Journal of Transportation Technologies, 2011, 1, 58-65
doi:10.4236/jtts.2011.13009 Published Online July 2011 (http://www.scirp.org/journal/jtts)
On the Comparison of Two Vehicular Safety Systems in
Realistic Highway Scenarios
A. Amoroso, G. Marfia, M. Roccetti, C. E. Palazzi*
Computer Science Department, University of Bologna, Bologna, Italy
*Pure and Applied Math Department , University of Padua, Padua, Italy
Received May 13, 201 1; revised June 15, 2011; accepted July 3, 2011
Abstract
The application of wireless VANET technology to accident warning systems is gaining an increasing interest.
These systems can significantly increase the safety of daily driving and are based on a technology that is
steadily becoming mature. We present an experimental comparison between two effective approaches that
cope with realistic scenarios. Both rapidly broadcast alert messages throughout platoons of vehicles, and are
based on wireless vehicle-to-vehicle (V2V) communications. However, with one approach an alert message
propagates through the farthest relay at each hop, whereas with the other it propagatesusing the farthest
spanning relay (i.e., the relay that can retransmit farthest away an alert message). With this study we will see
retransmitting through the farthest spanning relay at each hop can improve the performance by a factor of
two in terms of propagation delay, in comparison to choosing the farthest relay.
Keywords: Intelligent Transportation Systems, Accident Warning System, VANET
1. Introduction
A promising usage of emerging new technologies is the
application of inter-vehicular communications to signifi-
cantly improve vehicular safety in highway scenarios. In
fact, an effective warning system between vehicles could
be built either by means of pre-installed infrastructures,
or by means of wireless vehicle-to-vehicle (V2V) tech-
nologies. The latterapproach, however,appearsmore fea-
sible, less costly and more efficient on a whole network
of highways. Warning systems based on V2V technolo-
gies offer good performance and effectiveness, as alert
messages are directly broadcast between vehicles and do
not experience the delay of flowing through a centralized
server. Therefore, both the academic research and the
industries are proposing several safety systems based on
V2V technologies.
As a result, it is now a widely shared idea that effec-
tive vehicular warning systems could be built upon inte-
gration between vehicular ad-hoc networks (VANETs)
and pervasive sensor technologies, as the most promising
VANETs are based on the standard IEEE 802.11p [1-5].
The 802.11p standard defines V2V communications that
do not rely on any infrastructure, and that are built by
means of wireless devices installed on vehicles.
However, a few common proposals are also based on
3G technologies (e.g., smartphones). To summarize, these
proposals usually assume that a smartphone equipped
with an accelerometer (or similar technologies) is on
board ofvehicles. In case an accident occurs, generating
an abnormal acceleration, the smartphone (i.e., the acci-
dent warning software running on it) sends an alert mes-
sage to a centralized server, via the cellular infrastructure.
In turn, the centralized server advertises such event to all
overcoming vehicles, via FM radio or again via the cel-
lular infrastructure, for example.
Although cellular-based solutions are theoretically
feasible, the results that have been so far presented sug-
gest that they cannot match the requirements posed by
efficient accident warning system [6,7]. An accident war-
ning message of a few kilobytes, transmitted between
two moving vehicles through a cellular connection could
often experience latencies in the order of several seconds,
thus resulting almost useless in th is scenario.
Hence, several proposals have been so far presented in
the scientific literature to rapidly broadcast alert mes-
sages among vehicles, by means of V2V communica-
tions; these proposals are too many to be cited.However,
most of them do not take into account the following
characteristics of realistic scenarios:

A. AMOROSO ET AL.
59
 Different vehicles could have different transmission
ranges;
 The transmissions ranges of each vehicle could
change while traveling.
As a consequence of these characteristics, it might
happen that vehicles experience asymmetric communica-
tions: a given vehicle could receive messages from an-
other vehicle but not vice versa. To face suchproblem, a
new class of V2V algorithms to spread alert messages is
emerging, aimed at optimally transmitting alert messages,
while taking into account the above-mentioned realistic
conditions.
The purpose of this paper is the comparison of two
among the most effective V2V algorithms that work un-
der realistic assumptions [2,8].The first one adopts the
strategy of propagating alert messages through the far-
thest relay, while the second chooses the farthest span-
ning relay. To clearly understand the performance differ-
ences, in terms of propagation delay, that these two
choices entail, we compared both protocols under the
same experimental conditions in wide set of simulations
and different propagation scenarios. Anticipating here
our results, using the farthest spanning relay algorithm it
is possible halve the time require to disseminate alert
messages, compared to using the farthest relay.
It goes without saying that our comparison could in-
clude any new proposals, as these will meet the same
aims.
The remainder of this paper is organized as follows.
The next Section outlines the motivations of our work.
Section 3 describes which are the main challenges in
VANET highway scenarios, while Section 4 provides a
short description of the main ideas behind the two algo-
rithms that we compare. Section 5 describes the main
experimental results that we acquired by means of our
extensive simulation analysis. To end, Section VI con-
tains some concluding remarks.
2. Motivation
We discuss in the following example the positive effect
that VANET-based technologies may have in ensuring
vehicular safety. The adoption of VANET-based acci-
dent warning system reduces the number of vehicles that
could potentially be involved in an accident.
Specifically, we simulated a vehicle crash on a three-
lane highway and estimated how many vehicles could,
on average, be involved in it. In the simulation, we ac-
counted for both realistic driver response times, and ve-
hicles lengths. According to the measurements reported
in [9] and [10], drivers' response times were randomly
drawn from the (0.75, 1.4) s range, while vehicle lengths
from the (3.5, 5) m interval. Hence, in our simulation
model, just as it happens in reality, a driver waits before
braking, for a time equal to his/her response time, after
realizing that the vehicle that immediately precedes
him/her is braking.
We compared the amount vehicles involved in two
scenarios: with and without an accident warning system
in place. Both scenarios were simulated in the cases of
congested and not-congested traffic flows. Additionally,
we also considered wet and dry pavement conditio ns (i.e.,
different friction constants).
Finally, to be as realistic as possible, vehicle speeds
and related time-headway distributions were drawn from
real measurements [11]. Specifically, in the uncongested
scenario, vehicles moved at 110 km/h and their time-
headway distribution caused a linear density of 20 vehi-
cles per km, on a per lane basis. In the congested sce-
nario, speed was on average equal to 40 km/h and the
linear density of vehicles was of 40 per kilometer. The
kinetic friction constant between the tires and the asphalt
was equal to 0.2 when wet, and to 0.8 when dry, accord-
ing to [12].
We ran 100 simulations for each one of the 8 different
scenarios described above. The average numbers of
crashed vehicles involved in an accident are shown in
Figure 1, with their related 95% confidence intervals.
The darkest bars represent the average number of vehi-
cles involved in an accident in case only the traditional
warning system was used (i.e. drivers alerted by the pre-
ceding vehicles brake lights), while the light gray bars
represent the cases where a VANET-based accident
warning system was running.
Firstly, let us focus on the contexts when the p avement
is dry, thus when braking is more effective (first and
third pair of bars in Figure 1). When the pavement is dry,
the deployment of VANET-based accident warning sys-
tems reduces the average number of crashed vehicles by
nearly the 40%. In the worst case, instead, which occurs
when the pavement is wet, the average benefit deriving
Figure 1. Average number of vehicles involved in an acci-
dent.

60 A. AMOROSO ET AL.
from the adoption of a VANET-based accident warning
system amounts to about 25%; this still represents a sig-
nificant result.
From the above simulation results, it should be clear
that VANET-based alert systems could provide valid
solutions to accident warning challenges. The adoption
of such solutions could soon become a reality as it is
reasonable to assume that in the next few years a large
number of vehicles will be equipped with both GPS-
based navigation systems and 802.11 p networking capa-
bilities. In [13] the author reports the most prominent
ongoing projects in th is field carried by major car manu-
facturers.
3. Challenges in High way Scenario s
As widely discussed in literature, there are several dif-
ferences between VANETs and traditional ad hoc net-
works [3,14,15]; the main differences could be summa-
rized as follows:
 Higher speeds in vehicular environments than in
traditional ad hoc networks;
 Vehicles typically move along one-dimensional to-
pologies in highway scenarios, whereas nodes usually-
move along bi-dimensional trajectories in typical ad hoc
network scenarios (e.g., military).
Moreover, due to their high speeds, vehicles are sub-
jected to highly varying surroundings that affect their
transmission ranges. As an example, consider a vehicle
following a long truck; in such case the forward trans-
mission range of this vehicle is severely reduced while
remaining behind the truck. When the same vehicle over-
takes the truck, its forward transmission range abruptly
increases, while its backward transmission range could
instead experience the inverse phenomenon. In a similar
manner, several events can affect the transmission ranges
of vehicles while driving along a highway. As examples
consider: tunnels, road dips and hills, surrounding build-
ings and trees, weather conditions, and so on.
For these reason, in this work we compare two acci-
dent warning system proposals th at take into account the
fact that the transmission ranges of vehicles can radically
change in time at a fast pace, while travelling in a high-
way scenario.
In fact, under the realistic assumptions of different and
varying transmission ranges, many accident warning
system proposals might not exhibit satisfactory per-
formances, or even not work at all [4,16]. The rationale
for the poor performances of many proposals depends on
many different factors:
 Transmission ranges are not constant in time;
 Communications can be asymmetric;
 Global network topo logy knowledge is often unfea-
sible to obtain.
Recently, new approaches emerged to rapidly broad-
cast alert messages; these new methods account for the
realistic assumption that transmission ranges may be
anything, but equa l and constant in time.
Among these methods, we have considered as prom-
ising the one presented in [2]. This proposal attains very
fast broadcast of alert messages by minimizing the num-
ber of hops, i.e. minimizing the number of vehicles that
relay the alert message. Its intuition is that the farthest
vehicle that receives an alert message should relay it; a
lightweight mechanism selects the farthest receiver of
each message. To do so, this approach estimates the ac-
tual transmission range of each vehi c le .
Under the same realistic assumptions, an additional
method that effectively minimizes the number of hops
has been proposed in [8]. To achieve such result, the
latter scheme chooses as a relay, among all the vehicles
that receive an alert message, the vehicle whose
re-transmission will span farthest away in space (i.e., the
farthest spanning relay).
4. Choosing the Best Relay in Realistic
Scenarios
In this Section we briefly outline the main ideas behind
the two chosen protocols, namely [2] and [8]. It is worth
noticing that we are not interested in providing in this
paper an exhaustive discussion of all the technical details
underlying these protocols, as these can be found in the
referenced papers. Rather, we want to succinctly discuss
their most prominent characteristics.
Before proceeding, we anticipate here that both pro-
tocols assume that GPS and navigation data are locally
available on each vehicle. Moreover, to build the local
knowledge they utilize, both protocols share the same
technique of exchang ing utility messages between neighbor-
ing vehicles within a given platoon. Finally, accident-
warning messages are in general asynchronous with re-
spect to utility messages and both of the chosen protocols
append some data to the these messages in order to
choose the best relay.
4.1. Farthest Relay
Let us start with [2] that selects as its best relay, at each
step, the farthest receiver of a message. This method re-
lies on two relevant features: the assessment of the
transmission ranges, and the probabilistic mechanism of
the re-transmission procedure.
Each utility message sent by a vehicle contains itsup-
dated position and the IDs of the set of all vehicles

A. AMOROSO ET AL.
61
whose transmissions have been recently received by that
given vehicle. Upon receiving a utility message a vehicle
can estimate its transmission range computing its dis-
tance from the sender of that utility message. Assuming
that utility messages are frequent enough, each vehicle
can be aware of all the vehicles that have received its
recent transmissions. Obviously, this mechanism as-
sumes that communications are symmetric.
When the accident-warning software running on a ve-
hicle generates an alert message, it also appends to that
alert message the estimated transmission ranges of the
vehicle. This info rmation, in turn, is used by all the v ehi-
cles that receive that message to determine if they are
near to (or far from) the boundaries of the transmission
range of the vehicle that generated that alert. Utilizing
such information, each vehicle computes a contention
window for the re-transmission of the alert message, that
is inversely proportional to its distance from the sender
of that alert. The closer a vehicle is to the boundary of
the transmission range, the shorter its contention window
and the higher the probability th at it will act as the relay.
This mechanism has also the advantage of reducing the
possibility of collision between relays. In fact, each ve-
hicle waits a random time inside its contention window
before re-transmitting the message, and the re-transmis-
sion of a message takes place solely if none of the other
vehicles did it before.
Unfortunately, if transmission ranges are different on a
per vehicle basis, choosing as a relay the farthest vehicle
among all that receive a given message is not the best
strategy to minimize the number of hops. It could happen,
in fact, that the relay has a shorter transmission range in
comparison to that of another vehicle, traveling in be-
tween the sender and the relay. Hence this second vehi-
cle could span the alert farther than the relay that the
discussed method has chosen. In such case, the proposed
algorithm would select a sub-optimal relay.
4.2. Farthest Spanning Relay
To optimize the number of hops, the method proposed in
[8] selects as the best relay of an alert message that vehi-
cle, among all the receivers, whose re-transmission will
span farthest. This approach really achieves the resu lts of
minimizing the number of involved hops.
This result is achiev ed through different modifications
to the scheme of the previous algorithm, the first of
which is the idea of better exploiting the information on
transmission ranges, which is now inserted in the utility
messages. Hence, each vehicle of a platoon sends utility
messages to inform its peers of: its position, its transmis-
sion range, and the IDs of the set of vehicle from which
it has recently heard communication messages. Upon
receiving these utility messages, each vehicle can com-
pute an updated estimation of its own transmission range
and also obtain the ID of the set of receivers of its mes-
sages plus an estimation of their correspondent transmis-
sion ranges. All this can be used to overcome the prob-
lem of asymmetric communications.
Suppose, in fact, that vehicle r hears vehicle s, but not
vice versa. As all vehicles now receive utility messages
containing the set of information mentioned above, a
given vehicle i could exist, between r and s, that is able
to detect this anomaly in the communication between the
two peers, as it hears the utility messages from both r
and s. At this point the problem is sorted out as vehicle
ican add information about r in its utility messages so
that when s receives them it candiscover that its mes-
sages span till r.
By virtue of the mechanism mentioned before, the ac-
tivity of broadcasting accident alert messages works as
follows. Any vehicle in the situation of generating an
alert message appends to it a list of possible relays, or-
dered based on the length of their re-transmission span.
This is possible only because utility messages transport
information concerning the transmission ranges of the
vehicle that have emitted those utility messages. As soon
as a certain vehicle receives an alert message, it waits a
time proportional to its position in the list of relays be-
fore re-transmitting the message. If none did it before,
the vehicle retransmits the alert message. Note that the
horizon of knowledge of each vehicle contains solely its
neighbors.
Clearly, the advantages that this scheme provides
come at an increased overhead cost, compared to its
competitor. In particular, authors of [2] show that their
scheme can operate with less than 1 kb/s of utility mes-
sages within a transmission area, opposed to 75 kb/s re-
quired by [8] in normal situations.
5. Simulation Assessment
For the sake of conciseness, we will refer to the afore-
mentioned algorith ms by using th e origin al names, which
were given by their designers. Hence, we denote with
PIVCA the algorithm that selects the farthest relay [2],
while we denote with FROV the one that exploits the
farthest spanning relay [8].We evaluated the performance
of PIVCA and FROV against three different parameters:
1) end-to-end delay, 2) number of hops, and 3) number
of lost messages.
Propagation times and the number of involved hops, in
particular, represent the most important figures of merit
as to the efficacy of the examined app roaches. The num-
ber of lost messages accounts instead for the reliab ility of
the methods, since none of them exploits ACK based

62 A. AMOROSO ET AL.
mechanism to guarantee that all the cars in a platoon
have received the warning of an accident.
We used Ns2 [17] to run our simulations, considering
alert messages of 1Kbyte sent within two different sce-
narios:
 Transmission ranges of vehicles remain constant
during the simulation;
 Transmission ranges of the vehicles change during
the simulation, as it happens in realistic situation s.
5.1. Constant Transmission Ranges
The simulation considered a platoon of 400 vehicles on
an 8 km long portion of a single lane road. At the begin-
ning of the simulation, we considered the road as divided
in slots, each of 20 m in length. We randomly placed one
vehicle in each slot.
Each of the 400 vehicles moved at a constant, but dif-
ferent speed. In literature, it is shown th at the distribution
of the speeds of vehicles on a freeway is a bell shaped
around a median value [18]. Therefore, we randomly set
the constant speeds of the 400 vehicles of our experi-
ments as shown in Figure 2.
Each vehicle had a forward (and a backward) trans-
mission range randomly chosen in the (100, 600) m in-
terval.
To measure propagation times, we considered two probe
vehicles: the first vehicle was at the beginning of the
platoon while the second vehicle was at the end of the
platoon. These specific vehicles constantly remained at a
distance of 8 km one from other, for the entire simulation
period. All measurements were taken with respect to
these two vehicles.
To put our system under severe stress, we supposed
that more than one vehicle could send an alert message.
We tested the cases of 1, 20, 40, 60, 80, 100 different
senders, respectively, randomly chosen within the pla-
toon. Obviously, any vehicle could send an alert message
Figure 2. Speed distribution.
We tested the cases of 1, 20, 40, 60, 80, 100 different
senders, respectively, randomly chosen within the pla-
toon. Obviously, any vehicle could send an alert message,
independently of other vehicles. Moreover, the sending
vehicles repeatedly sent alert messages on a periodical
basis, i.e. they sent alert messages randomly within a
period of (1, 1. 5) s.
5.1.1. Propagation Time
We repeated each simulation ten times, varying the ran-
dom seed at every new run. Figure 3 shows the average
propagation times, in milliseconds, for each different
case. From a first inspection of that figure, it clearly
emerges that FROV is at least twice as faster than
PIVCA.
We consider that each broadcast terminates when the
alert message reaches all the vehicle of the platoon, i.e.
when both of the benchmark vehicles received the alert
message.
In particular, the leftmost pair of points in Figure 3
shows the case of a single vehicle that sends alert mes-
sages. In this case, a FROV message took an average of
about 120 ms to reach all the vehicles of the platoon,
while a PIVCA message took more than twice that time
(295 ms).The remaining points show the average times
taken alert messages when several vehicles act as
sources.
It is interesting to note that in the cases of multiple
senders both PIVCA and FROV almost do not undergo
any performance degradation with respect to the case of
a single sender.
FROV assesses quite precisely the transmission ranges
of all the vehicles, thus resulting in a higher speed in
Figure 3. Propagation times.

A. AMOROSO ET AL.
63
reaching all the vehicles in the platoon (by a factor of
two at least). This also depends on the optimality of the
relay policy that FROV implements.
5.1.2. Number of Hops
Figure 4 shows the number of hops required by the
broadcasts discussed in the previous section. These re-
sults confirm what already observed with propagation
times.
Note that, on average FROV takes about 30% less
hops with respect to PIVCA.
The number of hops taken by each broadcast in FROV
is almost independent of the number of senders. While
the alert messages are independent of each other, their
broadcasts take the same number of hops.
5.1.3. Percentage of Lost Messages
As already mentioned, neither PIVCA nor FROV guar-
antee the delivery of each alert message to each vehicle
of the platoon, as they do not exploit ACKs. In practice,
it might happen that some alert messages get lost, with
the possibility that some of the vehicles in the platoon
are not informed about an accident. To measure this,
Figure 5 reports the average number of lost messages
(with their 95% intervals of confidence) experienced by
PIVCA and FROV in the above-described scenarios.
Interestingly, even in the most stressful cases, the
number of lost messages was very low. This confirmsit
that is not necessary to implement an ACK mechanism to
effectively broadcast accident-warning messages.
The simulation shows that the number of lost mes-
sages with FROV is directly proportional to the number
of sender s, and therefo re to the number of alert messages
to broadcast. The number of messages lost by PIVCA,
Figure 4. Number of hops.
Figure 5. Percentage of lost messages.
instead, seems to be quite independent on the number of
broadcasts.
5.2. Variable Transmission Ranges
We finally tested the capability of both PIVCA and
FROV to adapt to the realistic variations of vehicles’
transmission ranges. To do this, we considered a platoon
of vehicles traversing a tunnel, factor that may signifi-
cantly and suddenly reduce the transmission ranges of all
the involved vehicles. Hence, to stress this point, we set
that as vehicles entered the tunnel, their transmission
ranges abruptly halved .
In particular, flowing through the tunnel had the fol-
lowing effects on the transmission ranges of vehicles,
depending on their p osi t i on:
 Entering the tunnel: the portion of the forward
transmission range that falls inside the tunnel gets halved.
The same event occurs to the backward range,as the ve-
hicle passes the entrance of the tunnel;
 Inside the tunnel: both forward and backward
ranges are halved with respect to their initial values;
 Exiting the tunnel: when the vehicle exits the tunnel
its forward range suddenly doubles, restoring its initial
value. The portion of backward range that still falls in-
side the tunnel remains halved with respect to its initial
value.
We simulated a platoon of 100 vehicles. At the begin-
ning of the experiments, we distributed the vehicles of
the platoon on a portion of road that was 2 km long, fol-
lowing the same scheme discussed in Section V.A. The
speeds of the vehicles were chosen based on the same
distribution mentioned above. In this set of experiments,
the benchmark vehicles were initially at the two ends of

64 A. AMOROSO ET AL.
the platoon, i.e. they remained about 2 km apart during
the experiment. The tunnel was 1 km long and at the
beginning of the experiments it was ahead of the first
benchmar k veh icle.
We limited to 10 the number of vehicles that sent alert
messages; each one of these vehicles implemented the
sending scheme discussed above.
5.2.1. Propagation Time
We repeated a set of 10 experiments for each case, vary-
ing the random seed each time. Figure 6 shows the av-
erage propagation times for both PIVCA and FROV. We
reported the usual 95% intervals of confidence atop of
each bar. The Figure shows two different cases: when the
platoon traverses a clean portion of road, i.e. without the
tunnel, and when the platoon traverses the tunnel. In the
latter case, the experiment was long enough to allow
both the benchmark vehicles to traverse the tunnel.
Measurements were taken since the moment when the
first benchmark vehicle entered the tunnel, till the mo-
ment when the second benchmark vehicle left the tunnel.
We computed the average time taken by alert messages
to reach both the benchmark vehicles. Figure 6 shows
two important results: 1) FROV is remarkably faster than
PIVCA in any circumstance, but 2) it suffers the tunnel
much more than PIVCA. In other words, FROV experi-
ences a loss of performance close to 30%, when the pla-
toon passes through the tunnel. Our insight here is that
the relative loss of performance of FROV is mainly
caused by the mechanism of assessment that takes some
time to detect the reduction of the transmission ranges. In
fact, a vehicle would probably insert in the list of possi-
ble relays several other vehicles that, instead, are no
longer reachable. This leads some vehicles to errone-
ously consider their transmission ranges longer than in
reality. Thus, as soon as the mechanism of transmission
Figure 6. Propagation times with and wi thout the tunne l.
range assessment updates its values, FROV returns to
operate as usual. PIVCA, instead, suffers less the pertur-
bations of trans- mission ranges brought by the tunnel.
This effect depends on the intrinsic nature of PIVCA,
which ignores asymmetric communications, thustheir
disruption of them does not cause any interference.
5.2.2. Number of Hops
In contrast with the measurements taken for propagation
times, the average number of hops appears to remain
quite stable even in the presence of the tunnel for both
PIVCA and FROV. Figure 7 shows the average number
of hops we measured in all the examined cases.
5.2.3. Percentage of Lost Messages
Figure 8 shows the percentage of messages lost with
PIVCA and with FROV, respectively, in the presence of
the tunnel. While with a clean road the performance of
FROV are one order of magnitude better than PIVCA,
the presence of the tunnel causes FROV to lose about ten
times more messages. This causes the fact that, when the
platoon traverses the tunnel, the average number of lost
messages by PIVCA and by FROV is basically the same.
In any case, the percentage of lost messages is still very
low, thus confirming the fact that these methods are re-
liable enough even without exploiting ACK-based me-
chanisms. Again the sharp increase of lost messages ex-
perienced by FROV depends on the temporary flawed
gauges of the assessment mechanism, which suffers
when vehicles enter and exit the tunnel.
6. Conclusions
6.1.1. Percentage of Lost Messages
Figure 8 shows the percentage of messages lost with
PIVCA and with FROV, respectively, in the presence of
Figure 7. Number of hops wi th and without the tunnel.

A. AMOROSO ET AL. 65
the tunnel. While with a clean road the performance of
FROV are one order of magnitude better than PIVCA,
the presence of the tunnel causes FROV to lose about ten
times more messages. This causes the fact that, when the
platoon traverses the tunnel, the average number of lost
messages by PIVCA and by FROV is basically the same.
In any case, the percentage of lost messages is still very
low, thus confirming the fact that these methods are re-
liable enough even without exploiting ACK-based me-
chanisms. Again the sharp increase of lost messages ex-
perienced by FROV depends on the temporary flawed
gauges of the assessment mechanism, which suffers
whenvehicles enter and exit the tunnel.
7. Conclusions
We here described the results of an extensive experi-
mental comparison between two approaches that cope
with realistic V2V scenarios. Both of these approaches
aim at delivering fast broadcast of alert messages by
minimizing the number of retransmission. However, the
simulation results have shown that FROV achieves the
best performances in terms of dissemination delay, at a
cost of a higher, but feasible, amount of protocol over-
head.
8. Acknowledgements
The Italian FIRB DAMASCO funded this work.
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