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Blue- Fi : Enhancing Wi-Fi Prediction using Bluetooth Signals

Blue- Fi : Enhancing Wi-Fi Prediction using Bluetooth Signals. Ganesh Ananthanarayanan and Ion Stoica Reliable, Adaptive, Distributed Systems Lab (RAD Lab) University of California, Berkeley. Wi-Fi: The good and the bad. Energy-efficient data transfer

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Blue- Fi : Enhancing Wi-Fi Prediction using Bluetooth Signals

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  1. Blue-Fi: Enhancing Wi-Fi Prediction using Bluetooth Signals Ganesh Ananthanarayanan and Ion Stoica Reliable, Adaptive, Distributed Systems Lab (RAD Lab) University of California, Berkeley

  2. Wi-Fi: The good and the bad • Energy-efficient data transfer • 5 J/MB for Wi-Fi (vs. 100 J/MB for cellular) • Idle power consumption is high • 0.77W for Wi-Fi vs. (~0W for cellular, 0.01W for bluetooth) • Detect Wi-Fi availability without scanning but use it whenever available • Background applications like Email clients and RSS feed synchronizers

  3. Location-based prediction • Learn Wi-Fi availability (Rahmati et al.) • Correlate Wi-Fi availability with locations • Localization • Global Positioning System • Accurate • Power-hungry • Poor signals indoors and in urban high-rise settings • Cell-tower fingerprinting • Power-efficient • Coarse grained granularity  Fine-grained and practical indoor localization…

  4. Bluetooth Fingerprinting • Bluetooth Contact Patterns • Users tend to repeatedly encounter the same set of bluetooth devices Looks like I am under Wi-Fi coverage… I have to download an email attachment … Bluetooth Discovery RAD Lab Bluetooth Printer Ion’s Device

  5. Challenges • High Mobility • Potentially low temporal and spatial constancy leading to low predictability • Low range • Possibly within Wi-Fi hotspot but just out of range of bluetooth devices… • Discovery Time • High start-up times for network jobs Learning reliable devices Combine with cell-tower signatures Periodic discovery and caching

  6. Learning Process • Periodic logging and correlation of network signals • Identifying reliable predictors • Predictability: Confidence measure of a signal’s presence indicating Wi-Fi availability • “Whenever I see Ion’s phone, I have Wi-Fi connectivity” • Constantly refined to account for new mobility patterns

  7. Prediction of Wi-Fi Availability • Prediction schemes evaluated using: • Coverage: Fraction of Wi-Fi connectivity chances that are predicted • Accuracy: Fraction of Wi-Fi connectivity predictions that are accurate • Bluetooth-based Prediction: High accuracy but low coverage (low range) Cell-tower-based Prediction: Low accuracy but high coverage (high range)

  8. Hybrid Prediction Scheme • Fine-grained learning (Accuracy) using bluetooth devices, and use cell-towers as a fall-back (Coverage) • Helps in finer prediction within a larger area covered by cell-towers • Learning phase identifies both the reliable as well as the unreliable bluetooth predictors

  9. Why is the hybrid scheme better? • Coverage is equal to pure cell-tower prediction Erroneous Prediction • Best of both worlds – Coverage as well as Accuracy! Accurate Prediction

  10. Prediction Reliability Threshold • What is the threshold of predictability over which we consider a device as reliable? • Predict-Signal Matrix Prediction of Wi-Fi availability p p__ 1. Probe for Wi-Fi network when there is Wi-Fi availability (p1) 2. Use the cellular interface in the presence of Wi-Fi (p2) s Wi-Fi Signal Availability 3. Waste energy to probe for Wi-Fi networks (p3) 4. Use the cellular interface because there is no Wi-Fi availability (p4) s__

  11. Reducing Energy Wastage • Minimize the expected energy wastage • Case 2: Function of size of data transfer as well as p2 • Case 3: Function of p3 • p2 and p3 are functions of Accuracy, which in turn is only dependent on the threshold • Please refer to the paper for the derivation

  12. Bluetooth Discovery • Bluetooth discovery takes ~11 seconds • High latency in prediction and application start-up • Periodic discovery and use last discovered list • Stationary  No change in Wi-Fi prediction • Euclidean distance of cell-tower signatures

  13. All bluetooth devices are not equal! • Landmark Devices: • Stationary bluetooth devices • Bluetooth printers, computer peripherals (keyboard, mouse), bluetooth access points (CoolSpots) • Shared across different users • Mobile Accessories: • Personal bluetooth gadgets • Bluetooth headphone, bluetooth-enabled media players • Eliminate from logs; introduces error in prediction

  14. Identification Algorithm • Calculate diversity for bluetooth devices • Variance among the set of locations sighted • using K-Medians clustering technique • Landmark Device: Any device whose diversity is low, and whenever a signature similar to its cluster occurs, it is present • Personal Accessory: Occur in high fraction of log entries

  15. Evaluation – Log Collection • Twelve volunteers collected logs for a period of two-three weeks • Graduate students in Berkeley and working professionals in the San Francisco Bay Area • HTC i-mate PDAs – Windows Mobile 5.0 • Log all <Wi-Fi SSID/BSSID, cell-tower identifiers, bluetooth MACs> every minute • Wi-Fi connectivity varies between 32%-68% • Bluetooth devices are visible up to 77% of the time

  16. Coverage and Accuracy Hybrid Scheme has good Accuracy as well as Coverage

  17. Energy Consumption [1] • Workload modeled on background synchronization applications • Periodically, wake up and download data • Starting with full charge, measure the number of synchronizations until the device dies • Comparison with two common strategies: • Ecellular: Use the cellular interface always • EWi-Fi: Scan for Wi-Fi networks, and use if available • Improvement of 19-62% w.r.t. Ecellular and • 20-40% w.r.t. EWi-Fi

  18. Energy Consumption [2] • Blue-Fi is most effective: • w.r.t. Ecellular when Wi-Fi coverage is moderate-high • w.r.t. EWi-Fi when Wi-Fi coverage is low-moderate Availability of Wi-Fi Networks

  19. Energy Consumption [3] • Blue-Fi is most effective: • w.r.t. Ecellular for moderate-high downloads • w.r.t. EWi-Fi for low-moderate downloads Size of data downloaded

  20. Diversity of bluetooth devices • Most devices have low diversity • Users see bluetooth devices only at select locations • Landmark devices have to be sighted every time the user is present at that location

  21. Future Work • Multi-hop bluetooth discovery • Chasm between range of Wi-Fi and bluetooth signals • Increase the Coverage of bluetooth-based prediction • Reference bluetooth devices • Deploy bluetooth landmark devices • Indoor spatial monitoring system for sensor applications • E.g., cooling within an office, Wi-Fi coverage

  22. Summary • Wi-Fi prediction is necessary due to the dichotomy in energy characteristics • Prediction strategy using bluetooth signals • Fine-grained indoor localization scheme • Combination of bluetooth and cellular based predictions produce encouraging results

  23. Questions/Feedback

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