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
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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
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
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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!