
  • RightSPOT – FM radio triangulation
  • TempIO – Inside/outside from temperature
spot watch location
SPOT Watch Location

with Adel Youssef, Ed Miller, Gerry Cermak, Eric Horvitz

traffic

weather

dining

movies

Commercial FM: transmit new data every ~2 minutes

Filter on watch to take what it wants

Watch displays “personalized” data

location sensitive features
Location-Sensitive Features
  • Nice to have
  • Local traffic
  • Nearby movie times
  • Nearby restaurants

Need to know location of device …

use fm radio signal strengths
Use FM Radio Signal Strengths

Scan signal strengths of 32 FM radio stations at 1 Hz

ranking approach
Ranking Approach

Redmond: KPLU < KMTT < KMPS

Bellevue: KMTT < KPLU < KMPS

Issaquah: KMTT < KMPS < KPLU

Any monotonically increasing function of signal strength preserves ranking

Measured Power

N radio stations → N! possible rankings

  • A B C
  • A C B
  • B A C
  • B C A
  • C A B
  • C B A

A

B

C

Input Power

  • Each watch scales signal strengths differently
  • Impractical to calibrate every watch
slide30
Test

Six suburbs and six radio stations

81.7% correct from 8 radio stations

avoid manual training
Avoid Manual Training

Seattle

KMPS 94.1 MHz

KSER 90.7 MHz

classify into grid cell
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

outline33
Outline
  • Introduction
  • LOCADIO – Wi-Fi triangulation
  • NearMe – Wi-Fi proximity
  • RightSPOT – FM radio triangulation
  • TempIO – Inside/outside from temperature
tempio inside outside classification
TempIO – Inside/Outside Classification

with Ramaswamy Hariharan

Suunto X9 – GPS, altimeter, thermometer

  • Are you inside or outside?
  • Turn off GPS if inside to save batteries
  • Metadata for digital photos
  • Higher-level context reasoning

Suunto N3 – SPOT watch, knows outside temperature, location

Bayes Net

world weather stations
World Weather Stations

6509 weather stations → http://weather.noaa.gov/weather/metar.shtml→ our web service

inside outside from temperature
Inside/Outside from Temperature

Kyoto

  • From hourly temperature data in five US cities, 2003
  • Average correct 81%