Indoor localization techniques using wireless network and artificial neural network

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INDOOR LOCALIZATION TECHNIQUES USING WIRELESS NETWORK AND ARTIFICIAL NEURAL NETWORK

AIMAN BIN IBRAHIM

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INDOOR LOCALIZATION TECHNIQUES USING WIRELESS NETWORK AND ARTIFICIAL NEURAL NETWORK

AIMAN BIN IBRAHIM

A thesis submitted in fulfilment of the requirements for the award of the degree of

Master of Philosophy

Faculty of Electrical Engineering Universiti Teknologi Malaysia

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ACKNOWLEDGEMENT

Preparing this thesis involves cumulative efforts from many kind-hearted individuals providing vast contributions in terms of constructive inputs, tips, advices, knowledge and thoughts essential for completing the research works presented. I would like to address my gratitude to my main supervisor, Assoc. Prof. Ir. Dr. Sharul Kamal Abdul Rahim for the excellent supervision, encouragement, support and guidance. I also wish to express my sincere appreciation to my co-supervisor, Dr. Leow Chee Yen for being the superhero in my heart, whom always been there to help and assist especially at times when I was lost in the dark. My research work would have never been able to shed some daylight without their repeated support and interest. My gratitude extends to the lecturers whom allow me to attend their classes to get some insight on knowledge relating to my research, namely Dr. Norhudah Seman, Prof. Dr. Zuhaimy Ismail, Prof. Dr. Martin Haardt and Prof. Dr. Matthias Hein. I am also thankful to librarians of UTM library and TU Ilmenau library for their assistance in providing relevant literatures supplies throughout my study. I would like to highlight my recognition the Ministry of Higher Education Malaysia for providing scholarship for my study through the MyBrain15 scheme. Additionally, I feel indebted to the Research Management Centre UTM for providing allowances through the research student grant. Furthermore, sincere acknowledgement goes to UTM International Office for organizing plus providing the funds for the student exchange program I attended in Germany. Finally, many thanks to my parents, family members and friends for their love friendship and support which with no doubt helped propelled my research work into smooth sailing towards the end of completion.

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ABSTRACT

This research focuses on improving indoor localization using wireless network and artificial neural network (ANN). This involves strategic study on wireless signal behavior and propagation inside buildings, suitable propagation model to simulate indoor propagation and evaluations on different localization methods such as distance based, direction based, time based and signature based. It has been identified that indoor signal propagation impairments are severe, non-linear and custom to a specific indoor location. To accommodate these impairments, an ANN is proposed to provide a viable solution for indoor location prediction as it learns the location specific parameters during training, and then performs positioning based on the trained data, while being robust to severe and non-linear propagation effects. The versatility of ANN allows different setup and optimization possibilities to affect location prediction capabilities. This research identified the best feedforward backpropagation neural network configuration for the generated simulation data and introduced a new optimization method. Indoor-specific received signal strength data were developed with the Lee’s in-building model according to a custom indoor layout. Simulation work was done to test localization performance with different feedforward backpropagation neural network setups with the generated received signal strength data as input. A data preparation method that converts the received signal strength raw data into average, median, min and max values prior to be fed into the neural network process was carried out. The method managed to increase location prediction performance using feedforward neural network with two hidden layers trained with Bayesian Regularization algorithm producing root mean squared error of 0.0821m, which is 50% better in comparison to existing research work. Additional tests conducted with six different relevant scenarios verified the scheme for localization performance robustness. In conclusion, the research has improved the performance of indoor localization using wireless network and ANN.

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ABSTRAK

Kajian ini memberi tumpuan kepada menambah baik penyetempatan lokasi tertutup menggunakan rangkaian isyarat tanpa wayar dan rangkaian neural tiruan (ANN). Ini merangkumi kajian tentang kelakuan isyarat tanpa wayar dan perambatan dalam bangunan, model perambatan yang sesuai untuk simulasi perambatan dalam kawasan tertutup dan penilaian ke atas kaedah penyetempatan berbeza seperti kaedah berdasarkan jarak, kaedah berdasarkan arah, kaedah berdasarkan masa dan kaedah berdasarkan corak. Telah dikenal pasti bahawa ketaksempurnaan perambatan isyarat di lokasi tertutup adalah teruk, tidak sekata dan unik untuk lokasi tertutup tertentu. Bagi mengatasi ketaksempurnaan ini, satu ANN dicadangkan untuk memberikan penyelesaian baik kepada penyetempatan lokasi tertutup kerana ianya dapat mempelajari maklumat khusus lokasi semasa latihan dan melakukan jangkaan lokasi tertutup berdasarkan data latihan, sambil kekal teguh kepada kesan perambatan teruk dan tidak sekata. Kepelbagaian ANN membolehkan persediaan berbeza dan kemungkinan pengoptimuman bagi mempengaruhi kemampuan ramalan lokasi. Oleh itu, kajian ini mengenal pasti konfigurasi rangkaian neural suap depan rambatan balik terbaik untuk data simulasi yang dibangunkan sambil memperkenalkan kaedah penambahbaikan yang baharu. Data kekuatan isyarat penerima khusus untuk lokasi tertutup telah dibangunkan menggunakan model dalam bangunan Lee berdasarkan susun atur lokasi tertutup unik. Simulasi dijalankan untuk menguji prestasi penyetempatan menggunakan rangkaian neural suap depan rambatan balik berbeza menggunakan data kekuatan isyarat yang telah dibangunkan sebagai input. Satu kaedah penyediaan data yang menukarkan data kekuatan isyarat yang diterima kepada nilai purata, nilai tengah, nilai minimum dan nilai maksimum sebelum dimasukkan ke dalam proses rangkaian neural telah dilaksanakan. Penggunaan kaedah ini berjaya meningkatkan prestasi jangkaan lokasi menggunakan rangkaian neural suap depan rambatan balik dengan dua lapisan tersembunyi yang dilatih dengan algoritma pengaturan Bayesian, menghasilkan ralat min punca kuasa dua pada 0.08210 meter, iaitu 50% lebih baik berbanding kajian terkini. Ujian-ujian tambahan yang dijalankan dengan enam senario berbeza membuktikan keteguhan prestasi jangkaan lokasi menggunakan kaedah yang diperkenalkan dalam kajian ini. Kesimpulannya, kajian ini berjaya menambah baik prestasi penyetempatan lokasi tertutup menggunakan rangkaian isyarat tanpa wayar dan ANN.

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TABLE OF CONTENTS

CHAPTER TITLE PAGE

DECLARATION ii

DEDICATION iii

ACKNOWLEDGEMENT iv

ABSTRACT v

ABSTRAK vi

TABLE OF CONTENTS vii

LIST OF TABLES xi

LIST OF FIGURES xii

LIST OF ABBREVIATIONS xvi

LIST OF SYMBOLS xviii

LIST OF APPENDICES xx

1 INTRODUCTION 1

1.1 Background 1

1.2 Problem Statement and Research Motivation 3

1.3 Objectives of Research 4 1.4 Scope of Research 5 1.5 Research Contributions 6 1.6 Thesis Organization 7 2 LITERATURE REVIEW 8 2.1 Introduction 8

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2.2 Radio Signal Propagation 9

2.2.1 Signal Fading 9

2.2.1.1 Large-scale fading 11

2.2.1.2 Small-scale fading 12

2.2.2 Basic Propagation Mechanisms 13

2.2.2.1 Reflection 14

2.2.2.2 Diffraction 17

2.2.2.3 Scattering 18

2.2.3 Critical review on Radio Signal Propagation literature 19

2.3 Localization Methods 20

2.3.1 Closeness to a point of association 21

2.3.2 Distance based techniques 22

2.3.2.1 Time of Arrival/Time Difference of Arrival 27 2.3.2.2 Received Signal Strength 28

2.3.2.3 Received Signal Phase 30

2.3.3 Direction based techniques 31

2.3.4 Signature based techniques 31

2.3.5 Hybrid positioning schemes 34

2.3.6 Critical review on Localization Methods literature 35

2.4 Artificial Neural Network 36

2.4.1 Multiple-layered Perceptrons 36

2.4.2 Transfer functions 37

2.4.3 Training algorithms 39

2.4.4 Research work on neural network based localization 40 2.4.5 Critical review on Artificial Neural Network literature 44

2.5 Summary 44

3 METHODOLOGY 46

3.1 Introduction 46

3.2 Research Organizational Structure 48

3.3 Simulation Data Development 49

3.3.1 The Lee’s in-building model 51

3.3.2 LOS condition 52

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3.3.3.1 Receiver in close-in region 55 3.3.3.2 Receiver beyond close-in region 57 3.3.4 Application of Lee’s in-building model to custom indoor

layout 60

3.4 Applying neural network for location prediction 71 3.4.1 Preprocessing and post-processing of data 73 3.4.2 Location prediction process with neural network 74

3.4.2.1 Training 75

3.4.2.2 Testing 75

3.4.2.3 Validation 76

3.4.2.4 Number of exemplar sets 76

3.4.3.5 Number of hidden layers and neurodes per

hidden layer 78

3.5 Simulation process 80

3.5.1 Location prediction with feedforward backpropagation

neural network 80

3.5.2 Data preparation method to optimize neural network

based localization 84

3.6 Summary 88

4 NEURAL NETWORK LOCATION PREDICTION WITH

DATA PREPARATION METHOD 90

4.1 Introduction 90

4.2 Matlab neural network toolbox plots 91 4.3 Location prediction with feedforward backpropagation neural

network 92

4.3.1 Single hidden layer feedforward backpropagation neural

network 92

4.3.2 2 hidden layer feedforward backpropagation neural

network 94

4.4 Applying data preparation method on neural network based

localization 95

4.5 Location prediction comparison with the weighted mean filter

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4.6 Overall analysis and discussion 100

4.7 Summary 101

5 PERFORMANCE EVALUATION OF NEURAL NETWORK LOCATION PREDICTION WITH DATA PREPARATION

METHOD 102

5.1 Introduction 102

5.2 Scenario 1: 2 receivers; 1m x 1m transmitter resolution 103 5.21 Scenario 1 neural network location prediction 108 5.3 Scenario 2: 4 receivers; 1m x 1m transmitter resolution 110 5.31 Scenario 2 neural network location prediction 117 5.4 Scenario 3: 5 receivers; 1m x 1m transmitter resolution 117 5.41 Scenario 3 neural network location prediction 126 5.5 Scenario 4: 0.5m x 0.5m transmitter resolution; 3 receivers 127 5.51 Scenario 4 neural network location prediction 133 5.6 Scenario 5: 2m x 2m transmitter resolution; 3 receivers 134 5.61 Scenario 5 neural network location prediction 141 5.7 Scenario 6: Untrained transmitter points; 3 receivers 141 5.71 Scenario 6 neural network location prediction 143

5.8 Overall analysis and discussion 144

5.9 Summary 145

6 CONCLUSION AND FUTURE WORKS 146

6.1 Conclusion 146

6.2 Recommendations for Future Works 147

REFERENCES 149

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LIST OF TABLES

TABLE NO. TITLE PAGE

2.1 Permittivity and conductivity values of different material 15

2.2 Triangulation application scenarios 22

2.3 List of transfer functions for neural network 38 2.4 Neural network training algorithm references 40 2.5 Comparison of research works on neural network based

localization 43

3.1 Symbol definitions for custom indoor layout 61 3.2 Lee’s in-building model constant variables 68

3.3 Neural network training algorithms 75

4.1 Location prediction with 1 hidden layer FFBNN 93 4.2 Location prediction with 2 hidden layer FFBNN 94 4.3 Location prediction with 2 hidden layer FFBNN and DPM 96 4.4 Location prediction with 2 hidden layer FFBNN and WMF 98 5.1 Scenario 1 results with 2 hidden layer FFBNN, BR and DPM 108 5.2 Scenario 2 results with 2 hidden layer FFBNN, BR and DPM 117 5.3 Scenario 3 results with 2 hidden layer FFBNN, BR and DPM 126 5.4 Scenario 4 results with 2 hidden layer FFBNN, BR and DPM 134 5.5 Scenario 5 results with 2 hidden layer FFBNN, BR and DPM 141 5.6 Scenario 6 results with 2 hidden layer FFBNN, BR and DPM 143 5.7 Location prediction performance evaluation analysis for

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LIST OF FIGURES

FIGURE NO. TITLE PAGE

2.1 Classification of signal fading 10

2.2 Graphical representation of large-scale fading versus

small-scale fading 10

2.3 Classification of small-scale fading 13

2.4 Smooth surface reflections 14

2.5 Comparison of reflection between horizontally and

vertically polarized radio waves: (a) horizontal polarization

(b) vertical polarization 16

2.6 Illustration of diffraction 18

2.7 Classification of localization methods 21

2.8 (a) An illustration of the three-circle Venn diagram

(b) The geometry of a circular triangle displaying involved

chords and radii 24

2.9 A method of addressing the specifications of a circular triangle in ordinance of a main coordinate system (x,y) relative to

complimentary sub coordinate systems (x’, y’) and (x”, y”) 25 2.10 Classification of fingerprinting algorithms 32

2.11 The multilayered perceptrons 36

3.1 General blueprint of the research 49

3.2 Process of simulation data development 50

3.3 Application of Lee’s in-building model in LOS situation 52 3.4 Fresnel zone between the reactive near-field and far-field zones 54

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3.5 Application of Lee’s in-building model in NLOS situation

within the close-in region 55

3.6 Application of Lee’s in-building model in NLOS situation

beyond the close-in region 57

3.7 Custom indoor layout with 3 receivers 60

3.8 Distance calculations between the transmitter at grid point {3,4} and receiver 1, receiver 2 and receiver 3 62 3.9 Depiction if distances d1 up to d5 along transmission path

between the mobile transmitter at point {1,9} and the receiver 2 64 3.10 Set 1 of simulated RSS values: (a) at receiver 1 (b) at receiver 2

(c) at receiver 3 69

3.11 Set 2 of simulated RSS values: (a) at receiver 1 (b) at receiver 2

3.13 Neural network process flow 73

3.14 Flowchart of neural network location prediction process 81 3.15 Neural network structure for first stage simulation process 82 3.16 Neural network structure for first stage simulation process in

matlab 82

3.17 2 hidden layer neural network structure for first stage simulation

process 83

3.18 2 hidden layer neural network structure for first stage simulation

process in matlab 83

3.19 Path loss data generated by the log-normal shadowing model 85 3.20 Data preparation applied to data generated by the log-normal

shadowing model 85

3.21 2 hidden layer neural network structure for simulation process

with DPM 87

3.22 2 hidden layer neural network structure for simulation process

with DPM in matlab 88

4.1 Location prediction performance comparison with 1 hidden layer FBNN with different training algorithms 93

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4.2 Location prediction performance comparison between 1 hidden

layer FBNN and 2 hidden layer FBNN 95

4.3 Location prediction performance comparison between 2 hidden layer FBNN with and without data preparation method applied 96 4.4 Location prediction performance comparison between 2 hidden

layer FBNN with WMF and with DPM 99

5.1 Location prediction performance evaluation in different

scenarios 102

5.2 Custom indoor layout with two receivers 104

5.3 Set 1 of simulated RSS values: (a) at receiver 1 (b) at receiver 2 105 5.4 Set 2 of simulated RSS values: (a) at receiver 1 (b) at receiver 2 105 5.5 Set 3 of simulated RSS values: (a) at receiver 1 (b) at receiver 2 106 5.6 2 hidden layer neural network structure for third stage scenario 1

simulation process 107

5.7 2 hidden layer neural network structure for third stage scenario 1

simulation process in matlab 108

5.8 Symmetrical problem between 2 receivers 109

(c) at receiver 3 (d) at receiver 4 112

simulation process 115

simulation process in matlab 116

5.16 Set 1 of simulated RSS values: (a) at receiver 1 (b) at receiver 2 (c) at receiver 3 (d) at receiver 4 (e) at receiver 5 120 5.17 Set 2 of simulated RSS values: (a) at receiver 1 (b) at receiver 2

(c) at receiver 3 (d) at receiver 4 (e) at receiver 5 122 5.18 Set 3 of simulated RSS values: (a) at receiver 1 (b) at receiver 2

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(c) at receiver 3 (d) at receiver 4 (e) at receiver 5 123 5.19 2 hidden layer neural network structure for third stage scenario

3 simulation process 124

5.20 2 hidden layer neural network structure for third stage scenario

3 simulation process in matlab 126

5.21 Custom indoor layout with 0.5m transmitter grid 127 5.22 Set 1 of simulated RSS values: (a) at receiver 1 (b) at receiver 2

4 simulation process with matlab 133

5.27 Custom indoor layout with 2m transmitter grid 135 5.28 Set 1 of simulated RSS values: (a) at receiver 1 (b) at receiver 2

5 simulation process in matlab 141

5.33 Untrained transmitter locations: (a) between 2m grids (b)

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LIST OF ABBREVIATIONS

WSN - Wireless Signal Networks

GNSS - Global Navigation Satellite Systems GPS - Global Positioning System

GLONASS - Global Navigation Satellite Systems CNSS - Chinese Navigation Satellite System QZSS - Quasi-Zenith Satellite System

IRNISS - Indian Regional Navigational Satellite System

LOS - Line of sight

NLOS - Non line of sight

RSS - Received signal strength ISI - Intersymbol interference TOA - Time of arrival

TDOA - Time difference of arrival

DGPS - Differential global positioning system AOA - Angle of arrival

DOA - Direction of arrival

SMP - Smallest M-vertex polygon SOM - Self organizational map PCA - Principle component analysis

BP - Backpropagation

MLP - Multiple layered perceptrons ART - Adaptive resonance theory BAM - Bidirectional associative memory RBF - Radial Basis Function

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SVM - Support vector machine

FANN - Feedforward artificial neural network LQI - Link Quality Indicator

CIR - Channel impulse response PSO - Particle swarm optimization

FFBNN - Feedforward backpropagation neural network RBFNN - Radial basis function neural network

BFGS - Broyden-Fletcher-Goldfarb-Shanno

LM - Levenberg-Marquardt

BR - Bayesian regularization OSS - One step secant

RP - Resilient backpropagation CG - Conjugate gradient

GD - Gradient descent

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LIST OF SYMBOLS

𝑃𝑡 - Transmitted power

𝑃_𝑟 - Received power

𝐺_𝑡 - Transmitting antenna gain 𝐺𝑟 - Receiving antenna gain

𝐿 - Loss factor

𝜆 - Wavelength

𝑓 - frequency

d - Distance

𝑋_𝜎 - Gaussian random variable

𝜎 - Standard deviation

v - Speed

𝐵𝑠 - Bandwidth of transmitted signal

𝑇₀ - Time of arrival

𝑇_𝑠 - Reciprocal bandwidth

𝜎𝜏 - RMS delay spread

𝐵_𝐻 - Coherence bandwidth

𝑅_𝑆 - Signal data rate

𝑓_𝑑 - Doppler frequency

𝑛 - Refraction index

𝜃₁ - Incident angle

𝜃2 - Reflected angle

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𝑎_𝑣 - Verticle reflection coefficient 𝜀_𝑐 - dielectric constant

𝜀_𝑟 - permittivity

𝛽 - Brewster angle

ℎ_𝑐 - Critical height of surface protuberance Γ - Reflection coefficient of smooth surface Γ_{𝑟𝑜𝑢𝑔ℎ} - Reflection coefficient of rough surface 𝜌_𝑠 - Scattering loss factor

c - speed of light 𝜏 - transmission time α - Decay rate b - Bias 𝐷𝑐 - close-in distance ℎ_𝑡 - height of transmitter ℎ_𝑟 - height of receiver

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LIST OF APPENDICES

APPENDIX TITLE PAGE

A List of Author’s Publications 158

B Simulation Data Development with Microsoft Excel 159

C MATLAB ANN Toolbox Settings 161

D Complete Location Prediction Results with 1 Hidden

Layer FBNN 166

E Complete Location Prediction Results with 2 Hidden

Layer FBNN 176

F Complete Location Prediction Results with 1 Hidden

Layer FBNN and DPM 186

G Complete Location Prediction Results with 2 Hidden

Layer FBNN and DPM 196

H Complete Location Prediction Results with 2 Hidden

Layer FBNN and WMF 206

I Simulation Data Development for Performance

Evaluation Scenario 1 216

J Complete Location Prediction Results for Performance

K Simulation Data Development for Performance

L Complete Location Prediction Results for Performance

M Complete Location Prediction Results for Performance

N Simulation Data Development for Performance

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O Complete Location Prediction Results for Performance

P Simulation Data Development for Performance

Q Complete Location Prediction Results for Performance

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CHAPTER 1

INTRODUCTION

1.1 Indoor Localization

Localization is a process of determining the position of devices within a specific area. It is also recognized as geolocation, radiolocation, parameter sensing or positioning [1]–[5]. Normally localization determines the coordinates of a target node in two or three dimensions, specifying the latitude and longitude of where the node is located. Localization leads to many pioneering application potentials which can be developed by utilizing this technology especially when the targeted device’s coordinates can be identified wirelessly or even better, involving capability to engage mobile or non-static devices. Consequently, over the past decades a lot of research work have been done due to the interest of improving current systems in terms of accuracy performance, computational cost, complexity and production cost [6]. While resolving and finding solutions for many issues, research work also contribute in identifying new problems and limitations. This therefore introduces opportunities for further research work to be done.

In reference to Elliot D. Kaplan et. al [7], a popular method for localization would be through the implementation of the Global Navigation Satellite Systems (GNSS), which estimates the target node positions by calculating the transmitted data from multiple satellites to the receiver. The most well-known among currently available GNSSs is the Global Positioning System (GPS) which is managed by the United States government. Other options of GNSSs include the Global Navigation

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Satellite System (GLONASS), Galileo, Chinese Navigation Satellite System (CNSS) named Compass/Beidou, Japanese Quasi-Zenith Satellite System (QZSS) and the Indian Regional Navigational Satellite System (IRNSS). In total more than 100 satellites are involved to operate GNSSs around the globe. These satellites transmit their time and position data continuously while the receivers on the ground monitor and process these data with equations that estimate location via calculations involving measurements of deviation with the true time relative to the satellites position. In order to achieve acceptable coordinating performance, a minimum of 4 satellites must be in view of the receiver for sufficient data computation [8].

As a result, particular limitations are introduced upon urban areas where satellite signals are most likely to be blocked by the high density of buildings and infrastructures. Therefore, an alternative method is required to apply localization especially in indoor situations where GNSSs do not work [9]. Many researchers are going through the route of using Wireless Signal Networks to achieve this as it is already widely used inside buildings for telecommunication and data transmissions [1]. However, various errors on location prediction are occurring due to non-linear propagation effects, which are more severe in indoor areas and are sensitive to the performance of the localization system [10].

Having an accurate and reliable GPS like system indoors would open up towards many possibilities of useful applications [11]. For example, during an emergency situation in a busy hospital, where a patient must be attended as soon as possible, having a system that would know the positions of every doctor or medical officer and call the closest one available may contribute to having an efficient personnel management in complicated environments while increasing the chances of being a life saver. This may also apply similarly to other situations where people can be quickly allocated and assembled for immediate meetings in business, government, educational or law enforcement organizations. On the other hand, asset or equipment management can be improved through parameter fencing system where attached nodes can be monitored on real-time basis preventing misplacement and also making sure the security is optimized by applying triggering mechanisms when the asset or equipment is taken out from a certain set of parameters. Additionally, an indoor

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navigation system would benefit users on finding desirable destinations more effectively and conveniently within a large building such as shopping malls, faculty buildings, stadiums or expo halls.

1.2 Problem Statement and Research Motivation

In order to understand the relationship between radio signal propagation and radio signal transmission distance, it is essential to know behavior of electromagnetic radiation relative to the space between the transmitter and receiver. According to David M. Pozar [12], research on the nature of electromagnetic radiation has started since the 18th century where James Clerk Maxwell developed his theory stating that radio waves move through free space at finite velocity with an order of magnitude similar to the of speed of light while involving instantaneous propagation effects which were not consistent with ‘action at a distance’ theories. Maxwell’s theory was then verified by Heinrich Hertz whom undertook controlled experiments to confirm that the electromagnetic behavior predictions by Maxwell were indeed true.

Fundamental theories provide a basis of the understanding radio propagation with the assumption of radio signal transfer in free space or in a uniform dielectric medium. However, real live implementation requires further considerations as it involves contact with large variety of transfer medium which are different in properties, shapes, sizes and distribution. This results to radio signal impairments and distortion as it goes through the transmission process from the transmitter to the receiver. Subsequently, the impairments of radio signal transfer also contribute to error in location measurements based on radio propagation. The effects of radio propagation include reflection, diffraction, scattering and fading.

Indoor localization introduces a very tough environment for signal propagation. The signal distortion and impairments are more severe inside buildings due to the existence of multiple objects of different materials affecting the transmission of signal between the transmitting antenna and the receiving antenna. Different surface

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shapes and roughness, object sizes and composition and object location and distribution all lead to different propagation effects. Furthermore, the higher density of these objects indoors increases the complexity of signal propagation as non-line of sight (NLOS) situations are more likely. Indoor environments are very different at different locations. This means signal distortion and impairments are also different depending on the location. Therefore, in general, a parameter adaptive method of location estimation is necessary to solve non-linear propagation effects for indoor localization scheme.

The main motivation of this research is the profound ability of artificial neural networks to classify non-linear problems for pattern recognition. Artificial neural network mimics the brain function of learning and solving problems based on the information learnt. This concept can be applied directly for indoor localization which needs an adaptive mechanism to estimate position based on the parameters of a certain location. The signal propagation will provide a specific pattern for each location and these data will be taught and trained into the artificial neural network. Then during the online phase, the neural network will predict the location based on the training data. There are many ways to configure the neural network plus there are numerous optimization possibilities within the application of neural network process.

As severe indoor propagation causes non-linear and location specific signal patterns relative to the distance of signal transmission, this creates challenging input data for the neural network to process and utilize to predict the mobile node location. Therefore, there is a need of an effective optimization technique to allow the neural network to receive a more distinguishable pattern which would lead to better performance in mobile location prediction.

1.3 Objectives of Research

The paramount objective of this research is to provide a valid location prediction solution for the problems occurring during indoor parameter sensing using

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wireless networks, following the path of our research motivations, with the end goal of producing a better performance for indoor localization system by utilizing suitable artificial neural network processes. Specifically, the objectives of this research are stated in the list below:

i. To investigate for a feedforward backpropagation neural network configuration suitable for indoor localization using the developed simulation data.

ii. To optimize neural network based indoor localization with proposed data preparation strategy for improved location prediction accuracy.

iii. To conduct comprehensive performance evaluation on the proposed method of indoor localization.

1.4 Scope of research

The research focuses on WSN localization for indoor parameter sensing. Centralized localization method is used where the computation takes place at the receivers which are interconnected while mobile nodes only act as transmitters. The study is based on 5.8GHz radio frequency suitable for short range femtocell applications. The basic triangulation technique is adopted where the received signal strengths (RSS) from three different receiver base station locations provide input parameters for the neural network to process and conduct location prediction accordingly. The primary simulation tool is the Matlab software. The neural network toolbox is utilized to process the obtained simulation data with different types of Artificial Neural Network algorithm configurations for location prediction. With exemption to the max epoch and max validation error, all training parameters are according to default values of the neural network toolbox. Data preparation provides strategic input pattern to the Artificial Neural Network. The prepared data is limited to 100 values generated from 100 raw data. The research is based on static receivers and ignores the effect movements of the mobile transmitter nodes as indoor localization applications normally involve static or low velocity movements of the mobile

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transmitter nodes. The received signal strength data is developed using the Lee’s in-building model according to a custom indoor layout. The indoor layout design is limited to an empty building structure.

1.5Research Contributions

The research work done provide contributions by introducing methodologies which are compliant to improving indoor localization approach and application as listed below.

i. The research provides an indoor specific development of simulation data by utilizing the Lee’s in-building model to provide received signal strength data in locations according to a custom indoor layout. In contrary to the log-normal shadowing model used by many research works related to indoor localization [13]–[17], the Lee’s in-building model takes into consideration of additional indoor-specific parameters including wall thickness, wall material, floor material, height of the transmitter and the receiver, Fresnel zone distance between the transmitter and receiver, LOS and NLOS properties of the signal path and the number of rooms within the signal path to model the received signal strength accordingly. Therefore, a more relevant simulation data is produced which better simulates severe radio signal propagation in indoor environments.

ii. The research also proposes the application of a novel data preparation method on the RSS data to optimize location prediction with 2 hidden layer feedforward backpropagation neural network trained with Bayesian Regularization training algorithm. The proposed data preparation method involves manipulating the raw RSS data into min, max, median and average values which provide a more strategic input to be processed by the neural network for location prediction. This method is contrary to traditional filtering methods where portions of valuable RSS information is eliminated to create a desirable range of RSS data. The proposed data preparation method enables

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respectable location prediction accuracy with RMSE of 0.08210m which better than the most recent related research work [18]. This research also provides comprehensive analysis on indoor localization with the proposed data preparation method by evaluating several different scenario applications.

1.6 Thesis Organization

This thesis is organized into 6 chapters respectively. The first chapter provides introductory insight into the research by laying out the fundamental background of the research topic, the problem statements, the research objectives, the scope which limits the research and the contributions yielded by the completion of the research. The second chapter provides elaborate discussion on the theories and previous research work related to the research topic. By conducting critical review of relevant literatures, the research problem is magnified which consequently reveals existing research gaps providing reference for the direction of research. The third chapter establishes the general blueprint of the research. This is where the methodology framework is presented in detail, presenting actions taken to achieve the objectives of the research. The fourth chapter provides analysis discussions on the indoor localization performance of different feedforward backpropagation neural network configurations, the effects of the data preparation method to location prediction and comparison analysis between the proposed data preparation method and the weighted mean filter method. The obtained results are analyzed to verify that the fulfillment of objectives 1 and 2 of the research. Chapter 5 proceeds with location prediction performance evaluation analysis of the proposed method in different controlled scenarios to satisfy the third objective of the research. The last chapter synthesizes the overall research work into a complimentary conclusion and recommends future research works to further improve indoor location prediction.

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REFERENCES

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