Transcript BreadCrumbs: Forecasting Mobile Connectivity Anthony J. Nicholson and Brian D. Noble Presented by Hao He Slides adapted from Dhruv Kshatriya.

BreadCrumbs: Forecasting Mobile Connectivity
Anthony J. Nicholson and Brian D. Noble
Presented by
Hao He
Slides adapted from Dhruv Kshatriya
Observations
Access points come and go as users move
Not all network connections created equal
Limited time to exploit a given connection
2
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
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
6
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
8
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)
9
Mobility model example
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
What will the available downstream bandwidth
be in 10 seconds (next step)?
0.61*72.13 + 0.17*0.00 + 0.22*141.84 = 75.20 KB/s
current
0.17
72.13
0.00
141.84
12
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
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AP statistics
14
Forecast accuracy
15
Application: opportunistic writeback
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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
Summary
Humans (and their devices) are creatures of habit
Mobility model + AP quality DB = connectivity forecasts
Minimal application modifications yield benefits to user
20
Future work
Evaluation: not representative
Energy efficient
Modification to software
Limited to certain applications: ex. download
Thank you!