a comparative study of implementation techniques …

A COMPARATIVE STUDY OF IMPLEMENTATION

TECHNIQUES FOR INDOOR LOCALIZATION

SYSTEMS

BY

RATCHASAK RANRON

A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE

REQUIREMENTS FOR THE DEGREE OF MASTER OF

ENGINEERING (INFORMATION AND COMMUNICATION

TECHNOLOGY FOR EMBEDDED SYSTEMS)

SIRINDHORN INTERNATIONAL INSTITUTE OF TECHNOLOGY

THAMMASAT UNIVERSITY

ACADEMIC YEAR 2017

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Abstract

A COMPARATIVE STUDY OF IMPLEMENTATION TECHNIQUES

FOR INDOOR LOCALIZATION SYSTEMS

by

RATCHASAK RANRON

Bachelor of Science, King Mongkut's University of Technology North Bangkok, 2012

Master of Engineering, Sirinhorn International Institute of Technology, 2018

As technology advances, the demand for localization systems have increased. The

usefulness of such systems cannot be ignored, localization systems such as GPS have

become a daily part of every individual’s life. Indoor localization systems have recently

gained popularity in the commercial, health care and industrial sectors. The use case

involves monitoring the stock movement within a warehouse, navigating and guiding

people to reach the intended destinations etc. However, in the recent years there has

been a rise in different technologies and protocols to enable indoor localization systems

where traditional systems such as GPS fails. This study explores the most widely

popular alternative technologies; ZigBee, Ultrawide band and Bluetooth. The research

presents the methodology and the results of implementations for each technology, while

explaining their strength and weakness.

Keywords: Localization, Indoor, ZigBee, UWB, Bluetooth

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Acknowledgments

First and Foremost, I would like to extend my sincere gratitude to my advisor Asst.

Prof. Dr. Prapun Suksompong, who kindly advises, supports and patiently motivates

me to achieve all the milestones. Furthermore, I would like to thank my Co-advisor,

Dr. Kamol Kaemarungsi who introduced me to ICTES program and give me many

supports all along this journey. Dr. Kamol provided guidance, patience and constructive

feedback on each stage of this research. I could not have completed this journey without

their support and encouragement.

I would like to thank the other committee members, Prof. Tsuyoshi Isshiki and Assoc.

Prof. Dr. Chalie Charoenlarpnopparut for their valuable advice and comments during

the progress presentation. Additionally, The TAIST-Tokyo tech scholarship has

provided me with an opportunity to learn and grow, opening new doors towards the

future.

I could not live this comfortable researcher life without support from TGIST (Thailand

Graduate Institute of Science and Technology) scholarship which supports my spending

in daily life throughout this research period.

Lastly, this research would not have been completed without the constant love and

support of my beloved friends and family members, who have been there for me

throughout my life.

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Table of Contents

Chapter Title Page

Signature i

Abstract ii

Acknowledgments iii

Table of Contents iv

List of Tables vii

List of Figures viii

1 Introduction 1

1.1 Motivation and Objectives 2

1.2 Thesis Structure 3

2 Background and Related Works 4

2.1 Indoor Localization System Applications 4

2.2 Performance Metrics 5

2.2.1 Accuracy 5

2.2.2 Precision 6

2.2.3 Complexity 6

2.2.4 Robustness 6

2.2.5 Scalability 7

2.2.6 Cost 7

2.3 Positioning Technology 7

2.3.1 Ultrasound 7

2.3.2 Visible Light Communication (VLC) 7

2.3.3 RFID 8

2.3.4 ZigBee 8

2.3.5 Bluetooth 9

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2.3.6 Wi-Fi 9

2.3.7 Ultra-Wide Band (UWB) 10

2.4 Localization Algorithm 11

2.4.1 Range-Based 11

2.4.2 Fingerprint-Based 12

3 ZigBee Indoor Localization System 16

3.1 System Design 16

3.1.1 System Architecture 16

3.1.2 ZigBee’s RSSI Location Protocol 18

3.1.3 Location Estimation Algorithm 20

3.2 Experimentation and Results 21

3.2.1 Experiment Design 21

3.2.2 Evaluation 22

4 UWB Indoor Localization System 24


4.1.1 System Architecture 24

4.1.2 Asymmetric Double Sided Two-Way Ranging (ADS-TWR) 25

4.1.3 Trilateration algorithm 26



4.3 Evaluation 29

5 Bluetooth Indoor Localization System 31


5.1.1 System Design 31

5.1.2 Hardware Design 33

5.1.3 Bluetooth’s Ranging Model 34

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5.1.4 Geo-N algorithm 35



5.3 Evaluation 39

5.4 Conclusion 40

6 Conclusion and Future Work 41

References 45

Appendix 49

Appendix A 50

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List of Tables

Tables Page

4.1 Operation Modes for TREK1000 28

6.1 The comparison of technologies and algorithms from implemented systems 43

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List of Figures

Figures Page

3.1 ZigBee Wireless Sensor Network Setup 17

3.2 ZigBee – RSSI Location Protocol 19

3.3 Deployment Map 22

4.1 ZigBee – RSSI Location Protocol 25

4.2 Deployment Map 28

4.3 2D (X-Y) and 3D (X-Y-Z) performance comparison 30

5.1 System Design for Bluetooth ILS 33

5.2 Anchor’s Component Diagram 34

5.3 Procedure for Geo-N algorithm 37

5.4 Bluetooth ILS - Deployment Map on 1st Floor of NECTEC building 38

5.5 Bluetooth ILS - Deployment Map on 3st Floor of NECTEC building 39

5.6 Bluetooth ILS - Localization Performance 39

6.1 The comparison of localization performance 43

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Chapter 1

Introduction

Nowadays location services are one of the important tools in daily life. Location

tracking is available on most of the mobile devices to assist us in navigating through

unknown places. This service helps a lot of businesses by bringing people to the

indented locations by reducing the time required to find the place, tracking lost devices

etc. Another domain of application for such services is automation for day-to-day tasks

such as driving by enabling self-driving cars.

Indoor localization systems can be applied for various purposes like navigation within

a large building such as a shopping mall or airport, in order to help people reach the

product they need or their respective boarding gates or even navigating the robots to

their intended working area. As a tracking system, indoor localization can help in

keeping the history of object’s location in the warehouse, where all goods movements

need to be analyzed, or in hospitals, where patients needed to be tracked to help reach

them faster should an emergency arise. This system is very helpful in a lot of ways to

improve the overall quality of life.

A major challenge facing such systems is the low accuracy caused by high amount of

noise in the indoor environment. The accuracy can be improved by either using a more

accurate ranging technology such as laser ranging or modifying existing algorithms to

eliminate the noise as much as possible. The more accurate ranging technologies are

costly and have limitations such as unsupported Non-Line-of-Sight (NLOS) ranging for

laser or VLC (Visible Light Communication) ranging. In order to modify the existing

algorithms to be better deal with noise, numerous positioning algorithms were

developed.

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1.1 Motivation and Objectives

Typically, in indoor localization system, the main problem that effects the system

performance is noises from indoor environment. Many of works were based on

simulation even though the indoor noise is hard to model, this make those results

unrealistic. Besides, a number studies were evaluated on small area such as a small

room and give a good result compared to other works even though it’s quite

unreasonable to compare the results because the size of areas as well as the

environments are different.

This research is aimed to explore the practical problems and performance of popular

technologies along with the implementation of the different algorithms to help people

understand the advantages and limitations of each of these technologies for building

systems most suitable for their area of application. There are three main technologies

and four algorithms presented in this research. The technologies are ZigBee, UWB

(Ultra-Wide Band) and Bluetooth which are already popular for indoor localization

systems. The algorithms implemented were fingerprint-based Single Nearest Neighbor

(SNN), fingerprint-based K-Nearest Neighbor (KNN), range-based Trilateration

algorithm and range-based Geo-N algorithm. The systems were developed and

deployed based on these technologies and algorithms.

For contribution, this research developed real-world localization systems and deploy

on the same environment that make a fair comparison for the different systems, and the

deployed area is relatively large compared to the most of studies. Finally, the

performance in various aspects are compared and learned practical problems are

explained. Moreover, this work implemented and evaluated the Geo-N algorithm based

on Bluetooth technology which is not found in previous works.

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1.2 Thesis Structure

This thesis contains six chapters, beginning with the surveys of related works and

background, followed by the explanations for each of our systems, and ending with a

summary and the recommendation for future work.

Chapter 2 presents previous work done in the field of indoor localization system.

Several technologies and algorithm’s performance, application and their problems are

explained and summarized.

Chapter 3 discusses the implementation of indoor localization system based on ZigBee

technology using the SNN and KNN algorithms. The system design and performance

of the two algorithms were compared and discussed in details.

Chapter 4 discusses the implementation of the UWB based indoor localization system

using the range-based Trilateration algorithm. The results and limitations of the system

are explained in this chapter.

Chapter 5 discusses the implementation of the Bluetooth based localization system

using range-based Geo-N algorithm. The results and performance were compared to

the previous implementations of localization system.

Chapter 6 presents the summary of the overall system. The problems were analyzed

and the performances were compared.

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Chapter 2

Background and Related Works

The ILS (Indoor Localization System) is an emerging technology that have become in

focus during recent years. This system can be applied in wide range of application

which can support the business growth and help simplify the people life. The ILS can

be applied to many wireless technology with several algorithms.

This chapter gives the background and related work for this thesis. Beginning with the

motivated applications of the ILS, followed by the standard metrics for evaluating the

system. Afterward, popular positioning technologies used today is discussed. Finally,

the location algorithms are categorized and explained.

2.1 Indoor Localization System Applications

An author in [1] has applied the ILS as a navigation service which is designed especially

for blind and visually impaired people who want to go to a specific place or room in a

building. The people can ask for the place to the service via mobile application and

listen to the guiding sound for going to that place. This system also utilized the sensors

data created by the mobile phone from the Inertial Measurement Unit (IMU) (magnetic,

gyroscope, acceleration) working with a k-Nearest Neighbor fingerprinting technique

based on Bluetooth and Wi-Fi’s RSSI (Receive Signal Strength Indication) for

predicting next position.

Another emerging example is a patient tracking application developed by [2]. This

application can help the patient’s doctor or family to locate the patient who has a health

problem. This application usually integrated with health monitor sensors such as fall

detection, blood or pressure sensor to detect the problem and automatically notify the

person who are taking care of them. This system is based on ZigBee WSN (Wireless

Sensor Network) using fingerprint-based technique with Calibrated Fuzzy C-Means

clustering algorithm and the measured result is an average error of 0.51 m.

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The work in [3] have implemented the indoor localization system based on RFID

technology for an object management system. A main challenge of this system is to be

able to scale the number of tag or object as much as possible such as in Warehouse

where the number of tracking goods may be up to many thousands. The reason made

the RFID the most suitable solution because of its very competitive cost. The

implementation used Active RFIDs as a Tag that can broadcast its identification signal

and will be read by RFID readers. After a vibration sensor integrated in the tag has

sensed its movement, the tag is re-calculated by location engine that has got all the tags

information from 3 sensors which are floor sensor, RSSI (Receive Signal Strength

Indicator) (from RFID readers) and the vibration sensor. This system use the

fingerprinting technique with classification algorithm to find the unknown location.

2.2 Performance Metrics

Most of the previous work and studies done on localization systems consider the

location estimation error as the most reliable measure of the system performance.

Although the accuracy of a system is a very important performance indicator, however,

the practical application of localization systems depends on various other performance

factors such as accuracy, precision, complexity, robustness, scalability and cost. Each

of the performance indicators are described in details below.

2.2.1 Accuracy

Most of the existing applications for localization system consider the accuracy of the

location estimation as the sole measure for system performance. The accuracy of any

localization system is determined by computing the Euclidean distance between the

actual location of the mobile node compared to the estimated location. The mean

distance of the estimated errors is considered the overall accuracy in localization

systems. A higher accuracy rate suggests the implemented system is good, however, it

is important to understand that not all applications for localization systems require a

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100% accuracy rate. The accuracy can be traded for other more desirable characteristics

of the system.

2.2.2 Precision

If accuracy is the mean of the distance error, then precision for most cases is considered

to be the standard deviation in the error data between the true distance and estimated

distance over multiple trials. The precision metric indicates a consistent performance

of the system. The cumulative distributive function (CDF) of the distance error is used

to describe the precision of the system in most studies. The CDF describes precision

using percentage, for example, the precision is 90% at a distance of 3m for the system.

When making a comparison between 2 systems with same precision rates, the CDF

graph that indicates a steeper rise for higher probability values faster will be selected.

2.2.3 Complexity

An importance factor to indicate the performance of ILS is a time to produce each

successive estimated location, the more time used the more resources are consumed,

this can be caused by many component such as hardware, software and many factors.

However, most of the time, many researchers refer to this metric as a computational

complexity such as calculation time for each unknown position or storage used in the

system.

2.2.4 Robustness

Robustness is ability for the algorithm to sustain the result even if the information for

calculation is incomplete. For example, when some of anchors are not seen by tag that

make the tag unable to calculate the result, a good algorithm should still produce a good

result.

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2.2.5 Scalability

A scalability for localization system can mean that the system can still giving the

satisfied result with acceptable calculation time even if the coverage area or the devices

in the system are increased. This can also mean how works are required or the cost for

expanding the system such as installing new device or data preparation.

2.2.6 Cost

A factor that every project need to optimize is a budget, after the main acceptable

performance such as accuracy or precision is defined, the technologies and

methodologies would be considered to get the best cost while the requirement is still

acceptable. The cost can vary depends on many factors such as accuracy, coverage

area, a number of devices or energy constraint.

2.3 Positioning Technology

2.3.1 Ultrasound

Ultrasound is a sound waves with frequencies higher than human hearing ability which

is approximately 20 kilohertz up to many gigahertz. The characteristics of low speed

propagation, unable to travel through the wall, centimeter level accuracy and low cost

make this technology popular in ILS. The distance between two nodes can be calculated

by the time-of-flight (TOF) between the emitter and receiver multiplied by the sound

propagation time. For this reason, the time synchronize is required in this system to

calculate the accurate time between the two nodes. Active Bat [4] and Cricket [5] are

ultrasound-based ILSs, the systems generate ultrasound pulses from transmitters works

as anchor. The pules receive by receivers works as tag or mobile node and calculate its

position. The works have achieved accuracies of few centimeters.

2.3.2 Visible Light Communication (VLC)

Visible Light Communication (VLC) is an emerging technology due to the popularity

of LEDs that allows the competitive cost in ILS. The technology utilizes the

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electromagnetic signal between the frequency of 430 THz up to 790 THz which can be

sensed by human eye. The data is modulated and transmitted to the receiver based on

the frequency range. In ILS, the LEDs on the ceiling is usually works as anchor and

transmit the signal to all in-sight receiver which will be used in ranging estimation. The

ranging techniques available for this technology is receive signal strength (RSS) [6],

time difference of arrival (TDOA) [7] and phase difference of arrival (PDOA) [8]. The

work’s location errors in [6] and [7] is less than 0.5mm and 4.5mm respectively.

2.3.3 RFID

RFID is a most competitive price technology due to the very cheap tag price. The

technology composes of 2 types of device: reader and tag, the tag can be classified into

2 types which are passive tag and active tag. The passive utilizes the electromagnetic

fields to identify the tag automatically while the active tag transmits its identity signal

periodically. There are many approaches and algorithms have been used to design the

system which are all range-based, fingerprint-based and proximity-based. For range-

based approach, many of distance estimation method can be used such as received

signal strength (RSS), time of arrival (TOA), time differential of arrival (TDOA) and

Phase of Arrival (POA) [9]. For implemented system in previous works, The SpotON

[10] is a range-based system using RSS ranging technique implemented by using the

active tag, this system can produce sub meter accuracy. The Landmarc [11] is based on

fingerprint-based with kNN algorithm, the location error of this system is less than 2

meters.

2.3.4 ZigBee

ZigBee is a low power Wireless Sensor Network (WSN). This technology allows the

data to communicate throughout the low-rate wireless personal area networks (WPAN)

with much less power consumption compared to other wireless technology. ZigBee

operated based on IEEE 802.15.4 specification over the 2.4 GHz radio band. The main

challenge of the 2.4 GHz signal to the ILS is the noise created by indoor environment

the furniture and multi-path effect. For distance estimation for range-based algorithm,

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RSS [14], TOA and TDOA [15] have been used in previous works. Moreover,

Fingerprint based algorithms are also applied in this technology [16]. The works in [14]

were implemented using RSS with range-based algorithm in outdoor environment and

achieve an average localization error of 2.6m. The works in [16] have implemented the

system based on fingerprint technique using Nearest Neighbor method and Weighted

K Neighbor method deployed in a small area of 7.4 m * 6.6 m and get the average error

of 1.24 m and 1.01 m respectively.

2.3.5 Bluetooth

Bluetooth is one of the popular technology in ILS because of its low price of the

Bluetooth device and its availability on almost mobile phone which allows users to use

their mobile’s Bluetooth transceiver for localizing their location. This technology is

worked based on IEEE 802.15.1 over the 2.4 to 2.485 GHz ISM band. Bluetooth Low

Energy (BLE) an improved version of Bluetooth has introduced a new hardware

concept called Beacon which broadcast its signal periodically for using in location

estimation. The works in [17] use the RSSI from the beacon’s signal to estimate the

beacon location based on range based technique with trilateration model, this work also

improve the location accuracy by using Particle Filter and achieve the average error of

0.427m. Fingerprint can be also used in Bluetooth ILS as well as in [18], the author

compare the performance of K-Nearest Neighbor (KNN), Neuron Network (NN) and

Support Vector Machine (SVM) in term of model training time and the accuracy. The

best average error of the system is about 4 meter which is belong to kNN-r (kNN

regression) algorithm.

2.3.6 Wi-Fi

Wi-Fi is a most popular wireless technology enabled in almost every internet-connected

device such as computer and mobile phone. The WiFi ILS can utilize the installed Wi-

Fi access point as the anchor so the mobile device can use this system in almost every

building without installing additional anchor. As same as the ZigBee and the Bluetooth,

WiFi uses the same radio band (2.4 GHz) and many similar algorithms can be applied.

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The author in [19] studied the RSS characteristic and system parameters used in Wi-Fi

ILS based on fingerprint techniques such as RSS deviation, grid spacing and number

of AP. An interesting work in [20] proposed the novel method for using the fingerprint

technique without site survey by utilize the user’s movement and construct the

fingerprint map based on the movement. The KMeans was used to predict the room of

tracking user and the correction rate achieve about 90%.

2.3.7 Ultra-Wide Band (UWB)

UWB is the focusing ILS technology due to its sub-centimeter level accuracy. This

technology reduces the effect of interferences and multi-path in indoor environment by

sending impulse signal with high bandwidth on very high radio frequency band from

3.5 to 6.5 GHz. Many time-based ranging estimation method can be used such as the

time of flight (TOF) and time differential of arrival (TDOA) method, this make time

synchronization between the 2 nodes required to be implemented such as two-way

ranging (TWR) method [21]. The works in [22] compared the performance of

DecaWave, BeSpoon and OpenRTLS the UWB localization chipset and confirmed that

the DecaWave and BeSpoon which is based on TOF can be produced less than 20cm

accuracy with the maximum error of 1.5 meter. OpenRTLS is outperform the two

chipsets by using TDOA algorithm with 2cm accuracy and 0.5mm precision.

Base on surveyed technologies, a main aspect to consider the technologies for our

studies is an ease to use of the system. The technologies that are not compatible with

NLOS such as Ultrasound and VLC usually need other type of communication to

exchanging the information such as ZigBee that make the system more complex,

moreover, it may hard to deploy in private area or uninstallable zone. For RFID, the

cheap passive tag has a limited range to operate which is the same problem for LOS

technology while the active tag has an expensive cost and inconvenient conditions such

as its large size and heavy. For the Wi-Fi, there are many researchers who are

contributing to this technology because of its popularity; so, the application is quite

saturated. Therefore, the interesting technologies to study for this work is the ZigBee,

Bluetooth and UWB.

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2.4 Localization Algorithm

The localization algorithm play an important role to give the precise positioning

solution. A suitable algorithm depends on many factor such as technology’s

characteristic, required accuracy, environment and the complexity. Some algorithms

require extra works such as site survey or data preparation to overcome some

limitations. However, the more work doesn’t mean the more accurate system depends

on the mentioned factors. Localization algorithms are generally categorized into 2

approaches which are range-based and the fingerprint-based (also called scene analysis)

approach as following. In this section, the works related to our selected algorithms are

reviewed.

2.4.1 Range-Based

The range-based approach calculates the location based on the estimated range on each

pair of nodes. The estimated range can be calculated by several methods such as RSS,

ToF and TOA as reviewed in the review of localization technologies. In this thesis, we

implemented the trilateration algorithm on UWB technology and the Geo-N on

Bluetooth technology.

2.4.1.1 Trilateration

The works in [26] implemented the UWB localization system based on trilateration

technique and two different ranging models were compared which are RSS and TOA.

The evaluation of the system was tested in 12.5 x 6.36 m2 and gives an average location

error of 0.48m for the RSS model and 0.21m for TOA model. The studies in [27]

applied this algorithm together with the Particle Filter and a fusion of wheel encoder.

The experiment is tested on a staying still, moving through straight line and the running

along the rectangle situation of a robot, the fusion of the UWB, Particle Filter and the

motion controller gives a very high accuracy location on moving robot compared to the

normal position calculated by only odometry.

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2.4.1.2 Geo-N

The Geo-N algorithm is originally proposed by the studies in [13] which lies on the

ideas of filtering out the useless anchors that aren’t contribute the better result. The

farther anchors are usually the huge source of errors due to the signal sensitivity of the

wireless’s device, especially when using the RSS-based ranging. The run-time

complexity and the memory requirement were analyzed which are Ο(𝑁4) and Θ(𝑁2)

respectively. Moreover, the authors also located the homogeneous spatial distribution

characteristic of the position error which mean the accuracy can be sustained in any

location of the coverage area. The works also show the real-world evaluation by

implementing using 2.4 GHz radio transceiver based on TOF technique with Kalman

filtered where the ranging precision is around 2.56m on average, the system achieve

the average location error of 1.55m in dense anchor area which is outperformed the

other compared algorithm. Geo-N also has a better performance when operating in

lacking anchor area. However, the studies that implemented Geo-N algorithm based on

Bluetooth technology is not found, making this research be a first implementation of

Geo-N algorithm based on Bluetooth technology.

2.4.2 Fingerprint-Based

In range-based approach, the range estimation requires ranging models to predict the

accurate distance such as path-loss model which is needed to be fine-tuned to make the

model robust to noise. The fingerprint-based approach eliminates the ranging work but

required additional phase for collecting the environment data in each location of the

deployed area called offline phase. When calculating the real-time location called

online phase, the algorithms do the pattern recognition of the collecting data from the

locating tag compared to the environment data collected in the offline phase. In this

research, the K-Nearest Neighbor algorithm is chosen to be implemented on ZigBee

technology.

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2.4.2.1 K-Nearest Neighbor (KNN)

KNN is a simple algorithm for the pattern recognition process to find the run-time

location from the fingerprint database. The work in [28] deployed the algorithm based

using ZigBee device on an area of 10.5 x 12 m2 compared with Weighted Distance

Fingerprint (WDF), K-Means Clustering and Genetic algorithm. The KNN algorithm

give an acceptable performance of about 80% correction rate for estimating the location

at each fingerprint location while the computational time is relatively low. The works

in [16] also implemented the system with in a 7.4 x 6.6 m2 area and achieve the error

of 1.24m on average. It can be observed that the similar works have relatively small

area compared to our deployed area. Especially, in the studies of [16] the error of 1.24m

is a quite high accuracy because of the deployment area is too small which is not

realistic for the real-world application.

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Chapter 3

ZigBee Indoor Localization System

This chapter explains the design and methodology for the implementation of indoor

localization system on ZigBee WSN (Wireless Sensor Network). This study aims to

analyze the performance and issues for location estimation based on the fingerprint

technique using the SNN (Single Nearest Neighbor) and KNN (K-Nearest Neighbor)

algorithms. An experiment was conducted to evaluate the performance of both the

algorithms based on the positioning error.

3.1 System Design

The deployment of the indoor localization system constitutes of three main parts: (1)

RSSI location protocol, (2) Location estimation algorithms and (3) Hardware of

wireless sensor network. This section describes the system architecture by presenting

an overview for the ZigBee network setup followed by the hardware specifications, the

RSSI (Received Signal Strength Indicator) protocol used and the location estimation

algorithms used for detecting the accuracy.

3.1.1 System Architecture

The indoor localization system is comprised of three main components for any system:

the anchor node, tag node (or location node) and the processing engine. The anchor

node communicates with the tag node to exchange the RSSI data and to forward all the

data to the processing engine, which is usually running on a server or personal

computer.

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Figure 3.1 ZigBee Wireless Sensor Network Setup

In order to benefit from the low power ZigBee technology, the information is

distributed throughout the ZigBee network, which is designed to compensate for the

excessive power consumption required for exchanging data. Unfortunately, the ZigBee

network does not support sending the data from the anchor nodes and the tag nodes

directly to the processing problem due to the limitation of this low energy device.

The gateway node solves this problem by acting as an intermediary for bridging the

data between the ZigBee network and the processing node. The simplest way to send

the data from ZigBee Gateway embedded device to the PC is by using a simple RS232-

UART interface. An application can be developed to contact the Server’s UART

interface for reading the data from the ZigBee network.

Once all the data has arrived at the Processing Engine, it will be processed using the

selected algorithm and the result will be displayed on the application. The application

was developed using Java programming language and the SWT (Standard Widget

Toolkit) framework. Java was selected because of its cross-platform compatibility and

numerous third-party libraries for building the application.

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The ZigBee network comprises of 3 primary components: (1) location gateway, (2)

anchor node and (3) location node. These components are implemented on the

MC13224V chip designed by Freescale (Currently is NXP). The chip includes a 32-bit

ARM7 processor with a 2.4 GHz IEEE 802.15.4 communication module, which is a

power consumption centric designed. Freescale also provides the ZigBee development

kit ecosystem, which contains the ZigBee software codebase called BeeStack. It helps

in quick implementation of ZigBee applications.

The ZigBee documentation [12] specifies the data exchange protocol used by ZigBee

WSN. However, the protocol was not implemented in the ZigBee development

framework developed by Freescale. Since this study uses the Freescale ZigBee

integrated chip (MC13224V), the standard protocol defined in the ZigBee

documentation was implemented.

3.1.2 ZigBee’s RSSI Location Protocol

The most important part of designing the system includes the protocol for exchanging

the RSSI data over the ZigBee network. The RSSI-based localization requires

exchanging RSSI values for location estimation of the mobile node. The ZigBee

standard protocol for exchanging the RSSI values is called the RSSI location cluster.

The ZigBee Cluster Library (ZCL) contains a set of standardized commands and

attributes grouped together in a cluster. The RSSI location protocol is a cluster in the

General functional domain of the ZCL, with the cluster ID 0x000B. The overview for

the protocol is depicted in Figure 3.1.

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Figure 3.2 ZigBee – RSSI Location Protocol

The initialization of the protocol starts by the anchor nodes notifying the gateway of its

existence by sending an Anchor Announce command. In order to collect the RSSI data

from the mobile node and its neighboring anchor node, the gateway sends an RSSI ping

command to the mobile node. The RSSI Ping command tells the mobile node to send

RSSI Pings to the anchor node for N defined times.

After sending the last RSSI ping to the anchor node, the mobile node waits for a few

seconds to ensure that the anchor node has successfully received all the RSSI

commands. Once the wait period is over, the mobile node sends an RSSI request

command to retrieve the average of the RSSI values. The command is followed by an

RSSI response from the anchor node. Finally, the mobile node sends the received

average RSSI value to the gateway, to be used in the location estimation algorithm.

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3.1.3 Location Estimation Algorithm

This study implements the fingerprint-based technique for localization as explained in

Chapter 2. There are 2 main algorithms used for this fingerprint-based localization

technique to classify the samples of the RSSI fingerprints. For the implementation of

the location estimation algorithm using the fingerprinting technique, the fingerprints of

the mobile node are collected in 2 phases, the offline phase and the online phase. In the

offline phase the RSSI values are obtained at a set of predefined locations. For the

online phase, the mobile node sends the current RSSI estimated values, which are used

by both the algorithms to compute the difference of the signal distance (Euclidean

distance).

The location estimation algorithm used in study includes the Single Nearest Neighbor

(SNN) algorithm and the K-Nearest Neighbor (KNN) algorithm. The SNN algorithm

relies on the RSSI values obtained in the offline phase of fingerprinting technique. The

algorithm selects the closest fingerprint to the current obtained RSSI value and sets the

location value to the same value as the one obtained during the offline fingerprinting.

The KNN algorithm addresses the shortcomings of the location estimation based on the

SNN algorithm by selecting the K nearest fingerprints from the sorted fingerprints and

use the locations of the K fingerprints to estimate the current location of the mobile

node (XE, YE) by using the following equations:

𝑋𝐸 = 1

𝐾 ∑ 𝑋𝑖

𝐾

𝑖=0

(3.1)

𝑌𝐸 = 1

𝐾 ∑ 𝑌𝑖

𝐾

𝑖=0

(3.2)

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The (𝑋𝑖 , 𝑌𝑖) in Equation 3.1 and Equation 3.2 represents the selected ith fingerprint

location. Hence, the estimated values are simply the average of the location values from

the selected K fingerprints. Although the KNN algorithm is an improvement over the

SNN algorithm, increasing K does not improve the accuracy of the location estimation.

3.2 Experimentation and Results

3.2.1 Experiment Design

The system was deployed on 5th floor of the National Electronics and Computer

Technology Center (NECTEC) building, which covers a rectangular area of 9 x 57 m2.

The endpoints are installed on a corridor of the floor, which includes 3 anchor nodes

and 1 gateway node. The gateway node can also work as an anchor node, increasing

the anchor node count to 4. The offline phase of the fingerprinting technique used a

total of 171 grid points that were spaced 1.5m apart as depicted in Figure 3.3.

For each grid point, 30 samples of RSSI values were collected. Hence, the fingerprint

is an average of all the received RSSI values. For the experiment, the same grid points

(171 points) were used as testing points. The SNN and KNN location estimation

algorithm was deployed for 30 samples at each grid point. The results include a total of

10,260 location estimations.

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Figure 3.3 Deployment Map

3.2.2 Evaluation

The performance of the ZigBee localization system was assessed using the cumulative

distributive function (CDF) on distance error for all the 10,260 estimated locations. The

results from both the algorithms (SNN and KNN) were compared. The errors observed

from the SNN and KNN algorithm at 50% precision were 5.41m and 4.88m. Whereas

for 90% precision was 17.46m and 16.38m respectively.

It can be seen from Figure 3.4, that the KNN algorithm performs slightly better than

the SNN algorithm overall. The performance of this localization experiment compared

to other studies [23 - 25] is lower. However, it is important to note that these studies

have used a higher number of anchor nodes but deployed in a smaller area, improving

the location estimation accuracy.

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The performance of the localization system can be further improved by using more

advanced techniques such as particle filtering. Particle filtering is a statistical model for

selecting the next particle (position) using probability distribution of current particle

and weight of the next particle. The particle with the highest weight will be selected

more often. This method reduces estimation noise and can be used with fingerprinting

technique for localization.

Figure 3.4 SNN vs KNN localization performance

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Chapter 4

UWB Indoor Localization System

This chapter explains the implementation of indoor localization system based on Ultra-

Wide Band (UWB) technology using trilateration algorithm. The system development

and the mechanism used in this system are explained. Finally, the performance of the

system measured on the deployed system is discussed and problems found in this

system is raised.

4.1 System Design

4.1.1 System Architecture

This study is aimed to measure the performance of UWB positioning based on

TREK1000 evaluation kit which implemented a high accuracy ranging technology

innovated by DecaWave. This evaluation kit consists of integrated DW1000

IEEE802.15.4-2011 UWB Wireless Transceiver IC and the STM32F105 ARM Cortex

M3 Processor.

The device has separated 2 modes which is anchor mode and tag mode which is

configurable by a switch on the board. When the anchor mode is set, the device is

responsible to process the Two-Way Ranging (TWR) mechanism with the tag mode’s

devices to estimate the distance between the two devices through the UWB

communication. The Tag nodes are just listen to the anchor signal to begin the TWR

process and exchange the TWR protocol when it’s needed. Once the TWR process to

all neighbor tag node had finished, the anchor forward the data to a computer through

an UART interface. DecaWave also provided a windows software with source code

that can read the data from the anchor device, then the software estimated the tag

location by doing the trilateration algorithm. Finally, the location of all unlocalized tags

is shown in the software GUI graphically. (Figure 4.1)

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Figure 4.1 ZigBee – RSSI Location Protocol

4.1.2 Asymmetric Double Sided Two-Way Ranging (ADS-TWR)

Due to the UWB signal and transmission characteristic that can eliminate the effect of

multi-path fading, this enable an ability to calculate a “time of flight” (ToF) from a

sending signal where the calculated time can be transformed to be a distance from

Equation 4.1.

𝑑 = 𝑐 × 𝑇𝑜𝐹 (4.1)

where

𝑑 is distance between the measuring node,

𝑐 is the speed of light, and

𝑇𝑜𝐹 is the time of flight.

However, by the requirement of the high-resolution clock synchronization, time of

flight cannot be calculated directly by differencing of sending time and received time

due to the clock drift problem. Therefore, the ADS-TWR mechanism is applied to

eliminate the clock drift time by initiating a ADS-TWR scheme. The scheme is

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composed of 3 sequenced message Poll, Response and Final which begin by the Anchor

to the Tag nodes. The Time of flight can be gathered by Equation 4.2.

𝑇𝑜𝐹 =

(𝑇𝑟𝑜𝑢𝑛𝑑1 × 𝑇𝑟𝑜𝑢𝑛𝑑2) − (𝑇𝑟𝑒𝑝𝑙𝑦1 × 𝑇𝑟𝑒𝑝𝑙𝑦2)

𝑇𝑟𝑜𝑢𝑛𝑑1 + 𝑇𝑟𝑜𝑢𝑛𝑑2 + 𝑇𝑟𝑒𝑝𝑙𝑦1 + 𝑇𝑟𝑒𝑝𝑙𝑦2 (4.2)

where

𝑇𝑟𝑜𝑢𝑛𝑑1 is a round trip time of the Poll and Response message stamped by the

anchor,

𝑇𝑟𝑜𝑢𝑛𝑑2 is a round trip time of the Response and Final message stamped by the

tag,

𝑇𝑟𝑒𝑝𝑙𝑦1 is a processing time used between the Poll message received time and the

Response message transmitted time stamped by the tag, and

𝑇𝑟𝑒𝑝𝑙𝑦2 is a processing time used between the Poll message received time and the

Response message transmitted time stamped by the anchor.

Note that DW1000 IC has an ability to stamp the time when the transmit and

receive event occurs to avoid a delay caused by the slower clock on the MCU.

4.1.3 Trilateration algorithm

The trilateration is a popular range-based algorithm which is finding the unknown

position from the intersection point of 3 sphere created by the anchor position and its

radius. The intersection point can be calculated by this derived equations (Equation 4.3-

5).

𝑥 =

𝑟12 − 𝑟2

2 + 𝑥22

2𝑥2 (4.3)

𝑦 =

𝑟12 − 𝑟3

2 + 𝑥32 + 𝑦3

2 − (2𝑥3𝑥)

2𝑦3 (4.4)

𝑧 = √𝑟1

2 − 𝑥2 + 𝑦2 (4.5)

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where

(𝑥, 𝑦, 𝑧) is the finding intersection point,

(𝑥1, 𝑦1), (𝑥2, 𝑦2), (𝑥3, 𝑦3) is the center point of the 1st, 2nd and the 3rd circle

respectively, and

𝑟1, 𝑟2, 𝑟3 is the radius of the 1st, 2nd and the 3rd circle respectively.



The experiment was conducted on the 5th floor of the NECTEC building, with 3 anchor

nodes for the first test followed by 4 anchor nodes. The goal of the experiment was to

compare the location estimation accuracy using 3 and 4 anchor nodes subsequently.

The experiment was setup in such a way, that for some cases the tag (mobile node) and

anchor node was in non-line of sight (NLOS) and for some cases there was good line-

of-sight (LOS). Additionally, the anchor nodes did not necessarily lie in the line of sight

of each other.

The anchor nodes remain stationary at known positions and known heights throughout

the entire experiment. In contrast, the tag node is moved freely within the experiment

area. All the UWB endpoint locations are given by x-y coordinates. The positions of

the tags were estimated by measuring the distance between the tag node and anchor

nodes using ToF and TWR techniques. All the calculations were done by the

DecaRangeRTLS software running on the PC based on the trilateration algorithm. The

algorithm factors in the number of anchor nodes as input for trilateration computation,

which can be configured manually. Each time the tag node was moved to a new

position, 500-1000 data samples were collected and their position was estimated.

The DecaWave’s TREK1000 evaluation kit used in this experiment has 4 modes of

operation as depicted in Table 4.1. The modes allow switching between different data

rates and frequency channels depending on the requirements of the application. The

TREK1000’s user manual specifies that lower data rate and frequency allows for longer

range of measurements. While performing the experiment, it was noted that the low

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data rate mode increased the latency in computing the tag’s location even in

environments with minimal interference and a good line of sight for even a single

sample. Hence, for this particular experiment only the mode S2 was enabled.

Table 4.1 Operation Modes for TREK1000

Mode (range) Data Rate Channel (frequency)

L2 (long) 110 kbps 2 (3.993 GHz)

L5 (long) 110 kbps 5 (6.489 GHz)

S2 (short) 6.8 Mbps 2 (3.993 GHz)

S5 (short) 6.8 Mbps 5 (6.489 GHz)

The setup for this experiment is depicted in Figure 4.2, where the green triangles

represent the anchor nodes along the corners of the hallway and the blue circles denotes

the locations of the tag. The tag was positioned 1 meter apart on both the x- and y- axis

each time. The first anchor node which will also act as the gateway node is placed on

the lower left corner of the hallway and given the coordinate (0,0) as a reference point

relative to the tag’s location.

Figure 4.2 Deployment Map

The gateway node is connected to a PC running the DecaRangeRTLS software. The

missing blue circles indicate that the results of the tag node at those particular locations

could not be obtained. From Figure 4., it can be seen from the positioning of the tag

node that a rectangular area of 9 x 55 m was covered. The area was an open space with

a cluster of 6 concrete pillars (1m diameter) in the middle, and a conference room on

the extreme right end of the hallway.

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The black markings in the Figure denotes the pillars and the conference room, which

acts as obstruction for the radio signals. The gray area represents open air spaces which

is free of any obstruction, hence the tag cannot be placed in the gray areas. During the

experiment, both the anchor nodes and the tag were placed at fixed height of 130 cm.

4.3 Evaluation

The DecaRangeRTLS software outputs the measurements into a comma-separated-

value (CSV) file. The output includes the measured ranges between the tag and the

anchor node in meters along with the x-y-z coordinates of the tag in meters for each

anchor using DecWave’s implementation of the trilateration algorithm. The key factors

assessed in this experiment was the precision and accuracy of the location estimation

for indoor localization which is presented as the cumulative distributive function of the

distance error. The location accuracy is reported as the deviation of the estimated

position from the actual position, while the precision is given by the percentage. The

distance error is simply calculated using the Euclidean distance between the estimated

coordinate and the actual coordinate of the tag.

It is important to note that for certain locations such as the area behind the meeting

room, the software did not return data. It is assumed that the trilateration algorithm

failed to estimate the location for the tag in such cases. The other important constraint

for the algorithm to work or estimate the location of the tag successfully includes the

tag lying in the field of intersection between all the anchor nodes, else the algorithm

fails to estimate the location.

For all the positions whose location was successfully estimated, a plot of cumulative

distribution function was created (as shown in Figure 4.3). The CDF plot depicts the

performance of the RTLS using 3 anchors and subsequently 4 anchors by the distance

error in 2D (x-y) and 3D (x-y-z). The experiment results show no difference in the

performance of the localization system between the setup including 3 anchor nodes and

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4 anchor nodes. The 2D and 3D graph plotted for both the scenarios were identical.

Almost all the distance error values at 100% precision rate were below 10m. The 3D

performance was worse than the 2D performance overall. At 50% precision the

accuracy for the 3D performance was 3m, whereas for the 2D performance it was

approximately 0.5m.

Figure 4.3 2D (X-Y) and 3D (X-Y-Z) performance comparison

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Chapter 5

Bluetooth Indoor Localization System

This chapter discusses the implementation of the indoor localization system based on

the Bluetooth technology. The concept of IoT (Internet of Things) was applied to

estimate the object location using the Range-based technique with Geo-N algorithm.

The location estimation experiments were performed to evaluate the performance of

this implementation and the results are discussed.

5.1 System Design

5.1.1 System Design

This system was designed to address 3 main issues facing the indoor localization

system, which includes reducing the cost, the scalability factor, and solving the

unsolvable equation problem.

The cost behind the technology used in the localization system is an important factor

for practical application of such systems. As described in previous sections, both

ZigBee and UWB devices have a relatively high cost which impacts the scalability of

the overall system. Due to the low cost of the Bluetooth devices, it is easier to track

multiple objects within the coverage area of the localization system.

The Bluetooth tag is a low-cost and low-powered device that operates by broadcasting

the Bluetooth beacon signal periodically. The signal is read by the Bluetooth reader in

order to extract the RSSI value from the received signal. The Bluetooth reader acts as

an anchor node, that is responsible for sending the tag’s information including the RSSI

value used in calculating the position on the processing engine.

Another important problem is scalability, which is hard to manage when the number of

anchor nodes and tag nodes are increased. For example, in the ZigBee scenario when

the network is larger, the load distribution requires usage of multiple gateways.

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In UWB, the lack of a standardized network protocol presents a significant challenge

for managing the endpoints when scaling up. Therefore, the IoT concept would be the

most suitable for sending data from the anchor node directly to the server over the

internet. This allows scaling to infinite number of anchors using cloud computing. One

of the most suitable media for transmitting data is Wi-Fi due to its popularity and

availability.

The third problem is the unsolvable equation problem. The most popular algorithm for

range-based technique is the trilateration algorithm, that works by calculating the

intersection of at least 3 circular coverage areas of 3 different anchor nodes. However,

when it comes to real implementation, there are great chances that the tag node does

not lie in between the intersection of the coverage area due to the noise of wireless

signal. Consequently, the Geo-N algorithm can handle this problem. The Geo-N

algorithm also provides filters to cut out the tags that do not contribute to better results.

In summary, this system is composed of 3 components: tag node, anchor node and the

location engine. The tag node is of type Bluetooth beacon tag. The anchor node is

simply a Bluetooth reader attached to a microcontroller with a Wi-Fi module. The

anchor node reads the data from the beacon and transmits it over the Wi-Fi network

connected to the internet while forwarding it to the location engine through the MQTT

protocol. The location engine is a server hosted on a cloud service. The server processes

the received data from the MQTT protocol and calculates the location of the tags. Once

the locations are estimated, this data is then sent back through the MQTT service and

displayed on a web application (as shown in Figure 5.1).

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Figure 5.1 System Design for Bluetooth ILS

5.1.2 Hardware Design

The three main hardware devices used in this system are the Bluetooth reader,

Bluetooth tag and the Wi-Fi microcontroller.

For Bluetooth reader, a Bluetooth development kit was used. The board contains an

integrated ARM® Cortex®-M4F processor with Bluetooth reader and the Bluetooth

Low Energy (BLE) Stack. The board is programmed based on Keil Microcontroller

Development Kit (K-MDK), which is used for interfacing with the Bluetooth interface

and reading the Bluetooth beacon broadcasted from the tag. The board also provides a

UART interface that can be used to forward the tag’s information to another controller

responsible for interfacing with the Wi-Fi network.

The Wi-Fi Microcontroller is used to forward the data from the Bluetooth Reader to the

Internet. This controller is an open development board integrated with a 1T1R 802.11n

Wi-Fi module on the Embedded MIPS24KEc (575/580 MHz) processor which is

operated on OpenWrt Linux Distribution. This board also provides a separated 8-bit

Microchip AVR RISC-based microcontroller that is compatible with the Arduino

platform. The Linux and Arduino controller have a UART-Serial connection which

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allows the Arduino controller to transmit data directly to the Linux controller without

any additional connections. The Arduino controller was implemented using Arduino to

read the data coming from the Bluetooth controller via the UART-Serial port and send

to the Linux controller module via interconnected UART-Serial (Figure 5.2). The Linux

controller is programmed by the Embedded Linux Firmware that allows us to create a

software using NodeJS. NodeJS is installed on the Linux OS to establish a connection

to the internet and to let the OS manage the Wi-Fi connection. The NodeJS application

reads the data from the serial interface and transmits the data through the MQTT

protocol to the location engine.

For the Bluetooth tag, it contains a BLE (Bluetooth Low Energy) chip that broadcasts

the device’s universal unique identifier (UUID) over the Bluetooth signal also called

iBeacon. The tag is a card-sized (85.5 x 54mm) iBeacon device based on the DA14580

SoC, which is a low-energy consumption design and is very cost effective.

Figure 5.2 Anchor’s Component Diagram

5.1.3 Bluetooth’s Ranging Model

For gathering the distance between the Bluetooth tag and the anchors for estimating the

location using the positioning algorithm, the distance needs to be calculated from the

given RSSI value using log-distance path loss model as follows (Equation 5.1):

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𝑃𝐿 = 𝑃𝐿0 + 10𝛾 log10

𝑑

𝑑0+ 𝑋𝑔 (5.1)

where

𝑃𝐿 is the total path loss in Decibel which is our measuring RSSI,

𝑃𝐿0 is the path loss at the reference distance 𝑑0,

𝛾 is the exponent for path loss,

𝑑 is the length of the path,

𝑑0 is the reference distance, usually 1 meter, and

𝑋𝑔 is a normal (or Gaussian) random variable with zero mean.

The 𝑋𝑔 parameter reflects the attenuation (in decibel) caused by flat fading, shadow

fading, fast fading or other types of noises. Due to a lack of noise characterization for

this system, the parameter is set to zero.

As seen from the Equation 5.1, there is a need to find the length of the path (𝑑) given

the RSSI value, which will be replaced by the total path loss parameter (𝑃𝐿). The rest

of the parameters that need to be tuned are 𝛾, 𝑃𝐿0 and 𝑑0. The 𝑃𝐿0 and 𝑑0 parameter

can be found by measuring the RSSI at a distance of 1 meter. However, we have to find

the best 𝛾 that provides the best ranging result. In this system the parameter 𝛾 is set to

2.5, which is the smallest average error of each 𝛾 between 2.0 and 3.0 with a stepping

value of 0.1. This comparison was provided by LAI laboratory of NECTEC.

5.1.4 Geo-N algorithm

In the case of UWB localization system, the trilateration algorithm has a unsolvable

equation problem that occurs due to the signal noise. This algorithm was introduced by

authors in [13] to reduce the error caused by the NLOS signal propagation.

Figure 5.3, explains the procedure of estimating the position of the tagged object. The

procedure can be classified to 4 main steps:

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1. Find all the intersection points for every pair of circle created by the anchor

location and its distance, these points will be in IP (Intersection Point) list. If

there is no such intersection the algorithm will approximate the point to be

between those circles and the approximated intersection points is kept in AIP

(Approximated Intersection Point) list.

2. Filter out intersection points from the IP list that are covered by the anchors less

than the number of all anchors minus 2. This filter is called Filter 1.

3. Merge all the intersection points from IP and AIP together. Calculate the sum

of distances to all other intersection points for each of the points and find the

median of those sum of distances. Then, the intersection point which contains

the sum of distances less than the median value will be eliminated. This filter is

called Filter 2.

4. Calculate the centroid of the remaining intersection points and use it as the

algorithm result.

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Figure 5.3 Procedure for Geo-N algorithm

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This experiment was designed to test the overall accuracy of this system. It was

deployed in the NECTEC building on the 1st floor and 3rd floor over an area of 30m x

75m per floor. The testing points were selected randomly and they had to cover every

characteristic of the floor such as the door, the hallway, the lift.

The selected testing points are illustrated as blue dots in the Figure 5.4 and Figure 5.5.

The number on the blue dots indicate the tag number, and there are 2 tags on every

selection location points. There is a total of 58 positions selected including both the

floors. The orange icon on the Figure describes the position of the anchor nodes, they

have been attached on the wall for the selected position. Note that the red dot indicates

the nodes that are covered by less than 3 anchors, so these locations cannot be estimated

by the algorithm.

Figure 5.4 Bluetooth ILS - Deployment Map on 1st Floor of NECTEC building

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Figure 5.5 Bluetooth ILS - Deployment Map on 3st Floor of NECTEC building

5.3 Evaluation

After the experiment, the error of the estimated locations was calculated. The system

gives the performance of 4.57m at 50% precision and 8.06m at 90% shown in Figure

5.6.

Figure 5.6 Bluetooth ILS - Localization Performance

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5.4 Conclusion

The purpose of the implementation for this system was to address the issues

encountered by the previous implementation of the indoor localization systems using

ZigBee and UWB technologies. The aim was to target issues which included cost,

scalability and unsolvable equation problem. This implementation using Bluetooth

technology along with the Geo-N algorithm addresses all the issues mentioned above

and returns precisions of 4.57m at 50% and 8.06m at 90%. However, the limitations of

this system lies in managing the large bandwidth of data being transferred from each of

the anchor nodes independently through the internet.

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Chapter 6

Conclusion and Future Work

This chapter provides a summary overview of the research work and discusses, how the

proposed techniques in this research can contribute to the field of Wireless Indoor

Localization.

The research focuses on implementation and deployment of indoor localization using 3

different technologies and 4 different algorithms. The first phase of this study discusses

the implementation of the Indoor Localization System on ZigBee Wireless Sensor

Network using the Fingerprint-based technique with KNN (K-Nearest Neighbor) and

SNN (Single Nearest Neighbor) algorithm. The second phase involves the

implementation of the localization system based on UWB (Ultra-Wide Band) using the

range-based technique with the trilateration algorithm. The last implementation is based

on Bluetooth technology using range-based technique with the Geo-N algorithm.

The first system was implemented on the ZigBee Wireless Sensor Network to collect

location information and estimate the location based on the fingerprint technique using

the KNN and SNN algorithm. The system was deployed on the 5th floor of NECTEC

(The National Electronics and Computer Technology Center) for the indoor localization

experiment. This system achieved an average error of 5.41 meters and 17.46 meters at

90% precision for the SNN algorithm. For the KNN algorithm, an average error rate of

4.88 meters and 16.00 meters at 90% precision was obtained. This system was designed

to take advantage of the low power consumption of the ZigBee network. The variation

of the RSSI (Receive Signal Strength Indicator) was normalized by using the

fingerprinting technique. The system required more work during the offline phase but

changing environment and unstable RSSI measurements in the indoor environment

caused a great impact on the accuracy.

The second system was developed on the UWB technology using range-based TWR

(Two-way Ranging) technique with the trilateration algorithm. The main idea of this

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study was to measure the performance of the UWB over varying distances. The 2D and

3D characteristics of the localization algorithm considered the effect of the antenna

direction on the range of UWB distances. For comparison and evaluation purposes, the

system was deployed at the same place as the previous system. The implementation on

UWB gives an accuracy of 3 meters on average for 3D evaluation and 0.5 meters of

average error rate for the 2D evaluation. The UWB distance estimation provided a very

high accuracy rate due to the signal characteristics and the TWR technique. The

differences between the 2D and the 3D algorithm influence the system performance.

However, the unsolvable equation problem on the Trilateration algorithm created by

signal’s noise is the main issue on this system. Moreover, the cost of the UWB device

is also a problem for scaling up the experiment and may be ruled out for

commercialization purposes.

The third system was built on the Bluetooth technology using the range-based technique

with Geo-N algorithm. This system was designed as the most cost-effective solution

compared to the other more expensive alternatives. The Geo-N algorithm was used to

solve the unsolvable equation problem observed using the Trilateration algorithm, and

the Kalman filter was applied to stabilize the noise. For evaluation, this experiment

used 50 anchors and 58 testing points deployed on two floors, with the test area of 30

x 75 square meters each. The system produced precisions of 4.57m at 50% and 8.06m

at 90%. The Geo-N algorithm along with the Kalman filter made the system very stable.

However, the main challenge of this system is the distance estimation error due to the

channel characteristics which needs to be further improved.

In conclusion, this research aimed to compare the advantages and disadvantages of each

of the technologies and techniques used for the purpose of wireless localization. There

is very little disparity in terms of performance between all of the systems compared in

this study. However, the systems are compared on various aspects such as performance,

and cost-effectiveness for practical real-world applications as summarized in Table 6.1

and the performance aspect is illustrated in Figure 6.1.

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Figure 6.1 The comparison of localization performance

Table 6.1 The comparison of technologies and algorithms from implemented systems

Comparison

Technology

ZigBee UWB Bluetooth

SNN KNN Trilateration Geo-N

Cost Moderate High Low

Ranging Accuracy - High Low

Precision at 50% 5.41m 4.88m 0.5m 4.57m

Precision at 90% 17.46m 16.38m 1.34m 8.06m

Advantages Built-in network,

Doesn’t need ranging

algorithm

Simple and fast Robust to noise

Disadvantages Need offline phase,

Need more storage

Unsolvable

Solution

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For contribution, this research has developed real applications of the localization

system based on three technologies and four algorithms, then deploy on the same

environment that make a fair comparison for the different systems, and the deployed

area is relatively large compared to the most of studies. Finally, the performance in

various aspects are compared and learned practical problems are explained. Moreover,

this work implemented and evaluated the Geo-N algorithm based on Bluetooth

technology which, to the best of the author’s knowledge, is not found in previous works.

For future work, all advantages from the result should be integrated to a more robust

system, more signal characteristics needs to be studied to improve the distance ranging

accuracy such as Path Loss Model and more intelligent algorithms to adjust the

estimated position using machine learning techniques. For example, Particle Filter and

SLAM (Simultaneous localization and mapping).

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Appendix

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Appendix A

List of Publications

1. Ranron, R., Suksompong, P., and Kaemarungsi, K. (2014). Deployment of

ZigBee wireless sensor network localization system. 29th International Technical

Conference on Circuits/Systems, Computers and Communications (ITC-CSCC

2014) [CD-ROM], 1-4 July 2014, Phuket, Thailand, pp. 885-888.

2. Chantaweesomboon, W., Suwatthikul, C., Manatrinon, S., Athikulwongse, K.,

Kaemarungsi, K., Ranron, R. and Suksompong, P. (2016). On performance study

of UWB real time locating system. 7th International Conference of Information

and Communication Technology for Embedded Systems (IC-ICTES 2016),

Bangkok, Thailand, pp. 19-24.

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