Slides - Sigmobile

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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
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
2
Airport
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]
 Commercial
productsto achieve high accuracy localization
Is it possible
using most prevalent WiFi infrastructure?
Google Map
Shopkick
Localization error up to 10 meters
Locate at the granularity of stores
3
Root Cause of Large Localization Errors
Received Signal Strenth (dBm)
Am I
here?
45 ~ 2 meters
40
I am around
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
32: Permanent
[ -22dB, -36dB,
-29dB, -43dB
] such as furniture placement and walls.

environmental
settings,
Physically distant locations share
similar
WiFi Received Signal Strength !
48: Transient
[ -24dB, -35dB,

factors, -27dB,
such as -40dB]
dynamic obstacles
and interference.
Orientation, holding position, time of day, number of samples
4
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
5
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
6
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.
7
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
signal
Fast ranging
 Peer phones willing to help send their IDs to the server
Unobtrusive to human ears
 Sound signal design
 Beep emission strategy
Robust to noise
 Employ virtual synchronization
based onMall
time-multiplexting
Airport
Train Station scheme
Shopping
Lab
 Deploy extra timing buffers to accommodate
variations
in the
reception
Change
point
detection
of the schedule at different phones, e.g., 20 ms
 Acoustic signal detection
Correlation method
8
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.
Rigid Graph G’ based on
acoustic ranging
Graph G based on WiFi
position estimation
9
System Work Flow
WiFi position
estimation
Rigid graph
construction
Peer recruiting &
ranging
 Peer assisted localization
Acoustic ranging graph
WiFi based graph
Translational
Movement
Graph
Orientation
Estimation
10
Peer assisted
localization
Prototype and Experimental Evaluation
Prototype
 Devices
HTC EVO
ADP 2
Trace-driven statistical test
 Feed the training data as WiFi samples
 Perturb distances with errors following the same
distribution in real environments
11
Localization Accuracy
 Localization performance across different real-world
environments (5 peers)
90% error
Median error
Lab
Train Station
Shopping Mall
Airport
Peer assisted method is robust to noises in different environments
12
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
13
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
14
Conclusion
 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
15
Related Work
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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.
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Thanks
&
Questions?
17