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 EngineeringStevens Institute of Technology

Fan YeIBM T. J. Watson Research Center

MobiCom 2012August 25, 2012

DAISYDAISYData Analysis and Information SecuritY Lab

1

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

Smartphone Indoor Localization - What has been

done? Contributions in academic research

Commercial products

Localization error up to 10 meters

Google MapGoogle Map ShopkickShopkick

Locate at the granularity of stores

WiFi indoor localization

High accuracy indoor localization

WiFi enabled smartphone indoor localization

RADAR [INFOCOM’00], Horus [MobiSys’05], Chen et.al[Percom’08]

Cricket [Mobicom’00], WALRUS [Mobisys’05], DOLPHIN [Ubicomp’04], Gayathri et.al

[SECON’09]

SurroundSense [MobiCom’09], Escort [MobiCom’10], WILL[INFOCOM’12], Virtual Compass [Pervasive’10]

3

Is it possible to achieve high accuracy localization using most prevalent WiFi

infrastructure?

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10152025303540

45

AP 1 AP 2 AP 3 AP 4

6 - 8 meters~ 2 meters

Root Cause of Large Localization Errors

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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]

Orientation, holding position, time of day, number of samples Orientation, holding position, time of day, number of samples

Physically distant locations share similar WiFi Received Signal Strength !

Physically distant locations share similar WiFi Received Signal Strength !

Rec

eive

d S

igna

l Str

enth

(d

Bm

)

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?

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

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Basic Idea

WiFi Position Estimation Acoustic Ranging

Interpolated Received Signal Strength Fingerprint Map

Exploit acoustic signal/ranging to construct peer constraintsTarget

Peer 1Peer 2

Peer 3

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.

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Rigid graph construction

Sound signal design

Acoustic signal detection

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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 KHz16 – 20 KHz

ADP2ADP2

Lab Train Station Shopping Mall Airport

HTC EVOHTC EVO

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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

Rigid graph construction

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System Work Flow

Peer assisted localization

Peer recruiting & ranging

Rigid graph construction

Peer assisted localization

WiFi position estimation

Peer assisted localization

Graph Orientation EstimationTranslational Movement

WiFi based graphAcoustic ranging graph

PrototypeDevices

Trace-driven statistical testFeed the training data as WiFi samplesPerturb distances with errors following the same

distribution in real environments

Prototype and Experimental Evaluation

ADP 2ADP 2HTC EVOHTC EVO

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Localization performance across different real-world environments (5 peers)

Localization Accuracy

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Peer assisted method is robust to noises in different environmentsPeer assisted method is robust to noises in different environments

Median errorMedian error 90% error90% error

Lab Train Station Shopping Mall Airport

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

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Pose little more latency than required in the original WiFi localization about 1.5 ~ 2 sec

Pose little more latency than required in the original WiFi localization about 1.5 ~ 2 sec

Peer Involvement

Movements of users

Triggering peer assistance

Discussion

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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 target’s localization

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

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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

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

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Thanks &

Questions?

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