1. Push the Limit of WiFi based Localization for Smartphones
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
IBM T. J. Watson Research Center
MobiCom 2012
August 25, 2012 1

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

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

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

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

7. System Design Goals and Challenges
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?
How to design and detect acoustic signals?
Need to complete in short time.
Not annoy or distract users from their regular activities.

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

9. System Work Flow Rigid graph construction
WiFi position estimation
Rigid graph construction
Rigid graph construction
Peer assisted localization
Peer recruiting & ranging
Rigid graph construction
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 9

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

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

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

13. 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. Discussion 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

15. Conclusion
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

16. Related Work
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. 16

17. Thanks
&
Questions? 17