HABITS: A History Aware Based Wi-Fi Indoor Tracking System

Eoghan Furey

Supervisors: Dr. Kevin Curran, Prof. Paul Mc KevittFaculty of Computing and Engineering,

University of Ulster, Magee College, Derry

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Introduction

Location Aware Computing (LAC) - Academia and Industry

Applications – medical, military, logistical and social

Positioning algorithms - period of rapid innovation

Optimize self-location estimates on Wi-Fi enabled devices

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Research Problem

Weaknesses of currently available positioning systems

They don’t work indoors

High levels of inaccuracy due to signal distortion in an indoor environment

Need for expensive equipment

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Research Objectives

New algorithm - improve location accuracy indoorsExtend the algorithm: Models of Movement history Multiple floors Effective Isotropic Radiated Power (EIRP) of the

Access Points (AP)

PlaceLab software platform - large 802.11 network

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Criteria for Evaluation

Accuracy and precision Yield and Consistency Overhead Power Consumption Latency Roll out and operating costs

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Classification of Positioning Infrastructures Integrated and Standalone Systems

E.g. Cellular vs. GPS

Terminal and network based positioning systems E.g. Laptop tracking vs. Wi-Fi tag tracking

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Positioning Infrastructures Satellite Infrastructures

Cellular Infrastructures

Indoor Infrastructures

Various Positioning Infrastructures

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Basic Positioning Methods

Proximity sensing Lateration

Circular Lateration Hyperbolic Lateration

Angulation Dead Reckoning Pattern matching Hybrid Approaches Received Signal Strength (RSS)

Circular lateration in 2D

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Indoor positioning systems Proximity Sensing WLAN Fingerprinting RFID Infrared Ultrasound

Fingerprinting with 3 Access Points

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Market Leading Indoor Positioning Systems Ekahau Real Time Location Systems (RTLS) Trapeze Networks Location Appliance LA200 PlaceLab – Intel Research Aeroscout Ubisense

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Ekahau Real Time Location Systems (RTLS) The current market

leader Wi-Fi tags may be

tracked in an indoor environment

RTLS may be used to provide historical movement data

Ekahau Finder Application

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Trapeze Networks Location Appliance LA200 Similar to Ekahau

except that it is a Network only based approach

Wi-Fi Fingerprints are taken at the access points

Historical data will also be collected using LA200

LA200 Dashboard showing locations of AP fingerprints

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PlaceLab – Intel Research

PlaceLab (LaMarca et al. 2005) consists of three key elements:Radio beacons in environmentDatabases holding beacon location informationPlaceLab clients - estimate their location from data PlaceLab Architecture (LaMarca et al. 2005)

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Effective Isotropic Radiated Power (EIRP)

Tsoulos (1999) defines EIRP as “the radiated power from the antenna referenced to a theoretical point source”.

GSM networks – EIRP - median accuracy - 71.3m improvement (Hubrich and Curran 2007)

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Project proposal

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Predictions based on History of Movement

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Data Mining and Machine Learning techniquesData Mining Cross Industry

Standard Process for Data Mining (CRISP-DM)

Association Rules (AR)

Machine Learning (ML) Inductive Learning Reinforcement learning Bayes network learning Continuous time

Markov Chains (CTMCs)

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Software Analysis

Software:Ekahau/LA200Clementine Data Mining PackagePlaceLab-Eclipse IDEHUGIN Bayesian CPN toolWEKA Toolkit/Matlab – Bayes Net Toolbox

Hardware: Wi-Fi TagsWireless NICsWi-Fi Access Points

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Comparison to other work

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Project Schedule

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Conclusion

More accurate algorithm for Wi-Fi positioning in indoor environment

History of movement - predict most likely paths traveled by Wi-Fi enabled users

Movement history & EIRP - new method - location estimates

Test in creative technologies software application

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Questions