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
2 introduces the related work and identifies the knowl-
edge gap in the literature. Section 3 presents a new con-
text-aware scheme for beaconing rate adaptation. The
performance of the proposed scheme is evaluated in Sec-
tion 4. Section 5 discusses and analyzes the experimental
results. Finally, Section 6 concludes this work and pro-
poses future research direction.
2. Related Work
The IEEE 802.11p WAVE standards [4,5] define a sin-
gle control channel to be used exclusively for all safety
Figure 1. Example of a simple traffic situation with differing
danger severity between vehicles.
related communication, which includes beaconing. Since
the channel is shared by all communication nodes, bea-
coning may saturate the channel bandwidth in a dense
traffic situation. To ensure safety, the beacon messages
must be prevented from overloading the control channel.
In literature, there are two categories of schemes that
have been proposed to improve the performance of bea-
coning: schemes that increase the effective capacity, and
schemes that control the beaconing load.
The effective capacity can be increased by controlling
the transmission timing to reduce the possibility of packet
collisions, improve reception rate, and ensure fairness to
channel access time. An example of this approach is a
collision-free scheduling of packet transmissions into time
slots [14]. Controlling the transmission timing does not
prevent channel congestion as it does not reduce or limit
the actual beaconing load generated by vehicles. There-
fore, it is not the main solution to ensure the function of
safety application.
The beaconing load can be controlled by tuning vari-
ous parameters that contribute to the communication
density. Communication density is described as the prod-
uct of vehicle density, message size, message generation
rate, and transmission range [15]. Since vehicle density is
determined based on the actual road traffic conditions,
only the three other parameters can be optimized: mes-
sage size, message generation rate, and transmission
range. Several studies on the effect of these parameters to
network performance [9,10,16] indicate the need for adap-
tive algorithms to control the channel load by adjusting
the parameters dynamically based on the surrounding
traffic conditions.
The message size can be reduced by utilizing a mes-
sage dispatcher to control all data exchanges between ap-
plications and prevent the same elements from being
transmitted multiple times by different applications [17].
The transmission range can be reduced by adjusting the
per-packet transmission power based on the estimated
vehicle density on the road [18,19]. The message genera-
tion rate (beaconing rate) parameter is the main focus in
this article. In contrast to the other two parameters, the
message generation rate directly affects the CCWS safety
performance. Therefore, the beaconing rate should be
minimized without reducing tracking accuracy and com-
promising safety.
Existing studies of adaptive beaconing rate schemes
use the metric of tracking accuracy to measure the safety
performance. Their goal is to reduce or control the bea-
coning rate while maintaining a sufficient level of track-
ing accuracy. Rezaei et al. [20] presented a scheme to
adapt the beaconing rate depending on a position predict-
tion error. Since the movement of a vehicle is predictable
to some degree, a beacon message needs to be sent only
when the prediction error is greater than a specified error
Copyright © 2012 SciRes. IJCNS
threshold. For example, a prediction error can be caused
by a relatively noticeable change of course such as ac-
celeration or a change of direction. Armaghan et al. [21]
further improved the idea by dynamically adapting the
error threshold and the number of estimation steps based
on safety distance. Each vehicle estimates its location
ahead for several intervals and sends the information
along with its actual current position. While the esti-
mated information is available, there is no new transmis-
sion unless any estimation errors are detected. Note that
the defined maximum error can actually be exceeded due
to message loss. Considering the dynamic nature of traf-
fic density, it is possible that even the reduced beaconing
rate is still relatively high enough to cause channel con-
gestion. For example, on a wide highway with many
lanes, a traffic jam will cause frequent occurrences of a
sudden change of movement (stop and go situation) that
will result in many beacon messages being sent fre-
To maintain a consistent beaconing performance in all
traffic situations with varying density, the actual or esti-
mated channel load must be considered. In principle,
beaconing rate should be reduced when the channel load
becomes higher. Saito et al. [11] proposed a scheme that
can estimate the channel load based on the number of
reception messages and detected reception errors, and
adapt the beaconing rate accordingly. Huang et al. [22]
proposed a similar scheme that calculates a transmission
probability based on the estimated tracking error. The
tracking error is stochastically decided depending on the
estimated channel load. The approach had been shown to
be more scalable to various vehicle densities and can be
complemented with a simple adaptive transmission range
scheme [12].
All the aforementioned adaptive schemes are unable to
prioritize the vehicles that are in a more dangerous situa-
tion than vehicles in a relatively safer situation. To ad-
dress this problem, we propose a new scheme that uses
the vehicle interaction graph [23] to prioritize vehicles in
the most danger and improve both the communication
and safety performance of the CCWS. Since the tracking
accuracy required in preventing a collision depends rela-
tively to interactions among vehicles, safety performance
is evaluated by simulating the number of potential colli-
3. Beaconing Rate Adaptation Using Context
Every vehicle repeatedly sends a beacon message to all
other vehicles with an interval I between two consecutive
transmissions. The beaconing interval I directly deter-
mines the beaconing rate, which is the number of mes-
sages sent per second. The interval I can be predeter-
mined as a constant for all time or can be determined
dynamically in real time.
A vehicle v1 that receives a beacon message sent by
other vehicle v2 at time t1 knows the state of vehicle v2 at
time t1. Tracking accuracy is defined as the difference
between the state of vehicle v2 at time t1 as tracked by
vehicle v1 and the actual state of vehicle v2 at current
time tnow = t1 + t. The most relevant metric for tracking
accuracy is positional distance error [12,20], which
measures the distance between the tracked position and
the actual position. Generally, the tracking accuracy is
higher if the duration t is shorter, which can be achieved
by increasing the reception rate of beacon messages.
Since the communication channel capacity is limited, the
reception rate cannot be increased indefinitely by in-
creasing the beaconing rate, which limits the achievable
tracking accuracy.
Higher tracking accuracy will result in a more accurate
collision prediction [1,24]. As an inaccurate prediction
may lead to a collision, tracking accuracy has been used
as an indicator to assess the safety performance of bea-
coning schemes. However, the possibility of a collision
mostly depends on the danger severity of a vehicle’s traf-
fic situation. For example, consider a vehicle vs that is in
a safe situation as opposed to another vehicle vu that is in
an unsafe situation. Vehicle vs has a smaller possibility of
being involved in a collision, and therefore vehicle vs
does not require its warning or tracking accuracy to be as
high as of vehicle v
u. One vehicle at a particular time
may not require the same tracking accuracy as another
vehicle to maintain the same safety performance.
Danger severity is determined from a time duration
that is available to perform an evasive action before a
collision becomes unavoidable, given a particular inter-
action between vehicles. A longer duration
provides a
driver more time and opportunity to react and avoid a
possible collision. To optimize channel usage without
compromising safety, the beaconing interval I must be
made adaptive based on an estimated load and the danger
severity. The beaconing interval I of a vehicle can be
proportionally adjusted based on the ratio between the
danger severity of itself and of its neighboring vehicles.
The basic principle is to give the highest priority (short-
est interval) to vehicles in the most danger to avoid any
possible collisions and at the same time control the chan-
nel load.
3.1. System Assumptions
This work assumes that every vehicle is equipped with a
wireless communication device that complies to the
IEEE 802.11 p WAVE standards, which define the pro-
tocols for PHY, MAC, and network layers. All the com-
munication devices operate in ad hoc mode and there is
no roadside infrastructure available. A vehicle is as-
sumed to be able to determine its own position on the
Copyright © 2012 SciRes. IJCNS
road, with an accuracy suitable for safety purposes, using
a combination of a Differential Global Positioning Sys-
tem (DGPS) and internal motion sensors [1,2,25].
3.2. Problem Definition
Let v be a subject vehicle and V' be a set of vehicles
within one-hop communication range of v. The state of
vehicle is defined as a tuple (x, y, w, l,
, s,
), where x and
y are the position coordinates, w is the width, l is the length,
is the heading, s is the speed, and
is the maximum
deceleration of the vehicle. The identity and state of each
vehicle u V' are obtained from a received beacon mes-
sage. The state of the subject vehicle v is obtained from
its internal positioning system. A set of vehicles V = {v}
V' is maintained locally by every subject vehicle v.
Given a constant message size S and the physical data
rate of wireless communication R, a transmission dura-
tion for a single message TSR
can be calculated.
The duration T excludes the extra time taken by PHY or
MAC protocol overhead. For example, a single transmis-
sion of a message with a size of 500 bytes using a data
rate of 6 Mbps will occupy the communication channel
for 0.6 milliseconds. The channel load
can be estimated
based on the number of nodes in the transmission range
of each other n, the message generation rate of each node
fi, and the transmission duration T:
For example, given the number of nodes n = 50, the
same generation rate for each node f = 10 Hz, and trans-
mission duration T = 0.0006 s, the channel load will be:
= 50 10 0.0006 = 0.3. In the CSMA-based protocol
such as IEEE 802.11 p, the channel becomes more con-
gested as
approaches 1. To control the channel load
consumed by beaconing, the beaconing rate f for each
vehicle must be adapted contextually. At each point in
time, the rate f may differ for each vehicle as each vehi-
cle may have different neighboring vehicle density and
danger severity (i.e., the context information).
Therefore, the problem of adaptive beaconing rate can
be defined as follows:
Input: The set of vehicles V, the transmission dura-
tion of a single beacon message T, and the maximum
beaconing load
max as a parameter to control channel
Output: The beaconing interval I or rate 1
for each subject vehicle such that
max is not exceeded
and the most endangered vehicles are assigned with
the shortest interval.
3.3. Multi-Vehicle Interaction Graph
The danger severity of vehicles in a certain proximity is
estimated by finding interactions among multiple vehi-
cles. Previously, we have proposed a multi-vehicle inter-
action graph model [23] to represent the interaction be-
tween multiple vehicles in a specific region and at a point
in time. The original model has been extended to include
the calculation of the danger severity.
The vehicle interaction graph is defined as a weighted
directed graph G = (V, E), where V represents the vehicles
in a specific area and E represents the interactions among
vehicles. An edge eij E represents an interaction between
vehicles i and j, where vehicle i is influencing vehicle j.
An edge weight
ij, 0 <
ij 1 is a real number that in-
dicates the danger severity or intensity of the interaction
eij. The value of
ij = 1 indicates an interaction with the
highest severity while
ij = 0 indicates no interaction.
Each vehicle maintains its own interaction graph.
Given the set of vehicles V tracked by each subject vehi-
cle, an interaction graph G is constructed by generating
the set of edges E and the corresponding edge weight.
Initially, each vehicle v creates a graph G = (V, E), where
V = {v} and E = . Every time a vehicle v receives a
beacon message from other vehicle vi V', vehicle v
updates its interaction graph G by enumerating each ve-
hicle vj V. For each pair (vi, vj), i j, the interactions
between vi and vj are calculated based on their position,
speed, and heading. Depending on the result, an edge eij,
eji, or both edges, may be added to the set of edges E. As
an example, Figure 2 shows a possible interaction graph
that represents the traffic situation shown in Figure 1.
An interaction is determined if there is a trajectory
contention between a pair of vehicles and their avoidance
is less than or equal to the maximum reaction time
Tmax. The avoidance time
is the time available for the
driver of the influenced vehicle to react in order to avoid
the collision. The maximum reaction time Tmax is a pa-
rameter that reflects the worst possible reaction time for a
driver. The minimum reaction time Tmin reflects the best
possible reaction time for a driver. The danger severity,
represented as an edge weight
, is determined based on
the value of
scaled proportionally with Tmin and Tmax.
Any interaction with an avoidance time less than Tmin
will be treated as having the same severity. Based on the
statistics [26], this study assumes the value of Tmin = 0.2 s
and Tmax = 2.5 s. The following equations are used to
calculate the danger severity:
max ,T (2)
max min
 (3)
Figure 2. Example of an interaction graph that represents
the traffic situation shown Figure 1.
Copyright © 2012 SciRes. IJCNS
Trajectory contention is calculated by considering
three distinct cases covering all the possible traffic sce-
narios without road information from a digital map. De-
pending on the case, the avoidance time
is calculated
differently based on their vector geometry and kinematic
calculations. For the sake of simplicity, the absolute po-
sition of vehicle v is represented by a Cartesian coordi-
nate (xv, yv), referenced as the center point of the vehicle.
The heading of the vehicle
v is in radian where 0
v <
2, and
v = 0 means heading north. Let A and B be a
pair of vehicles to be processed.
Following: This is a case where one vehicle is fol-
lowing another vehicle (vehicle A is following B or vice
versa). This case applies if vehicles A and B are moving
in the same direction and have overlapping paths. After
determining the following vehicle F and the leading ve-
hicle L, the net distance dt can be calculated using Equa-
tion (4), where da is the actual longitudinal distance be-
tween vehicles F and L, dmin is a parameter of the ex-
pected minimal distance between vehicles, and lF and lL
are the length of vehicles F and L, respectively.
ll 
ddd (4)
There are two conditions in this case that can lead to a
possible collision. First, the follower is faster than the
leader. Second, the distance between them is less than the
safety distance. The second condition is used to anticipate
an event when the leader brakes abruptly. The conditions
are modeled using a different avoidance time
1 and
if 0
ot her w is e
 (6)
where sF and sL are the speed of follower and leader ve-
hicles, and
F and
L are the maximum deceleration of
follower and leader vehicles. The avoidance time
1 is
calculated using Equation (7):
min ,
 (7)
Opposite: This is a case where a vehicle is heading
toward another vehicle and there is a possibility of a col-
lision. This case applies if vehicles A and B are moving
in the opposite direction and have overlapping paths.
Similar to the previous case, the net distance dt is calcu-
lated using Equation (4). The avoidance time
2 is calcu-
lated using Equation (8):
Intersection: This is a case where two vehicles have
intersecting paths and therefore there is a possibility of a
collision. This case applies if trajectory lines of vehicles
A and B intersect each other. This case covers any other
conditions besides the previous two cases. Given the two
trajectory lines of the vehicles, an intersection point C(xC,
yC) can be computed using simple geometry calculations.
Using the intersection point, the expected time-to-inter-
section for both vehicles tAC and tBC can be calculated
using the following formulas:
signsin ,cos
signsin ,cos
2sin tan
 
2 sintan
 
where sA and sB are the speed of vehicles A and B, re-
spectively, AC is a vector from point A to point C, BC is
a vector from point B to point C, and sign() is a sign
function to identify if a vehicle has passed through the
intersection. A route contention exists if both vehicles are
expected to arrive at the intersection point around the
same time. This can be determined by defining a time
frame for each vehicle tA and tB, 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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