BreadCrumbs - PowerPoint Presentation

Slide1

BreadCrumbsForecasting Mobile Connectivity

Presented

by

Tao HUANG

Lingzhi

XUSlide2

ContextMobile devices need exploit variety of connectivity options as they travel. 

Operating

systems manage wireless networks in the

moment, reactively

choosing connections only when

circumstances change.

Problem

A

pplications

cannot make reliable assumptions about the quality of connectivity.

Observation

P

eople

are creatures of habit; they take similar paths every day.Slide3

BackgroundDetermining AP quality

Virgil

Estimating client location

GPS

Place labSlide4

Determining AP QualityCurrently

Select the unencrypted

AP with the strongest signal

Virgil

Connects

to reference servers

to estimate connection quality:

Downstream bandwidth

Whether AP

blocks certain

services

Estimated

latency

BreadCrumbs

uses Adapted

Virgil,

estimates three values:

Downstream

bandwidth,

Upstream

bandwidth,

Estimated latencySlide5

Estimating Client Location

GPS

Provide latitude and longitude (0.001◦×0.001◦, 110m*80m)

Place Lab

For devices without GPS

Works well indoors and in urban canyons

Rely on public

Wardriving

databaseSlide6

Contribution• Authors introduce the concept of

connectivity forecasts

for mobile devices.

• Authors demonstrate that such forecasts can be accurate over regular, day-to-day use, without requiring GPS hardware or extensive centralized infrastructure.

• Authors illustrate the potential benefits of the system through three example applications Slide7

Connectivity ForecastingIt is an estimate of the quality of a given facet of network connectivity at some future time.

Predicting future mobility

Forecasting future

conditions Slide8

Predicting Future Mobility

Each

state consists

of two

sets of coordinates: the location where the device

was during

the last state, and its current location

.

The frequency with

which

BreadCrumbs

estimates the

device’s GPS

location bounds the resolution of the

mobility model.Slide9

Forecasting Future Conditions (1)

Build

an AP quality

database to

estimate

the “quality

” of a connection to the

Internet

W

hen

BreadCrumbs

first encounters an unencrypted

AP, it

attempts

to

estimate (

1) downstream

bandwidth, (2) upstream bandwidth, and (

3) latency

to remote Internet hosts

.

The test database tracks access points both by

ESSID and

by MAC

address, and tags all AP test results with GPS coordinates.Slide10

Forecasting Future Conditions (2)

BreadCrumbs

combines the custom user mobility model

and the AP quality database to provide connectivity forecasts

.

One

step is τ seconds in the future.Slide11

ExampleSlide12

ImplementationScanning thread

S

cans

for access points and

fixes the

device’s

GPS every10 seconds

Updating the transition

probability

All data are stored in local database

Application interface

H

andle

application requests for

connectivity forecasts

.

R

equests consist of two value: criterion of interest and number of seconds in the futureSlide13

Evaluation: Methodology

Track movements for two weeks

Mobile:

Familiar

Linux on

iPAQ

+

WiFi

Mixture of walking and bus

First week as training set, while second week for evaluation

Authors use three sample applications to examine

how both the operating system and different mobile applications could benefit from

connectivity forecasts

.Slide14

Forecast Accuracy Location PredictionSlide15

Accuracy Bandwidth PredictionSlide16

Sample Applications: Map

V

iew

BreadCrumbs

’

forecasts could avoid

wasteful

pre-fetching.Slide17

Sample Applications: Streaming Media

C

onnectivity forecasts provide

the same playback experience

while

using significantly less of the mobile device’s

battery.Slide18

Sample Applications: Opportunistic

Writeback

Although

BreadCrumbs

completes slower than no prediction algorithm, it

makes

more efficient use of the wireless radio.Slide19

OverheadSlide20

Related workContext-for-wireless

:

Choose

between

WiFi

and cellular data

networks, no prediction

MobiSteer

:

Improve

wireless network connectivity in

motion by using a directional antenna.

Predictability of WLAN mobility and its effects on bandwidth extensive

:

Using different prediction methods to improve

bandwidth provisioning and handoff for VoIP telephony

Building realistic mobility models from coarse-grained traces

:

Build model from different client traces, more close real movementsSlide21

Future WorkD

eploy

BreadCrumbs

on

the

mobile

device to

investigate how

sharing of these databases among co-located users

can reduce

this scanning overhead further

.

Add encrypted

WiFis

which device has authority to access to database. Those encrypted

WiFis

own higher weight than those unencrypted.

Test whether it will improve performance. Slide22

Conclusion

Applications cannot make reliable assumptions about the quality of connectivity

. So it cannot provide relative stable connectivity performance.

BreadCrumbs

tracks device trend of connectivity quality as its owner moves around. The

predictions of the mobility model and

the AP

quality database yield

connectivity

forecasts.

BreadCrumbs

can

provide improved

performance while reducing power

consumption only

with one week training time.

It can works on devices without GPS.Slide23

Questions