No Need to War-Drive Unsupervised Indoor Localization

1. No Need to War-DriveUnsupervised Indoor Localization He Wang, SouvikSen, Ahmed Elgohary, MoustafaFarid, 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 waysstairs, 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 4m2 • 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 not stable Acc 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 – 4m2

15. Organic landmarks • WiFi Landmarks • MAC addresses, RSSI • Similarityfi(a): RSSI of AP a overheard at liA: set of AP heard at l1 and l2 • 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(1750m2), Engineering(3000m2), North gate shopping mall(4000m2) • 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