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SurroundSense : Mobile Phone Localization via Ambience Fingerprinting Ionut Constandache Co-authors: Martin Azizyan a

SurroundSense : Mobile Phone Localization via Ambience Fingerprinting Ionut Constandache Co-authors: Martin Azizyan and Romit Roy Choudhury. Context. Pervasive wireless connectivity + Localization technology = Location-based applications. Location-Based Applications (LBAs).

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SurroundSense : Mobile Phone Localization via Ambience Fingerprinting Ionut Constandache Co-authors: Martin Azizyan a

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  1. SurroundSense: Mobile Phone Localization via Ambience Fingerprinting IonutConstandache Co-authors: Martin Azizyan and Romit Roy Choudhury

  2. Context Pervasive wireless connectivity + Localization technology = Location-based applications

  3. Location-Based Applications (LBAs) • For Example: • GeoLife shows grocery list when near Walmart • MicroBlog queries users at a museum • Location-based ad: Phone gets coupon at Starbucks • iPhoneAppStore: 3000 LBAs, Android: 500 LBAs

  4. Location-Based Applications (LBAs) • For Example: • GeoLife shows grocery list when near Walmart • MicroBlog queries users at a museum • Location-based ad: Phone gets coupon at Starbucks • iPhoneAppStore: 3000 LBAs, Android: 500 LBAs • Location expresses context of user • Facilitates content delivery

  5. Location is an IP address As if for content delivery

  6. Thinking about Localization from an application perspective…

  7. Emerging location based apps need place of user, not physical location Starbucks, RadioShack, Museum, Library Latitude, Longitude

  8. Emerging location based apps need place of user, not physical location Starbucks, RadioShack, Museum, Library Latitude, Longitude We call this Logical Localization …

  9. Can we convert from Physical to Logical Localization?

  10. Can we convert from Physical to Logical Localization? • State of the Art in Physical Localization: • GPS Accuracy: 10m • GSM Accuracy: 100m • Skyhook (WiFi+GPS+GSM) Accuracy: 10m-100m

  11. Can we convert from Physical to Logical Localization? • State of the Art in Physical Localization: • GPS Accuracy: 10m • GSM Accuracy: 100m • Skyhook (WiFi+GPS+GSM) Accuracy: 10m-100m Widely-deployable localization technologies have errors in the range of several meters

  12. Several meters of error is inadequate to logically localize a phone Physical Location Error

  13. Several meters of error is inadequate to logically localize a phone Starbucks RadioShack Physical Location Error The dividing-wall problem

  14. Contents • SurroundSense • Evaluation • Limitations and Future Work • Conclusion

  15. Contents • SurroundSense • Evaluation • Limitations and Future Work • Conclusion

  16. Hypothesis It is possible to localize phones by sensing the ambience such as sound, light, color, movement, WiFi …

  17. Sensing over multiple dimensions extracts more information from the ambience Each dimension may not be unique, but put together, they may provide a unique fingerprint

  18. SurroundSense • Multi-dimensional fingerprint • Based on ambient sound/light/color/movement/WiFi Starbucks RadioShack Wall

  19. Should Ambiences be Unique Worldwide? J P I H A Q B K C D L Q E M N R F O G

  20. Should Ambiences be Unique Worldwide? GSM provides macro location (strip mall) SurroundSense refines to Starbucks J P I H A Q B K C D L Q E M N R F O G

  21. SurroundSense Architecture Matching Ambience Fingerprinting Sound Test Fingerprint Color/Light + Acc. = WiFi Logical Location Fingerprint Database GSM Macro Location Candidate Fingerprints

  22. Fingerprints Acoustic fingerprint (amplitude distribution) 0.14 0.12 0.1 0.08 0.06 0.04 0.02 0 • Sound: (via phone microphone) • Color: (via phone camera) Normalized Count -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 Amplitude Values Color and light fingerprints on HSL space 1 0.5 0 Lightness 0 1 0.8 0.5 0.6 0.4 0.2 Hue 1 0 Saturation

  23. Fingerprints • Movement: (via phone accelerometer) Grocery Store Cafeteria Clothes Store Moving Static

  24. Fingerprints • Movement: (via phone accelerometer) Grocery Store Cafeteria Clothes Store Moving Static Queuing

  25. Fingerprints • Movement: (via phone accelerometer) Grocery Store Cafeteria Clothes Store Moving Static Queuing Seated

  26. Fingerprints • Movement: (via phone accelerometer) Grocery Store Cafeteria Clothes Store Moving Static Pause for product browsing

  27. Fingerprints • Movement: (via phone accelerometer) Grocery Store Cafeteria Clothes Store Moving Static Short walks between product browsing Pause for product browsing

  28. Fingerprints • Movement: (via phone accelerometer) Grocery Store Cafeteria Clothes Store Moving Static Walk more

  29. Fingerprints • Movement: (via phone accelerometer) Grocery Store Cafeteria Clothes Store Moving Static Walk more Quicker stops

  30. Fingerprints • Movement: (via phone accelerometer) • WiFi: (via phone wireless card) Grocery Store Cafeteria Clothes Store Moving Static ƒ(overheard WiFi APs)

  31. Discussion • Time varying ambience • Collect ambience fingerprints over different time windows • What if phones are in pockets? • Use sound/WiFi/movement • Opportunistically take pictures • Fingerprint Database • War-sensing

  32. Contents • SurroundSense • Evaluation • Limitations and Future Work • Conclusion

  33. Evaluation Methodology • 51 business locations • 46 in Durham, NC • 5 in India • Data collected by 4 people • 12 tests per location • Mimicked customer behavior

  34. Evaluation: Per-Cluster Accuracy Localization accuracy per cluster Accuracy (%) Cluster

  35. Evaluation: Per-Cluster Accuracy Localization accuracy per cluster Accuracy (%) Multidimensional sensing Cluster

  36. Evaluation: Per-Cluster Accuracy Localization accuracy per cluster Fault tolerance Accuracy (%) Cluster

  37. Evaluation: Per-Cluster Accuracy Sparse WiFi APs Localization accuracy per cluster Accuracy (%) Cluster

  38. Evaluation: Per-Cluster Accuracy Localization accuracy per cluster Accuracy (%) No WiFi APs Cluster

  39. Evaluation: Per-Scheme Accuracy

  40. Evaluation: User Experience Random Person Accuracy 1 WiFI Snd-Acc-WiFi Snd-Acc-Clr-Lt SurroundSense 0.9 0.8 0.7 CDF 0.6 0.5 0.4 0.3 0.2 0.1 0 0 10 20 30 40 50 60 70 80 90 100 Average Accuracy (%)

  41. Why does it work? The Intuition: Economics forces nearby businesses to be different Not profitable to have 3 coffee shops with same lighting, music, color, layout, etc. SurroundSense exploits this ambience diversity

  42. Contents • SurroundSense • Evaluation • Limitations and Future Work • Conclusion

  43. Limitations and Future Work • Energy-Efficiency • Localization in Real Time • Non-business locations

  44. Limitations and Future Work • Energy-Efficiency • Continuous sensing likely to have a large energy draw • Localization in Real Time • Non-business locations

  45. Limitations and Future Work • Energy-Efficiency • Continuous sensing likely to have a large energy draw • Localization in Real Time • User’s movement requires time to converge • Non-business locations

  46. Limitations and Future Work • Energy-Efficiency • Continuous sensing likely to have a large energy draw • Localization in Real Time • User’s movement requires time to converge • Non-business locations • Ambiences may be less diverse

  47. Contents • SurroundSense • Evaluation • Limitations and Future Work • Conclusion

  48. SurroundSense • Today’s technologies cannot provide logical localization • Ambience contains information for logical localization • Mobile Phones can harness the ambience through sensors • Evaluation results: • 51 business locations, • 87% accuracy • SurroundSense can scale to any part of the world

  49. Questions? Thank You! Visit the SyNRG research group @ http://synrg.ee.duke.edu/

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