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Push the Limit of WiFi based Localization for Smartphones Presenter: Yingying Chen Hongbo Liu, Yu Gan, Jie Yang, Simon Sidhom, Yan Wang, Yingying Chen.

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Presentation on theme: "Push the Limit of WiFi based Localization for Smartphones Presenter: Yingying Chen Hongbo Liu, Yu Gan, Jie Yang, Simon Sidhom, Yan Wang, Yingying Chen."— Presentation transcript:

1 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 IBM T. J. Watson Research Center MobiCom 2012 August 25, 2012DAISY Data Analysis and Information SecuritY Lab 1

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

3 Smartphone Indoor Localization - What has been done? Contributions in academic research Commercial products Localization error up to 10 meters Google Map Shopkick Locate at the granularity of stores WiFi indoor localization High accuracy indoor localization WiFi enabled smartphone indoor localization RADAR [INFOCOM00], Horus [MobiSys05], Chen et.al[Percom08] Cricket [Mobicom00], WALRUS [Mobisys05], DOLPHIN [Ubicomp04], Gayathri et.al [SECON09] SurroundSense [MobiCom09], Escort [MobiCom10], WILL[INFOCOM12], Virtual Compass [Pervasive10] 3 Is it possible to achieve high accuracy localization using most prevalent WiFi infrastructure?

4 6 - 8 meters ~ 2 meters Root Cause of Large Localization Errors 4 Permanent environmental settings, such as furniture placement and walls. Transient factors, such as dynamic obstacles and interference. Permanent environmental settings, such as furniture placement and walls. Transient factors, such as dynamic obstacles and interference. Am I here? I am around here. 32: [ -22dB, -36dB, -29dB, -43dB ] 48: [ -24dB, -35dB, -27dB, -40dB] 32: [ -22dB, -36dB, -29dB, -43dB ] 48: [ -24dB, -35dB, -27dB, -40dB] Orientation, holding position, time of day, number of samples Physically distant locations share similar WiFi Received Signal Strength ! Received Signal Strenth (dBm) 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?

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

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

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

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

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

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

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 Prototype and Experimental Evaluation ADP 2 HTC EVO 11

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

13 Overall Latency Energy Consumption Overall Latency and Energy Consumption 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 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 13 Pose little more latency than required in the original WiFi localization about 1.5 ~ 2 sec

14 Peer Involvement Movements of users Triggering peer assistance Discussion 14 Provides the technology for peer assistance Up to users to decide when they desire such help 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 Use incentive mechanism to encourage and compensate peers that help a targets localization

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 Conclusion 15 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 smartphones battery life time

16 RADAR [INFOCOM00]: P. Bahl and V. N. Padmanabhan. RADAR: An In-building RF-based User Location and Tracking System. INFOCOM00. Cricket [Mobicom00]: N. Priyantha, A. Chakraborty, and H. Balakrishnan. The Cricket Location-support System. MobiCom00. DOLPHIN [Ubicomp04]: M. Minami, Y. Fukuju, K. Hirasawa, and S. Yokoyama. DOLPHIN: A Practical Approach for Implementing A Tully Distributed Indoor Ultrasonic Positioning System. Ubicomp04. WALRUS [Mobisys05]: G. Borriello, A. Liu, T. Offer, C. Palistrant, and R. Sharp. WALRUS: Wireless Acoustic Location with Room-level Resolution Using Utrasound. MobiSys05. Horus [MobiSys05]: M. Youssef and A. Agrawala. The Horus WLAN Location Determination System. MobiSys05. Beepbeep [Sensys07]: C. Peng, G. Shen, Y. Zhang, Y. Li, and K. Tan. Beepbeep: A High Accuracy Acoustic Ranging System Using Cots Mobile Devices. Sensys07. Chen et.al [Percom08]: S. Chen, Y. Chen and W. Trappe. Exploiting Environmental Properties for Wireless Localization and Location Aware Applications. PerCom08. Gayathri et.al [SECON09]: 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. SECON09. SurroundSense [MobiCom09]: M. Azizyan, I. Constandache, and R. R. Choudhury. Surroundsense: Mobile Phone Localization via Ambience Fingerprinting. MobiCom09. Escort [MobiCom10]: I. Constandache, X. Bao, M. Azizyan, and R. R. Choudhury. Did You See Bob? Using Mobile Phones to Locate People. MobiCom10. Virtual Compass [Pervasive10]: N. Banerjee, S. Agarwal, P. Bahl, R. Chandra, A. Wolman, and M. Corner. Virtual compass: relative positioning to sense mobile social interactions. Pervasive10. WILL [INFOCOM12]: C. Wu, Z. Yang, Y. Liu, and W. Xi. WILL: Wireless Indoor Localization Without Site Survey. INFOCOM12. Related Work 16

17 Thanks & Questions? 17


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