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Review & PResentation

Push the Limit of WiFi based Localization for Smartphones Hongbo Liu, Yu Gan, Jie Yang, Simon Sidhom, Yan Wang, and Yingying Chen Stevens Institute of Technology MobiCom 2012


outline Background ~ Motivation ~ Accuracy & Abundant Peer Phone Research Solution & Design ~ Basic idea ~ Goals Design Challenge ~ Work Flow Experimental Results Discussion & Concluding Remarks

Global vs. indooR PositioninG system

indooR PositioninG system

ď ą Help users navigation inside large and complex indoor environment, e.g., airport, train station, shopping mall. ď ą Understand customers visit and stay patterns for business

Train Station

Shopping Mall


Smartphone Indoor Localization what has been done?  Contributions in academic research  WiFi indoor localization

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

 High accuracy indoor localization

Cricket [Mobicom’00], WALRUS [Mobisys’05], DOLPHIN [Ubicomp’04], Gayathri [SECON’09]

 WiFi enabled smartphone indoor localization

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

 Commercial products Is it possible to achieve high accuracy localization using most prevalent WiFi infrastructure?

Google Map Localization error up to 10 meters

Shopkick Locate at the granularity of stores

vaRious indooR localization solutions

Fingerprinting-based method becomes the promising solution for ubiquitous IPS.

site suRvey

FinGeRPRintinG-based techniques

PRimaRy test bed • The experimental area 12 m × 11 m • with hallways office wall dividers and Furnitures • 60 Received Signal Strength (RSS) samples • 71 known locations from 14 Aps • Each location can observe signals from 8- 9 APs on average • Repeat the above process for each of the 4 factors

the need FoR hiGh accuRacy smaRtPhone localization

Errors may cause a passenger make a wrong turn leading to a different in platform, or a store erroneously stock up for a section with much less real customer interests.

Train Station

Shopping Mall


Root cause oF laRGe localization Am I eRRoRs here?

ht nert Sl angi S devi ec e R ) mBd(

45 ~ 2 meters I am around 40 here. 35 30 25 WiFi as-is is not a suitable candidate for high accurate 20 localization due to large errors 15 10 5 0

6 - 8 meters

Is it possible to address this fundamental AP 1 limit AP 2without AP 3 the need of additional hardware or infrastructure?

AP 4

ď ś

Permanent environmental settings, such as furniture placement and walls. share Physically distant locations

ď ś

similar WiFi Received Signal Strength ! Transient factors, such as dynamic obstacles and interference. Orientation, holding position, time of day, number of samples Impact of Various Factor

insPiRation FRom 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

outline Background ~ Motivation ~ Accuracy & Abundant Peer Phone Research Solution & Design ~ Basic idea ~ Goals Design Challenge ~ Work Flow Experimental Results Discussion & Concluding Remarks

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

system desiGn Goals and challenGes • Peer assisted localization  Exactly what is the algorithm to search for the best fit position and quantify the signal similarity so that to reduce large errors?

• Fast and concurrent acoustic ranging of multiple phones  How to design and detect acoustic signals?

• Ease of use  Need to complete in short time.  Not annoy or distract users from their regular activities.

system woRk Flow WiFi position estimation Peer recruiting & ranging

Rigid graph construction

 Peer recruiting & ranging

Peer assisted localization

16 – 20 KHz

the impact activities Minimizing Identify nearby peerson users’ regular HTC EVO ADP2  Only phones close enough can detect recruiting Fastsignal ranging  Peer phones willing to help send their IDs to the server

Sound signal design  Beep emission strategy 

Unobtrusive to human ears

Robust to noise  Employ virtual synchronization based on time-multiplexting Airport Train Station scheme Shopping Mall Lab

 Deploy extra timing buffers to accommodate variations in point the reception of the Change detection schedule at different e.g., 20 ms Acoustic signalphones, detection

Correlation method

system woRk Flow WiFi position estimation Peer recruiting & ranging

Rigid graph construction

Peer assisted localization

 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

PRototyPe and exPeRimental evaluation Step 1: Compute edge directions from acoustic ranging. Other data from Step 2: Compute edge directions from initial WiFi localization Step 3: Graph Orientation estimation

Step 4: Set the Search Scopes Step 5: Joint location estimation


system woRk Flow WiFi position estimation

Rigid graph construction

Peer recruiting & ranging

ď ą Peer assisted localization

Acoustic ranging graph

WiFi based graph

Translational Movement Graph Orientation Estimation

Peer assisted localization

outline Background ~ Motivation ~ Accuracy & Abundant Peer Phone Research Solution & Design ~ Basic idea ~ Goals Design Challenge ~ Work Flow Experimental Results Discussion & Concluding Remarks

PRototyPe and exPeRimental evaluation • Prototype Devices & APP - WiFi RSS sampling, acoustic emitting and recording




Lenovo Thinkpad X201 with Intel Core i5 2.53GHz processor and 4GB DDR3 RAM

• 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

• Sound Signal Parameter

Beep Design : between 16kHz and 20kHz - making sure less back ground noise Beep Detection : Change-Point Detection Method Beep Length (BL) : length of 400 samples Beep Interval (BI) : beep interval of 3000 samples Beep Frequency Band: 16-17kHz range for ADP2 and 18-19kHz for HTC EVO Number of Beeps (NB): Three beeps

• Other data from – Mall supermarket train station Airport – Youtube 23

localization accuRacy

• Localization performance across different real-world environments (5 peers) 90% error

Median error


Train Station

Shopping Mall


Peer assisted method is robust to noises in different environments

oveRall latency and eneRGy consumPtion

• Overall Latency

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

• 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


outline Background ~ Motivation ~ Accuracy & Abundant Peer Phone Research Solution & Design ~ Basic idea ~ Goals Design Challenge ~ Work Flow Experimental Results Discussion & Concluding Remarks

discussion • Peer Involvement  Use incentive mechanism to encourage and compensate peers that help a target’s localization

• Movements of users  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

• Triggering peer assistance  Provides the technology for peer assistance  Up to users to decide when they desire such help 27


• Leverage abundant peer phones in public spaces to reduce large localization errors  Aim at the most prevalent WiFi infrastructure  Do not require any special hardware

• Exploit minimum auxiliary COTS sound hardware readily available on smartphones  Utilize much more accurate distance estimate through acoustic ranging to capture unique physical constraints

• Demonstrate our approach successfully pushes further the limit of WiFi localization accuracy Lightweight in computation on smartphones  In time not much longer than original WiFi scanning  With negligible impact on smartphone’s battery life time 28

ReFeRence • • • • • • • • • • • •

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 [Percom’08]: S. Chen, Y. Chen and W. Trappe. Exploiting Environmental Properties for Wireless Localization and Location Aware Applications. PerCom’08. Gayathri [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.


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Push the Limit of WiFi based Localization for Smartphones

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