BreadCrumbs: Forecasting Mobile Connectivity

Presented by Dhruv Kshatriya

Paper byAnthony J. Nicholson

Brian D. Noble

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Mobility complicates thingsOften 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

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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

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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

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

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Observations

Humans are creatures of habit

Common movement patterns

Leverage AP selection work Map AP distribution and

quality

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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

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Determining location

Best: GPS on device Unreasonable

assumption?

PlaceLab Triangulate 802.11

beacons

Wardriving databases

Other options Accelerometer, GSM

beacons

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Mobility modelSecond-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)

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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

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Connectivity forecasts

Applications and kernel query BreadCrumbs

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

Recursively walk tree based on transition frequency

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Forecast example: downstream BW

current

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

0.0072.13 141.84

0.22

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

0.61

0.1

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Evaluation methodologyTracked 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

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AP statistics

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Forecast accuracy

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Application: handheld map viewer

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Application: opportunistic writeback

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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!