1. Presented by Tao HUANG Lingzhi XU

2. Context Mobile devices need exploit variety of connectivity options as they travel. Operating systems manage wireless networks in the moment, reactively choosing connections only when circumstances change. Problem Applications cannot make reliable assumptions about the quality of connectivity. Observation People are creatures of habit; they take similar paths every day.

3. Background Determining AP quality Virgil Estimating client location GPS Place lab

4. Determining AP Quality Currently Select the unencrypted AP with the strongest signal Virgil Connects to reference servers to estimate connection quality: Downstream bandwidth Whether AP blocks certain services Estimated latency BreadCrumbs uses Adapted Virgil, estimates three values: Downstream bandwidth, Upstream bandwidth, Estimated latency

5. Estimating Client Location GPS Provide latitude and longitude (0.001◦×0.001◦, 110m*80m) Place Lab For devices without GPS Works well indoors and in urban canyons Rely on public Wardriving database

6. Contribution Authors introduce the concept of connectivity forecasts for mobile devices. Authors demonstrate that such forecasts can be accurate over regular, day-to-day use, without requiring GPS hardware or extensive centralized infrastructure. Authors illustrate the potential benefits of the syste m through three example applications

7. Connectivity Forecasting It is an estimate of the quality of a given facet of network connectivity at some future time. Predicting future mobility Forecasting future conditions

8. Predicting Future Mobility Each state consists of two sets of coordinates: the location where the device was during the last state, and its current location. The frequency with which BreadCrumbs estimates the device’s GPS location bounds the resolution of the mobility model.

9. Forecasting Future Conditions (1) Build an AP quality database to estimate the “quality” of a connection to the Internet When BreadCrumbs first encounters an unencrypted AP, it attempts to estimate (1) downstream bandwidth, (2) upstream bandwidth, and (3) latency to remote Internet hosts. The test database tracks access points both by ESSID and by MAC address, and tags all AP test results with GPS coordinates.

10. Forecasting Future Conditions (2) BreadCrumbs combines the custom user mobility model and the AP quality database to provide connectivity forecasts. One step is τ seconds in the future.

11. Example

12. Implementation Scanning thread Scans for access points and fixes the device’s GPS every10 seconds Updating the transition probability All data are stored in local database Application interface Handle application requests for connectivity forecasts. Requests consist of two value: criterion of interest and number of seconds in the future

13. Evaluation: Methodology Track movements for two weeks Mobile: Familiar Linux on iPAQ + WiFi Mixture of walking and bus First week as training set, while second week for evaluation Authors use three sample applications to examine how both the operating system and different mobile applications could benefit from connectivity forecasts.

14. Forecast Accuracy Location Prediction

15. Accuracy Bandwidth Prediction

16. Sample Applications: Map View BreadCrumbs’ forecasts could avoid wasteful pre- fetching.

17. Sample Applications: Streaming Media Connectivity forecasts provide the same playback experience while using significantly less of the mobile device’s battery.

18. Sample Applications: Opportunistic Writeback Although BreadCrumbs completes slower than no prediction algorithm, it makes more efficient use of the wireless radio.

19. Overhead

20. Related work Context-for-wireless: Choose between WiFi and cellular data networks, no prediction MobiSteer: Improve wireless network connectivity in motion by using a directional antenna. Predictability of WLAN mobility and its effects on bandwidth extensive: Using different prediction methods to improve bandwidth provisioning and handoff for VoIP telephony Building realistic mobility models from coarse-grained traces: Build model from different client traces, more close real movements

21. Future Work Deploy BreadCrumbs on the mobile device to investigate how sharing of these databases among co-located users can reduce this scanning overhead further. Add encrypted WiFis which device has authority to access to database. Those encrypted WiFis own higher weight than those unencrypted. Test whether it will improve performance.

22. Conclusion Applications cannot make reliable assumptions about the quality of connectivity. So it cannot provide relative stable connectivity performance. BreadCrumbs tracks device trend of connectivity quality as its owner moves around. The predictions of the mobility model and the AP quality database yield connectivity forecasts. BreadCrumbs can provide improved performance while reducing power consumption only with one week training time. It can works on devices without GPS.

23. Questions