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http://www.iaeme.com/IJMET/index.asp 496 [email protected]
International Journal of Mechanical Engineering and Technology (IJMET)
Volume 9, Issue 8, August 2018, pp. 496–507, Article ID: IJMET_09_08_054
Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=9&IType=8
ISSN Print: 0976-6340 and ISSN Online: 0976-6359
© IAEME Publication Scopus Indexed
DEVELOPMENT OF RFID BASED 2D
LOCALIZATION SIMULATION FOR
AUTONOMOUS GUIDED VEHICLE TRACKING
IN INDOOR ENVIRONMENT
Muataz Hazza Faizi Al Hazza*, Nur Izzati Zainal and Mohd Zuhaili Mohd Rodzi
Manufacturing and Materials Engineering Department, Faculty of Engineering,
International Islamic University Malaysia, Kuala Lumpur, Selangor, Malaysia
*Corresponding Author
ABSTRACT
The fast pace of technological development urges manufacturers to upgrade their
systems to compete with the current industry. Autonomous Guided Vehicle (AGV) is
one of the recent trends in the manufacturing. It promotes high efficiency and reduces
labor cost to the manufacturer. As an enhancement feature for the AGV, in this paper,
simulation of 2D localization for tracking an autonomous guided vehicle (AGV) using
RFID in an indoor manufacturing environment is presented. The localization
techniques are based on measuring the distance using path loss model from RSSI
values provided by RFID and coordinates calculation using trilateration algorithm
with multiple reference points. The mathematical process is coded in a software to
produce a graphical user interface (GUI) based simulation for 2D localization of AGV
tracking in indoor environment. Based from the data collected, the results show that
the distance is computed experimentally and theoretically resulted average errors of
4.00 % and the AGV locations is measured from the calculated distance using
trilateration algorithm. Therefore, the simulation can be enhanced for real time 2D
localization of AGV tracking in indoor environment.
Keywords: Localization algorithm, RSSI, trilateration and AGV.
Cite this Article: Muataz Hazza Faizi Al Hazza, Nur Izzati Zainal and Mohd Zuhaili
Mohd Rodzi, Development of RFID Based 2d Localization Simulation for
Autonomous Guided Vehicle Tracking in Indoor Environment, International Journal
of Mechanical Engineering and Technology, 9(8), 2018, pp. 496–507.
http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=9&IType=8
1. INTRODUCTION
The introduction of robots into manufacturing industry gives a huge impression toward
industrial players. In fact, it has galvanized study among researchers and inventors to develop
more prototypes that can contribute to the industry of making. Nowadays, a myriad of
RFID Based 2d Localization Simulation for Autonomous Guided Vehicle Tracking in Indoor
Environment
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automation technologies was designed to bring ease to manufacturing systems. One of the
current trends in factory automation is the implementation of autonomous guided vehicle
(AGV). Although the installation of AGV in the factory requires high initial investments,
considering high labor cost that becomes the major challenge for industries, AGV may
possibly assist the manufacturer to solve the problem. Moreover, manufacturers are normally
aim for tremendous productivity with minimal operational cost. According to market research
report prepared by Grand View Research in 2016, the Asia Pacific regional market is assumed
to experience a huge growth which has engendered an increasing demand for AGV in their
industry [1]. Since AGV promotes solution to the existing issues, its implementation inspires
developers to enhance more upgradation features in it.
Developing an AGV in a flexible factory system is incomplete without knowing its
current location. A real time AGV location provide crucial information for the factory
workers to identify unpredicted circumstances that may happen during production process as
well as to monitor current AGV status. Since the past few years, numerous researches have
been done to explore algorithms that are best suited for localization. Localization algorithm
that is simpler such as trilateration and triangulation offer more flexibility for the developer to
practically implement it in the AGV with predefined coordinates. Although sometimes, a
complex algorithm provides better outcomes, it requires developers to design a device that
can solve its complexity and it might be costly since they need high performance equipment.
Therefore, in this research, trilateration-based localization algorithm is proposed for AGV
tracking in indoor environment.
In identifying the location of AGV, one of the prerequisite parameter that need to be
measured is distance. There are various devices introduced in the market for distance
measurement such as radio frequency identification (RFID), Zigbee and laser. These devices
usually provide received signal strength indication (RSSI) value which assists in distance
measurement. However, for cost reduction, developers opt to choose RFID since its
implementation offers more flexibility. In addition, Market Research Future (MRF) in 2017
had reported that RFID market is growing rapidly over approximately 15.76% of Compound
Annual Growth Rate (CAGR). The CAGR is predicted to reach at nearly USD$ 31.8 billion
by the end of forecast period which is in 2023 as depicted in Figure 1 [2]. The upsurge in
CAGP percentage indicates that RFID technology and demand will kept on blooming over the
year. Therefore, selecting RFID based technology for localization provides more convention
community in the world.
Figure 1 Radio-Frequency Identification (RFID) Market Report [2]
The fusion of RSSI based distance measurement which is obtained using RFID device and
triangulation for localization algorithm gives alternatives to the developer in the market on
how they want to design their system in the factory. Therefore, this research discussed the
Muataz Hazza Faizi Al Hazza, Nur Izzati Zainal and Mohd Zuhaili Mohd Rodzi
http://www.iaeme.com/IJMET/index.asp 498 [email protected]
development of 2D localization simulation for AGV tracking in indoor manufacturing
environment as one of the potential solution as well as providing ideas and suggestion to the
researchers pertaining to the study on localization. In Section 2, some explications of related
works relevant to the topic of this research are discussed. Section 3 elucidates some details
explanation of the methodology involved in this study while Section 4 discussed the outcome
of the research based on the data collected and simulation performance. Finally, Section 5
briefly maps the research into a compact form of narration as a conclusion of the research.
2. RELATED WORKS
Researches on localization require a depth understanding in wireless transmission system in
indoor environment. The communication between RFID reader and tag is done through a
cordless medium. Therefore, there are several factors that could affect the reading process like
multipath propagation. However, RFID reader detection distance varies in different
frequency. Longer reading detection distance will be more affected by multipath propagation
compared to shorter distance. Therefore, this section elaborates some basic knowledge on
RFID, selected RFID operating frequency, RSSI based distance measurement and previous
work on localization algorithm.
2.1. UHF RFID for Localization System
RFID can be categorized into different operating frequency. Each range offer unique
functions suitable to the application involves in the implementation. Basically, there are three
basic ranges which are low frequency (LF, 30-300 kHz), high frequency (HF, 3-30 MHz), and
ultra-high frequency (UHF, 300-3 GHz) [3]. Figure 2 illustrates the frequency range of each
class in electromagnetic spectrum whereas Table I describes the RFID operating frequencies
and their respective passive reading distance.
Figure 2 RFID Frequency Range in Electromagnetic Spectrum [4]
Table I RFID Operating Frequencies [5]
Frequency Range Frequencies Passive Read
Distance
Low Frequency (LF) 120-140 kHz 10-20 cm
High Frequency (HF) 3-30 MHz 10-20 cm
Ultra High Frequency (UHF) 869-928 MHz 3m
This research requires tracking an AGV in indoor environment which needed reading
distance within few metres range. Among the three operating frequency classes of RFID,
UHF RFID is the best selection to be used in this research application. Therefore, UHF RFID
operating frequency is proposed in the hardware implementation.
RFID Based 2d Localization Simulation for Autonomous Guided Vehicle Tracking in Indoor
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2.2. Distance Measurement based on Received Signal Strength Indicator (RSSI)
using Friis Propagation Law
The received signal strength indicator (RSSI) referred to the measurement of power level
from RFID tag which is detected by the UHF RFID reader. There are some previous works on
UHF RFID that explain the usage of RSSI as a parameter to measure the distance between the
tag and the reader. Luh et al. (2013) had conducted a research on the measurement of
effective reading distance of UHF RFID passive tags using RSSI. The research elaborated the
theoretical background which involve the calculation of transmitted and received power in
wireless transmission system [6].
In another research, Dao et al. (2014) had discussed a study on an indoor localization
system using passive RFID. The study applied basic concepts of wireless transmission system
which is based on free space Radio Wave Propagation (RWP) [7]. Free space RWP
measurement is closely related with Friis transmission equation as shown in equation (1)
where in this case, PTag is power received by the RFID tag, PReader is power transmitted by the
reader, GReader is the gain of the UHF RFID antenna, GTag is the gain of the tag and PL is the
path loss [6] (Note that all the parameters are in dBm).
LaderTagaderTag PGGPP ReRe 1
Theoretically, the ideal equation of path loss, PL is shown in equation (2) where d is
referred to distance between the RFID tag and reader and λ is the wavelength of the signal [6].
(Note that the path loss is in dB).
dPL
4log20
2
However, due to various environment designs, path loss will be affected by many factors
[8]. Therefore, the path loss calculation is usually associated with another parameter which is
losses that occur due to the building interference (denoted as PL0). Thus, the model has
changed into an approximately linear log-distance form which is shown in equation (3) where
PL0 is the losses measured in the building and n is the named Ordinary Least Squares (OSL).
dnPP LL log200 3
In previous work done by Lau et al. (2007) in which the research elaborated more on RSSI
based distance estimation using equation (4) where RSSI is the RSSI in dBm, n is the path loss
exponent, d is distance between transmitter and receiver and A is RSSI value at 1meter
distance [9]. Therefore, by using reference distance equivalent to 1 metre, RSSI value can be
calibrated from experimental result. Thus, in this research, equation (4) may be used as
reference for measuring the distance.
)log10( 10 AdnRSSI 4
2.3. Related Works on Localization Algorithm
Over the past years, researchers have been actively studied on indoor algorithm using RFID
due to some demands such as security, safety and service. A lot of techniques proposed by
researchers for RFID based localization application in which the approaches are categorized
into three algorithms; distance estimation, scene analysis and proximity [10] [11].
Table II shows the list of RFIDs based localization algorithms and its class.
Muataz Hazza Faizi Al Hazza, Nur Izzati Zainal and Mohd Zuhaili Mohd Rodzi
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Table II Localization Algorithms and Its Class [11]
Class Algorithm (example)
Distance Estimation
Received Signal Strength
Time of Arrival
Time Difference of Arrival
Phase of Arrival
Angle of Arrival
Scene Analysis K Nearest Neighbor
Probabilistic Approach
Proximity -
Stefan et al. (2016) had performed a study on fingerprinting based 2D localization
algorithm using passive UHF RFID. The study proposed using outputs form multiple signal
classification (MUSIC) and Bartlett beamformer as fingerprint algorithm. The results are
evaluated using four different classification methods which are k Nearest Neighbour (kNN),
J48, Random Forest and Multi Perception Layer (MLP) algorithm [12]. Although the outcome
of the research proven to have the capability in localizing RFID tag within a segment of 0.23
m2 in 97% of the cases, the research approach requires in depth analysis since different places
need new observations. Besides that, the calibration requires a myriad of reference points
which are 1500-2000 points. Therefore, the methods are tough to be installed in a flexible
manufacturing environment.
In another research, Marton et al. (2016) had proposed a study on a robust trilateration
based indoor localization algorithm for omnidirectional mobile robots [13]. The distance is
measured based on time of flight of the signals travelling between the transmitters and
receivers. Then, localization algorithm is furthered calculated using the distance values. This
approach suggests sensor fusion method for fast localization by using measured information
provided by Inertial Measurement Unit (IMU) implemented on the robot. The results show
that mobile robots can be tracked using trilateration algorithm with accurately. However, in a
real-time application, the implementation is usually associated with some unpredicted but
acceptable errors.
2.4. Comparison of Previous Works
The fundamental of this research is focusing on development of RFID based 2D localization
simulation for AGV tracking in indoor environment. Based on the summary of previous
works listed in Table III, inaccurate distance measurement contributes major error in
localization calculation. Although Luh et al. and Dao et al. performed almost similar approach
for measuring distance, this approach is ideal for short distance range measurement [14].
Meanwhile, Stefan et al. use a complex approach of MUSIC algorithm which makes it harder
to physically mount the systems in real world scenario due to its complexity [15]. Besides
that, Marton et al. use IMU for measuring distance which is rarely used in localization
algorithm for autonomous mobile robot.
RFID Based 2d Localization Simulation for Autonomous Guided Vehicle Tracking in Indoor
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Table III Table of Comparison
Author (Year)
Algorithm Approach Limitation
Luh et al. (2013)
RSSI based distance measurement using Friis
transmission formula.
The usage of Friis transmission formula in real life incorporated with unpredictable signal loss which
engenders inaccuracy in the measurement.
Dao et al. (2014)
Distance estimation usng RWP model and kNN
based localization algorithm.
RWP model use similar concept of Friis transmission law. The result is associated with unexpected error due
to multipath propagation from surrounding.
Stefan et al. (2016)
Fingerprinting based localization algorithm
using MUSIC and Bartlett beamformer.
The calibration process requires a myriad of reference points which are 1500-2000 points. This might be due
to small distance detection range of RFID. Besides that, MUSIC algorithm is quite complex to achieve.
Marton et al. (2016)
Time of flight based distance measurement and
trilateration based localization algorithm.
There are a number of complicated process used to identify the location of mobile robots. In addition, IMU
is rarely used in localization application.
2.5. Open Issue
Despite of the contemporary research work discussed, majority of the localization algorithm
are simulated into several fractions. None of the researches design their own simulation
interface to solidify the idea of practically implement the AGV localization system in real
world scenario such as in indoor manufacturing environment. Therefore, in this research,
simulation of 2D localization using RFID for AGV tracking in indoor manufacturing
environment is proposed to provide optional solution to the developer and researcher.
3. METHODOLOGY
Planning an AGV system in a real working environment is a challenging task to the engineers.
Usually, an AGV is mainly programmed to deliver things to the target location. During the
delivery process, the person in charge must observe the AGV’s performance in term of
location, safety, speed, operation and a number of other things. One of the aim of this research
is to simulate the AGV location in real-time. This research proposed a trilateration-based
algorithm using RSSI value measured from RFID reader which is implemented in the AGV
system. The development of the proposed system required an accurate and stable data reading
within a desired distance. The methodology of the system implementation is illustrated in
Figure 3 which is began with path loss model, trilateration technique, predefined AGV
pathway and GUI application development.
Figure 3 Methodology of the System Implementation
Muataz Hazza Faizi Al Hazza, Nur Izzati Zainal and Mohd Zuhaili Mohd Rodzi
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3.1. Path Loss Model based on RSSI for Distance Estimation
The designation of path loss model requires data collection from the experiment. For
simplicity, the RSSI and distance, d abbreviations in Equation (4) are substituted with yrssi and
xd generating the following equation (equation (5)) [16]:
Axny drssi 10log10 5
The path loss exponent, n is approximated to 2.5 (median value of n inside a factory with
no line of sight), based on Table IV which is taken from the recorded data by Shahin in 2008
[16]. The value of n may be varied depend on the percentage error of the results.
Table IV Path Loss Exponent (n) for Different Environments [17]
n Environment
2.00 Free space
1.60 – 1.80 Inside a building, line of sight
1.80 Grocery store
1.80 Paper/cereal factory building
2.09 A typical 15m x 7.6m conference room with table and chair
2.20 Retail store
2.00 – 3.00 Inside a factory, no line of sight
2.80 Indoor residential
2.70 -4.30 Inside a typical office building, no line of sight
By referring the RSSI value from experimentation result which are taken within range of
0.5 meter to 1.5 meter from the UHF RFID, unknown value of A from Equation 5 is
calculated, resulting equation (6).
59log25 10 drssi xy 6
Using equation (5), By referring the RSSI value from experimentation result which are
taken within range of 1.5 meter to 3.0 meter from the UHF RFID, unknown value of A from
Equation 5 is calculated, resulting equation (6). The average percentage error for equation (6)
is 6.3025%. However, to optimize the accuracy level and reduce percentage of error, the n
parameter is varied as shown in Table V. Based on the result depicted below, the value of n is
reselected with lowest percentage of error.
Table V Path Loss Exponent, n and Its Percentage of Error
Path loss exponent, n Percentage of Error (%)
2.0 3.84522
2.1 4.04724
2.2 4.25031
2.3 4.45446
2.4 4.65970
2.5 4.87765
2.6 5.09890
2.7 5.32129
2.8 5.54483
2.9 5.76955
3.0 5.99546
Table V shows that the lowest percentage of error occur when n is equivalent to 2.0.
Therefore, the equation is improved by replacing n = 2.0, resulting equation (7) and the new
average percentage of error measured is 4.91371 %. Figure 4 illustrated the resulted graph of
RSSI versus distance.
RFID Based 2d Localization Simulation for Autonomous Guided Vehicle Tracking in Indoor
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Figure 4 Graph of RSSI (dBm) versus Distance (m)
3.2. Trilateration Technique to Compute AGV Coordinate in x and y-axis
In trilateration algorithm, distance will be the main parameter to solve the equation. This
value is already computed in section 3.1. Therefore, assume that distance between the AGV
and the reference point, Ri is denoted as di where i = 1,2,3 … N, the equation is shown in
equation (7).
222222
222
22 iiiiii
iii
yyyyyxxxxd
yyxxd
7
To simplify the calculation, equation (7) which is non-linear is subtracted with dN2 and di
2
resulting equation (8) which is linear.
2222222222 NNNNiiiiNi yyyxxxyyyxxxdd 8
Unknown value of x and y in equation (8) can be solved using matrix form as depicted in
equation (10). Meanwhile, b and A are represented in equation (11) and (12).
yyyxxxyxdyxd NiNiNNNiii 22222222
9
y
xAb
10
2222
1
2
1
2
1
2222
3
2
2
2
2
2222
1
2
1
2
1
NNNNNN
NNN
NNN
yxdyxd
yxdyxd
yxdyxd
b
11
NNNN
NN
NN
yyxx
yyxx
yyxx
A
11
22
11
2
12
Trilateration algorithm requires 3 reference points (N=3) so that equation (10) can be
solved when matrix A is inversed.
Muataz Hazza Faizi Al Hazza, Nur Izzati Zainal and Mohd Zuhaili Mohd Rodzi
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3.3. Predefined AGV Track in Cartesian Coordinate with Multiple Reference
Point
Before AGV’s coordinate is computed, AGV track is planned and sketched in a cartesian
coordinate system. Assume that the AGV pathway will be installed in indoor manufacturing
environment, the pathway is designed in a circulation layout with scale of 1:1 meter as
illustrated in Error! Reference source not found.. In the predefined AGV track, 15 reference
points labels as R1-R15 are required to ensure AGV’s coordinate computation is possible.
Furthermore, for every point along the AGV track, there should be exactly 3 reference points
detected. Therefore, by assuming the maximum distance for the RFID reader to record a
stable reading is 3 meters, based on all rules stated, the reference points are positioned as
shown in Error! Reference source not found.
Figure 5 Predefined AGV Pathway Layout in Cartesian Coordinate
3.4. GUI Development for 2D Localization Simulation for AGV Tracking
The process is continued with development of GUI. The idea of introducing GUI in this
research is to visually locate the AGV in real time scenario. In this section, the function for
each button in the application is elaborated.
Figure 6 2D AGV Tracking Simulation Application
RFID Based 2d Localization Simulation for Autonomous Guided Vehicle Tracking in Indoor
Environment
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Figure 6 illustrate the menu page of the application. This application consists of seven
button which function are listed in Table VI
Table VI GUI Buttons and Its Function
Button Function
Connect To establish connection between the device and the computer.
Disconnect To terminate connection between the device and the computer.
Read
To initiate reading process of the system. Once data is read, information such as RSSI value and tag ID will be displayed and automatically calculated. Meanwhile, the measured location is visually updated in the AGV layout shown in the application.
Pause To hold the reading process of the system.
Stop To stop the reading process of the system.
Save To save the recorded data by the application (RSSI, tag ID,
distance and location).
Info To show some introduction about the application.
4. RESULTS AND DISCUSSIONS
Further analysis on the experimentation results are elucidated in this section. Besides that, the
GUI application performance are also evaluated and elaborated. The discussion is started with
path loss model for estimating the distance based on RSSI and evaluation of developed
software for 2D localization for AGV tracking.
4.1. GUI Development for 2D Localization Simulation for AGV Tracking
In distance estimation, where RSSI value are collected for generating path loss model, the
actual and experimentation value of RSSI are recorded with distance ranging from 0.5 meter
to 1.5 meter. The RFID reader is programmed to operate at 20 dBm resulting the data as listed
in Table VII Based on calculated percentage error, the overall average error obtained is 4.00
% which is considered low and reasonable.
Table VII Distance, RSSI and Its Percentage of Error
Distance (m)
RSSI (dBm) (experiment)
RSSI (dBm) (theoretical)
Percentage of Error (%)
0.5 -54 -52.98 1.93
0.6 -56 -54.56 2.63
0.7 -59 -55.90 5.54
0.8 -60 -57.06 5.15
0.9 -62 -58.08 6.74
1.0 -62 -59.00 5.08
1.1 -60 -59.83 0.29
1.2 -58 -60.58 4.26
1.3 -59 -61.28 3.72
1.4 -60 -61.92 3.10
1.5 -59 -62.52 5.63
Figure 7 illustrate the difference of RSSI value in experimentation (labelled with blue dot)
and its theoretical value to graphically show the distinction between them. The logarithmic
line shown in Figure 7 represent equation (7) as measured in Section 3.
Muataz Hazza Faizi Al Hazza, Nur Izzati Zainal and Mohd Zuhaili Mohd Rodzi
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Figure 7 Graph of RSSI (dBm) versus Distance (m) (Experimentation and Theoretical)
4.2. Evaluation of Developed Software for 2D Localization for AGV Tracking
Simple approach of trilateration algorithm makes the programming process easier and faster.
Therefore, the AGV tracking can be done in a real time considering fast process computation
performed by the application. By using the GUI application, the data collected from the
application can be recorded and observed. The x and y coordinate are determined based on the
nearest AGV pathway coordinate. Based on the computed coordinate, AGV location is
automatically updated in the simulation as shown in Figure 8 for example.
Figure 8 Screenshot of 2D Localization Simulation for AGV Tracking
5. CONCLUSIONS
This research had produced positive outcomes in providing solution for AGV localization
implemented in the factory. The results show that the designed application can possibly be
installed and used in the manufacturing system. In relevance to the issue faced by developer
who want another alternative for localization algorithm for autonomous mobile robot, this
research had successfully proposed trilateration-based approach for computing the
autonomous mobile robot in predefined pathway. Therefore, the main objective of this
research which is to develop 2D localization simulation for AGV tracking in indoor
environment is successfully achieved.
RFID Based 2d Localization Simulation for Autonomous Guided Vehicle Tracking in Indoor
Environment
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ACKNOWLEDGEMENT
The authors would like to express their sincere gratitude to the Research Management Centre,
International Islamic University Malaysia and the Ministry of Science, Technology and
Innovation (MOSTI). This research was sponsored and published under the project SF15-017-
0067. This work was funded by e-science fund from MOSTI.
REFERENCES
[1] Grand View Research, “Automated Guided Vehicle (AGV) Market Size Report, 2024,”
California, US, 2016.
[2] Market Research Report, “Global Radio-Frequency Identification (RFID) Market
Research Report-Forecast 2023”, US, 2017.
[3] Finkenzeller, K., Different Features of RFID Systems, RFID Handbook: Fundamentals
and Applications in Contactless Smart Cards, Radio Frequency Identification and Near-
Field Communication, John Wiley & Sons, (2010).
[4] Dobkin, D. M., Link Budgets. The RF in RFID: UHF RFID in Practice, Elsevier Science.
2012.
[5] Weis, S. A., RFID (Radio Frequency Identification): Principles and Applications, 2007.
[6] Luh, Y. P., Liu, Y. C., Measurement of Effective Reading Distance of UHF RFID Passive
Tags, Scientific Research, 2013.
[7] Dao, T. H., Nguyen, Q. C. and Hoang, C. A., Indoor Localization System Based on
Passive RFID Tags in International Conference on Intelligent Systems, Modelling and
Simulation, 2014.
[8] Ben-Shimo, Y. and Blaunstein, N., Path Loss Spatial Distribution in Indoor/Outdoor RF
Environment, IEEE, 2012.
[9] Lau, E. E. L. and Chung, W. Y., Enhanced RSSI based Real Time User Location Tracking
System for Indoor and Outdoor Environments, International Conference on Convergence
Information Technology, 2007.
[10] Boontrai, D., Jingwangsa, T. and Cherntanomwong, P., Indoor Localization Technique
using Passive RFID Tags, International Symposium on Communication and Information
Technologies, 2009.
[11] Bouet, M. and Santos, A. L., RFID Tags: Positioning Principles and Localization
Techniques, Wireless Days Conference, IEEE, 2008
[12] Nosovic, S., Ascher, A., Lechner, J. and Bruegge, B., 2-D Localization of Passive UHF
RFID Tags Using Location Fingerprinting, 8th International Congress on Ultra Modern
Telecommunications and Control Systems and Workshops (ICUMT), 2016.
[13] Marchang, J., Ghita, B. and Lancaster, D., Location based Transmission using a
Neighbour Aware with Optimized EIFS MAC for Ad Hoc Networks, Elsevier, 2017.
[14] Marton, L., Nagy, C. and Ambrus, Z. B., Robust Trilateration Based Indoor Localization
Method for Omnidirectional Mobile Robots, European Control Conference, 2016.
[15] Li, Y., Dai, J., Zhou, J., Qi, J. and Zhao, J., An Improved DOA Estimation Method based
on MUSIC and MSCS, International Conference on Information and Communications
Technologies (ICT 2014), 2014.
[16] Oguejiofor O.S., Okorogu V.N., Adewale Abe and Osuesu B.O., Outdoor Localization
System Using RSSI Measurement of Wireless Sensor Network, International Jounal of
Innovative Technology and Exploring Engineering, 2013.
[17] Farahani, S., ZigBee Wireless Networks and Transceivers, Elsevier, US, 2008.
[18] N. Tamilselvan and N.Sivakumar, RFID Based International Library Management
System, International Journal of Library and Information Science (IJLIS), Volume 1,
Issue 1, January- April (2012), pp. 48-60.
[19] Amrita R.Palaskar and Prof Aruna P.Phatale, RFID Based Automated Guided Vehicle
System For Transportation, International Journal of Electrical Engineering and
Technology (IJEET), Volume 4, Issue 4, July-August (2013), pp. 56-61.