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BreadCrumbs: Forecasting Mobile Connectivity PowerPoint PPT Presentation

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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

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BreadCrumbs: Forecasting Mobile Connectivity

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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

  • 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

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

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


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


Humans are creatures of habit

  • Common movement patterns

    Leverage AP selection work‏

  • Map AP distribution and quality

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

  • Best: GPS on device

    • Unreasonable assumption?

  • PlaceLab

    • Triangulate 802.11 beacons

    • Wardriving databases

  • Other options

    • Accelerometer, GSM beacons

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)


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

Applications and kernel query BreadCrumbs

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

Recursively walk tree based on transition frequency




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





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

Forecast accuracy

Application: handheld map viewer

Application: opportunistic writeback


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!

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