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.
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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