Innovation on Indoor GeoLocalization Applications Based on Artificial Intelligence

17-19th of May, 2015 @ Elgazala Technopark

Elgazala Innovation Day 2015

Presented by:Noura BACCAR

n.baccar@cynapsys.deSupervised by:

Pr. Ridha BOUALLEGUE

Outlines

1. Introduction2. Indoor Geo-localization Techniques 3. Indoor Geo-localization Technologies4. Artificial intelligence in Indoor Geo-

localization Systems5. Innovative fuzzy logic based solutions 6. Conclusion & Perspectives

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GeoLocalization is natural

?????

To where…?

From Where…?

Right or wrong...??

Which direction…??

Where am I?

Where are You?

Introduction

GPS

GLONASS

GALILEO

Beidou

Why notGNSS??

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Introduction

6

Introduction

• Indoor GeoLocalization• Simply… An indoor geolocation system is a

geolocation system that operates indoors.• Indoor geolocation systems have emerged as

a means to render localization and navigation inside buildings to people and personnel

• Do you know the percentage of time people spend indoor????

90%

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Concepts

Geo-Localizing

Navigating

Tracking

Guiding

LBS

Mapping

Positioning

Indoor Tracking System

Indoor Location Based Service

Indoor Mapping

Indoor Positioning

Indoor Navigation

Indoor Personal Localization System (IPLS) Real Time Localization System (RTLS)

Indoor Location Market

In-Location Alliance

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Nao Campus

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Statistics• Market research company, ABI Research, estimates the indoor

mapping technology market will be worth $4 billion by 2018.• Another research conducted by a market analysis firm Opus

Research, predicts the market for indoor location and place based marketing and advertising to surpass $10 billion by 2018.

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Research problematic

• Standardization of LBS• Optimization of localization techniques• Indoor Mapping • Complexity of indoor environment• Energy harvesting in location processing• Confidentiality and privacy of positionning

data

2. Geo-localization Techniques

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Techniques of Geo-localization

Power strenght parameter: RSSI

Angle of Arrival parameter: AoA

Time Parameters: ToA, TDoA

Others: NFER( mesure of magnetic field) ...

Radio Signal Strength

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ZC

ZEDZR1

ZR2

ZR3

Affichage

RSSI2

RSSI3

RSSI1

d1

d3d2 (x,y)

• Trilateration is used to estimate the location of the unknown node

• 2D Trilateration• 3D Trilateration

Triangulation

Distances (d1,d2,d3) are measured by an RSSI signal.

Radio Signal Strength Indicator

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Classifications of Localization Techniques

Centralized vs Distributed

Anchor-free vs Anchor-based

Range-free vs Range-based

Mobile vs Stationary

• Range-Free– Local technics– Hop-Counting technics

• Range-Based– Received Signal Strength

Indicator (RSSI)• Attenuation• RF signal

– Time of Arrival (ToA) • time of flight

– Time Difference of Arrival (TDoA)

• requires time synchronization

• electromagnetic (light, RF, microwave)

• sound (acoustic, ultrasound)

– Angle of Arrival (AoA)• RF signal

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Localization in WSN

Distributed

Beacon

-based

distributed algorithm

sDiffusion

Bounding box

Gradient

Relaxation

-based

distributed algorithm

s

Coordinate system stitching based

distributed algorithm

s

Hybrid

localization algorithm

s

Interferometri

c ranging

based

localization

Error

propagatio

n aware localization

Centralized

Classifications of Localization Techniques

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Optimization techniques

• Kalman Filter• Advanced/Reduced Kalman Filter• Annealing algorithm• Data fusion and clustering algorithms• Particular filters• ….

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Art’s state

• Various comparative works exist in literature presents a comparative analysis between different localization techniques[1].

• Most of them presents every localization technique aside• But some combines two techniques such as multidimensional

scaling (MDS) and proximity based map (PDM) [2] or MDS and Ad-hoc Positioning System (APS)[3].

• Interferometric ranging based scheme localization has been proposed in [4], [5], [6].

• Error propagation, flip ambiguity because of channel fading and noise corruption are more and more discussed [7]and merging to the top.

3. Geo-localization Technologies

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3. Technologies of Geo-localization

WLAN WiFi

RFID Passive and active tags

WSN

Zigbee, Bluetooth, LowBluetooth

Infrared

Camera-

based

Alien, Tagsys…

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Technologies of Geo-localization

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Bibliographics• Wireless Sensors Networks(infrared, ultrasound, sound sensors, magnetic

field sensors) (Samama 2008; Haverinen & Kemppainen 2009; Bird & Arden 2011).

• Wireless Local Networks(WLAN, RFID et Radar) (Hui Liu et al. 2007; Bouet & dos Santos 2008).

• Mobile Telecommunication Networks(GSM, UMTS) (Samama 2008).• Additionnal systems, like INS (accelerometer, odometer, magnetometer)

(Hui Liu et al. 2007; Mautz 2009) and hybrid systems (inertial sensors intgrated with GNSS systems).

• UWB systems (Ultra Wide Band) (Gigl et al. 2007; Cemin Zhang et al. 2006; Fujii et al. 2007).

• Optical Systems (based on image processing) (Mautz & Tilch 2011).• Approaches SLAM (Simultaneous Localization and Mapping) (Mourikis and

Roumeliotis 2004).

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Localization SystemsLocalization

System

Ultrasound Radiofrequency Infrared

DistancesMeasurement Fingerprinting

AnglesMeasurement

PoA AoARSSIToATDoA

Cartography TriangulationTrilateration

Technology

Principe

Method

Technique

System Overview

4. Artificial Intelligence in Indoor Geo-localization Systems

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Indoor Fuzzy GeoLocalisation Systems

• Fuzzy locating is a rough but reliable method based on appropriate measuring technology for estimating a location of an object

Crisp LocationLinguistic Location

X, Y, Z, Angle°Right, left, RoomA…

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How does it work?

fuzzy locating determines the radial distances between entities involved in an operational process • reduces the required accuracy of

measurement to basic qualities of close, near or far and to relations simple as in or out.

• Such segregation shall be achieved with high reliability.

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ITU Site-general model for indoor propagation

• The indoor transmission loss model has the following form: Ltotal= 20 log10f+N log10d+Lf (n) – 28 dB where:• N : distance power loss coefficient; (N = 30)• f : frequency (MHz); (f = 2400 MHz)• d : separation distance (m) between the base station

and portable terminal (where d> 1 m);• Lf : floor penetration loss factor (dB); (Lf = 14)• n : number of floors between base station and portable

terminal (n 1). (n = 1)

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Fuzzy concept

• The Fuzzy modeling research field is divided into two sections:

• linguistic fuzzy modeling that is focused on interpretability, mainly the Mamdani model

• precise fuzzy modeling that is focused on accuracy, mainly the Takagi-Sugeno-Kang (TSK) model.

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Strategic Approaches

• 1st approach: developing a signaling system and a network infrastructure of location measuring units– Testbed of WSN

• Platform WSN• Zigbee communication

• 2nd approach: Using an existing wireless network infrastructure– WiFi Communication

• WiFi Fingerprinting + INS of the smart phone

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Fuzzy Fingerprinting

During the offline phase the fingerprints are generated and stored in the database. The usual method is to move a sensor node through the environment for taking measurements at known positions.If the fingerprint contains RSSI measurements fixed anchors are installed in the environment for referencing the RSSI measurements. Naturally the positions of the anchors should not change during the online phase.

Offline Phase

In the online phase a new measurement set called Son is generated, which will be used for searching the correct fingerprints from the database.

Online Phase

Fuzzy Fingerprinting

DBRSSI2

RSSI3

RSSI1FLI1 (RSS11,RSS12,RSS13)

FLI2 (RSS21,RSS22,RSS23)

… …

FLIn (RSS21,RSS22,RSS23)

?? (RSS21,RSS22,RSS23)

User

Inference Engine FLI

Location estimation

Online Phase

Learning Phase

IT2-Fuzzy processing

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IT2 Fuzzy System

5. Innovative fuzzy logic based solutions

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Experimental testbed (STM32W-RFCKIT)

ZC

ZigBee Boards of STMicroelectronics.

ZEDZR

34

Zigbee

Zigbee

zigzag bee

Zigbee Alliance

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Results

Table 1. Comparative Results of different tests

Imp-Method

Agg method

Range of

low

Range of

Medium

Range of

High

% of correct estimation

% fail Elapsed time (s)

[1] Test1: T1 FLS 'min' [2] Max [3] [-100 -88] [4] [-93 -80] [5] [-84 -70] [6] 75.55% [7] 24.44% [8] 2.329s

Test 2: T1 FLS 'min' Prob [-100 -88] [-93 -80] [-84 -70] 77.77% 22.22% 2.533

Test 3: T1 FLS min Prob [-100 -88] [-94 -77] [-84 -70] 87.77% 12.22% 2.74

Test 4: IT2 Prod Prob [-5 -98 0.1] [-5 -86 0.1] [-5 -77 0.1] 91.11% 8.88% 3.73s

Test 5: IT2 Prod Prob [-5 -94 0.1] [-5 -86 0.1] [-5 -77 0.1] 93.33% 6.66% 3.88s

Localization Algorithms % SuccessIT2 FLS 93.33%Fuzzy Based System 81,21%RepTree 79,71%

The 11th International Wireless Communications & MobileComputing Conference" (IWCMC 2015) : 24 - 27 Aout 2015 in Dubrovnik, Croatia

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Mobile app Architecture

A combined WiFi + INS protocol

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Why Wifi?

• Cheap, ubiquitoushardware• Indoor and outdoorcoverage• Privacy observant

Inertial Navigation Sensors (INS)

Accelerometer BarometerGyroscope

Activity classification

Step detection

Length step estimation

Amplitude estimation

Pedestrian Dead Reckoning (PDR)

Direction of Navigation

Sensors Smartphone

Navigation guiding

System Architecture

18/04/2023 40

Server Architecture

Cartographic Server

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Android App

Success Rate up to 90%

for zoom level

6. Conclusion & Perspectives

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Conclusion

• Geo-localization is Fuzzy Innovative Domain:• Advanced techniques of Fuzzy logic in localization has

proved better positioning results• Innovation in location system’s architecture is a wined

challenge• The system rely on the administrator experiment and thus

prior effort is needed• Unchangeable anchor’s infrastructure• Cynapsys consider the high level of competitiveness in such

field and rely on its Innovative solutions to gain the best position in the market

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Perspective

• Crowdsourcing• Handle Rssi fluctuations (through the footprint

of uncertainty (FOU) of the IT2FLS)• Linguistic rule-based• Precision highness on localization• Reduce the High computational cost

compared to Type 1 FLS

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Thank You For Your Attention

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Researcher, Research and Development Department at Cynapsys IT hotspot, Tunisia

Noura BACCAR was born in Monastir, Tunisia. She received the Engineering degree in Mechatronics in 2010 and the M.Sc. degree in Telecommunicating and intelligent systems in 2011 from the National Engineering School of Sousse (ENISo), Tunisia. She is currently working toward the Ph.D. Degree in Telecommunication systems at the National Engineering School of Tunis (ENIT) within the research Laboratory of Innovation, Communication and Cooperative Mobiles (Innov’COM) at the High School of Telecommunication of Tunis (SUP’com), Tunisia. Receiving full scholarship funding “Mobidoc” financed by the European Union (EU) within the framework of the PASRI program and partially supported by Cynapsys IT Enterprise, she is researcher within the department of Research and Development of Cynapsys IT.Contact:n.baccar@cynapsys.de