Localization of Wireless Terminals using Smart Sensing

Shahrokh ValaeeWireless and Internet Research Lab (WIRLab)Dept of Electrical and Computer EngineeringUniversity of Torontowww.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 Localization of Wireless Terminals Vehicle-to-vehicle Communication Cognitive Radios Cellular Networks Sensor networks Mesh networks ….

WIRLab

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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

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

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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.

Motivation

Regulations: E911

Commercial: shopping mall advertisement

Assistive: visually challenged

Precision

increases

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Where Am I?

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Sense the environment and find your location

Sensors in Mobile Phones RF Signal Scanner Accelerometer Gyroscope Barometer Magnetometer Thermometer Photometer …

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Software Sensors

Orientation

Rotation Matrix

Gravity

Linear Accelerometer

Rotation Vector

Game Rotation Vector

Camera GPS …

Integrated Solution

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Localize and

Track

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

Collect fingerprints and store

Measure and compare

Off-line

On-line

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Received Signal Strength (RSS)

?

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Fingerprint Matrix

)(

)2(

)1(

,2,1,

,22,21,2

,12,11,1

2

2

1

1

LAP

AP

AP

RRR

RRR

RRR

R

y

x

y

x

y

x

NLNL

N

N

N

N

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Online Localization

The problem is underdetermined if L < N infinite solutions

L: no. of WiFi access points

N: no. of fingerprints

Radio map MeasurementUnknown Location

Assuming sparsity

Compressive Sensing The location of user can be found via the following convex programming

Number of samples: C K log(N)24

versus

EITA-EITC 2012 [Valaee] 25

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Skip the details

Indoor Navigation

System

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

28Canadian National Institute for the Blind

Evaluation Results

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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)

Site Survey via Crowd Sourcing Accelerometer Sensing Step Counter

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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AP(2)

AP(L) AP(l)

AP(1)

Access Points (APs)

Labelled Points (reference points) Data Points

MAC1

MAC2

MAC3

MAC4

MAC5

MAC6

MAC7

- 89

- 78

- 91

- 85

- 92

- 77

- 72

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Off-line Phase: Speedup

Collect RSS readings while walking

Need for a location estimation method

AP(2)

AP(L) AP(l)

AP(1)

Access Points (APs)

Labelled Points Data Points

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.

Bahen Centre 4th floor, 70m x 80m

Auto-labeled data:347 labeled points in approx. 12 min.

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Reliability of Auto-labeled Data

Auto-labeled data is as useful as manually labeled data

Manually labelled dataAuto-labelled 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

Demos

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Floor DetectionPressure Sensing

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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P

Barometric Data Air pressure for different floors of Bahen

Centre.

440 5 10 15 20 25 30 35 40 45 50

994

994.5

995

995.5

996

996.5

997

997.5

998

998.5

999Air Pressure, Bahen building, sunny day

Time(s)

Pre

ssur

e(m

Bar

)

1st floor

2nd floor3rd floor

4th floor

5th floor

6th floor7th floor

8th floor

Floor detection

View of 3D Map

0

500

1000

1500

0

500

1000

1500

20000

2

4

6

8

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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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Transmit sensor data of the phone to a PC running MATLAB in real-time.

We deploy algorithms in MATLAB rather than JAVA. Much Faster!

Implementation of Algorithms

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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 48