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 beacons  Wardriving databases Other options  Accelerometer, GSM beacons

8 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

9 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

12 Forecast example: downstream BW current What will the available downstream bandwidth be in 10 seconds (next step)? * * * = KB/s

13 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

14 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

20 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!