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Enhancing Wi-Fi Prediction with Bluetooth Signals

Explore how Bluetooth signals can improve Wi-Fi prediction accuracy and coverage. Learn about the reliability of Bluetooth devices, prediction schemes, and energy efficiency in this research by Ganesh Ananthanarayanan and Ion Stoica from the RAD Lab at UC Berkeley.

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Enhancing Wi-Fi Prediction with 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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