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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
Ref. code: 25605622040664QON
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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ii
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 System Design 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
4.2 Experimentation and Results 27
4.2.1 Experiment Design 27
4.3 Evaluation 29
5 Bluetooth Indoor Localization System 31
5.1 System Design 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
5.2 Experimentation and Results 38
5.2.1 Experiment Design 38
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.
4.2 Experimentation and Results
4.2.1 Experiment Design
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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5.2 Experimentation and Results
5.2.1 Experiment Design
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.
Ref. code: 25605622040664QON
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