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Location in Pervasive Computing Shwetak N. Patel University of Washington More info: shwetak.com Special thanks to Alex Varshavsky and Gaetano Borriello for their contribution to this content design: use: build: university of washington

ubicomp lab university of washington

Computer Science & Engineering Electrical Engineering


Location 

A form of contextual information

Person’s physical position

Location of a device 

Device is a proxy of a person’s location

Used to help derive activity information

2


Location 

Well studied topic (3,000+ PhD theses??)

Application dependent

Research areas 

Technology

Algorithms and data analysis

Visualization

Evaluation 3


Location Tracking

4


Representing Location Information 

Absolute 

Relative 

Geographic coordinates (Lat: 33.98333, Long: -86.22444)

1 block north of the main building

Symbolic 

High-level description

Home, bedroom, work

5


No one size fits all! 

Accurate

Low-cost

Easy-to-deploy

Ubiquitous

Application needs determine technology 6


Consider for example… 

Motion capture

Car navigation system

Finding a lost object

Weather information

Printing a document

7


Others aspects of location information 

Indoor vs. outdoor

Absolute vs. relative

Representation of uncertainty

Privacy model

8


Lots of technologies!

GPS

WiFi Beacons

VHF Omni Ranging

Ultrasound

Ad hoc signal strength

Floor pressure

Laser range-finding Stereo camera

Array microphone Ultrasonic time of flight

Infrared proximity

E-911 Physical contact 9


Some outdoor applications

E-911

Bus view

Car Navigation

Child tracking 10


Some indoor applications

Elder care

11


Outline 

Defining location

Methods for determining location 

  

Ex. Triangulation, trilateration, etc.

Systems Challenges and Design Decisions Considerations


Approaches for determining location 

Localization algorithms

Proximity Lateration Hyperbolic Lateration Angulation

Fingerprinting

  

Distance estimates  

Time of Flight Signal Strength Attenuation

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Proximity 

Simplest positioning technique

Closeness to a reference point

Based on loudness, physical contact, etc

14


Lateration ď Ž

Measure distance between device and reference points

ď Ž

3 reference points needed for 2D and 4 for 3D

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Hyperbolic Lateration ď Ž

Time difference of arrival (TDOA)

ď Ž

Signal restricted to a hyperbola

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Angulation ď Ž

Angle of the signals

ď Ž

Directional antennas are usually needed

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Determining Distance 

Time of flight 

Signal strength 

Speed of light or sound

Known drop off characteristics 1/r^2-1/r^6

Problems: Multipath

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Fingerprinting 

Mapping solution

Address problems with multipath

Better than modeling complex RF propagation pattern

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Fingerprinting SSID (Name)

BSSID (MAC address)

Signal Strength (RSSI)

linksys

00:0F:66:2A:61:00

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starbucks

00:0F:C8:00:15:13

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newark wifi

00:06:25:98:7A:0C

23

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Fingerprinting 

Easier than modeling

Requires a dense site survey

Usually better for symbolic localization

Spatial differentiability

Temporal stability 21


Reporting Error ď Ž

Precision vs. Accuracy

22


Reporting Error 

Cumulative distribution function (CDF) 

Absolute location tracking systems CDF of Localization error 1 0.9

Percentage

0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Error (m)

Accuracy value and/or confusion matrix 

Symbolic systems 23


Location Systems 

Distinguished by their underlying signaling system 

IR, RF, Ultrasonic, Vision, Audio, etc

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GPS 

Use 24 satellites

TDOA

Hyperbolic lateration

Civilian GPS 

L1 (1575 MHZ) 

10 meter acc.

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Active Badge 

IR-based

Proximity

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Active Bat 

Ultrasonic

Time of flight of ultrasonic pings

3cm resolution

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Cricket ď Ž

Similar to Active Bat

ď Ž

Decentralized compared to Active Bat

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Cricket vs Active Bat 

Privacy preserving

Scaling

Client costs

Active Bat

Cricket 29


Ubisense 

Ultra-wideband (UWB) 6-8 GHz

Time difference of arrival (TDOA) and Angle of arrival (AOA)

15-30 cm

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RADAR 

WiFi-based localization

Reduce need for new infrastructure

Fingerprinting

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Place Lab 

“Beacons in the wild” 

WiFi, Bluetooth, GSM, etc

Community authored databases

API for a variety of platforms

RightSPOT (MSR) – FM towers

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ROSUM 

Digital TV signals

Much stronger signals, well-placed cell towers, coverage over large range

Requires TV signal receiver in each device

Trilateration, 10-20m (worse where there are fewer transmitters)

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Comparing Approaches 

Many types of solutions (both research and commercial) 

Install custom beacons in the environment 

Ultra-wideband (Ubisense), Ultrasonic (MIT Cricket, Active Bat), Bluetooth

Use existing infrastructure 

GSM (Intel, Toronto), WiFi (RADAR, Ekahau, Place Lab), FM (MSR) 34


Limitations 

Beacon-based solutions 

Requires the deployment of many devices (typically at least one per room)

Maintenance

Using existing infrastructure 

WiFi and GSM 

Not always dense near some residential areas

Little control over infrastructure (especially GSM) 35


ď Ž

Beacon-based localization

36


ď Ž

Wifi localization (ex. Ekahau)

37


ď Ž

GSM localization

Tower IDs and signals change Coverage? over time!

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PowerLine Positioning ď Ž

Indoor localization using standard household power lines

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Signal Detection ď Ž

A tag detects these signals radiating from the electrical wiring at a given location

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Signal Map

1st Floor

2 nd Floor 41


2

d ( x, y ) = ( ∑ ( x i − y i ) 2 ) i =1

Example

42


2

d ( x, y ) = ( ∑ ( x i − y i ) 2 ) i =1

Passive location tracking 

No need to carry a tag or device 

Hard to determine the identity of the person

Requires more infrastructure (potentially)

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2

d ( x, y ) = ( ∑ ( x i − y i ) 2 ) i =1

Active Floor 

Instrument floor with load sensors

Footsteps and gait detection

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2

d ( x, y ) = ( ∑ ( x i − y i ) 2 ) i =1

Motion Detectors 

Low-cost

Low-resolution

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2

d ( x, y ) = ( ∑ ( x i − y i ) 2 ) i =1

Computer Vision 

Leverage existing infrastructure

Requires significant communication and computational resources

CCTV

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2

d ( x, y ) = ( ∑ ( x i − y i ) 2 ) i =1

Other systems? 

Inertial sensing

HVACs

Ambient RF

etc.

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Considerations 

Location type

Resolution/Accuracy

Infrastructure requirements

Data storage (local or central)

System type (active, passive)

Signaling system 48

Presentation Patel  

Location in ubiquitous c

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