BreadCrumbs: Forecasting Mobile Connectivity

Presented by Hao He

Slides adapted from Dhruv Kshatriya

Anthony J. Nicholson and Brian D. Noble

2

Observations

Access points come and go as users move

Not all network connections created equal

Limited time to exploit a given connection

The big idea(s) in this paper

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

3

Road Map

Background knowledge

Connectivity forecasting

Evaluation

Conclusion

Background knowledge

Determining AP quality Wifi-Reports:

Improving Wireless Network Selection with Collaboration

Estimating Client Location

6

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

7

Determining location

Best: GPS on device Unreasonable

assumption?

PlaceLab Triangulate 802.11

beacons

Wardriving databases

Other options Accelerometer, GSM

beacons

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Connectivity Forecasting

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

Combine prediction with AP quality database to produce connectivity forecasts

Applications use these forecasts to take domain-specific actions

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Mobility model

Humans are creatures of habit Common movement patterns

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)

Mobility model example

11

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

7

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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

15

Forecast accuracy

16

Application: opportunistic writeback

Application: Radio Deactivation

Goal Conserving energy

Implementation Query BreadCrumbs to get a connectivity

forecast

If radio on & no connectivity in next 30 secs

Turn radio off

Else If radio off & BreadCrumbs predicts connectivity in next 30 secs

Application: Radio Deactivation

Application: Phone network vs. WiFi

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Summary

Humans (and their devices) are creatures of habit

Mobility model + AP quality DB = connectivity forecasts

Minimal application modifications yield benefits to user

Future work

Evaluation: not representative

Energy efficient

Modification to software

Limited to certain applications: ex. download

Thank you!