abs-introduction

11
Indoor Localization without Infrastructure using the Acoustic Background Spectrum Stephen P. Tarzia, Peter A. Dinda, Robert P. Dick, Gokham Memik MobiSys 2011 Jose Alvarez Sezaki Lab.

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Page 1: Abs-introduction

Indoor Localization without

Infrastructure using the

Acoustic Background

Spectrum

Stephen P. Tarzia, Peter A.

Dinda, Robert P. Dick, Gokham

MemikMobiSys 2011

Jose Alvarez

Sezaki Lab.

Page 2: Abs-introduction

Background & Objectives

Motivations

◦ Indoor Localization

◦ WiFi not always available and is not a

definite solution

◦ Improved accuracy

Page 3: Abs-introduction

Background & Objectives

Related works

◦ Radio

Sensors as beacons

WiFi

◦ Other than radio

Accelerometer and Compass

SoundSense and SurroundSense

Page 4: Abs-introduction

Background & Objectives

Distinctive elements of this method

◦ Transient noise v/s Background noise

=> Listen to background noises

◦ Look at frequency domain

Page 5: Abs-introduction

Points of Interest

69% accuracy for 33 rooms

Implementation using smartphones

(Batphone in AppleStore)

Effectively combined sound technique

with WiFi

Page 6: Abs-introduction

Proposed Method

Database side

◦ Training by gathering labeled samples (4

per room)

Client side

◦ Euclidian distance measure for selecting

candidates

Page 7: Abs-introduction

Proposed Method

Page 8: Abs-introduction

Results

Quality of the

recording device did

not impact accuracy

For 2 rooms

discrimination

accuracy goes up to

92%

Page 9: Abs-introduction

Results

Chatter state

performance

drops

dramatically

Choosing

different

frequency band

helps in

conversation

state

Page 10: Abs-introduction

Results

During the experiments the device

(Ipod touch) was with the screen on at

all times, with the brightness setting at

the dimmest => Battery life was 7.9

hours

11.5% CPU usage

16.7 MB of main memory

Battery life extended for a reasonable

time.

Page 11: Abs-introduction

Why this method?

Indoor localization is not solved

completely yet

Using room’s acoustic fingerprint

seems like a creative method for room

identification

Hardware on smartphones is more

than enough for a feasible

implementation (Batphone)