LIGHTWEIGHT INDOOR LOCALISATION AND

LINGUISTIC LOCATION AUTHORITY

BY

AKEEM OLOWOLAYEMO

A thesis submitted in fulfilment of the requirement for the

degree of Doctor of Philosophy in Computer Science

Kulliyyah of Information & Communication Technology

International Islamic University Malaysia

AUGUST 2015

ii

ABSTRACT

Indoor positioning and navigation unlike outdoor positioning requires different

techniques apart from the classical geometric based approached utilizing satellite

communications. This is due to the fact that satellite signal reception is poor in indoor

environment. Approaches to indoor localization using Received Signal Strengths

(RSS) are generally based on signal propagation models or location fingerprinting

methods, using different algorithms. All algorithms whether applied on signal

propagation models or location fingerprinting can be classified as heavyweight or

lightweight algorithms. Heavyweight algorithms generally have better accuracies but

require rigorous and complex computations thereby place critical strain on processing

power of mobile devices and suffer from location response delay due to the

complexity of the computation and extended time requirement. Lightweight

algorithms are less complex and do not require extensive time or processing power

compare to the heavyweight algorithms, however they perform relatively poorer in

accuracy. Lightweight algorithms have been investigated in this thesis for near

heavyweight accuracy and sufficiently accurate for indoor environments. The two

novel algorithms proposed achieve 95% room level accuracy and a maximum update

time of 2 seconds reducing update time considerably. The first one is Fuzzy Weighted

Aggregation of Received Signal Strengths of Wi-Fi signals with Compensated

Weighted Attenuation Factor (CWAF) in the form of fuzzy weighted signal quality

and noise while the second is lightweight localization approach based on the extreme

learning algorithm (ELM), a single hidden layer neural network. For every location

based system requires the representation of the location in effective and efficient

scheme. In order to provide suitable location authority for indoor positioning

approaches proposed, this work introduced a perception-based linguistic approach to

locations relative to landmarks to extend present location authority with a view to

making it more user-friendly. The idea is due to the realisation that people respond to

the question ―where are you?‖ naturally in linguistic forms such as ―I am close to Lab

A‖ rather than ―I am 5m to Lab A‖ etc., which is what entails in most positioning &

navigation devices such as GPS. Therefore, it is argued that positioning and

navigation systems should incorporate linguistic description of distances rather than

the present quantitative distances, such as 5m to Lab A. Three fuzzy schemes based on

α-cut, Gaussian and enhanced interval type-2 (EIA T2) have been proposed. The first

two gave above 80% accuracy while the third gave around 85% accuracy, given the

subjective validation data elicited from groups of subjects taken from ordinary mobile

users, experts and blind subjects. The two sets of algorithms compared favourably

with other traditional models such as Bayesian, Decision Tree, and ANFIS Type-1 &

Type-2. The EIA shows the best results in terms of accuracy though it requires more

processing power due to complexity than that of α-cuts and Gaussian models which

are less accurate but more efficient computationally.

iii

A

(RSS)

(CWAF)

(ELM)

GPS

. A α-cut (EIA T2)

ANFIS EIA a-cut

.

iv

APPROVAL PAGE

The thesis of Akeem Olowolayemo has been approved by the following:

_________________________

Abu Osman Md Tap

Supervisor

__________________________

Teddy Mantoro

Co-Supervisor

_________________________

Normaziah Abdul Aziz

Internal Examiner

_________________________

Mohamed Essaaidi

External Examiner

_________________________

Mustafa Mat Deris

External Examiner

_________________________

Mustafa Omar Mohammed

Chairman

v

DECLARATION

I hereby declare that this thesis is the result of my learned investigations, except where

otherwise stated. I also declare that it has not been previously or concurrently

submitted as a whole for any other degrees at IIUM or other institutions.

Akeem Olowolayemo

Signature …………………………. Date ………….......………….

vi

COPYRIGHT PAGE

INTERNATIONAL ISLAMIC UNIVERSITY MALAYSIA

DECLARATION OF COPYRIGHT AND AFFIRMATION OF

FAIR USE OF UNPUBLISHED RESEARCH

Copyright © 2015 by International Islamic University Malaysia. All rights reserved.

LIGHTWEIGHT INDOOR LOCALISATION AND LINGUISTIC

LOCATION AUTHORITY

No part of this unpublished research may be reproduced, stored in a retrieval system,

or transmitted, in any form or by any means, electronic, mechanical, photocopying,

recording or otherwise without prior written permission of the copyright holder

except as provided below.

1. Any material contained in or derived from this unpublished research may

only be used by others in their writing with due acknowledgement.

2. IIUM or its library will have the right to make and transmit copies (print or

electronic) for institutional and academic purposes.

3. The IIUM library will have the right to make, store in a retrieval system and

supply copies of this unpublished research if requested by other universities

and research libraries.

Affirmed by Akeem Olowolayemo

……………………… …………………

Signature Date

vii

ACKNOWLEDGEMENTS

All praises is due to Allah, the Lord of the World, the Entirely Merciful and the

Especially Merciful. May His peace, blessings and choicest benediction be upon the

noblest of mankind, the Prophet Muhammad (PBUH) and his entire family, his

companions and all who follow in their footsteps till the day of reckoning.

Words are indeed inadequate to express my profound gratitude to my

supervisor and mentor, Prof Dr. Abu Osman Md Tap, for his relentless efforts in

motivating, guiding and supporting me in every form morally possible during the

course of this study. This work would not have been accomplished without his

conscientious guidance and support. May Allah reward him abundantly and grant him

long life in good health, Amin.

I am also indeed very grateful to my co-supervisor, Prof Dr. Teddy Mantoro

for his suggestion from inception, invaluable advice and constructive contributions in

making this research into fruition. May Allah reward him abundantly.

I am also grateful to all academic advisors, mentors and well-wishers who were

concerned and offered advice in different forms that were useful to me in the process

of conducting and completing this work. Specifically, I am indeed very grateful to

Prof Abubakar Abefe Sanusi whose support and advice were instrumental to pursuing

this graduate programme in the first place. I will like to appreciate Prof Momoh

Salami for his fatherly support throughout the entire journey. I will also like to

acknowledge Prof Adam Shuaimi, Prof Mohamed Ridza Wahiddin, Prof Abdul

Rahman AbdulWahab, Assoc Prof Dr Imad Fahri, Assoc Prof Dr Messikh Az Eddine,

Prof Asadullah Shah, Prof Husnayati Hussin, Assoc Prof Dr Norshidah Mohamed,

Assoc Prof Dr AbdulRahman Ahlan, Assoc Prof Dr Murni Mahmud, Assoc Prof Dr

Normaziah Abdul Aziz, Assoc Prof Dr Mira Kartiwi, Dr Norsaremah Salleh, Dr Muhd

Rosydi Muhammad, Dr Mior Nasir Mior Nazri, Dr Noor Azizah Mohamadali, Dr

Zainatul Shima Abdullah, Dr Shuhaili Talib, Dr Mohd Izzuddin Moh Tamrin, Dr

Sherzod Turaev, Dr Muhamed Razi Muhamed Jalaldeen and Dr Hawira Sakti Yaacob

and all KICT family, all of whom were very supportive in different forms and offered

advice and words of encouragement to me when the going was very tough. May Allah

swt reward you all for your concerns, suggestions and motivation and encouragement

during this journey.

I will also like to show appreciation to all administrative staffs who provided

some form of assistance to me during the course of this work. Specifically, I will like

to acknowledge Mdm Sarimah Yahaya, Mdm Kamsiah Mohamed, Sr Narieta Bukhari,

Sr Pauziah Abas, Br Halmi Husain, Br Mhd Firdaus Abdullah, Br Aminudin Resat, Sr

Shahidah Mahbob, Sr Nurasnida Nurdin, Br Kamal Najib, Br Nurusan Jamree Yacob,

Sr Haryianie Marni, Br Mohd Affindee Haji Hamzah and Br Hafizee Razak. I say a

big thank you to you all.

Also worthy of special mention is my one and only Egbon, Dr Hakeem

Olawale Amuda, who ensured I was both financially and academically comfortable on

viii

my arrival here. I am also specifically indebted to Dr Sunday Olusanya Olatunji for

his support. I will also like to mention Dr Muritala Abioye Mustapha, Dr Musodiq

Bello, Dr Musa Aibinu, Deji Aderibigbe, Dr Fatai Anifowose, Dr Rasheedah

Olanrewaju, Dr Abideen Adewale, Dr Ishaq Oyebisi, Dr (Mrs) Misturah Sanni, Dr

Tunji Odejobi, Dr Babajide Afolabi, Prof Owolarafe, Musa Afeiye, Hamed Wasiu,

Engr Kamil Bello, Dr Ibrahim Eleyinla, Dr Hafiz Musa, Br Mahdi Umar Muhammad,

Luqman Salami, Mutiu Salami, Monsuru Saka, Ayodele Lasisi, Dr Lamin Sylla and

Mrs Nasir for their support throughout this journey.

I am also indebted to all my colleagues who have in one way or the other

offered some form of motivations, assistance, tips, or materials, all of which were

handy in making this research a reality. You are too numerous to provide an

exhaustive list. But specifically, I will like to mention Sharyar Wani, Dini Handayani,

Amjad Muhammad, Elbara Eldawi Elnour, Mahmud Ibrahim, Dr Idwayati Husein,

AbdulQayyum, Dr Wafaa Shams, Shakirat Raji, Dr Adamu Abubakar, Dr Abubakar

Folorunso, Iya Researcher Dr Nafisah Adeyemi, Dr Mboni Ruzegea, Dr Ikhlas Fuad

Zamzami, Rumeysa Cakmak and her friends for rendering the location maps, my

friend Selvarani who helped with blind subjects‘ data collection. I am indeed grateful

to you all. I also recognise all my students in KICT for your prayers and supports.

May Allaah swt bless you all.

To my immediate family, Egbon Lateef, my only Anti mi, Egbon Amir, my

Aburo the Defender, Hajji Moruf, my four blood wives, and my cousins, love you all

for your prayers, supports and for always being there. May Allah continue to unite our

family and assist all of us in all our endeavours.

To my Muslim brothers, who stood by me through the thick and thin, I am not

mentioning your names, Allah knows you all, and you know yourself too. May Allah

swt be your support always, in this world and the hereafter.

Lastly, to the Queen, the boyz and Princess Suzzy, I say sorry for all the pains I

caused during this period.

Oh Allaah, bless my parents for they tried within their humble capabilities to

give me the best, have mercy on them in their graves, and raise them among the

fortunate on the day that nothing profits except Your mercy, You are the All-forgiven,

the Entirely Merciful. Amin

ix

TABLE OF CONTENTS

Abstract .................................................................................................................... ii Abstract in Arabic .................................................................................................... iii Approval Page .......................................................................................................... iv Declaration ............................................................................................................... v Copyright Page ......................................................................................................... vi

Acknowledgements .................................................................................................. vii List of Tables ........................................................................................................... xii

List of Figures .......................................................................................................... xiii List of Abbreviations ............................................................................................... xv List of Symbols ........................................................................................................ xvii

CHAPTER ONE: INTRODUCTION .................................................................. 1 1.1 Overview ................................................................................................ 1 1.2 Problem Statement ................................................................................. 4 1.3 Research Philosophy .............................................................................. 6 1.4 Research Objectives ............................................................................... 9

1.5 Scope of Study ....................................................................................... 10 1.6 Expected Results and Contribution ........................................................ 11

1.7 Research Methodology ........................................................................... 12 1.8 Thesis Organisation ................................................................................ 15

CHAPTER TWO: LOCATION AWARENESS AND LOCATION

AUTHORITY ......................................................................................................... 16 2.1 Introduction ............................................................................................ 16 2.2 Ubiquitous Computing ........................................................................... 16

2.2 Location Awareness ............................................................................... 19 2.2.1 Context Awareness....................................................................... 19 2.2.2 Location Awareness ..................................................................... 20

2.2.3 Evaluation of Positioning Technologies ...................................... 21

2.2.4 Location Fingerprinting ............................................................... 23

2.2.5 Propagation Laws ......................................................................... 29 2.2.6 Positioning Levels of Orientations ............................................... 33

2.2.7 Accuracy and Coverage of Wireless Positioning

Technologies. ............................................................................... 37 2.3 Location Authority ................................................................................. 42

2.3.1 Geocentric coordinates (X, Y, Z) ................................................. 43 2.3.2 Topological Referencing .............................................................. 44

2.3.3 Qualitative spatial reasoning ........................................................ 45 2.3.4 Applications of Fuzzy logic to Indoor Localization. ................... 48 2.3.5 Applications of Fuzzy logic to Qualitative reasoning in spatial

Analysis. ...................................................................................... 51 2.3.6 Landmark-Based Localization ..................................................... 53

2.3.7 Perception-Based Localisation ..................................................... 54 2.4 Summary ................................................................................................ 56

x

CHAPTER THREE: MATHEMATICAL MODELS FOR

DEVELOPMENT OF INDOOR POSITIONING .............................................. 59 3.1 Introduction ............................................................................................ 59 3.2 Fuzzy Set Theory ................................................................................... 60

3.3 The Fuzzy Logic Based Compensated Weighted Positioning

Algorithm .............................................................................................. 62 3.3.1 Regression model variables formulation. ..................................... 65

3.4 Fuzzy Membership Functions ................................................................ 70 3.4.1 Fuzzy Rules .................................................................................. 71

3.4.2 Fuzzy Inference ............................................................................ 72 3.4.3 Localisation Membership Function.............................................. 73

3.5 Experimental Setup ................................................................................ 74 3.6 Extreme Learning Machines .................................................................. 78

3.6.1 Artificial Neural Network ............................................................ 78 3.6.2 Extreme Learning Machines Algorithm....................................... 80 3.6.3 How Extreme Learning Machine Algorithm Works .................... 82

3.6.4 Development of Extreme Learning Based Indoor Localisation

based on Location Fingerprinting ................................................ 84 3.6.5 Experimental Setup ...................................................................... 86 3.6.6 Simulation setting ........................................................................ 86

3.7 Summary ................................................................................................ 87

CHAPTER FOUR: EVALUATION OF POSITIONING MODELS ............... 88 4.1 Introduction ............................................................................................ 88

4.2 Positioning Performance of Signal Propagating Model, Using

Fuzzy-Based Compensated Approach. ................................................. 88 4.3.1 Performance Indicator & Results ................................................. 89

4.2 Performance Evaluation of Indoor Fingerprinting Positioning Based

on Extreme Learning Algorithms (ELM). ............................................ 95

4.3 Performance Indicator & Results ........................................................... 96 4.4 Summary ................................................................................................ 100

CHAPTER FIVE: DEVELOPMENT OF LINGUISTIC LOCATION

AUTHORITY AND FUZZY BASED UNCERTAINTY MANAGEMENT .... 101 5.1 Introduction ............................................................................................ 101 5.2 Location Authority ................................................................................. 102

5.3 Understanding Distance Linguistic Variable. ........................................ 105 5.4 Fuzzy Alpha-Cut: Horizontal Representation of Fuzzy sets. ................. 106 5.5 Type-2 Fuzzy Sets and Systems ............................................................. 108 5.6 Computing With Words (Cww) & Perceptual Computing (Per-C) ....... 109

5.6.1 Computing with Words (CWW) .................................................. 109

5.6.2 Interval Approach......................................................................... 111 5.7 Methodology .......................................................................................... 130 5.8 Detailed Elicitation ................................................................................. 132 5.9 Subjects .................................................................................................. 135

5.10 Summary .............................................................................................. 138

xi

CHAPTER SIX: EVALUATION OF LINGUISTIC LOCATION

AUTHORITY MODELS ....................................................................................... 139 6.1 Introduction ............................................................................................ 139 6.2 Performance of Linguistic Location Authority Model ........................... 139

6.3 Validation Process & Nested Sampling. ................................................ 146 6.4 Gaussian Linguistic Model .................................................................... 149 6.5 Fuzzy α-Cut Linguistic Membership Function ...................................... 151 6.6 Performance of Fuzzy Enhanced Interval Approach to Linguistic

Location Authority. ............................................................................... 154

6.7 Model Validation ................................................................................... 155 6.8 Indoor Navigation Android Application ................................................ 156

6.8.1 Algorithm ..................................................................................... 156 6.9 Summary ................................................................................................ 157

CHAPTER SEVEN: CONCLUSIONS AND RECOMMENDATION ............. 159 7.1 Introduction ............................................................................................ 159

7.2 Contributions to Knowledge .................................................................. 161 7.3 Future Work & Recommendations ........................................................ 163

REFERENCES ....................................................................................................... 165

APPENDIX A FUZZY COMPENSATED WI-FI SIGNAL STRENGTH

INDOOR POSITIONING ALGORITM ................................... 183

APPENDIX B ELM ALGORITHM FOR INDOOR LOCALISATION .......... 185

APPENDIX C ANDROID-BASED LOCATION FINGERPRINTING

SIGNAL CAPTURE APPLICATION ...................................... 192 APPENDIX D LANDMARK-BASED LINGUISTIC LOCATION

AUTHORITY ............................................................................ 193 APPENDIX E A* ALGORITHM ..................................................................... 194

APPENDIX F QUESTIONNAIRE ................................................................... 196 APPENDIX G VALIDATION DATA .............................................................. 202

xii

LIST OF TABLES

Table No. Page No.

2.1 Algorithms used in Signal Fingerprinting models 26

2.2 Norms used in deterministic models (Honkavirta et al., 2009). 28

2.3 Various Algorithms used in Signal Propagation models. 34

2.4 Localization systems with their accuracies and levels of complexity 39

3.1 Fuzzy Rule base for the Compensated Weight 74

3.2 Fuzzy Rule base for the Compensated Weight 86

4.1 Sample per Second Location WiFi Signal Data 91

4.2 Accuracy of the Fuzzy Compensated Localisation Algorithm 92

4.3 Sample Location Data for ELM Indoor Model 96

4.4 Precision of the Proposed Localisation Algorithm 97

5.1 Transformation of the Uniformly Distributed Data Interval 125

5.2 FOU Type (Wu et al., 2012) 125

5.3 Direct Rating Data 137

5.4 Interval Endpoints Data Conversion 137

6.1 Descriptive Data of the Indoor Distances (Experts & Users) 140

6.2 Hypothesis Test between Experts Ordinary Users. 142

6.3 Initial Piecewise Normalized Membership Functions 144

6.4 Comparison between the three levels of Elicitation. 145

6.5 Initial Performance Accuracy of the Models 146

6.6 The FOU of the EIA Type-2 for Linguistic Variables. 155

6.7 Performance Accuracy of the Models. 155

xiii

LIST OF FIGURES

Figure No. Page No.

1.1 Lightweight Indoor positioning and Linguistic Location Authority

methodology flow chart 14

2.1 Components of Ubiquitous Computing (Begole, 2010) 18

2.2 Location Fingerprinting phases 24

2.3 Location Fingerprinting phases 25

2.4 Traditional RSSI Positioning phases 32

2.5 An illustration of the geocentric coordinate system (Knippers, 2009). 43

3.1 RSSI Propagation Model 67

3.2 Location I – Restricted Computer Lab with Minimal Obstacles 75

3.3 Location II – Unrestricted Computer Lab with Minimal Obstacles 75

3.4 Location III – Partitioned Restricted Office Area with Lots of Obstacles 76

4.1 Accuracy of the Proposed Localisation Algorithm 93

4.2 Precision of the Proposed Localisation Algorithm 94

5.1 The Computing with Words paradigm (Mendel, 2007) 109

5.2 Conceptual Structure of the Per-C (Mendel, 2007a) 111

5.3 Double-ended slider used to collect interval endpoint data 112

5.4 The Data Part of Interval Approach (Liu & Mendel, 2008) 116

5.5 A Box and Whisker (MVP, 2014) 118

5.6 The Fuzzy Part of Interval Approach (Liu & Mendel, 2008) 123

5.7 Illustrations of the union of T1 MFs (dashed lines) (Wu et al., 2012). 124

6.1 Initial Gaussian Fuzzy Aggregated Linguistic Model 143

6.2 Normalised Fuzzy Aggregated Linguistic Model 144

xiv

6.3 Box and Whisker plots of Phase II Dataset 148

6.4 Gaussian Linguistic Model 150

6.5 Normalised Fuzzy α-cut Piecewise Aggregated Linguistic Variables 152

6.6 Enhanced Interval Approach to Linguistic Variables 154

xv

LIST OF ABBREVIATIONS

APs Access Points

A-GPS Assisted GPS

ANN Artificial Neural Networks

AOA Angle of Arrival

BEIDOUTM

Chinese Global Navigation Satellite System

CIO Conventional International Origin

CO Cell of Origin

CWAF Compensated Weighted Attenuation Factor

CWW Computing with Words

EIA Enhanced Interval Approach

E-OTD Enhanced Observed Time Difference

ELM Extreme Learning Algorithm

FAF Floor Attenuation Factor

FNN Fuzzy Neural Network

FOU Footprint of Uncertainty

FR Fuzzy Regression

IT2-EIA Fuzzy type-2 Enhanced Interval Approach

GALILEOTM

Russian‘s Global Navigation Satellite System

GNSS Global Navigation Satellite Systems

GPS Global Positioning Systems

GRNN Generalized Regression Neural Network

IA Interval Approach

IQR Interquartile Range

IT Information Technology

LMF Lower Membership Function

MAP Maximum-a-Posterior

ML Maximum-Likelihood

MF Membership Function

MFN Multilayered Feed Forward Neural Network

MLR Multiple Linear Regression

MU Mobile Unit.

Per-C Perceptual Computer

RFID Radio Frequency Identification

PG Path Gain

PR Precision

rmse Root Mean Square Error

RP Reception Point.

RSS Received Signal Strength

RSSI Received Signal Strength Indicator

SABPN Simulated-Annealing

SQ Signal Quality

SS Signal Strength

SVM Support Vector Machine

TDOA Time Difference of Arrival

TOA Time of Arrival

xvi

T1 FS Type-1 Fuzzy Sets

T2 FS Type-2 Fuzzy Sets

UMF Upper MF

VCC Ventana Coefficient of Consensus.

VCC' Modified Ventana Coefficient of Consensus.

WAF wall attenuation factor

WGS World Geodetic System

WLAN Wireless LAN

ήKNN ήK-Nearest Neighbour

xvii

LIST OF SYMBOLS

di,j distances between each of the beacon and the MU

ki number of walls

li a particular type of wall

Aα. alpha cuts (α-cuts)

d distance between the transmission and receiving source

ei error in positioning computation

Gr gains of the receiving antennas

Gt gains of the transmitting

h1; h2 the heights of the transmitting and receiving terminal antennas

SJ (A , Jaccard similarity

li linguistic labels

µA (x) membership function

mL mean of interval endpoint data

sL standard deviation of interval endpoint data

T error distance within limit

F error distance beyond limit

PR Precision

n path loss exponent; power decay index path; loss coefficient.

PGdB. power gains in decibels

Pr total power delivered to the receiving antenna.

Pt total power delivered to the transmission antenna

r2 coefficient of determination

r correlation coefficient

λ signal‘s wavelength

1

CHAPTER ONE

INTRODUCTION

1.1 OVERVIEW

The prime goal of pervasive computing is the concept of Information Technology (IT)

services every time and everywhere, by any means, with little intervention and

restriction on mobile users (Weiser, 1999; Stanton, 2001). The main idea in pervasive

computing is provisioning of IT-enabled services that are available and responsive to

users‘ needs in mobile environment by taking advantage of their context information.

This emphasizes the significant role that context aware computing constitute to

mobility services management.

Location-based service is the central motivation for context awareness.

Providing mobile users with adequate information based on reference to their location

and context information has been very crucial for today‘s mobile services and

management. Outdoor positioning, an integral component of positioning systems, rely

largely on Global Navigation Satellite Systems (GNSS) which include GALILEOTM

,

Russian‘s Global Navigation Satellite System (GLONASS , BEIDOUTM

, Chinese

Global Navigation Satellite System and most especially Global Positioning Systems

(GPS); has been adopted in several applications (Mautz, 2008) and established to be

adequate in outdoor environments. However, in indoors and undergrounds as well as

in urban environments, where there exists a lot of high walls and buildings, its

performance is adversely impacted. Current works to enhance positioning accuracies

include inter-nodal ranges capability in sensors, using improved signal strengths,

acceleration or angles for localization, higher sensitivity algorithms for signal

acquisition and tracking in harsh environments, as well as combined usage or

2

integration of different sensor systems and data sources (Mautz, 2008). The diversity

of available sensors has led to a variety of localization schemes such as triangulation,

trilateration, centroids, hyperbolic localization, and data/place matching localization

based on history data.

Location awareness is the most important component of context awareness

systems (Mantoro & Johnson, 2003). There have been tremendous attempts to better

approximate locations of mobile users (Honkavirta, et al., 2009; Kaemarungsi &

Krishnamurthy, 2004; Reyero & Delisle, 2008; Wallbaum, 2006; Kumar et al., 2006).

These are due largely to the ever growing need for better positioning for improved

location-based services management. Different approaches have been used and

proposed in the literature. Work in indoor positioning so far broadly relies either on

signal propagation models or location fingerprinting. Location fingerprinting is the

positioning of users based on differential signal attributes at different locations rather

than computing the distance between the signal transmitting points, usually the access

point, and mobile device terminal peculiar to propagation models or other network-

based approaches. The received signal strength is then compared to location history

data, databased in a radio map. On one hand, the latter approach is claimed to have

given better accuracy than the former, however it requires an added overhead of

surveying history data of a calibration of every indoor environment before the

approach can be used. Besides, if any of the mobile Access Points (APs) included in

the surveyed history data is down for any reason, the result of the location

fingerprinting approach is impacted.

All location-based computing systems make references to locations, assuming

a specific location authority. Location authority, any set of referents for location

references used to describe locations for location-based services could be geometrical,

3

topological or hybrid (Shafer, 2003). In geometrical such as used in GPS, the World

Geodetic System, based on WGS84, has remained the standard location authority for

use in cartography, geodesy, and navigation. In geometrical, the underlying approach

is Euclidean, though easier to develop and use for computers and easily manipulated

graphically for humans, it is however deficient in conveying intrinsic meaning to

ordinary humans. Topological on the other hand, can be expressed in hierarchical,

descriptive or symbolic form, such as room name, in a particular floor, in a particular

building or expressed as a displacement from some landmarks (Mantoro, 2006). It

expresses location as a set of atoms in the location authority which are more

meaningful to humans yet lack universality at present and are more complex to

implement. Hybrid location authority combines both approaches and it is the

approach used by most powerful location authorities (Shafer, 2003).

However, Location Authority standardization is a challenging yet continuous

process. Users refer to location with references to landmarks, such as ―the office

behind the main stairwell‖, more than ―I am on latitude and longitude so, so and so‖,

which are rather complex (Shafer, 2003). Therefore, an approach that could simplify

this complexity and make conveying locations more meaningful especially with

inexactness and imprecision is imperative, thereby making location authority more

user friendly. Fuzzy logic proposed by Zadeh, (1965), an extension and as opposed to

classical crisp logic of ―Yes‖ or ―No‖, ―True‖ or ―False‖ or binary 1 or 0, allows

intermediate truth values between 0 and 1. Using Linguistic variables ―variables

whose values are not numbers but words or sentences in a natural or artificial

language‖(Zadeh, 1973), based on fuzzy logic, allows variability of instances such as

full, high, near etc. to be accommodated instead of the classical crisp values. Zadeh

(1999) proposed the computation involving inexactness and imprecision referred to as

4

Computing with Words. This approach is suitable for computing in which there is

inherent complexity in dealing with imprecise information (Mendel et al., 2010).

Location authority in indoor space using topological approach in which users‘

description and natural references is the inclination and preference to provide a more

friendly location authority would be better resolved using fuzzy Computing with

Words, a system which ―greatly enhances the capability of computational

methodologies to deal with imperfect information, that is, information which in one or

more respects is imprecise, uncertain, incomplete, unreliable, vague or partially true”

(Zadeh, 2009). This is the approach proposed in this research.

1.2 PROBLEM STATEMENT

Location authority is required for every location-based service. Global positioning

systems (GPS) which is often used for location determination, has its efficiency and

suitability restricted to outdoor space, thereby performing poorly in indoor locations

where there are obstructions to signal reception such as tall buildings and walls. Even

despite the promise of indoor GPS from GPS device manufacturers, indoor

localization is still an issue of concern. Furthermore, location depiction and

references in GPS is mainly geometric or coordinate which is insufficient intrinsically

for humans who ―converse, communicate, reason and make rational decisions in an

environment of imprecision, uncertainty, incompleteness of information and partiality

of truth‖ (Mendel et al., 2010).

Again, there is the present framework for location authorities call for the need

to develop new opportunities to create some standard for location referencing if

location-based computing is to be truly ubiquitous (Shafer, 2003). This requirement

necessitates that all various ways in which locations are referenced and used must be

5

well evaluated to develop the possibility of a common framework to accommodate the

systematic co-existence of these variations. It is therefore necessary to explore an

approach towards standardizing location authority while at the same time making

location referencing more meaningful to human perception. Using Computing with

Words, an approach based on fuzzy linguistic approximation, which refers to a

methodology for reasoning, computing and decision making with information

described in natural language (Mendel et al., 2010), offers an appealing approach

suitable for description of human perception of locations relative to known positions

or landmarks. This, if properly investigated might be the required approach for

universality of location authorities and lead to an effective paradigm for location

authority.

The motivation for this is rooted in the fact that when gadgets such as GPS,

that is often used for positioning and location references are used for location updates,

its outputs are presented in numerical forms, such as 20km from/to a landmark. It is

argued that to human mind, the 20km is reprocessed to some linguistic variables such

as close or far, etc. That is, human mind does not measure or cannot ―reckon‖ the

20km in crisp measurement; what it does is to just imagines and linguistically

approximates the quantitative distance into a general linguistic variable such as very

close, close and far. So, the question is why the output from the GPS/other similar

devices can‘t be provided in this form, which is more appropriate and realistic to

humans? That is, presenting location authority in linguistic form which should without

enforcing unrealistic standard in the approximations of distances and in so doing, give

appropriate consideration for human natural understanding? It is therefore strongly

considered that users understanding of distance approximation in linguistic form

6

would be more appropriate and better simplify distances for positioning and location

authority.

It should be recognised however that there is inherent complexity in

representing distance based on human perception. This indicates that there is the need

to address the uncertainty that arise in modelling distances in this form. Sources of

uncertainty in distance approximation include consideration for different scales

(Clementini, et al., 1997; Hernández, et al., 1995) and the size of the space such as

within indoors, outdoor, a suburb, highway distance, distances between cities, from a

given country. What is an agreeable close or far etc. in each scale, and especially in

indoors which is the main focus of this work? What variations is allowed among the

linguistic values, taking into consideration the overlap between such linguistic values

very close and close, between close and intermediate or between intermediate and far

or between far and very far? The accuracies of the positioning devices or the

localization algorithms, uncertain boundaries of places as well as unequal spaces

within places of reference which makes it complex to actually determine the starting

and ending points in any two places under consideration and the precise distances

between any two points. The aforementioned and many more calls for an appraisal of

how linguistic approximation or qualitative distance representation can be

implemented in handheld mobile localization devices.

1.3 RESEARCH PHILOSOPHY

Indoor positioning and navigation have attracted tremendous work in recent times.

Unlike outdoor positioning, indoor localization requires different techniques apart

from the usual geometric based approached based on satellite communications. This is

due to the fact that satellite signal reception is poor in indoor environment.

7

Approaches to indoor localization using received signal strength (RSS) are generally

based on signal propagation models or location fingerprinting methods. Signal

propagation models involve painstaking complexity due to the effects of intervening

walls, floors, equipment, electronic sources and movement in the environment. The

presence of any of these objects has effects on the RSS received which should have

only been attenuated by the distance traversed. These affect the accuracy of such

models. The other approach based on location fingerprinting has been shown to give

better accuracy.

Location fingerprinting approaches rely on pattern matching of signal attributes

collected in real time with that which had been accumulated previously as known

history of such locations. Obviously, there is the task of surveying history data of a

calibration of every indoor environment which becomes unavoidable. Again, as often

the case sometimes, the mobile Access Points (APs) included in the surveyed history

data may be down at the online phase of localisation, the result of the approach then

may not be reliable. All algorithms whether applied on signal propagation models or

location fingerprinting can be classified as heavyweight or lightweight algorithms

depending on the extent of complexity involved in the algorithm and the speed

(Mantoro, 2006). Heavyweight algorithms generally have better accuracies but

require rigorous and complex computations. As a result, they place critical strain on

processing power of mobile devices and suffer from location response delay due to the

complexity of the computation and extended time requirement. Lightweight

algorithms, on the other hand, are relatively poorer in accuracy despite the fact that

they are less complex and do not require extensive time or processing power unlike

the heavyweight algorithms. Therefore, it is pertinent to investigate the likelihood of