1 / 48

Energy-efficient Localization Via Personal Mobility Profiling Ionut Constandache Co-authors:

Energy-efficient Localization Via Personal Mobility Profiling Ionut Constandache Co-authors: Shravan Gaonkar, Matt Sayler, Romit Roy Choudhury and Landon Cox. Context. Pervasive wireless connectivity + Localization technology =. Location-based applications (LBAs). Context.

sabine
Télécharger la présentation

Energy-efficient Localization Via Personal Mobility Profiling Ionut Constandache Co-authors:

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Energy-efficient Localization Via Personal Mobility Profiling Ionut Constandache Co-authors: Shravan Gaonkar, Matt Sayler, Romit Roy Choudhury and Landon Cox

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

  3. Context Pervasive wireless connectivity + Localization technology = Location-based applications (LBAs) (iPhoneAppStore: 3000 LBAs, Android: 600 LBAs)

  4. Location-Based Applications (LBAs) • Two kinds of LBAs: • One-time location information: Geo-tagging, location-based recommendations, etc.

  5. Location-Based Applications (LBAs) • Two kinds of LBAs: • One-time location information: Geo-tagging, location-based recommendations, etc. • Localization over long periods of time: GeoLife: shopping list when near a grocery store TrafficSense: real-time traffic conditions

  6. Localization Technology • LBAs rely on localization technology to get user position

  7. Localization Technology • LBAs rely on localization technology to get user position AccuracyTechnology 10m GPS 20-40m WiFi 200-400m GSM

  8. Localization Technology • LBAs rely on localization technology to get user position • LBAs executed on mobile phones AccuracyTechnology 10m GPS 20-40m WiFi 200-400m GSM

  9. Localization Technology • LBAs rely on localization technology to get user position • LBAs executed on mobile phones AccuracyTechnology 10m GPS 20-40m WiFi 200-400m GSM Energy Efficiency is important (localization for long time)

  10. Localization Technology Ideally Accurate and Energy-Efficient Localization

  11. Energy Battery Lifetime: GPS ~ 10h WiFi ~ 40h GSM ~ 60h … sample every 30s

  12. Energy Battery Lifetime: GPS ~ 10h WiFi ~ 40h GSM ~ 60h … sample every 30s Battery shared with • Talk time, web browsing, photos, SMS, etc.

  13. Energy Battery Lifetime: GPS ~ 10h WiFi ~ 40h GSM ~ 60h … sample every 30s Battery shared with • Talk time, web browsing, photos, SMS, etc. Localization energy budget only percentage of battery • 20% of battery = 2h GPS or 8h WiFi

  14. Energy Battery Lifetime: GPS ~ 10h WiFi ~ 40h GSM ~ 60h … sample every 30s Battery shared with • Talk time, web browsing, photos, SMS, etc. Localization energy budget only percentage of battery • 20% of battery = 2h GPS or 8h WiFi For limited energy budget what accuracy to expect?

  15. Problem Formulation L(t2) L(t3) L(t4) L(t6) L(t0) L(t1) L(t5) L(t7)

  16. Problem Formulation L(t2) L(t3) L(t4) L(t6) L(t0) L(t1) L(t5) L(t7) Localization Error t0 t1 t2 t4 t6 t7 t3 t5 Time

  17. Problem Formulation L(t2) L(t3) L(t4) L(t6) L(t0) L(t1) L(t5) L(t7) Localization Error t0 t1 t2 t4 t6 t7 t3 t5 Time GPS

  18. Problem Formulation L(t2) L(t3) L(t4) L(t6) L(t0) L(t1) L(t5) L(t7) Accuracy gain from GPS Eng.: 1 GPS read Localization Error t0 t1 t2 t4 t6 t7 t3 t5 Time GPS

  19. Problem Formulation L(t2) L(t3) L(t4) L(t6) L(t0) L(t1) L(t5) L(t7) Accuracy gain from GPS Eng.: 1 GPS read Localization Error Accuracy gain from WiFi Eng.: 1 WiFi read t0 t1 t2 t4 t6 t7 t3 t5 Time GPS WiFi

  20. Problem Formulation L(t2) L(t3) L(t4) L(t6) L(t0) L(t1) L(t5) L(t7) Accuracy gain from GPS Eng.: 1 GPS read Localization Error Accuracy gain from WiFi Eng.: 1 WiFi read Time t0 t1 t2 t4 t6 t7 t3 t5 GPS WiFi

  21. Problem Formulation Given energy budget B, known Trace T, location readings costs egps, ewifi, egsm: Schedule location readings to minimize Average Localization Error (ALE)

  22. Problem Formulation Given energy budget B, known Trace T, location readings costs egps, ewifi, egsm: Schedule location readings to minimize Average Localization Error (ALE) ALE = Avg. dist. between reported and actual location of the user

  23. Problem Formulation Given energy budget B, known Trace T, location readings costs egps, ewifi, egsm: Schedule location readings to minimize Average Localization Error (ALE) ALE = Avg. dist. between reported and actual location of the user Find the OfflineOptimal Accuracy

  24. Results B = 25% Battery

  25. Results B = 25% Battery OfflineOptimal ALE > 60m

  26. Results B = 25% Battery OfflineOptimal ALE > 60m Online Schemes Naturally Worse

  27. Our Approach: EnLoc • Reporting last sampled location increases inaccuracy

  28. Our Approach: EnLoc • Reporting last sampled location increases inaccuracy • Prediction opportunities exist • Exploit habitual paths • Leverage population statistics when the user has deviated

  29. Our Approach: EnLoc • Reporting last sampled location increases inaccuracy • Prediction opportunities exist • Exploit habitual paths • Leverage population statistics when the user has deviated • EnLoc Solution: • Predict user location when not sampling • Sample when prediction is unreliable

  30. EnLoc: Overview EnLoc Habitual Paths Deviations E.g. Regular path to office E.g. Going to a vacation

  31. EnLoc: Overview EnLoc Habitual Paths Deviations E.g. Regular path to office E.g. Going to a vacation Per-user Mobility Profile

  32. EnLoc: Overview EnLoc Habitual Paths Deviations E.g. Regular path to office E.g. Going to a vacation Per-user Mobility Profile Population Statistics

  33. Profiling Habitual Mobility • Intuition: Humans have habitual activities • Going to/from office • Favorite grocery shop, cafeteria

  34. Profiling Habitual Mobility • Intuition: Humans have habitual activities • Going to/from office • Favorite grocery shop, cafeteria • Habitual activities translate into habitual paths • E.g. path from home to office

  35. Profiling Habitual Mobility • Intuition: Humans have habitual activities • Going to/from office • Favorite grocery shop, cafeteria • Habitual activities translate into habitual paths • E.g. path from home to office • Habitual paths may branch • E.g., left for office, right for grocery • Q: How to solve uncertainty? • A: Schedule a location reading after the branching point.

  36. Per-User Mobility Graph Graph of habitual visited GPS coordinates User Habitual Paths

  37. Per-User Mobility Graph Graph of habitual visited GPS coordinates User Habitual Paths Logical Representation

  38. Per-User Mobility Graph Graph of habitual visited GPS coordinates Sample location after branching points Predict between branching points # of BPs < # of location samples (BP = branching point) User Habitual Paths Logical Representation

  39. Evaluation: Habitual Paths • 30 days of traces, loc. battery budget 25% per day • Assume phone speed known

  40. Evaluation: Habitual Paths • 30 days of traces, loc. battery budget 25% per day • Assume phone speed known

  41. Evaluation: Habitual Paths • 30 days of traces, loc. battery budget 25% per day • Assume phone speed known Average ALE 12m

  42. Deviations from habitual paths • Predict based on population statistics • If user on a certain street, at the next intersection predict the most probable turn.

  43. Deviations from habitual paths • Predict based on population statistics • If user on a certain street, at the next intersection predict the most probable turn. • Probability Maps computed from Google Map simulation

  44. Deviations from habitual paths • Predict based on population statistics • If user on a certain street, at the next intersection predict the most probable turn. • Probability Maps computed from Google Map simulation

  45. Evaluation: Population Statistics B = 25% Battery OptX: report last sampled location using sensor X (offline) EnLoc-Deviate: Equally spaced GPS + population statistics (online). ALE ~ 32m

  46. Future Work/Limitations • Assumed phone speed known • Infer speed using accelerometer • Energy consumption of accelerometer relatively small • Deviations from habitual paths • Quickly detect/switch to deviation mode • Probability Map hard to build on wider scale • Statistics from transportation departments

  47. Conclusions • Location is not for free • Phone battery cannot be invested entirely into localization • Offline optimal accuracy computed • For specified energy budget • Known mobility trace • However, online localization technique necessary • EnLoc exploit prediction to reduce energy • Personal Mobility Profiling • Population Statistics

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

More Related