Associate Professor Tao Gu, RMIT University, Australia INDOOR POSITIONING VIA SMARTPHONE SENSING

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2 Associate Professor Tao Gu, RMIT University, Australia INDOOR POSITIONING VIA SMARTPHONE SENSING

3 Outline Indoor Localization using smartphone magnetometer Floor localization Localization in Metro Trains

4 Applications Shopping malls, office buildings, airports, train stations, hospitals, trade shows, museums, warehouse, etc.

5 Smartphone Built-in Sensors

6 A Magnetic Fingerprinting Approach to Indoor Localization Use smartphone sensors magnetometer accelerometer compass gyroscope Do not rely on Wi-Fi Battery efficiency Accurate (1~2.8m )

7 Magnetic Fingerprint

8 Magnetometer Readings B B = [B x, B y, B z ] Magnetic Fingerprint: Not Unique!

9 Particle Filter Particle Motion Tracking Particle Weight Evaluation Particle Resampling

10 Initialization Heading changes Magnetic readings Default Particle Step Length=

11 Motion Tracking & Weight Estimation Heading changes Magnetic readings

12 Resampling Heading changes Magnetic readings

13 Motion Tracking & Weight Estimation Heading changes -- Magnetic readings

14 Resampling Heading changes Magnetic readings Get you!

15 P1: Particle Step Length & User Step Length Heading changes Magnetic readings User Actual Position Estimated Position Default Particle Step Length=1 User Actual Step Length=

16 P2: Localization Failure - Kidnapped Robot Problem Heading changes Magnetic readings Estimated Position User Actual Position We cannot realize the localization has failed until all particles weight become.

17 P3: Heading Offset Change Heading changes -- Magnetic readings 45 Estimated Position User Actual Position User s Heading direction Phone s Heading direction User answer a Call

18 P4: Calibrating Different Smartphones?

19 Our Contributions Dynamic step length estimation Kidnapped robot problem Heading offset change Calibrating different smartphones

20 Real Experiments in an Office Building

21 1-2.8 m Accuracy

22 Smartphone Battery

23 Floor Localization Motivation: Do not rely on Wi-Fi Leverage on smartphone sensing and crowdsourcing Applications: Location based services Emergency services

24 Bluetooth User Trace User Trace From Floor Cloud Server The Initial floors of users Mapping table To Floor Traveling Time Step Count 1 From 2 To 8 s Traveling 22 Step 1 Floor 3 Floor 12 Time s 44 Count s s 44 Traveling Time Elevator Download Stairs Step Count Acceleration Escalators Encounter Acceleration

25 Localization in Metro Trains Problem definition: how to locate mobile users in a metro train? Motivation: Do not rely on Wi-Fi No need to war-drive all the metro lines Smartphone sensing accelerometer magnetometer barometer Crowdsourcing

26 Different from Traditional Indoor Spaces Structure Infrastructure Movement Metro Line Simple Limited infrastructure High speed Predictable Complex Abundant Traditional Indoor Spaces Low speed Random

27 Cloud Server Trace merging Pattern map generation Localization map Crowdsourcing Stop event Recognition. Running event matching. User traces 1 location 2 A B C Logging accelerometer, barometer reading. Do train stop event Recognition. 11/2/215 Metro Train

28 More Information My website Related Publications Hongwei Xie, Tao Gu, Xianping Tao, and Jian Lu. A Reliability-Augmented Particle Filter for Magnetic Fingerprinting based Indoor Localization on Smartphone, IEEE Transaction on Mobile Computing (TMC), 215. Hongwei Xie, Tao Gu, Xianping Tao, and Jian Lu. MaLoc: A Practical Magnetic Fingerprinting Approach to Indoor Localization using Smartphones, In Proc. of the 214 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 214), Seattle, Washington, September 13-17, 214. HaiboYei, Tao Gu, Xianping Tao, and Jian Lu. SBC: Scalable Smartphone Barometer Calibration through Crowdsourcing, In Proc. of the 11th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous 214), London, December 2-5, 214. HaiboYei, Tao Gu, Xianping Tao, and Jian Lu. B-Loc: Scalable Floor Localization using Barometer on Smartphone, In Proc. of the 11th IEEE International Conference on Mobile Ad hoc and Sensor Systems (MASS 214), Philadelphia, Pennsylvania, October 28-3, 214. HaiboYei, Tao Gu, Xianping Tao, and Jian Lu. Crowdsourced Smartphone Sensing for Localization in Metro Trains, In Proc. of IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM 214), Sydney, Australia, June 16-19, 214.

29 Opportunities R&D Centers Joint research and development Joint funding Industry Partners Commercial Product development Australian Government Funding Collaboration between an Australian university and an industry partner (local or overseas). Venture Capital for creating a start-up 11/2/215