1. Zee: Zero-Effort Crowdsourcing for Indoor Localization
Anshul Rai, Krishna Kant Chintalapudi, Venkata N. Padmanabhan, Rijurekha Sen
Speaker: Huan Yang

2. Basic Idea
Zee is a system that makes the calibration zero-effort, by enabling training data to be crowdsourced without any explicit effort on the part of users. The only site-specific input that Zee depends on is a map showing the pathways and barriers. Zee tracks user walk distance and orientation. Using both of the track data and floor map, Zee can propose a user walk path. Then using the positions along the walk path and RSS correspondingly as training data to build a WiFi database and it will be updated during the time user using it. For any incoming query, Zee applies HORUS or EZ model on the database to estimate the user location.

3. Example Scenario Inferring a user’s location
Backward belief propagation
Recording WiFi measurements
Using past WiFi measurements to locate subsequent users

4. Architecture Placement Independent Motion Estimator
Counting Steps
Estimating Heading Offset Range
Augmented Particle Filter
WiFi Database

5. Counting Steps
Idle vs Motion: The STD is small when the user is idle. For the motion scenario the STD is very large.
The STD is under 0.01g with 99% probability when the user is idle, it is over 0.01g with almost 100% probability when the user is walking.

6. Counting Steps
Repetitive nature of walks: the acceleration pattern for a given user with a particular device placement repeats.

7. Counting Steps
Generates a step occurred event every samples while the user in the WALKING state.

8. Estimating Heading Offset Range
Magnetic offset: usually a characteristic of a given location, depending on the construction and other materials in the vicinity, and typically remains stable with time.
Placement offset: usually remains unchanged even when the user takes a turn and changes the direction of walking.
Heading offset:

9. Estimating Heading Offset Range
The spectrum of a typical walk: the second harmonic is either completely absent or is extremely weak in the accelerations experienced by the phone in the direction perpendicular to the user’s walk. It is however always present and dominant in the direction parallel to the user’s walk.

10. Estimating Heading Offset Range
Suppose the magnitude of the second harmonic in the Fourier transform along north is and that along west is
Heading offset: or
Error estimation: sectors

11. Augmented Particle Filter
As a user continues to walk in an indoor environment, navigating through hallways and turning around corners, the possibilities for the user’s path and location shrink progressively.
4-D particle
Particle update

12. Augmented Particle Filter
Forward pass

13. Augmented Particle Filter
Backward pass

14. WiFi Database HORUS Construct RSS probability distribution
Probability of observing RSS at any location
Using Bayesian inference to compute and find the maximum likelihood location

15. WiFi Database EZ Log Distance Path Loss model Distance estimation
Standard trilateration

16. Results
Without WiFi database

17. Results
With WiFi database

18. Results
Overall

19. Conclusion Strength Weakness No need of user participation
No need of user initial location
Independent of device placement
Active learning strategy of database refinement
Weakness
Floor map needed
Particle may not converge
For the early queries, the result may not precise

20. Questions?