1. NO NEED TO WAR-DRIVE UNSUPERVISED INDOOR LOCALIZATION He Wang, Souvik Sen, Ahmed Elgohary, Moustafa Farid, Moustafa Youssef, Romit Roy Choudhury -twohsien 2012.6.25

2. OUTLINE  Introduction  Architecture and Intuition  Design Details  Evaluation  Discussion and Conclusion

3. INTRODUCTION  Indoor localization is still not in the mainstream  Accuracy  Calibration overhead  Simultaneously harness sensor-based dead-reckoning and environment sensing for localization

4. OUTLINE  Introduction  Architecture and Intuition  Design Details  Evaluation  Discussion and Conclusion

5. ARCHITECTURE AND INTUITION  Seed Landmarks (SLMs)  Certain structures in the building that force users to behave in predictable ways stairs, elevators, entrances, escalators.  Dead Reckoning  Accelerometer, Compass, gyro  The error gets reset whenever use crosses any of the landmarks  Organic Landmarks (OLMs)  Cannot be known a priori, and will vary across different buildings

6. UNLOC ARCHITECTURE

7. DEAD-RECKONING ACCURACY Mean error 11.7m Mean error 1.2m

8. LANDMARK DENSITY  WiFi Landmarks  8 and 5 in two floor of engineering building, each of area less than 4m 2  Magnetic/Accelerometer Landmarks  6 and 8 for each floor

9. COMPUTING LANDMARK LOCATIONS  Combine all dead-reckoned estimates of a given landmark  Errors are random and independent

10. OUTLINE  Introduction  Architecture and Intuition  Design Details  Evaluation  Discussion and Conclusion

11. SEED LANDMARKS  Define sensor patterns that are global across all buildings Acc stableAcc not stable

12. DEAD RECKONING  Displacement from accelerometer  Step count * Step size  Step size: counting the number of steps for a known displacement

13. DEAD RECKONING  Relative angular velocity  Juxtaposes the gyroscope and compass

14. ORGANIC LANDMARKS  Distinct patterns  K-means clustering algorithm  Similarity threshold  Small area – 4m 2

15. ORGANIC LANDMARKS  WiFi Landmarks  MAC addresses, RSSI  Similarity f i (a): RSSI of AP a overheard at l i A: set of AP heard at l 1 and l 2  Magnetic and Inertial Sensor Landmarks  Bending coefficient

16. OUTLINE  Introduction  Architecture and Intuition  Design Details  Evaluation  Discussion and Conclusion

17. EXPERIMENT SETTINGS  Google NexusS phones  3 different users in 3 different university buildins  Computer science(1750m 2 ), Engineering(3000m 2 ), North gate shopping mall(4000m 2 )  Every user walked arbitrarily for 1.5 hours Questions:  How many landmarks are detected in different buildings? Are they well scattered?  Do real users encounter these landmarks?  Localization accuracy

18. SLM DETECTION PERFORMANCE  Trace from 2 malls in Egypt

19. DETECTING ORGANIC LANDMARKS  Number of landmarks detected inside different buildings

20. DETECTING ORGANIC LANDMARKS  Number of landmarks and accuracy increase over time

21. LANDMARK SIGNATURE MATCHING  Tradeoff between distinct signature and matching accuracy

22. LOCALIZATION PERFORMANCE

23. OUTLINE  Introduction  Architecture and Intuition  Design Details  Evaluation  Discussion and Conclusion

24. DISCUSSION AND CONCLUSION  Use the information of landmarks to recalibrate user’s location.  Median location errors is 1.69m Disadvantages:  Device limited  Energy