Int. J. Communications, Network and System Sciences, 2012, 5, 534-547 Published Online September 2012 (
Context-Aware Rate-Adaptive Beaconing for Efficient and
Scalable Vehicular Safety Communication
Alvin Sebastian, Maolin Tang, Yanming Feng, Mark Looi
School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane, Australia
Received May 31, 2012; revised July 11, 2012; accepted August 14, 2012
Vehicular safety applications, such as cooperative collision warning systems, rely on beaconing to provide situational
awareness that is needed to predict and therefore to avoid possible collisions. Beaconing is the continual exchange of
vehicle motion-state information, such as position, speed, and heading, which enables each vehicle to track its
neighboring vehicles in real time. This work presents a context-aware adaptive beaconing scheme that dynamically
adapts the beaconing repetition rate based on an estimated channel load and the danger severity of the interactions
among vehicles. The safety, efficiency, and scalability of the new scheme is evaluated by simulating vehicle collisions
caused by inattentive drivers under various road traffic densities. Simulation results show that the new scheme is more
efficient and scalable, and is able to improve safety better than the existing non-adaptive and adaptive rate schemes.
Keywords: VANET; DSRC; Vehicular Safety Communication; Safety Applications; Adaptive Beaconing; Context-Aware
1. Introduction
Recent advances in wireless communication technology
have resulted in the development of a Cooperative Colli-
sion Warning System (CCWS) that can actively prevent
accidents, and therefore may improve road safety sig-
nificantly. Several concepts and prototypes of the CCWS
have been proposed and developed [1-3], demonstrating
the technical feasibility of the CCWS. The CCWS works
by having vehicles to continually exchange safety mes-
sages via wireless ad hoc networks. The safety messages,
termed as beacon messages, contain up-to-date vehicle
state information, such as position, speed, heading, and
other kinematics or motion information. The dissemina-
tion of beacon messages, termed as beaconing, allows
each vehicle to realize and track the existence and the
state information of its neighboring vehicles within a cer-
tain range. Using the state information, each vehicle can
predict any possible collision and provide early warnings
to its driver accordingly.
The wireless technology used in the CCWS will be
based on the IEEE 802.11 p [4] and the IEEE 1609 Wire-
less Access in Vehicular Environments (WAVE) [5]
standards. Extensive studies on the performance of the
standards [6,7] indicate that the standards can provide an
adequate signal reception in an environment with high-
speed mobility. However, the standard alone cannot en-
sure time-critical message dissemination in dense road
traffic conditions, such as in traffic jams. Dense traffic
conditions induce a high communication channel load,
which causes a higher rate of packet collisions and sig-
nificantly deteriorates the communication performance
[6]. To ensure fast and reliable delivery of beacon mes-
sages to all relevant vehicles in any traffic conditions, it
is necessary to develop application level protocols that
can utilize the communication channel more efficiently.
Typical CCWS and other safety applications assume
that a vehicle broadcasts beacon messages periodically at
a constant rate of ten messages per second [8]. The con-
stant rate beaconing strategy is simple and easy to im-
plement, but is not scalable to various road traffic situa-
tions. Road traffic is a very dynamic environment, in
which the vehicle density can vary significantly over
time. If the broadcast rate and other parameters such as
radio range and packet size are constant, the communica-
tion performance can vary depending on the vehicle den-
sity. A dense traffic condition may lead to a high rate of
packet loss that can compromise the safety performance
of the CCWS significantly. Therefore, to reduce channel
congestion and improve communication performance,
the beaconing rate should be continuously adapted to the
traffic situation [9,10]. Existing rate-adaptive beaconing
schemes [11-13] are designed to improve mainly the
communication performance. However, they do not con-
sider the differences in the danger severity of an interact-
tion between two vehicles that may lead to a possible
collision. By prioritizing vehicles based on their danger
severity, it may be possible to further improve the safety
opyright © 2012 SciRes. IJCNS
In this article, we propose a new context-aware bea-
coning scheme that considers the danger severity of ve-
hicle interactions in reducing the beaconing rate. For
example, Figure 1 shows a simple traffic situation where
vehicles v1, v2, and v3 are following each other with an
unsafe following distance while vehicles v4 and v5 are
moving independently. Assuming a high channel usage
in the vicinity, each vehicle needs to cooperatively re-
duce their beaconing rate. Because of the unsafe condi-
tions, reducing the beaconing rate of vehicle v1 may sig-
nificantly increase the possibility of collisions with vehi-
cles v2 and v3. In contrast, reducing the beaconing rate of
vehicle v5 will not significantly increase the possibility of
collisions between v5 and other vehicles. Vehicles that
endanger other vehicles such as v1 and v2 should have a
higher beaconing rate compared to vehicles that are
unlikely to endanger other vehicles, such as v4 and v5.
The original contribution of this work is a new bea-
coning scheme that continuously adapts the beaconing
rate to the estimated channel load and the danger severity
of the interactions among vehicles. The objective of this
research is to optimize the beaconing rate of each vehicle
in order to improve the capability of the CCWS collision
prevention in various traffic conditions. The improve-
ment is achieved by controlling channel usage to avoid
congestion, and most importantly, by prioritizing the
most endangered vehicles. The performance of the new
scheme is evaluated by simulating vehicle collisions
caused by inattentive drivers. Simulation results show
that the adaptive rate scheme consistently provides a bet-
ter safety level on highways in various traffic densities
compared to the existing constant and adaptive rate
The rest of this article is organized as follows: Section
B, where tAC tA (tAC + cA)
and tBC tB (tBC + cB), such that tA tB signifies a
route contention. The contention time windows cA and cB
are determined by considering the intersection angle
C =
ACB and each vehicle size and speed.
sin tan
sin tan
If there is a route contention then the avoidance times
3A and
3B are calculated using the following equations:
 
of the outgoing edges of vi:
3.4. Determining Danger Severity
Since a vehicle can endanger more than one other vehicle,
the beaconing interval should be adjusted according to
the interaction that has the highest danger severity. Given
the interaction graph G = (V, E), the maximum danger
severity of a vehicle vi V can be obtained from the in-
teraction graph by finding the highest weight
ij from all
Copyright © 2012 SciRes. IJCNS
ij j
vv E
max, each subject vehicle calculates the sum of
aximum weight
The sum of maxm weight
reflects a temporary
log lcal knowledge of the beaconinoad within the radio
range of the subject vehicle. If a vehicle knows the value
in its neighboring area, it can estimate the beacon-
ingte of other neighboring vehicles, which is equiva-
lent to the beaconing load. The value of
of each subject
vehicle is included in every beacon mesge sent. Hence,
each vehicle can obtain the sum of
for all its neighbor-
ing vehicles, defined as v
, v . The total sum of
danger severity
max in itseighboring area is calculated
by finding the largest v
xmax v
3.5. Rate-Adaptive Beaconing Protocol
e has been
Algorithm 1. Context-aware adaptive rate protocol.
1) f
2) if |V| = 1 then
3) Calculate default interval Is using Equation (19)
4) return Is
5) Calculate interval I using Equation (20)
6) if I < Imi n then I Imin
7) else if I > Imax then I Imax
8) return I
9) procedure SendMessage()
10) Get the vehicle self state v from the positioning system
11) Update(G,v)
12) Create a new beacon message m that contains the current self
13) Transmit m using WSMP
14) t
prev tnow
15) I
new CalculateInterval()
16) Execute SendMessage() after interval Inew
17) procedure ReceiveMessage(m)
18) Retrieve the vehicle state vi from m
19) Update(G,vi)
20) if tprev is define d then
21) Cancel any scheduled transmission
22) I
new CalculateInterval()
23) Inow tnowtprev
24) if Inow < Inew then
25) Execute SendMessage() after interval (InewInow)
26) else
27) SendMessage()
The proposed concept of rate adaptation schem
developed and implemented as a Context-aware Adap-
tive Rate (CAR) beaconing protocol. Algorithm 1 de-
scribes the CAR protocol, which in principle works as
unction CalculateInterval()
1) When a vehicle receives a beacon message from
as used to
bmay not have the
s interaction graphs. However,
cost likely have similar in-
message repeatedly with a
tction graph. Whenever the interact-
min is the lower bound that is
uto the smallest rea-
sed 126 km/h
(35 m/s) can travel 1.75 m within an interval of 50 ms.
Tmall enough to
ghan two meters.
TImax is the upper bound that is
u reasonable value.
Fo one second
t message is always sent at least
rn byk. The syst cl
ced by using the GPS.
ehicle speed using Equation (19):
nother vehicle, information from the message i
tion graph, which is locally mapdate the interac
y the vehicle. Two different vehicles
ame information in their
losely spaced vehicles will m
2) A vehicle sends a beacon
ynamic interval, which is calculated using a funct
hat utilizes the intera
ion graph is updated or modified, the interval is also re-
interval IThe minimum
sed to limit the beaconing interval
onable value. For example, a vehicle at spe
hissval is s
tance error of less t
means that the 50 m inter
ive a reasonable dis
imhe maxum interval
sed to limit the interval to the largest
or example, the Ix parameter can be set t
ato ensure th a beacon
ne every second. A time tnow is the present or most cur
the system clocemockent time give
an be globally synchroniz
The CalculateInterval() function calculates
the beaconing interval based on the danger severity of the
current road traffic situation. If a vehicle has no neigh-
boring vehicle, which means that there are no other vehi-
cles within its communication range, this function returns
a default interval I'. The default interval is calculated
based on the v
min min
if 0 and
where s is vehicle current speed and et is an error toler-
ance threshold. A higher speed will result in a smaller
interval to keep a possible distance error less than the
threshold et. The threshold et is a parameter that can be
set based on an assumption of acceptable position or dis-
tance error in the CCWS. If a vehicle has one or more
neighboring vehicles, this function returns the interval I
calculated using Equation (20):
max max
max maxmax
The formula calculates an interval proportionally based
on a vehicle’s danger severity
max and the sum of
neighboring vehicles’ danger severity
max, in which the
resulting channel load is restricted to the maximum bea-
coning load
max. The resulting interval I is bounded to
Copyright © 2012 SciRes. IJCNS
the minimum and maximum interval such that Imin I
A vehicle v starts sending beacon messages after its
engine has been started. A beacon message is transmitted
by invoking the SendMessage() procedure. This pro-
cedure first acquires current vehicle sel
position, speed, and heading, from the positioning system.
The Update (G, v) procedure updates and recalculates the
ReceiveMessage() procedure is called when
a vehicle v receives a beacon message m
vehicle vi. This procedure decodes the state of vehicle vi
from m and updates the interaction graph G of vehicle v
with the
extending the
ing error on neighboring vehicles. To clearly demonstrate
fety and communication performances
ncy and scalability. A scheme is effi-
e, the number
it is received by
y gives a better chance for
he actual channel usage is
ceived by
f state, such as
interaction graph G with the new information. A new
data packet that encodes the state information is created
and transmitted using WAVE Short Message Protocol
(WSMP) as defined in the IEEE 1609.3 standard [5]. The
time of transmission is kept in tprev. The next beacon
transmission is then scheduled by executing the Send
Message() procedure after an interval calculated by
the CalculateInterval() function.
from another
new information. Every time a beacon message
is received, it is likely that the interactions among neigh-
boring vehicles have changed. Therefore, any scheduled
beacon transmission is canceled and the next transmis-
sion is scheduled with a new interval. The new interval
Inew is calculated using the CalculateInterval()
function. The actual current interval Inow is the duration
elapsed since the last transmission time tprev until the
current time tnow. If the new interval is longer than the
actual current interval, the next beacon transmission is
then scheduled at time InewInow. Otherwise, a beacon
message must be sent immediately by invoking the
SendMessage() procedure.
4. Evaluation
The performance of the Context-aware Adaptive Rate
(CAR) scheme is evaluated by performing an integrated
simulation of a vehicular wireless network, vehicles
moving on a straight road with multiple lanes, and colli-
sions between vehicles caused by unsafe situations. The
simulation program is implemented by
-3 network simulator (version 3.8) [27].
The performance of the CAR scheme is compared
with several Constant Rate (CR) schemes and an existing
Probabilistic Adaptive Rate (PAR) scheme [12]. In the
CR schemes, beacon messages are periodically sent at a
constant interval. Four different intervals were selected
and represented by the CR-50 (50 ms interval), CR-100
(100 ms interval), CR-200 (200 ms interval), and CR-500
(500 ms interval) schemes. The PAR scheme is a rela-
tively recent adaptive beaconing scheme that improves
tracking accuracy under various traffic conditions by cal-
culating transmission probability based on suspected track-
the benefits of the new context-aware adaptive technique,
all the compared schemes are implemented without using
any kind of position prediction model.
4.1. Performance Metrics
We evaluate the sa
in terms of efficie
cient if it generates less network load to maintain a cer-
tain safety level. A scheme is scalable if it is able to
maintain safety and communication performances in
various traffic scenarios with different density.
The aim of the CCWS is to improve road safety by
preventing vehicle collisions caused by the error or lim-
ited perception of human drivers. Therefor
vehicle collisions is used as the metric to assess the
safety performance (as in [28]). A beaconing scheme has
a better safety performance if using the scheme results in
a smaller number of potential vehicle collisions. The
number of potential vehicle collisions is measured by
simulating an accident scenario on a typical highway. To
study the effect of different beaconing schemes on the
number of potential collisions, the simulation is designed
in such a way so that a collision will occur only if a bea-
con message is not received in time.
The communication performance involves the metrics
of dissemination latency or delay, actual channel usage,
and probability of message reception. The latency is the
duration between the time when a beacon message is sent
to the MAC layer and the time when
other vehicles. A lower latenc
a vehicle to avoid a collision. T
measured by averaging channel busy time from all nodes
during the simulation time. As such, the measured usage
includes the PHY and MAC protocols overhead. Higher
channel usage increases the possibility of channel con-
gestion. The probability of message reception is the prob-
ability that a beacon message is successfully re
node located at a particular distance from a sender node.
Higher probability of message reception indicates fewer
packet collisions.
A good overall performance is indicated by both safety
and communication performance. This means that a good
scheme must achieve a low number of collisions, low
latency, low channel usage, and high probability of mes-
sage reception. However, emphasize is given to the
number of potential collisions metric since the ultimate
goal of the CCWS is to improve safety.
4.2. Simulation Design and Setup
4.2.1. Wireless Communication
ch vehicle repeatedly sends beacon messages during
the simulation duration at an interval determined by the
beaconing schemes. For example, the CR-100 scheme
Copyright © 2012 SciRes. IJCNS
sends a beacon message every 100 milliseconds. The
beacon message size is set to a constant value of 500
bytes, excluding the MAC protocol specific header. A
constant message size is used to provide a consistent
comparison result. The transmission power is configured
to 19 dBm. The probabilistic Nakagami distribution is
selected for the radio propagation loss model, as field
tests on highways showed that the Nakagami distribution
is suitable to be used on vehicular communication in
highway scenarios [19]. The parameter of m = 1 is set to
simulate severe fading conditions; therefore, demon-
strating the beaconing performance in the worst case
The parameters for PHY and MAC protocols are set
according to the IEEE 802.11 p draft standard, which
operates at 5.9 GHz on a 10 MHz control channel (CCH).
The PHY data rate is configured to 6 Mbps, which is the
optimal value for safety communication [29]. The chan-
nel switching scheme is currently not implemented, so
the CCWS applications can utilize the entire 10 MHz
CCH bandwidth. The MAC layer is configured to ad hoc
A mechanism as
riority for beacon mes-
mode with QoS support using the EDC
described in IEEE 802.11e. The p
sages is set to AC_VI (second highest). All beaconing
schemes are implemented as application level protocols
in the simulator that use the IEEE 1609 WAVE Short
Message Protocol (WSMP) [5]. Common configuration
parameters related to communication are summarized in
Table 1.
4.2.2. Road Traffic and Accident Scenario
The simulation of vehicles moving on a road is staged on
a typical multi-lane 2 km highway as illustrated in Fig-
ure 3. To demonstrate the scalability of the CAR scheme,
five scenarios with different average vehicle densities are
evaluated: VD-30, VD-60, VD-90, VD-120, and VD-150.
The vehicle density starts from 30 vehicles/km (VD-30)
up to 150 vehicles/km (VD-150). Each scenario is de-
signed with different numbers of vehicles and lanes to
create a realistic situation with a desired density. Table 2
shows the parameters of the scenarios. The number of
Table 1. Common configuration parameters.
Parameter Value
PHY and MAC protocol 802.11 p
802.11p data rate 6 Mbps
Propagation loss model 1 Three log distance
Propagation loss model 2 Nakagami
Transmission power 19 dBm
Beacon message size 500 bytes
Beacon priority level AC_VI
CAR parameters Imin = 50 ms, Imax = 1000 ms
vehicles on each lane is randomized. Vehicles on the
same lane travel at the same speed, which is determined
based on the vehicle density. The distance between two
alue is en-
void tnce of
the CC by
ahat some drivers e distracted or inatten-
ter canptly react to avoid a
cleading vehless they are warned
bent the con, the CCWS must
wright of warning
i the ate of neighboring
vate beacones were not promptly
rng calcull be inaccurate, and
a cocur accordinTherefore, the safety
perfferent bng schemes can be
e the numbe that
consecutive vehicles di,j is random, but the v
sured to be greater than the required safety distance. As
such, a collision is always avoidable provided that a
beacon message is received on time. For each scenario,
simulations with different random seeds were performed
50 times. Each simulation instance uses a random road
traffic situation (random speed and inter-vehicle dis-
tance). Results from the simulation are averaged from the
50 runs.
The simulation implements a basic CCWS function for
each vehicle. If a collision is likely to occur, the CCWS
arns the driver, which will then stop the vehicle to w
ahe collision. To evaluate the safety performa
WS, collisions between vehicles are simulated
ssuming tbecom
ive. A distracted drivnot prom
ollision with a
y the CCWS. To prev
icle, un
arn the driver at the time. The timing
s calculated based ontracked st
ehicles. If up-to-d messag
eceived, the warniation wil
llision may oc
formance of di
valuated basedr of potential collisions
cannot be prevented.
The percentage of distracted or inattentive drivers in
each simulation instance is determined using a parameter.
The performance of the beaconing schemes can be fully
demonstrated by using a worst case scenario that as-
sumes all the drivers are inattentive. However, to make
the simulation more realistic, the number of inattentive
Figure 3. Illustration of the simulated highway scenarios.
Table 2. Specific parameters for scenarios with different
vehicle densities.
Scenario Number of
Number of
Speed variation
range (m/s2)
VD-30 60 2 25 - 30
VD-60 120 3 15 - 25
VD-90 180 3 10 - 15
VD-120 240 6 15 - 25
VD-150 300 6 10 - 15
Copyright © 2012 SciRes. IJCNS
drivers is set to 15 percent of the total vehicles in each
scenario. The percentage is obtained from statistics of
letely right at the end of the road. To avoid a collision, a
following vehicle must decelerate at the right time de-
pending on the relative position and speed of its leading
vehicle. A normal vehicle will start decelerating based on
the calculation using the actual position and speed of its
leading vehicle. A vehicle with a distracted driver will
start decelerating only after its warning system predicts a
eed of its leading vehicle, instead of the actual position
ollisions depending on the interaction
between vehic
n pa relatehe vehin
in . Drivs reaction time is set tot
value of 1.5 s. A minimum inter-vehicle gap of 2 m is
use toleranffer in thcalculationision
pre. The olerance threshold et is set to the
sae as thinimum g A simulattance
finin allcles stop mng.
ver inattention in the US [30], which are based on the
analysis of five years of data.
The simulation models a situation when vehicles stop
at a red traffic light. Each vehicle at the front end of each
lane vlaneId,1 will start decelerating normally at 4.9 m/s2
when approaching the end of the road, until it stops com-
ollision based on the known (tracked) position and
and speed. Inaccurate position and speed prediction may
result in some c
rametersCommod to tcles are give
Table 3er’ a constan
d as ace bue of coll
dictionerror t
me value map.ion ins
shes whe vehiovi
4.3. Simulation Results
Since the CAR scheme is expected to perform differently
given a different maximum beaconing load, the perform-
ance of CAR scheme is firstly evaluated by varying the
max parameter from 0.1 to 1.0. The average results from
all scenarios show that the parameter
max = 1.0 gives the
least number of collisions. However, there is no signify-
cant difference in the number of collisions with
0.6, in which all the average collisions are below 0.2.
The distance error is measured from the simulations by
accumulating the distance between the tracked position and
the actual position of a vehicle every 100 ms and aver-
aging the result. The results indicate that the error de-
creases significantly as the
max increases. A higher value of
max implies a shorter beaconing interval, which also re-
sults in a higher actual channel usage. And as expected in
a wireless network that uses the CSMA MAC protocol,
Table 3. Common parameters for the highway scenario.
arameter Value
Driver’s reaction time 1.5 s
Vehicle length 4 m
Min. inter-vehicle gap 2 m
Vehicle deceleration 4.9 m/s2
Highway length 2000 m
the overall probability of message reception decreases as
the channel usage increases. Although the CAR scheme
tion probability, it
scen e
beaconing rate of 2 messages per second enough in
msure safety. The CR-scheme has
the best average result compared to the otR schemes.
Terformance comparonly
iom CR-100, PAR, a schemes.
heme can prevent all pcollisions
max = 1.0 has the lowest recep
has the fewest number of collisions. From the initial
evaluation, we conclude that the CAR scheme performs
the best using the parameter
max = 1.0. The results also
confirm the proposition that safety performance cannot
be measured solely by the tracking accuracy metric or by
the communication performance such as successful mes-
sage reception rate.
The performance of the CAR scheme with
max = 1.0
is then compared to CR and PAR schemes. The average
number of collisions resulting from the use of each
scheme in each scenario is shown in Table 4. The total
average of the results from all scenarios indicates the
overall safety performance of each scheme. From the
safety perspective, the CR-50 scheme has the worst per-
formance in the scenario with the highest vehicle density
(VD-150). Such a result indicates severe channel conges-
ion because the channel capacity is overloaded. The t
CR-500 scheme has the worst performance in all othe
arios (VD-30 to VD-120), which indicates that th
is not
ost situations to en100
her C
herefore, further pisons will
nclude the result frnd CAR
The CAR scotential
in the VD-60 and VD-90 scenarios and has the lowest
number of collisions in the VD-120 and VD-150 scenar-
ios. The total average shows that the CAR scheme has
the best safety performance, followed by the PAR, CR-
100, CR-200, CR-500, and CR-50 schemes. The average
number of collisions for the CAR scheme in every sce-
nario is always less than 0.23, which demonstrates that it
can ensure safety in various traffic situations.
Figure 4 plots the percentage of occurred collisions
calculated by normalizing the number of collisions to the
maximum number of possible collisions. The result
shows the magnitude of safety improvement that the
CAR scheme is able to achieve in comparison to the
CR-100 and PAR schemes. The average latency of one
hop transmissions is shown in Figure 5. The latencies for
Table 4. Number of vehicle collisions in different scenarios.
ScenarioCR-50CR-100 CR-200 CR-500 PARCAR
VD-300.00 0.10 0.34 3.16 0.020.02
VD-600.20 0.16 0.74 4.34 0.000.00
VD-900.24 0.04 0.34 2.38 0.080.00
VD-1203.04 1.50 1.78 9.78 0.540.20
VD-15025.341.22 1.68 8.44 0.660.22
Average5.7640.604 0.976 5.620 0.2600.088
Copyright © 2012 SciRes. IJCNS
Copyright © 2012 SciRes. IJCNS
Figure 4. Percentage of occurred collisions.
Figure 5. Latency of one hop transmissions.
all the compared schemes are all below 6 ms, which
make their differences relatively insignificant. However,
CAR scheme can maintain the latency below 2 ms in all
scenarios. Average channel usage during the simulation
duration is shown in Figure 6. It demonstrates the effi-
ciency of the CAR scheme compared to the CR-100
scheme in most scenarios. The PAR scheme has the
lowest channel usage, but it generates more vehicle colli-
sions compared to the CAR scheme. Both the PAR and
CAR schemes are more scalable because they can main-
tain channel usage below 65% even in high density sce-
To evaluate the communication reliability, Figure 7
compares the probability of message reception between
the CR-100, PAR, and CAR schemes. The probability is
plotted with respect to the distance between a receiver
and a sender. Figures 7(a) and (b) show that the reliabil-
ity of the CR-100 and PAR schemes decreases signifi-
cantly when the vehicle density increases. In contrast,
Figure 7(c) shows that the reliability of the CAR scheme
does not change significantly with different vehicle den-
sities. In the VD-30 scenario, the overall probabilities
of message reception of the CR-100 and CAR schemes
are relatively similar. However, the resulting number
of collisions in the same scenario for the CAR scheme
is smaller than for the CR-100 scheme because the
CAR scheme is able to prioritize vehicles in the most
The results of communication performance show that,
in general, a higher channel usage causes a higher la-
tency and a lower probability of message reception. The
CAR scheme can limit its channel usage and keep the
probability of message reception within acceptable levels
in any scenario with different vehicle densities. Although
the CAR scheme cannot achieve a very high probability
of message reception, its safety performance is the best.
5. Discussion
Simulation results demonstrate the safety, efficiency, and
scalability of the proposed CAR scheme. In terms of
safety, the CAR scheme constantly pe
e other schemes for all tested scenarios with different
have the same safety performance in low density scenar-
ios (VD-30 and VD-60). However, CAR scheme signifi-
cantly outperforms PAR scheme in high density scenar-
ios because of the prioritization strategy. This shows that
prioritizing vehicles based on their danger severity can
improve safety.
In terms of efficiency, the CAR scheme can maintain
its actual channel usage between 45% and 65% of the
capacity in all the scenarios. It is better than the CR-100
schemes that can utilize almost 90% of channel capacity
in high density scenarios, but with a lower safety per-
formance. Safety performance of the CR-100 scheme is
the lowest in the VD-120 and VD-150 scenarios because
of the high channel usage. It is clear that using a constant
rate scheme may cause channel congestion that can sig-
nificantly reduce safety, particularly when road traffic
becomes denser such as in a traffic jam. Although the
PAR scheme utilizes the least channel capacity, the PAR
safety performance is lower than for the CAR. The result
shows that the best safety performat achievable
hannel usage without prioritizing ve-
anger. The result confirms that the
and PAR schemes in all tested scenarios, as indicated by
ions, latency, and probability of mes-
rforms better than by only reducing c
hicles in the most d
vehicle densities, as indicated by the average of the vehi-
cle collisions. Our experiments show that the CR-100
scheme has the best overall performance among the con-
stant rate schemes. It seems that the popular assumption
of using an interval of 100 ms for beaconing [8,19,31]
may not be without grounds. As expected, the adaptive
schemes (CAR and PAR) perform better than all the
constant rate schemes because the adaptive schemes are
able to control channel congestion. CAR and PAR schemes
nce is no
CAR scheme is able to achieve its objective, which is to
improve both efficiency and safety.
The safety and communication performances of the
CAR scheme are more scalable than those of the CR-100
the vehicle collis
sage reception. In low density scenarios, all the schemes
perform relatively well because the channel capacity is
still sufficient. In high density scenarios, the CAR scheme
ring the simulation duration. Figure 6. Channel usage du
Copyright © 2012 SciRes. IJCNS
Figure 7. Probability of message reception with respect to the distance from the sender. (a) CR-100 scheme; (b) PAR scheme;
(c) CAR scheme with
max = 1.0.
Copyright © 2012 SciRes. IJCNS
Copyright © 2012 SciRes. IJCNS
significantly outperforms all the other schemes. The av-
erage number of collisions indicates the safety perform-
ance for all the scenarios. The CAR scheme comes with
the smallest average number of collisions of 0.088, fol-
lowed by the PAR scheme with an average number of
collisions of 0.260, which is almost three times larger
than the CAR’s result. The CAR scheme has the best
safety performance in almost all scenarios. Figure 5
shows that the latency for the CAR scheme is kept at
below 2 ms in all scenarios while the latency for the
CR-100 and PAR schemes can exceed 3 ms in some sce-
narios. The CAR scheme can ensure a more stable and rela-
tively high probability of message reception in all scenarios.
The result demonstrates the scalability of the CAR scheme
to ensure the CCWS safety and communication perfor-
mances under various road traffic conditions.
It is expected that the maximum beaconing load pa-
max cannot control the channel usage precisely
because each vehicle only relies on its own local one-hop
knowledge. For
max 0.3, the actual channel usage is
slightly more than the specified limit. For
max > 0.3, the
actual channel usage is getting much lower than the
specified limit as its value increases. The discrepancy is
reasonable because the parameter
max is used just as a
maximum limit of the channel usage estimation. Since
the beaconing interval is bounded between 50 ms and
1000 ms, the maximum limit may not be reached in some
situations, such as when vehicles are not moving. The
objective of the CAR scheme is not about precise control
of the actual channel usage.
6. Conclusions
In this article, we presented a new context-aware adaptive
beaconing rate scheme to improve the performance of
vehicular safety communication. The original contribution
of this research is a new method to adapt the beaconing
rate dynamically to the context, which includes the esti-
mated channel load and the danger severity. The pro-
posed scheme estimates the danger severity of each vehi-
cle by using the interaction graph model. Vehicles with
the highest danger severity are facing the highest risk of
collision, and therefore must be prioritized. The beacon
messages are sent at a shorter interval for these vehicles
to increase their chance to avoid a possible collision.
Simulation results have demonstrated that the pro-
posed scheme outperforms both the existing adaptive rate
and non-adaptive rate schemes in terms of the efficiency,
scalability, and safety. The proposed scheme is able to
reduce the potential collision rate significantly, and there-
fore improve safety. Efficiency is demonstrated by hav-
ing a lower channel usage compared to the existing
schemes of a similar safety performance. Scalability is
ss various scenarios
with different vehicle densities.
The context-aware adaptive scheme can be extended
by incorporating existing ideas and concepts to improve
the beaconing performance. In future work, we will study
the benefits of combining our proposed scheme with a
prediction scheme that uses a threshold policy to further
reduce the beaconing rate and an aggregation or piggy-
backing scheme to improve the successful message re-
ception rate. To further improve the beaconing efficiency,
the next step would be to investigate an extended scheme
that adapts both the repetition interval and the transmis-
sion power.
7. Acknowledgements
This research was supportedy Queensland University
of Technology (QUT) and the Commonwealth of Austra-
lia, through the Cooperative Research Center for Ad-
vanced Automotive Technology (AutoCRC). Computa-
tional resources and services used in this work were pro-
vided by the High Performance Computing and Research
Support Unit, QUT, Brisbane, Australia.
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