Localization of Wireless
Terminals using
Smart Sensing
Shahrokh Valaee
Wireless and Internet Research Lab (WIRLab)
Dept of Electrical and Computer Engineering
University of Toronto
www.comm.utoronto.ca/~valaee
Wireless and Internet Research Laboratory
(WIRLab)
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A laboratory built by funds from:
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Canadian Foundation for Innovation (CFI)
Ontario Innovation Trust (OIT)
Several industrial partners
The research focus at WIRLab is on Wireless
Networks and Signal Processing
2
WIRLab Architecture

The equipment is organized into multiple layers to
emulate various networking architectures:
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Core network with high-end L2/L3 switches and soft
routers;
Several access points with capability for multiple
standard support;
Numerous wireless devices such as notebooks, PDAs,
wireless cameras, etc, for mesh or multi-hop
communications;
Wireless robots for mobility management;
Sensors equipped with localization devices for
environmental monitoring and location estimation;
DSRC/WAVE devices for fast MAC and rapid network
acquisition used in mobile communications at vehicular
speeds.
The lab can simulate almost all network
configurations and various topologies.
3
Team of Researchers last six years
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Director: Shahrokh Valaee
Professors on Sabbatical: 7
Visiting Researchers: 4,
(LG Electronics, SONY, ETRI)
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Post-doctoral Fellows: 6
PhD Students: 15
MASc Students: 15
Visiting PhD Students: 7
Visiting MASc Students: 1
Undergrad students: 40+
4
Sample Projects
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Localization of Wireless Terminals
Vehicle-to-vehicle Communication
Cognitive Radios
Cellular Networks
Sensor networks
Mesh networks
….
WIRLab
5
Cellular Networks
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High Bandwidth
communication for
Maglev Trains
PAPR reduction through
network coding (LGE)
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Joint patent
Instantly Decodable
Network Coding (IDNC)
Spectrum Sensing
6
Vehicular Networks
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Low latency communications
for vehicular environment
Opportunistic Network Coding
for data broadcast
Enhanced reliability through
Positive Orthogonal Codes
V2X (pedestrian, cyclists)
communications
Localization of vehicles
7
Localization of Wireless Nodes
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Localization of mobile phones
Compressive Sensing
 Patent licensed
Android and Windows
implementation
SLAM
Crowdsourcing
Using Camera for Localization
8
WIRLab
Projects
Signal Processing
Networking
Communications
Localization
Vehicular
Networks
Cognitive Radios
9
Localization of Wireless
Terminals using Smart
Sensing
Indoor Localization
Objective

To design an accurate indoor
navigation system that can be
easily deployed on
commercially available mobile
devices without any hardware
modification.
11
Motivation
Regulations: E911
Precision
increases
Commercial: shopping mall advertisement
Assistive: visually challenged
12
Where Am I?
Sense the environment and find your location
13
Sensors in Mobile Phones
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RF Signal Scanner
Accelerometer
Gyroscope
Barometer
Magnetometer
Thermometer
Photometer
…
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Camera
GPS
…
Software Sensors
Orientation
Rotation Matrix
Gravity
Linear Accelerometer
Rotation Vector
Game Rotation Vector 14
Integrated
Solution
Localize
and
Track
15
RF Sensing & Localization
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Beacon-based
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RSS-based
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Proximity
Fingerprinting
Time-of-Arrival
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GPS
16
iBeacon
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Uses Bluetooth Low Energy
(BLE)
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Small battery-operated
transmitters
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Used in consumer market
17
Localization based on Proximity
18
Localization via RF
Fingerprinting
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Off-line measurements (site survey)
On-line localization
Fingerprinting
Off-line
Collect fingerprints and store
On-line
Measure and compare
20
Received Signal Strength (RSS)
?
21
Fingerprint Matrix
 x1 
 
 y1 

 R1 ,1

R 2 ,1

R 
 

 R L ,1
 x2 
 
 y2 


R1 , 2

R2,2



R N ,2

 xN 
 
 yN 

R1 , N  

R2,N  
 

R L , N  
AP (1)
AP ( 2 )

AP ( L )
22
Online Localization
Unknown Location
Radio map
é
ê R1,1
ê R
ê 2,1
ê
ê R
êë L,1
R1,2
R2,2
RN,2
Measurement
ù
R1,N ú é x1 ù é y1 ù
ê
ú ê
ú
ú
R2,N ê x2 ú ê y2 ú
úê
ú=ê
ú
úê
ú ê
ú
RL,N úúû êë x N úû êë yL úû
L: no. of WiFi access points
N: no. of fingerprints
Assuming sparsity
é
ê
ê
ê
ê
ê
ê
ë
0
1
0
0
ù
ú
ú
ú
ú
ú
ú
û
The problem is underdetermined if L < N
 infinite solutions
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Compressive Sensing
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The location of user can be found via
the following convex programming
min x
1
s.t. y = Rx
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Number of samples: C K log(N)
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1
versus
2
min ( x1 + x2 )
min ( x + x
s.t. y = R1 x1 + R2 x2
s.t. y = R1 x1 + R2 x2
2
1
EITA-EITC 2012 [Valaee]
2
2
)
25
Indoor
Navigation
System
Skip the
details
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Patents and Licenses

S. Valaee, C. Feng, and A. W. S. Au, “System, Method, and Computer
Program for Anonymous Localization,” US non-prov patent, EFS ID
9022070, Application ID 12/966493 filed Dec 2010, Notice of Allowance
issue on 12/05/2014.
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S. Valaee, C. Feng, and A, Au, “System, Method, and Computer Program
for Anonymous Localization,” Canadian patent, Reference no. 100
5050700 M, filed Dec 2010.
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S. Valaee and C. Feng, “System, Method, and Computer Program for
Dynamic Generation of a Radio Map for Indoor Positioning of Mobile
Devices, “US Patent Application, Application number 13/927510, Filed
June 26, 2013.
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CNIB Testbed Demo
Canadian National Institute for the Blind
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Evaluation Results
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30 blind subjects interviewed by a doctor
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15 testing group
15 control group
3 tests for each subject
Overall Success Rate
– Testing Group
Overall Success Rate
– Control Group
7%
Success
40%
Success
Failure
Failure
60%
93%
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Bayview Village Shopping Center
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Accuracy (positioning in BV)
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Site Survey via Crowd
Sourcing
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Accelerometer Sensing
Step Counter
Off-line Phase
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A radio map includes
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A grid of points
(labeled points) in
the service area
RSS measurements
at each point
AP(L)
MAC1
- 89
MAC2
- 78
MAC3
- 91
MAC4
- 85
MAC5
- 92
MAC6
- 77
MAC7
- 72
AP(1)
AP(2)
AP(l)
 Access Points (APs)
 Data Points
Labelled Points
(reference points)
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Off-line Phase: Speedup
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Collect RSS readings
while walking
AP(1)
AP(2)
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Need for a location
estimation method
AP(L)
 Access Points (APs)
Labelled Points
 Data Points
Auto-Labelled Points
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AP(l)
Android Motion Sensors
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Take advantage of various sensors information.
Each Android device has a combination of:
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Accelerometer
Linear Acceleration Information
Gyroscope
Magnetic Field sensor (compass)
….
Position Estimation with Step Counter
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Position can be estimated given the initial location, speed, and
heading directions
With the help of accelerometer, it is possible to make a step
counter to estimate the coordinates of RSS readings
Acceleration
samples
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Step Counter Accuracy
Test1
Test2
Test3
Test4
Test5 Test6
Phone
Samsung Samsung Samsung Motorola
S1
S1
Tab
RAZR
HTC LG
Desire Nexus 4
Z
Tester id
P1
P1
P1
P1
P2
P3
Actual steps: 40
60
60
80
50
100
Counted
steps:
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60
60
79
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98
Accuracy
97.5%
100%
100%
98.75%
98%
98%
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Speedup in Data Acquisition
Manually labeled data:
21 labeled points in approx. 15 min.
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Auto-labeled data:
347 labeled points in approx. 12 min.
Bahen Centre 4th floor,
70m x 80m
Reliability of Auto-labeled Data
Manually labelled data
Auto-labelled data
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Auto-labeled data is as useful as manually labeled data
Crowd Sourcing
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Traces from casual users
 The answer to several issues:
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Using Graph theory, we can build a completely
unsupervised system
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Removing the training phase
Radio map maintenance
Combine traces from multiple users to build the radio map
Demos
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Floor Detection
Pressure Sensing
Barometer
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Air pressure of the environment ( P ).
Barometer is useful in floor detection.
Power consumption: 0.003mA
Unit: mBars
Max. sample rate : 30 Hz
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Barometric Data
Air pressure for different floors of Bahen
Centre.
Air Pressure, Bahen building, sunny day
999
1st floor
2nd floor
3rd floor
4th floor
5th floor
6th floor
7th floor
8th floor
998.5
998
997.5
Pressure(mBar)

997
996.5
996
995.5
995
994.5
994
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0
5
10
15
20
25
Time(s)
30
35
40
45
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Floor detection
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View of 3D Map
8
6
4
2
0
2000
1500
1500
1000
1000
500
500
0
0
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Confusion Matrix for Floor Detection
Floor 1 Floor 2 Floor 3 Floor 4 Floor 5 Floor 6 Floor 7 Floor 8
Floor 1
0.9980
0.0020
0
0
0
0
0
0
Floor 2
0
1.0000
0
0
0
0
0
0
Floor 3
0
0
1.0000
0
0
0
0
0
Floor 4
0
0
0
1.0000
0
0
0
0
Floor 5
0
0
0
0
1.0000
0
0
0
Floor 6
0
0
0
0
0
1.0000
0
0
Floor 7
0
0
0
0
0
0
0.9998
0.0002
Floor 8
0
0
0
0
0
0
0
1.0000
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Implementation of Algorithms
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Transmit sensor data of the phone to a PC
running MATLAB in real-time.
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We deploy algorithms in MATLAB rather
than JAVA. Much Faster!
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Conclusion
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Sensory data from smartphones can be used to
localize wireless devices indoors
Compressive Sensing is used to enhance sensing
and localization
Accelerometer and Gyro are used for
crowdsourcing
Pressure sensor is used for floor detection
Direct connection between sensor data and
MATLAB reduces the implementation time
48