Push the Limit of WiFi based Localization for Smartphones Presenter: Yingying Chen

1. DAISY Data Analysis and Information SecuritY Lab Push the Limit of WiFi based Localization for Smartphones Presenter: Yingying Chen Hongbo Liu, Yu Gan, Jie Yang, Simon Sidhom, Yan Wang, Yingying Chen Department of Electrical and Computer Engineering Stevens Institute of Technology Fan Ye IBMT.J. Watson Research Center MobiCom 2012 August 25, 2012

2. The Need for High Accuracy Smartphone Localization Train Station • Help users navigation inside large and complex indoor environment, e.g., airport, train station, shopping mall. • Understand customers visit and stay patterns for business Shopping Mall Airport

3. Smartphone Indoor Localization - What has been done? RADAR [INFOCOM’00], Horus [MobiSys’05], Chen et.al[Percom’08] Is it possible to achieve high accuracy localization using most prevalent WiFi infrastructure? • Contributions in academic research • WiFi indoor localization Cricket [Mobicom’00], WALRUS [Mobisys’05], DOLPHIN [Ubicomp’04], Gayathri et.al [SECON’09] • High accuracy indoor localization Shopkick SurroundSense [MobiCom’09],Escort [MobiCom’10], WILL[INFOCOM’12], Virtual Compass [Pervasive’10] • WiFi enabled smartphone indoor localization Google Map • Commercial products Localization error up to 10 meters Locate at the granularity of stores

4. Root Cause of Large Localization Errors Received Signal Strenth (dBm) I am around here. WiFi as-is is not a suitable candidate for high accurate localization due to large errors Is it possible to address this fundamental limit without the need of additional hardware or infrastructure? Am I here? ~ 2 meters 6 - 8 meters 32: [ -22dB, -36dB, -29dB, -43dB ] 48: [ -24dB, -35dB, -27dB, -40dB] • Permanent environmental settings,such as furniture placement and walls. • Transient factors,such as dynamic obstacles and interference. • Physically distant locations share similar WiFi Received Signal Strength ! Orientation, holding position, time of day, number of samples 4

5. Inspiration from Abundant Peer Phones in Public Place Increasing density of smartphones in public spaces Peer 1 Peer 2 How to capture the physical constraints? Provide physical constraints from nearby peer phones Target Peer 3

6. Basic Idea Peer 2 Peer 1 Peer 3 Target Exploit acoustic signal/ranging to construct peer constraints Interpolated Received Signal Strength Fingerprint Map WiFi Position Estimation Acoustic Ranging 6

7. Peer assisted localization Fast and concurrent acoustic ranging of multiple phones Ease of use • Exactly what is the algorithm to search for the best fit position and quantify the signal similarity so that to reduce large errors? System Design Goals and Challenges • How to design and detect acoustic signals? • Need to complete in short time. • Not annoy or distract users from their regular activities.

8. Fast ranging Unobtrusive to human ears Robust to noise • Employ virtual synchronization scheme based on time-multiplexting Airport Train Station Shopping Mall Lab Change point detection Correlation method System Work Flow WiFi position estimation Rigid graph construction Peer assisted localization Peer recruiting & ranging Peer recruiting & ranging • Peer recruiting & ranging 16 – 20 KHz Minimizing the impact on users’ regular activities • Identify nearby peers • Beep emission strategy HTC EVO ADP2 • Only phones close enough can detect recruiting signal • Peer phones willing to help send their IDs to the server • Sound signal design • Acoustic signal detection • Deploy extra timing buffers to accommodate variations in the reception of the schedule at different phones, e.g., 20 ms 8

9. Construct the graph G and G’ based on initial WiFi position estimation and the acoustic ranging measurements. System Work Flow WiFi position estimation Peer assisted localization Rigid graph construction Rigid graph construction Peer recruiting & ranging • Rigid graph construction Rigid Graph G’ based on acoustic ranging Graph G based on WiFi position estimation 9

10. System Work Flow WiFi position estimation Peer assisted localization Peer assisted localization Rigid graph construction Peer recruiting & ranging • Peer assisted localization WiFi based graph Acoustic ranging graph Translational Movement Graph Orientation Estimation 10

11. Prototype Devices Trace-driven statistical test Feed the training data as WiFi samples Perturb distances with errors following the same distribution in real environments HTC EVO ADP 2 Prototype and Experimental Evaluation

12. Localization performance across different real-world environments (5 peers) Airport Train Station Shopping Mall Lab Localization Accuracy 90% error Median error Peer assisted method is robust to noises in different environments

13. Overall Latency Energy Consumption Overall Latency and Energy Consumption • Pose little more latency than required in the original WiFi localization about 1.5 ~ 2 sec • Negligible impact on the battery life • e.g., with additional power consumption at about 320mW on HTC EVO - lasts 12.7 hours with average power of 450mW

14. Peer Involvement Movements of users Triggering peer assistance • Use incentive mechanism to encourage and compensate peers that help a target’s localization • Do not pose more constraints on movements than existing WiFi methods • Affect the accuracy only during sound-emitting period • Happens concurrently and shorter than WiFi scanning • Provides the technology for peer assistance • Up to users to decide when they desire such help Discussion

15. Leverage abundant peer phones in public spaces to reduce large localization errors Exploit minimum auxiliary COTS sound hardware readily available on smartphones Demonstrate our approach successfully pushes further the limit of WiFi localization accuracy • Aim at the most prevalent WiFi infrastructure • Do not require any special hardware • Utilize much more accurate distance estimate through acoustic ranging to capture unique physical constraints • Lightweight in computation on smartphones • In time not much longer than original WiFi scanning • With negligible impact on smartphone’s battery life time Conclusion

16. RADAR [INFOCOM’00]: P. Bahl and V. N. Padmanabhan. RADAR: An In-building RF-based User Location and Tracking System. INFOCOM’00. Cricket [Mobicom’00]: N. Priyantha, A. Chakraborty, and H. Balakrishnan. The Cricket Location-support System. MobiCom’00. DOLPHIN [Ubicomp’04]: M. Minami, Y. Fukuju, K. Hirasawa, and S. Yokoyama. DOLPHIN: A Practical Approach for Implementing A Tully Distributed Indoor Ultrasonic Positioning System. Ubicomp’04. WALRUS [Mobisys’05]: G. Borriello, A. Liu, T. Offer, C. Palistrant, and R. Sharp. WALRUS: Wireless Acoustic Location with Room-level Resolution Using Utrasound. MobiSys’05. Horus [MobiSys’05]: M. Youssef and A. Agrawala. The Horus WLAN Location Determination System. MobiSys’05. Beepbeep [Sensys’07]: C. Peng, G. Shen, Y. Zhang, Y. Li, and K. Tan. Beepbeep:A High Accuracy Acoustic Ranging System Using Cots Mobile Devices. Sensys’07. Chen et.al [Percom’08]: S. Chen, Y. Chen and W. Trappe. Exploiting Environmental Properties for Wireless Localization and Location Aware Applications. PerCom’08. Gayathri et.al [SECON’09]: G. Chandrasekaran, M. A. Ergin, J. Yang, S. Liu, Y. Chen, Marco Gruteser and Rich Martin. Empirical Evaluation of the Limits on Localization Using Signal Strength. SECON’09. SurroundSense [MobiCom’09]: M. Azizyan, I. Constandache, and R. R. Choudhury. Surroundsense: Mobile Phone Localization via Ambience Fingerprinting. MobiCom’09. Escort [MobiCom’10]: I. Constandache, X. Bao, M. Azizyan, and R. R. Choudhury.Did You See Bob? Using Mobile Phones to Locate People. MobiCom’10. Virtual Compass [Pervasive’10]: N. Banerjee, S. Agarwal, P. Bahl, R. Chandra, A. Wolman, and M. Corner. Virtual compass: relative positioning to sense mobile social interactions. Pervasive’10. WILL [INFOCOM’12]: C. Wu, Z. Yang, Y. Liu, and W. Xi. WILL: Wireless Indoor Localization Without Site Survey. INFOCOM’12. Related Work 16

17. Thanks & Questions?