1. BreadCrumbs: Forecasting Mobile Connectivity Presented by Dhruv Kshatriya Paper by Anthony J. Nicholson Brian D. Noble

2. 2 Mobility complicates things Often 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

3. 3 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

4. 4 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

5. 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 5

6. 6 Observations Humans are creatures of habit Common movement patterns Leverage AP selection work Map AP distribution and quality

7. 7 Improved Access Point Selection Conventionally APs 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

8. 8 Determining location Best: GPS on device Unreasonable assumption? PlaceLab Triangulate 802.11 beacons Wardriving databases Other options Accelerometer, GSM beacons

9. 9 Mobility model 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)

10. 10 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

11. 11 Connectivity forecasts Applications and kernel query BreadCrumbs Expected bandwidth (or latency, or...) in the future Recursively walk tree based on transition frequency

12. 12 Forecast example: downstream BW current What will the available downstream bandwidth be in 10 seconds (next step)? 0.0072.13141.84 0.22 0.61*72.13 + 0.17*0.00 + 0.22*141.84 = 75.20 KB/s 0.61 0.17

13. 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. 14 AP statistics

15. 15 Forecast accuracy

16. 16 Application: handheld map viewer

17. 17 Application: opportunistic writeback

18. 18 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

19. Thank you!