Localization of Wireless Terminals using Smart Sensing Shahrokh Valaee Wireless and Internet Research Lab (WIRLab) Dept of Electrical and Computer Engineering University of Toronto

2 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

3 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.

4 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+

5 Sample Projects  Localization of Wireless Terminals  Vehicle-to-vehicle Communication  Cognitive Radios  Cellular Networks  Sensor networks  Mesh networks  …. WIRLab

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

7 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 8  Localization of mobile phones  Compressive Sensing  Patent licensed  Android and Windows implementation  SLAM  Crowdsourcing  Using Camera for Localization

9 WIRLab Projects Signal Processing Localization Networking Vehicular Networks Communications Cognitive Radios

Localization of Wireless Terminals using Smart Sensing Indoor Localization

11 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 12

Where Am I? 13 Sense the environment and find your location

Sensors in Mobile Phones  RF Signal Scanner  Accelerometer  Gyroscope  Barometer  Magnetometer  Thermometer  Photometer  … 14 Software Sensors Orientation Rotation Matrix Gravity Linear Accelerometer Rotation Vector Game Rotation Vector  Camera  GPS  …

Integrated Solution 15 Localize and Track

RF Sensing & Localization  Beacon-based Proximity  RSS-based Fingerprinting  Time-of-Arrival GPS 16

iBeacon  Uses Bluetooth Low Energy (BLE)  Small battery-operated transmitters  Used in consumer market 17

Localization based on Proximity 18

Localization via RF Fingerprinting  Off-line measurements (site survey)  On-line localization

20 Fingerprinting Collect fingerprints and store Measure and compare Off-line On-line

21 Received Signal Strength (RSS) ?

22 Fingerprint Matrix

23 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

26 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 , Application ID 12/ 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 M, filed Dec  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,

CNIB Testbed Demo 28 Canadian National Institute for the Blind

Evaluation Results 29  30 blind subjects interviewed by a doctor 15 testing group 15 control group  3 tests for each subject

30 Bayview Village Shopping Center

31 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 33 AP(2) AP(L) AP(l) AP(1)  Access Points (APs) Labelled Points (reference points)  Data Points MAC1 MAC2 MAC3 MAC4 MAC5 MAC6 MAC

34 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

35 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

36 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

37 Step Counter Accuracy Test1Test2Test3Test4Test5Test6 PhoneSamsung S1 Samsung Tab Motorola RAZR HTC Desire Z LG Nexus 4 Tester idP1 P2P3 Actual steps: Counted steps: Accuracy97.5%100% 98.75%98%

38 Speedup in Data Acquisition Manually labeled data: 21 labeled points in approx. 15 min. Bahen Centre 4 th floor, 70m x 80m Auto-labeled data: 347 labeled points in approx. 12 min.

39 Reliability of Auto-labeled Data Auto-labeled data is as useful as manually labeled data Manually labelled data Auto-labelled data

40 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 41

Floor Detection Pressure Sensing

Barometer  Air pressure of the environment ( ). Barometer is useful in floor detection.  Power consumption: 0.003mA  Unit: mBars  Max. sample rate : 30 Hz 43

Barometric Data  Air pressure for different floors of Bahen Centre. 44

Floor detection  View of 3D Map 45

Confusion Matrix for Floor Detection Floor 1Floor 2Floor 3Floor 4Floor 5Floor 6Floor 7Floor 8 Floor Floor Floor Floor Floor Floor Floor Floor

 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 47

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