Vol. 5, No. 1 January 2014
ISSN 2079-8407
Journal of Emerging Trends in Computing and Information Sciences
©2009-2014 CIS Journal. All rights reserved.
http://www.cisjournal.org
Suitability of Passive RFID Technology for Fast Moving
Vehicle Identification
1
1
H. Khali, 2 A. Araar, E. 3 Zennal Abdulla
Assoc. Prof., Office of Academic Affairs, Canadian University of Dubai, PO Box 117781 Dubai, UAE
2
Assoc. Prof., College of Information Technology, Ajman University, UAE
3
MSIS, College of Information Technology, Ajman University, UAE
ABSTRACT
This research paper aims at analyzing the feasibility of using passive RFID-based technologies and related ALOHA-based
anti-collision algorithms to identify moving vehicles. It has been motivated by the availability of long-range passive RFID
tags that can be acquired at a distance up to 35 meters. The analysis performed in this research work highlights the main
strengths and weaknesses of existing anti-collision algorithms which assume a static RFID tag population and are not
really efficient in identifying mobile tags like vehicles. Several key factors are taken into account, such as: reader’s range,
reader’s reading rate, vehicle speed, and size of tag population. Three main configurations were analyzed: one RFID reader
per traffic lane, two RFID readers per two traffic lanes and one RFID reader per multiple traffic lanes. Simulation models
have been developed in C++ and validated under a certain confidence level to estimate the car loss ratio (CLR). Main
results showed that when slow RFID readers are used, CLR is low when each car lane is assigned an RFID reader.
However, in case of fast RFID readers, several car lanes can share a single RFID reader, leading to a reduced CLR. These
results have been consolidated by a cost analysis which showed the cost effectiveness of sharing the same RFID reader by
several car lanes while maintaining a low CLR. Finally, several design issues have been identified for future research
work.
Keywords: Passive RFID, ALOHA, Vehicle Identification
1. INTRODUCTION
RFID
(Radio
Frequency
Identification)
technology is used in order to identify objects located
within a reading range. RFID offers a means of storing
and retrieving data through electromagnetic transmission
using a radio frequency (RF)-compatible integrated
circuit. Today, RFID has various applications such as
supply-chain tracking, retail stock management, parking
access control, object tracking, electronic security keys,
toll collection and healthcare [1]. An RFID system
consists of a tag made up of a microchip with an antenna,
and an interrogator or reader with an antenna [2]. The tags
are attached or embedded in objects that need to be
identified or tracked. The reader sends out RF waves
which are detected by RFID tags within the range of the
reader. The range of a RFID reader depends on the type of
the tag: passive; semi-active or active. It varies from a few
meters to hundreds of meters. These tags will respond by
sending out their IDs stored in their local memory. In our
application, RFID tags are attached to cars. The reader
will acquire these IDs and sends back through a
communications means to an identification system which
hosts a background database that provides mapping
between IDs and objects. Figure 1 shows a basic
identification process.
Fig 1: Basic RFID-based Vehicle Identification.
The main RFID tag selection factor for vehicle
identification is the availability or not of a power source.
Three types of RFID tags can be considered:
Passive RFID tags: They do not have an
internal source of power. They obtain their
power from the RF waves sent out by the
reader.
b) Semi-passive RFID tags: They use an
internal battery to power the microchip’s
circuitry. However, the communication unit
still gets its power from the incoming RF
waves.
c) Active RFID tags: they possess an
independent power source to run all tag’s
components (microchip and communication
units).
a)
44
Vol. 5, No. 1 January 2014
ISSN 2079-8407
Journal of Emerging Trends in Computing and Information Sciences
©2009-2014 CIS Journal. All rights reserved.
http://www.cisjournal.org
Figure 2 shows the format of a 96-bit passive
RFID tag.
Fig 2: Structure of EPC type 1 Passive RFID tag (96 bits)
Active RFID technology can be seen as the ideal
choice for vehicle identification and tracking applications
due to the long range detection capability (hundreds of
meters). However, active RFID tags are costly and require
battery replacement. The research community is showing
a high interest in using passive RFID technology to
determine vehicle position in supply chain management
applications in general [3] and automatic vehicle
identification in particular [4]. Several countries have
developed systems based on passive RFID technologies to
control toll gates, such as America, Japan and the Salik
system in UAE. Many research companies are
investigating the design and development of passive tags
that can be used for vehicle identification. They mainly
focus their research on two important factors:
o
o
Tag detection range using special antennas.
Vehicle speed.
Based on our research, companies such as OmniID [5] and Gaorfid [6] have reported successful detection
of passive RFID tags at 35 m. In addition to that, Gaorfid
and 7-id [7] have successfully detected cars at more than
200 km/h using passive RFID tags. These major
technology advancements show the possibility of using
passive RFID tags in vehicle identification and tracking at
high speeds.
This research work investigates the suitability of
designing cost-effective vehicle identification systems
using passive tags and combining more than one car lane
per each RFID reader. This will mainly rely on the
analysis of the limits of passive RFID anti-collision
protocols as a function of several key factors such as:
reader’s range, vehicle speed and size of tag population.
The organization of the paper is as follows:
Section 2 presents RFID anti-collision protocols;
section 3 discusses the main design factors in a moving
vehicle identification system; section 4 presents the
simulation models and related result; section 5 concludes
the paper.
2. RELATED WORK
As stated in the introduction, RFID tags are
attached to objects to be identified. In our research work,
each vehicle is identified by a unique passive RFID tag.
When several vehicles are in the range of the RFID
reader, they will all send their Tag IDs at the same time
which will lead to tag collisions. The tags will have to
retransmit their data, which wastes the tag reading time
and degrades the system performance. Solving the tag
collision requires retrieving the tag’s ID accurately with
low transmission power, low computational complexity
and minimum time delay. A good anti-collision algorithm
will try to read all tag IDs correctly or minimize the tag
loss ratio (TLR) [8]. RFID tag collisions can be handled at
physical or MAC layers.
2.1 Anti-Collision Protocols at Physical Layer
The most common techniques are:
o FDMA (Frequency Division Multiple Access);
o TDMA (Time Division Multiple Access);
o SDMA (Space Division Multiple Access);
o CDMA (Code Division Multiple Access);
o CSMA (Carrier Sense Multiple Access).
Most of these solutions are not cost-effective
because they require tags with additional complexity
leading to a higher cost. This is why most applications
seek to solve tag collision problems at the MAC layer.
The additional complexity will be transferred to the RFID
receiver side.
2.2 Anti-Collision Protocols at Mac Layer
At MAC layers, RFID tag collisions can be
resolved using either deterministic or probabilistic
methods.
Deterministic
methods
are
mainly
characterized by the construction of an identification tree
where leaves represent tags. Several algorithms were
derived from this approach such as the bit-arbitration
algorithm, splitting tree algorithm (memory based
protocols), a tree working algorithm, a query tree
algorithm, and a collision tracking tree algorithm
(memory less based protocols). These algorithms split
colliding tags into two subgroups until all tags are
identified [9]. Other variants about tree-based approach
can be found in [10]. The main weakness of deterministic
methods is the need to rebuild the tree for any new
incoming tag which leads to higher delays and significant
memory overhead.
Most of the probabilistic methods are based on
ALOHA approach. In general, before tag responds to the
Reader, it does not check the channel whether it is busy or
free. The ALOHA-based protocols are designed to reduce
the probability of occurring tag collisions [9] and are
divided into four main groups [8, 11]:
o
o
o
o
PA (Pure Aloha);
SA (Slotted-Aloha);
BFSA (Basic Frame Slotted-Aloha);
DFSA (Dynamic Frame Slotted-Aloha).
In a PA protocol, since data is sent when
available without checking the status of the transmission
channel, the expected performance is low. Theoretical
analysis shows that only 18.4% of the time is used for
successful transmissions. In a SA protocol, data is sent at
the beginning of time slots only in order to reduce
collisions. The maximum throughput that can be achieved
is 36.8% which is an improvement of the PA version.
45
Vol. 5, No. 1 January 2014
ISSN 2079-8407
Journal of Emerging Trends in Computing and Information Sciences
©2009-2014 CIS Journal. All rights reserved.
http://www.cisjournal.org
In BFSA and DFSA protocols, time slots are grouped into
frames. In BFSA, the number of slots per frame (M) is
static where is DFSA it is dynamic. In our research work,
we will use the BFSA protocol. This choice is mainly
motivated by two main reasons:
-
Vehicles entering and leaving the reader’s range
at various speeds need to be given more than one
chance to be identified.
The total number of vehicles within the reader’s
range can be estimated as it will be shown in the
next sections.
Figure 3 shows an example related to BFSA
where the number of slots per frame is 3. In this example,
when vehicles enter the reader’s range, they will be asked
to send their RFID tag IDs during a randomly selected
time slot. Two vehicles or more may select the same slot
during the same frame which leads to a collision. These
vehicles may send their RFID IDs during the next frames
for correct identification as long as they are within the
reader’s range. However, when collisions occur during the
last frame (Frame n in figure 3), the vehicles are lost and
couldn’t be identified.
When designing an automatic RFID-based
identification system for moving vehicles, several factors
need to be taken into account. The most important ones
are:
- Number of RFID readers.
- Reader’s range.
- Reader’s tag rate (number of RFID tags read per
second).
- Number of road lanes.
- Vehicle’s speed.
- Vehicles arrival rate (number of vehicles per
hour).
These factors when combined together will have
a direct impact on the performance of the BFSA protocol
and its attributes, namely the total number of frames and
the number of time slots per frame. Let’s assume that R is
the reader’s range; S is the vehicle’s speed; L is the
number of road lanes; T G is the time required to read one
tag successfully and V L the average vehicle length. The
total time T needed by a vehicle to cross the reader’s
range is given by:
T = R/S
(1)
The total number of frames N F available to a
vehicle for correct identification can be estimated by:
N F = T /( M × TG )
(2)
The total number of vehilces N V that can enter
the reader’s range can be estimated by:
NV = L × R / VL
The value of
Fig 3: FBSA Protocol with M = 3.
When RFID tags are properly identified, they
may be muted by the reader to avoid unnecessary
transmission during the remaining frames.
3.
RELATED WORK
In our research work, we will assume that
passive RFID tags are strapped to the moving vehicle
windshield while the RFID reader is usually placed in a
portal on the road as shown in figure 1 [12]. This common
configuration provides the best line of sight between the
reader and the tag. Some research works have studied the
feasibility of having the RFID reader mounted on a car
while the RFID tag is static [13]. This configuration can
be used for applications where moving vehicles require
reading or consuming information when passing near tags
which produce information.
(3)
NV will represent an estimate of
the total RFID tag population that needs to be identified
by the RFID reader. Therefore, in all our research work,
the number of time slots per frame is fixed and at a given
time, only NV vehicles need to be identified by the BFSA
protocol. Three configurations will be investigated:
-
Configuration 1: one RFID reader and one road
lane (see figure 1).
Configuration 2: two RFID readers and two road
lanes (see figure 4).
Configuration 3: one RFID reader and two road
lanes (see figure 5).
46
Vol. 5, No. 1 January 2014
ISSN 2079-8407
Journal of Emerging Trends in Computing and Information Sciences
©2009-2014 CIS Journal. All rights reserved.
http://www.cisjournal.org
Fig 4: Two road lanes / two RFID readers
Fig 6: Simulation Environment.
4.1 Configuration 1
In configuration 1, only one reader and one lane
are used. Several car arrival rates (AR) have been
simulated. Figure 7 and figure 8 show the results for AR =
60 and AR = 360 cars/hour respectively.
Fig 5: Two road lanes / One RFID reader.
Configurations 1 and 2 represent an all-hardware
solution where each RFID reader monitors only one road
lane. Configuration 3 is a cost-effective approach where
each RFID reader monitors more than one road lanes. For
each configuration, we will pay attention to find the
maximum speeds for which the tag loss ratio (TLR) is 0%
and 100% with respect to the design factors presented
earlier. This will help at the system level to choose
between on RFID reader per road lane or one RFID reader
per multiple road lanes.
Fig 7: One reader / One lane (AR=60 cars/hour)
4. SIMULATION MODELS & RESULTS
The three configurations presented in the
previous section have been implemented as discret event
models and programmed in C++. Simulations are
validated using the validation model described in [14, 15]
are supposed to be IID. The complete simulation
environment is presented in figure 6. For all simulations
we will use α = 0.1.
Fig 8: One reader / One lane (AR = 360 cars/hour)
Figure 7 shows that for a reading range of 25 m,
the TLR is 0% up to a speed of 140 Km/H, while it is
100% for a speed exceeding 240 Km/H, assuming AR =
60 cars/h. Figure 8 shows that for the same reading range
47
Vol. 5, No. 1 January 2014
ISSN 2079-8407
Journal of Emerging Trends in Computing and Information Sciences
©2009-2014 CIS Journal. All rights reserved.
http://www.cisjournal.org
(25 m), a TLR of 0% is achieved for a speed up to 120
Km/H, while it is 0% for a speed exceeding 240 Km/H,
assuming AR = 360 cars/h. These results show that for
road with one lane only, the TLR is mainly affected by the
reading range of the reader. Additional simulatins showed
that for a reading range of 35 m, as published by
GaoRFID, TLR = 0% for a maximum speed of 200 Km/h.
This means that under normal conditions, all cars will be
identified.
4.2 Configuration 2
In configuration 2, two readers and two lanes are
used. Figure 9 and figure 10 show the results for AR = 60
and AR = 360 cars/hour respectively. The interferences
between RFID readers are not taken into account and are
out of the scope of this research work.
-
Reader’s bit rate (bits/s): 1000 (slow), 5000
(average) and 20000 (fast).
Car speeds (Km/H): 80, 150 and 200.
Number of lanes: from 2 to 6.
Regarding the reader’s bit rate, it can also be
expressed as the number of tags that can be read per
second. In this case, we will consider 10 tags/s (slow), 50
tags/s (average) and 200 tags/s (fast). We will assume a
reading range of 35 m and BFSA protocols for all
simulations. The total number of frames N F available to a
vehicle for correct identification and the total number of
vehicles N V that can enter the reader’s range are estimated
using equations 2 and 3 respectively. For each simulation,
the TLR is computed.
Scenario 1: Impact of a slow RFID reader
In scenario 1, a slow RFID reader (10 tags/s) is
used. The number of slots per frame is varied, assuming
three different speeds. Figures 11, 12 and 13 show the
related results.
Fig 9: Two readers / Two lanes (AR=60 cars/hour)
Fig 11: TLR variation using a slow reader & S=80
Fig 10: Two readers / Two lanes (AR = 360 cars/hour)
The results shown in figures 9 and 10 are similar
to those shown in figures 7 and 8. This similariy suggests
that the approach of having one reader per lane is scalable
and leads to the best performance. However, the the total
cost increased with the number of readers as well.
Fig 12: TLR variation using a slow reader & S=150
4.3 Configuration 3
In configuration 3, one reader is shared between
two lanes. This configurations aims at reducing the
system cost while maintaining an acceptable pre-set TLR.
To analyze the performance of this configuration, the
following main parameters are used:
48
Vol. 5, No. 1 January 2014
ISSN 2079-8407
Journal of Emerging Trends in Computing and Information Sciences
©2009-2014 CIS Journal. All rights reserved.
http://www.cisjournal.org
Fig 13: TLR variation using a slow reader & S=200
Figures 11, 12, and 13 show that when a slow
RFID reader is used, the TLR is increasing with the car
speed and number of lanes as well. They also show that
the TLR is minimum when the number of lanes is 2 due to
the reduced size of the RFID tag population.
Scenario 2: Impact of an average RFID reader
In scenario 2, an average RFID reader (50 tags/s)
is used. The number of slots per frame is varied, assuming
three different speeds. Figures 14, 15 and 16 show the
related results.
Fig 16: TLR variation using an average reader & S=200
By increasing the speed of the RFID reader from
slow to average, the TLR has decreased. When the speed
S = 150Km/H and the number of slots per frame is 22, the
TLR is around 20% for a number of lanes between 2 to 4.
However, for a speed of 200 km/H, the TLR increases
substantially for the same number of lanes and slots per
frame as well.
Scenario 3: Impact of a fast RFID reader
In scenario 3, a fast RFID reader (200 tags/s) is
used. The number of slots per frame is varied, assuming
three different speeds. Figures 17, 18 and 19 show the
related results.
Figures 17, 18 and 19 show that when a fast
RFID reader is used, the TLR can be substatialy decreased
(less than 1%) even for S = 200Km/H and the number of
lanes = 6, assuming the number of slots per frame is
between 15 and 22. This promising resuts clearly shows
the feasibility of sharing a fast RFID reader by several
lanes while maintaining a low TLR. Finally, this result
can be achieved by a proper choice of the number of slots
per frame to minimize the TLR. This aspect is clearly
illustrated by figure 19.
Fig 14: TLR variation using an average reader & S=80
Fig 15: TLR variation using an average reader & S=150
Fig 17: TLR variation using an a fast reader & S=80
49
Vol. 5, No. 1 January 2014
ISSN 2079-8407
Journal of Emerging Trends in Computing and Information Sciences
©2009-2014 CIS Journal. All rights reserved.
http://www.cisjournal.org
5. CONCLUSION
Fig 18: TLR variation using an a fast reader & S=150
In this research work, authors have investigated
the suitability of passive RFID technology for fast moving
vehicle identification. Key design factor have been taken
into account such reader’s range, reader’s rate, number of
readers, vehicle’s speed, number of road lanes and vehicle
arrival rate. Two strategies have been proposed: 1) each
lane is assigned a reader; and 2) two lanes or more share a
reader. Simulation results show that when slow RFID
readers are used, strategy 1 is recommended. However, it
leads to a higher cost since it increases linearly with the
number of lanes. Strategy 2 is recommended when fast
RFID readers are available. In this case, with a reading
rate higher than 200 tags/s and the use of frame-slotted
ALOHA, simulation results show a tag loss ratio less than
1% is achieved with up to 4 lanes sharing the same reader
and a vehicle speed reaching 200 Km/H. As a future
work, authors will investigate the design of a smart node
which combines RFID and wireless technologies, solar
panels to address applications related to vehicle
identification and tracking.
REFERENCES
[1]
H. Liu, M. Bolic, A. Nayak and I. Stojmenovic, “
Taxonomy and Challenges of the Integration of
RFID and Wireless Sensor Networks”, IEEE
Network Magazine, Vol. 22, No. 6, 2008, pp. 2632.
[2]
H. Liu, L. Cheng and D. Li, “Design of Smart
Nodes for RFID Wireless Sensor Networks”, First
International Workshop on Education Technology
and Computer Science, 2009, pp. 132-136.
In order to estimate the cost of a given hardware
configuration, a research was conducted on the prices of
RFID readers with respect to their throughputs. Table 1
shows the results obtained from GaoRFID [6].
[3]
S. Park and H. Lee, “Self-Recognition of Vehicle
Position Using UHF Passive RFID Tags”, IEEE
Transactions on Industrial Electronics, Vol. 60,
NO. 1, 2013, pp. 226-234.
Table 1: Prices of some GaoRFID passive readers
[4]
W. Hongjian and T. Yuelin, “RFID Technology
Applied to Monitor Vehicle in Highway”, Third
International Conference on Digital Manufacturing
& Automation, 2012, pp. 736-739.
[5]
“Omni-ID
id.com.
[6]
“116041
UHF
Passive
Datasheet,www.gaorfid.com.
[7]
www.7-id.com.
[8]
J.V-Alonso, M.V. Bueno-Delgado, E. Egea-López,
J.J. Alcaraz-Espín and F.J. González-Castaño,
“Characterization of the Identification Process
RFID Systems”, Chapter 2 in Radio Frequency
Identification Fundamentals and Applications,
Bringing Research to Practice, INTEC, 2010.
[9]
Okkyeong Bang, J.H.Choi, D.W. Lee ,H.J.Lee,
(2009), “Efficient Novel Anti-collision Protocols
Fig 19: TLR variation using an a fast reader & S=200
RFID Reader
216012
236023
236018
Throughput
(bits/second)
15000
20000
30000
Price ($)
1150
1395
1495
Table 1 shows that a reasonable trade-off
between cost and performance can be achieved in order to
design a cost effective RFID system. As an example, it is
possible to double the troughtput of a passive RFID reader
at a 30% cost increase (216012 vs. 236018). This fact, in
addition to our simulation results, show the strength of
our proposed approach which is to share a fast RFID
reader among several lanes in order to design a cost
effective RFID vehicle identification system.
ULTRA”,
Datasheet,
Tag:
www.omni-
ULTRA”,
50
Vol. 5, No. 1 January 2014
ISSN 2079-8407
Journal of Emerging Trends in Computing and Information Sciences
©2009-2014 CIS Journal. All rights reserved.
http://www.cisjournal.org
for Passive RFID Tags,” Auto-ID Labs White
Paper
WP-HARDWARE-050,
www.autoidlabs.org.
[10]
P. Pupunwiwat and B. Stantic, “Unified Q-ary Tree
for RFID Tag Anti-Collision Resolution”,
Proceeding of the 20th Australian Database
Conference (ADC 2009).
[11]
Dheeraj K. Klair, Kwan-Wu Chin, RaadRaad: A
Survey and Tutorial of RFID Anti-Collision
Protocols. IEEE Communications Surveys and
Tutorials 12(3): 400-421 (2010).
[12]
S.R.M. Gonzalez and R. L. y Miranda, “Passive
UHF RFID Technology Applied to Automatic
Vehicle Identification”, Chapter 9 in Advanced
RFID Systems, Security, and Applications, IGI
Global, 2013.
[13]
Eun-Kyu Lee, Young Min Yoo, Chan Gook Park,
Minsoo Kim & Mario Gerla, "Installation and
Evaluation of RFID Readers on Moving Vehicles",
Proceeding of Vehicular Ad Hoc Networks,
September 2009, pp. 99-108.
[14]
Fishman G., Discrete-Event Simulation: Modeling,
Programming and analysis, Springer-Verlag,
Berlin, 2001.
[15]
Headrick T., Fast fifth-order polynomial transforms
for generating univariate and multivariate no
normal distributions, Computational Statistics and
Data Analysis, 40 (4), 2002, pp. 685-711,.
AUTHOR PROFILES
Hakim Khali is an associate professor at the Canadian
University of Dubai, UAE. Dr. Hakim Khali obtained his
BSc in computer engineering from INI, Algeria, in 1989,
and his MSc and PhD in 1993 and 2000, respectively,
from Ecole Polytechnique of Montreal, Canada. His
research interests are hardware-software co-design, RFID
applications, VLSI architectures, and FPGA-based
designs for image processing and cryptography. Dr.
Hakim Khali also worked as a system designer for
Mirotech Microsystems on reconfigurable computing
systems. He is an IEEE Senior member.
Abdelaziz Araar is an associate professor at the college of
Information Technology at Ajman University of Science
and Technology Network, UAE. He received his BSc in
computer science from University of Annaba in 1983, his
MSc in computer science in 1986, and then his PhD in
1991 from Case Western Reserve University, Cleveland,
Ohio, USA. His area of interest is mainly on advanced
simulation
models
for
networking,
wireless
communication,
intelligent
systems,
and
other
applications.
51