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Published byOwen Heath Modified over 9 years ago
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BreadCrumbs: Forecasting Mobile Connectivity Presented by Hao He Slides adapted from Dhruv Kshatriya Anthony J. Nicholson and Brian D. Noble
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2 Observations Access points come and go as users move Not all network connections created equal Limited time to exploit a given connection
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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
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Road Map Background knowledge Connectivity forecasting Evaluation Conclusion
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Background knowledge Determining AP quality Wifi-Reports: Improving Wireless Network Selection with Collaboration Estimating Client Location
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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
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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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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
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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)
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Mobility model example
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11 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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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
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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
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14 AP statistics
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15 Forecast accuracy
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16 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
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Application: Radio Deactivation
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Application: Phone network vs. WiFi
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20 Summary Humans (and their devices) are creatures of habit Mobility model + AP quality DB = connectivity forecasts Minimal application modifications yield benefits to user
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Future work Evaluation: not representative Energy efficient Modification to software Limited to certain applications: ex. download
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Thank you!
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