Localization of Wireless Terminals using Smart Sensing

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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

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Wireless and Internet Research Laboratory
(WIRLab)

• A laboratory built by funds from
• Canadian Foundation for Innovation (CFI)
• Ontario Innovation Trust (OIT)
• Several industrial partners
• The research focus at WIRLab is on Wireless
  Networks and Signal Processing

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WIRLab Architecture

• The equipment is organized into multiple layers
  to emulate various networking architectures
• 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.

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Team of Researchers last six years

• Director Shahrokh Valaee
• Professors on Sabbatical 7
• Visiting Researchers 4,
• (LG Electronics, SONY, ETRI)
• Post-doctoral Fellows 6
• PhD Students 15
• MASc Students 15
• Visiting PhD Students 7
• Visiting MASc Students 1
• Undergrad students 40

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Sample Projects
WIRLab

• Localization of Wireless Terminals
• Vehicle-to-vehicle Communication
• Cognitive Radios
• Cellular Networks
• Sensor networks
• Mesh networks
• .

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Cellular Networks

• High Bandwidth communication for Maglev Trains
• PAPR reduction through network coding (LGE)
• Joint patent
• Instantly Decodable Network Coding (IDNC)
• Spectrum Sensing

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Vehicular Networks

• 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

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Localization of Wireless Nodes

• Localization of mobile phones
• Compressive Sensing
• Patent licensed
• Android and Windows implementation
• SLAM
• Crowdsourcing
• Using Camera for Localization

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Localization of Wireless Terminals using Smart
Sensing

• Indoor Localization

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Objective

• To design an accurate indoor navigation system
  that can be easily deployed on commercially
  available mobile devices without any hardware
  modification.

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Motivation
Regulations E911
Precision increases
Commercial shopping mall advertisement
Assistive visually challenged
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Where Am I?
Sense the environment and find your location
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Sensors in Mobile Phones

• RF Signal Scanner
• Accelerometer
• Gyroscope
• Barometer
• Magnetometer
• Thermometer
• Photometer

• Camera
• GPS

Software Sensors
Orientation
Rotation Matrix
Gravity
Linear Accelerometer
Rotation Vector
Game Rotation Vector
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Integrated Solution
Localize and Track
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RF Sensing Localization

• Beacon-based
• Proximity
• RSS-based
• Fingerprinting
• Time-of-Arrival
• GPS

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iBeacon

• Uses Bluetooth Low Energy (BLE)
• Small battery-operated transmitters
• Used in consumer market

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Localization based on Proximity
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Localization via RF Fingerprinting

• Off-line measurements (site survey)
• On-line localization

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Fingerprinting
Off-line

• Collect fingerprints and store

On-line
Measure and compare
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Received Signal Strength (RSS)
?
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Fingerprint Matrix

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Online Localization
Measurement
Unknown Location
Radio map
L no. of WiFi access points N no. of
fingerprints
Assuming sparsity

• The problem is underdetermined if L lt N ?
  infinite solutions

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Compressive Sensing

• The location of user can be found via the
  following convex programming
• Number of samples C K log(N)

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versus
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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.
• 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.
• 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

• 30 blind subjects interviewed by a doctor
• 15 testing group
• 15 control group
• 3 tests for each subject

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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

• Accelerometer Sensing
• Step Counter

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Off-line Phase

• A radio map includes
• A grid of points (labeled points) in the service
  area
• RSS measurements at each point

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Off-line Phase Speedup

• Collect RSS readings while walking
• Need for a location estimation method

Auto-Labelled Points
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Android Motion Sensors

• Take advantage of various sensors information.
• Each Android device has a combination of
• Accelerometer
• Gyroscope
• Magnetic Field sensor (compass)
• .

Linear Acceleration Information
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Position Estimation with Step Counter

• 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 S1 Samsung S1 Samsung Tab Motorola RAZR HTC Desire Z LG Nexus 4
Tester id P1 P1 P1 P1 P2 P3
Actual steps 40 60 60 80 50 100
Counted steps 39 60 60 79 49 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.

Auto-labeled data 347 labeled points in approx.
12 min.
Bahen Centre 4th floor, 70m x 80m
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Reliability of Auto-labeled Data
Auto-labeled data is as useful as manually
labeled data
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Crowd Sourcing

• Traces from casual users
• The answer to several issues
• Removing the training phase
• Radio map maintenance
• Using Graph theory, we can build a completely
  unsupervised system
• Combine traces from multiple users to build the
  radio map

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Demos
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Floor Detection

• Pressure Sensing

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Barometer

• Air pressure of the environment ( ). 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.

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Floor detection

• View of 3D Map

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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

• Transmit sensor data of the phone to a PC running
  MATLAB in real-time.
• We deploy algorithms in MATLAB rather than JAVA.
  Much Faster!

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Conclusion

• 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