Encounter-based Noise Cancelation For Cooperative Trajectory Mapping

1. Encounter-based Noise Cancelation For Cooperative Trajectory Mapping Wei Chang, Jie Wu, and Chiu C. Tan IEEE PerCom 2012, March Lugano, Switzerland Temple University, USA

2. Introduction • An issue: routine in an unfamiliar region. • The limitations of GPS-based map construction: • The unavailability of GPS signals • High energy consumption of GPS

3. Introduction • A new way for map. • Multi-sensors embedded in smartphones: • Accelerometer • Electronic compass • Encounter sensor (a Bluetooth module) • Pros: • No signal issue: users record their own data • Save energy

4. System model Accelerometer Compass Encounter detection:Mutual encounter or AP encounter

5. Problems: • Spatially, there are two types of errors may occurred during map construction: • False Positive • False Negative

6. Prior research A’ Error detected • Linear adjustment • Only use physical encounter A Reported path The real path No Physical encounter Physical encounter

7. Limitations of existing solution A A

8. Limitations of existing solution

9. Our purpose • Improve accuracy of the map • Provide shortest navigation by using spatial intersection of trajectories • Correct error by using both false positive and false negative information.

10. Challenges • Every user has different systematic error

11. Challenges • Two types of errors are needed to be corrected. • Since users may not move at constant speed, false positive may hard to detect.

12. Our assumption

13. Solution framework Step 1: Kalman filter Step 2: Collects data, constructs map, and detects false positive and false negative; Step 3: Position adjustment; Step 4: Estimation of error parameter.

14. Step 2: error detection

15. Step 3: adjustment force • False negative: • False positive (hypothesis): • The adjustment force of at a node i:

16. Details: historical error cancellation • Users may not have any encounter in a period of time. • Once we know how to adjust the instant position, the historical positions could be corrected from the latest corrected position. • The whole trace in a period of time will be rotated and elongated.

17. Details: gradually reposition • When changing one user’s historical path, the server may detect more false positive and false negative. • We gradually reposition users’ paths by only moving , where G is a server specified granularity.

18. Details: Hypothesis verification • Two types of verification • Physically encounter • Definitely no encounter

19. Step 4: Error parameter estimation • Interpret data in the same way. • Estimate relative error. • The function of APs • Pre-adjustment

20. Evaluation • Simulation used data: • Metric:

21. Simulation results

22. Conclusion • In this paper, we consider the problem of accumulative measurement errors in cooperative trajectory mapping. • We use a realistic noise model and propose an encounter-based error cancelation algorithm that is effective against measurement errors. • Future work: we plan to build the system on a real platform such that we can test the following items: the magnitude of the measurement errors, the impacts of road structures, traveling patterns, and battery drain on multi-sensors.

23. Thank you! IEEE PerCom 2012 , March Lugano, Switzerland