Presented by dhruv kshatriya paper by anthony j nicholson brian d noble l.jpg
This presentation is the property of its rightful owner.
Sponsored Links
1 / 19

BreadCrumbs: Forecasting Mobile Connectivity PowerPoint PPT Presentation


  • 134 Views
  • Uploaded on
  • Presentation posted in: General

Presented by Dhruv Kshatriya Paper by Anthony J. Nicholson Brian D. Noble. BreadCrumbs: Forecasting Mobile Connectivity. Mobility complicates things. Often optimize for local conditions Laptop user stationary at a café Mobile scenario less stable

Download Presentation

BreadCrumbs: Forecasting Mobile Connectivity

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Presented by dhruv kshatriya paper by anthony j nicholson brian d noble l.jpg

Presented by

Dhruv Kshatriya

Paper by

Anthony J. Nicholson

Brian D. Noble

BreadCrumbs: Forecasting Mobile Connectivity


Mobility complicates things l.jpg

Mobility complicates things

Often optimize for local conditions

  • Laptop user stationary at a café

    Mobile scenario less stable

  • Network quality and availability in flux

  • Multiple networks, multiple administrators

  • Handheld devices, always-on links

  • Want to use connectivity opportunistically

    Volatile quality and availability is a fact of life


The derivative of connectivity l.jpg

The derivative of connectivity

Access points come and go as users move

Not all network connections created equal

Limited time to exploit a given connection

Consider trends over time, not spot conditions


The big idea s in this talk l.jpg

The big idea(s) in this talk‏

1. Maintain a personalized mobility model on the user's device to predict future associations

2. Combine prediction with AP quality database to produce connectivity forecasts

3. Applications use these forecasts to take domain-specific actions


Contributions l.jpg

Contributions

Introduce the concept of connectivity forecasts

  • Show how such forecasts can be accurate for

  • everyday situations w/o GPS or centralization

  • Illustrate through example applications


Observations l.jpg

Observations

Humans are creatures of habit

  • Common movement patterns

    Leverage AP selection work‏

  • Map AP distribution and quality


Improved access point selection l.jpg

Improved Access Point Selection

Conventionally AP’s with the highest signal strength are chosen.

Probe application-level quality of access points

  • Bandwidth, latency, open ports

  • AP quality database guides future selection

    Real-world evaluation

  • Significant improvement over link-layer metrics


Determining location l.jpg

Determining location

  • Best: GPS on device

    • Unreasonable assumption?

  • PlaceLab

    • Triangulate 802.11 beacons

    • Wardriving databases

  • Other options

    • Accelerometer, GSM beacons


Mobility model l.jpg

Mobility model

  • Second-order Markov chain

    • Reasonable space and time overhead (mobile device)‏

    • Literature shows as effective as fancier methods

  • State: current GPS coord + last GPS coord

    • Coords rounded to one-thousandth of degree(110m x 80m box)


Breadcrumbs l.jpg

BreadCrumbs

User-level daemon, periodically:

  • Scan for APs

  • Estimate GPS location from 802.11 beacons

  • Test APs not seen before

  • Write test results to AP quality database

  • Update mobility model

  • Accepts application requests for Conn forecast

  • Convert from sec to no of state transitions


Connectivity forecasts l.jpg

Connectivity forecasts

Applications and kernel query BreadCrumbs

Expected bandwidth (or latency, or...) in the future

Recursively walk tree based on transition frequency


Forecast example downstream bw l.jpg

0.17

0.22

0.61

Forecast example: downstream BW

What will the available downstream bandwidthbe in 10 seconds (next step)?

0.61*72.13 + 0.17*0.00 + 0.22*141.84 = 75.20 KB/s

current

72.13

0.00

141.84


Evaluation methodology l.jpg

Evaluation methodology

  • Tracked weekday movements for two weeks

    • Linux 2.6 on iPAQ + WiFi

    • Mixture of walking, driving, and bus

  • Primarily travel to/from office, but some noise

    • Driving around for errands

    • Walk to farmers' market, et cetera

  • Week one as training set, week two for eval


Ap statistics l.jpg

AP statistics


Forecast accuracy l.jpg

Forecast accuracy


Application handheld map viewer l.jpg

Application: handheld map viewer


Application opportunistic writeback l.jpg

Application: opportunistic writeback


Summary l.jpg

Summary

Humans (and their devices) are creatures of habit

Derivative of connectivity, not spot conditions

Mobility model + AP quality DB = connectivity forecasts

Minimal application modifications yield benefits to user


Thank you l.jpg

Thank you!


  • Login