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Vehicle-to-Vehicle Communication: Fair Transmit Power Control for Safety-Critical Information

Authors:
  • Eurecat, Barcelona
  • Datagnan UG

Abstract and Figures

Direct radio-based vehicle-to-vehicle communication can help prevent accidents by providing accurate and up-to-date local status and hazard information to the driver. In this paper, we assume that two types of messages are used for traffic safety-related communication: 1) Periodic messages (ldquobeaconsrdquo) that are sent by all vehicles to inform their neighbors about their current status (i.e., position) and 2) event-driven messages that are sent whenever a hazard has been detected. In IEEE 802.11 distributed-coordination-function-based vehicular networks, interferences and packet collisions can lead to the failure of the reception of safety-critical information, in particular when the beaconing load leads to an almost-saturated channel, as it could easily happen in many critical vehicular traffic conditions. In this paper, we demonstrate the importance of transmit power control to avoid saturated channel conditions and ensure the best use of the channel for safety-related purposes. We propose a distributed transmit power control method based on a strict fairness criterion, i.e., distributed fair power adjustment for vehicular environments (D-FPAV), to control the load of periodic messages on the channel. The benefits are twofold: 1) The bandwidth is made available for higher priority data like dissemination of warnings, and 2) beacons from different vehicles are treated with ldquoequal rights,rdquo and therefore, the best possible reception under the available bandwidth constraints is ensured. We formally prove the fairness of the proposed approach. Then, we make use of the ns-2 simulator that was significantly enhanced by realistic highway mobility patterns, improved radio propagation, receiver models, and the IEEE 802.11p specifications to show the beneficial impact of D-FPAV for safety-related communications. We finally put forward a method, i.e., emergency message dissemination for vehicular environments (EMDV), for fast and effective multihop information di- - ssemination of event-driven messages and show that EMDV benefits of the beaconing load control provided by D-FPAV with respect to both probability of reception and latency.
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3684 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 58, NO. 7, SEPTEMBER 2009
Vehicle-to-Vehicle Communication: Fair Transmit
Power Control for Safety-Critical Information
Marc Torrent-Moreno, Jens Mittag, Student Member, IEEE, Paolo Santi, and Hannes Hartenstein, Member, IEEE
Abstract—Direct radio-based vehicle-to-vehicle communication
can help prevent accidents by providing accurate and up-to-date
local status and hazard information to the driver. In this paper,
we assume that two types of messages are used for traffic safety-
related communication: 1) Periodic messages (“beacons”) that
are sent by all vehicles to inform their neighbors about their
current status (i.e., position) and 2) event-driven messages that
are sent whenever a hazard has been detected. In IEEE 802.11
distributed-coordination-function-based vehicular networks, in-
terferences and packet collisions can lead to the failure of the
reception of safety-critical information, in particular when the
beaconing load leads to an almost-saturated channel, as it could
easily happen in many critical vehicular traffic conditions. In this
paper, we demonstrate the importance of transmit power control
to avoid saturated channel conditions and ensure the best use of
the channel for safety-related purposes. We propose a distrib-
uted transmit power control method based on a strict fairness
criterion, i.e., distributed fair power adjustment for vehicular
environments (D-FPAV), to control the load of periodic messages
on the channel. The benefits are twofold: 1) The bandwidth is
made available for higher priority data like dissemination of
warnings, and 2) beacons from different vehicles are treated with
“equal rights,” and therefore, the best possible reception under
the available bandwidth constraints is ensured. We formally prove
the fairness of the proposed approach. Then, we make use of the
ns-2 simulator that was significantly enhanced by realistic high-
way mobility patterns, improved radio propagation, receiver mod-
els, and the IEEE 802.11p specifications to show the beneficial
impact of D-FPAV for safety-related communications. We finally
put forward a method, i.e., emergency message dissemination for
vehicular environments (EMDV), for fast and effective multihop
information dissemination of event-driven messages and show that
EMDV benefits of the beaconing load control provided by D-FPAV
with respect to both probability of reception and latency.
Index Terms—Active safety, contention, fairness, information
dissemination, power control, vehicle-to-vehicle communication.
Manuscript received February 4, 2008; revised December 18, 2008. First
published March 16, 2009; current version published August 14, 2009. The
work of M. Torrent-Moreno was supported in part by the German Ministry of
Education and Research and the NEC Deutschland GmbH for the “Network
on Wheels” Project under Contract 01AK064F. The work of J. Mittag was
supported in part by the Ministry of Science, Research, and the Arts of Baden-
Württemberg under Contract Az: Zu 33-827.377/19,20. The review of this
paper was coordinated by Dr. L. Cai.
M. Torrent-Moreno was with the Institute of Telematics, University of
Karlsruhe, 76131 Karlsruhe, Germany. He is now with Advanced Automotive
Communications®, a joint venture of Ficosa and GMV, 08028 Barcelona, Spain
(e-mail: marc.torrent.moreno@gmail.com).
J. Mittag is with the Decentralized Systems and Network Services Research
Group, Karlsruhe Institute of Technology, 76124 Karlsruhe, Germany (e-mail:
jens.mittag@kit.edu).
P. Santi is with the Instituto di Informatica e Telematica del Consiglio
Nazionale delle Ricerche, 56124 Pisa, Italy (e-mail: paolo.santi@iit.cnr.it).
H. Hartenstein is with the Steinbuch Centre of Computing, Karlsruhe Insti-
tute of Technology, 76128 Karlsruhe, Germany (e-mail: hartenstein@kit.edu).
Color versions of one or more of the figures in this paper are available online
at http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/TVT.2009.2017545
I. INTRODUCTION
DIRECT vehicle-to-vehicle communication based on radio
technologies represents a key component for improving
safety on the road. Various public and private organizations
worldwide are funding national and international initiatives
that are devoted to vehicular networks, such as the InternetITS
Consortium [1] in Japan, the Vehicle Infrastructure Integration
(VII) Initiative [2] in the U.S., the Car2Car Communication
Consortium (C2CCC) [3] in Europe, and the Network on
Wheels (NoW) Project [4] in Germany. Currently, the IEEE
802.11p Working Group [5] is developing a standard that is
based on carrier-sense multiple access (CSMA) and tailored
to vehicular environments. The effort is assisted by initiatives
from various parts of the globe.
Direct radio-based vehicle-to-vehicle communication can
provide a fundamental support to improve active safety, i.e.,
accident prevention, by making information available beyond
the driver’s (or other car sensor’s, e.g., radar) knowledge with
almost minimal latency. Note that active safety is composed
of sensing and communication activities. In this paper, we are
concerned with active-safety-related communications.
When considering safety-related communication, two types
of messages can be identified: 1) periodic and 2) event driven.
Periodic exchange of “status” messages that contain the vehi-
cle’s position, speed, etc. (also called beacons in the follow-
ing discussion) can be used by safety applications to detect
potentially dangerous situations for the driver (e.g., a highway
entrance with poor visibility). It is assumed that every equipped
vehicle will also contain a global navigation satellite system
(GNSS), e.g., Global Positioning System (GPS), to determine
its absolute position. On the other hand, when an abnormal
condition (e.g., an airbag explosion) or an imminent peril is
detected by a vehicle, an event-driven message (also called
emergency message in the following discussion) is generated
and disseminated through parts of the vehicular network with
the highest priority.
While, from a safety perspective, one key challenge for direct
vehicle-to-vehicle communication technologies in the market
introduction phase will be to achieve a significant penetration
rate of equipped vehicles, it will be even more challenging in
fully deployed high-density vehicular scenarios due to the high
data load on the channel solely caused by beaconing. With
CSMA, a high load on the channel is likely to result in an
increased amount of packet collisions and, consequently, in a
decreased “safety level,” as seen by the active-safety applica-
tion. In particular, beacon messages will not successfully be
decoded, even when sent by a nearby vehicle, and event-driven
0018-9545/$26.00 © 2009 IEEE
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TORRENT-MORENO et al.: VEHICLE-TO-VEHICLE COMMUNICATION: FAIR TRANSMIT POWER CONTROL 3685
messages will show a slow unreliable dissemination process. To
counter the issue of channel saturation, we proposed to make
use of packet-level interference management based on per-
packet transmit power control to give packets “relative” weights
that control the introduced interferences and, implicitly, the
ability to capture packets.
In this paper, we analyze vehicle-to-vehicle communication
from an active-safety perspective and identify the challenges
and required strategies to improve performance through packet-
level interference management. We start by observing that,
with the proposed technology, i.e., the IEEE 802.11p [5], the
load on the wireless medium that results from periodic message
exchange should carefully be controlled to prevent deterioration
of the quality of reception of safety-related information. To this
purpose, we propose a distributed transmission power control
strategy called distributed fair power adjustment for vehicular
environments (D-FPAV) that controls the beaconing load under
a strict fairness criterion that has to be met for safety reasons.
D-FPAV also allows a clear prioritization of event-driven over
periodic messages. We then turn our attention to a fast and
effective dissemination of event-driven emergency messages.
We design a contention-based strategy called emergency
message dissemination for vehicular environments (EMDV)
that ensures a fast effective dissemination of alerts in a target
geographical area in cooperation with D-FPAV. Finally, we
evaluate the performance of the protocols in a highway traffic
scenario with the use of a significantly extended version of
the ns-2 [6] simulator that has been improved to account for
the IEEE 802.11p draft and for more realistic propagation
and interference patterns. Simulation results clearly show the
following results: 1) D-FPAV can successfully control the bea-
coning load on the channel while ensuring that the probability
of beacon reception is still high within the safety distance to
the sending vehicle; 2) D-FPAV significantly increases the
probability of one-hop reception of event-driven messages for
all distances to the sender; and 3) when used in combination
with D-FPAV, the EMDV protocol achieves a fast and effective
dissemination of event-driven messages. The proposed suite of
protocols provides a comprehensive solution for active-safety
communications in IEEE 802.11-based vehicular networks.
The remainder of this paper is structured as follows.
Section II identifies the communication challenges that exist
in IEEE-802.11-based vehicular environments. Furthermore, it
defines the goals that communication strategies for sending bea-
cons and for sending emergency messages should accomplish.
Section III presents recent studies most relevant to our work.
Section IV formally defines the basis of our strategy to maintain
the beaconing load under control, i.e., D-FPAV, which is also
formally proven to achieve fairness among sending vehicles. In
Section V, we propose the EMDV method to quickly and effec-
tively disseminate emergency information within a geographi-
cal area. The simulator setup and configuration, as well as the
modules that we developed to enhance the simulator, are given
in Section VI. The performance evaluation of the proposed pro-
tocols is presented in Section VII. Finally, Section VIII summa-
rizes the main results and presents an outlook on future work.
In this paper, great care has been taken to thoroughly analyze
the challenges of power control as well as the proposed solu-
tion under realistic assumptions (in particular with respect to
mobility, radio propagation, interferences, and protocol details
of the IEEE 802.11p). At the same time, a formal and rigorous
treatment of the challenges and of the proposed solutions is
presented. However, a parameter-estimation problem occurs to
bridge the gap between the rigorous treatment and the practical
application for which we derive and evaluate an estimation
procedure. We therefore start with the more formal treatment in
Sections IV and V and move to a simulative assessment under
realistic assumptions in Sections VI and VII. The results show
that desired features can be maintained when moving from the
formal treatment to realistic assumptions.
II. IDENTIFYING CHALLENGES AND DEFINING GOALS
As outlined in the Introduction, safety applications can be
enabled by two types of messages: 1) periodic and 2) event
driven. Periodic status messages are intended to exchange state
information from the sending vehicle, i.e., position, direction,
speed, etc., and, possibly, aggregated information of the sur-
roundings. Through this beaconing activity, safety applications
acquire an accurate knowledge of the surroundings and can
therefore detect potentially dangerous situations for the driver.
The key challenge related to this beaconing activity is to
control the channel load to avoid channel congestion. This
assessment is supported by the following facts. As defined
by the U.S. Federal Communications Commission [7], we
assume the existence of a single 10-MHz wide channel where
only safety information is exchanged.1The data rates provided
by the IEEE 802.11p [5] range from 3 to 27 Mb/s, where
the lower ones will be preferred for safety applications due
to their robustness against noise and interferences [8]. The
channel access mechanism of IEEE 802.11 systems, i.e., the
distributed coordination function (DCF), is an asynchronous
approach that cannot efficiently utilize the wireless medium.
According to previous studies [9], [10] and the Vehicle Safety
Communications Project Final Report [11], it is envisioned
that several messages per second from each vehicle will be
needed to provide the required accuracy for safety applica-
tions. Furthermore, additional transmission repetitions could
be considered to overcome the effects of packet losses due to
collisions and fading. Finally, according to recent studies [12],
safety-related messages will be relatively large, i.e., between
250 and 800 bytes, due to security-related overhead (e.g., digital
signatures and certificates). A back-of-the-envelope calculation
easily shows that (for example, with 100 neighboring nodes
that send ten packets per second, each of size 500 bytes) the
generated load can be much higher than the available bandwidth
(3 Mb/s with the most robust modulation/coding scheme).
In a previous study [13], we evaluated the reception rates of
periodic broadcast messages in a setup as previously described
for different configurations of transmission power and packet-
generation rate. On one hand, the results of our evaluation show
that, as expected, increasing the generation rate of beacon mes-
sages decreases the probability of successful reception of each
1Such a channel has been coined a high-availability low-latency (HALL)
channel.
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3686 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 58, NO. 7, SEPTEMBER 2009
Fig. 1. Expected probability of successful reception of periodic and event-
driven messages in case the communication behavior and the resulting channel
load is uncontrolled. In comparison, the performance of periodic and event-
driven messages is shown, as it should be achieved by active packet-level
interference management.
of them. On the other hand, we have observed that, although
increasing the transmission power extended the communication
range to farther distances, it could also lead to a congested
wireless medium where reception rates for vehicles close to the
sending vehicle decreased due to packet collisions. Section VII
presents the simulation results of configuring different trans-
mission power values for beacon messages.
Accounting for these observations, we propose to fix the
packet generation rate at the minimum required by safety
applications and to adjust the transmission power of beacons
in case of congestion. This mechanism should keep the load
on the wireless medium below a certain level, called the maxi-
mum beaconing load (MBL). To illustrate our design goal, we
schematically show in Fig. 1 the communication performance
that can be achieved by applying packet-level interference
management based on transmit power control to all periodic
message transmission. Without control of the channel load,
the probability of successful message reception will already
significantly drop at close distances, and emergency messages
will not experience a better reception performance than peri-
odic messages. On the other hand, controlling and managing
the interference introduced by periodic beacon messages, as
illustrated in Fig. 1, the desired performance for active-safety-
related communications can be achieved, i.e., periodic mes-
sages experience a high reception probability at close distances,
and event-driven emergency messages achieve an enhanced
performance. Consequently, we might need to accept lower re-
ception probabilities for periodic messages at farther distances.
Note that the transmit power control mechanism must be fully
distributed and quickly react to the very dynamic topologies of
vehicular networks. In addition, strict fairness must be guar-
anteed, because it is very important that every vehicle has a
good estimation of the state of all vehicles (with no exception)
in its close surroundings. More specifically, a higher transmit
power should not be selected at the expense of preventing
other vehicles from sending/receiving their required amount
of safety information. In Section IV, we propose D-FPAV,
which is a distributed strategy for adjusting the transmission
power of periodic messages inspired by a max-min principle;
the minimum of the transmission power of vehicles has to be
maximized while confining the beaconing load below the MBL.
Contrary to beacons, event-driven messages follow a reactive
strategy, i.e., they are issued when a hazard has been detected.
Event-driven messages need to be quickly and effectively dis-
seminated within the geographical area where the danger can be
a threat. The main challenge for the information dissemination
scheme is related to the fact that event-driven messages will
share the wireless channel with periodic messages. Thus, in
case of high vehicular traffic density, a high data load will
be experienced on the channel, which, in turn, can result in
longer channel access time and an increased number of packet
collisions (see [13]). Furthermore, vehicular networks are a
challenging environment with respect to radio-wave propaga-
tion due to a high number of reflecting mobile obstacles that
can randomly degrade the strength of the received signal (see
[14], where the analysis of empirical data is summarized).
In Section V, we propose EMDV, a strategy for disseminating
emergency information within a geographical area with short
delay. EMDV has been designed by taking into account prob-
abilistic radio propagation characteristics and potentially high
channel load. For safety reasons, information dissemination and
beaconing to be “correctly” balanced. As a basis, a prioritized
channel access mechanism, e.g., enhanced distributed channel
access (EDCA) [15], as suggested in the IEEE 802.11p draft,
should be used to reduce the channel access time for event-
driven emergency messages. On top of that, we propose to use
D-FPAV to adjust (through the MBL parameter) the amount
of bandwidth available for unexpected emergency information,
thereby increasing the probability of successful emergency
message reception.
III. RELATED WORK
Periodic one-hop broadcast communications are the basic
mechanism for supporting safety applications, and their perfor-
mance has been addressed in several vehicle-to-vehicle commu-
nication studies. In this context, Xu et al. [9] identify “infeasible
regions” (situations) where potential safety applications re-
quirements cannot be satisfied due to technological limitations.
Their assessment is based on an evaluation of the performance
of several layer-2 repetition strategies in terms of the number
of updates per period of time and the probability of reception
failure for different fractions of channel capacity assigned to
this type of messages. In a previous work [16], we studied the
probability of successful reception with respect to the distance
from the transmitter of periodic IEEE 802.11 one-hop broadcast
messages in vehicular scenarios. In addition, the effects of a
probabilistic radio propagation model and the EDCA scheme
suggested in IEEE 802.11p were shown. The results demon-
strated that the CSMA/CA approach is highly challenged when
coordinating broadcast transmissions in high vehicular traffic-
density scenarios with probabilistic propagation phenomena.
Furthermore, the study confirmed the beneficial effect of EDCA
on channel access time for messages with higher priorities.
However, to the best of our knowledge, none of the exist-
ing approaches aims at controlling the load on the wireless
channel where safety-related information exchange will take
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TORRENT-MORENO et al.: VEHICLE-TO-VEHICLE COMMUNICATION: FAIR TRANSMIT POWER CONTROL 3687
place. Furthermore, congestion control strategies that were
designed for nonvehicular environments do not address the
specific challenges of vehicular networks due to their different
goal, e.g., commonly focusing on unicast flows for end-to-end
connections.
Existing power control studies in mobile networks frequently
intend to maximize the overall system capacity, energy
consumption, or connectivity for point-to-pint communications
and, therefore, are not applicable to vehicular networks (see the
work of Kawadia and Kumar [17] for a description of the design
principles of power control in wireless ad hoc networks). The
increasing interest caused by the potential of vehicle-to-vehicle
communication has encouraged some researchers to adopt con-
ventional power control or fairness approaches to vehicular en-
vironments. In this group, Artimy et al. [18] and Wischhof et al.
[19] propose a power control scheme to maximize connectivity
and a utility fair function to share the broadcast medium,
respectively. Although the proposed strategies could perfectly
be valid when focusing on nonsafety applications, they still fail
to satisfy all the safety constraints outlined in Section II.
With respect to information dissemination, we can find sev-
eral strategies in the field of vehicle-to-vehicle communication
that take advantage of the existence of positioning systems,
e.g., GPS, to improve simple flooding. These approaches are
designed according to different criteria that correspond to dif-
ferent types of applications and environments.
On one hand, there is a group of studies that address non-
safety applications and, therefore, are not designed accord-
ing to strong reliability constraints and provide little or no
attention to a reduction of the delay experienced during the
dissemination process. These schemes, e.g., [20]–[25], intend
to deliver information over large distances, i.e., from several
kilometers to complete cities. In addition, there are nonsafety
information dissemination schemes that address smaller areas,
e.g., to enable cooperative driving, such as [26].
On the other hand, several proposals exist, which consider
time-critical safety applications, such as [27]–[30], which in-
tend to deliver the information to all vehicles within local areas
(up to a couple of kilometers) with low delay. Durresi et al.
propose in [27] to construct a hierarchical structure among
cars that drive in the same direction to efficiently manage the
dissemination process. However, highly dynamic topologies
would not be supported, e.g., with cars entering or leaving the
road. Sormani et al. [28] suggest selecting message forwarders
by the use of a probabilistic scheme, which is not proven to be
a valid approach to reliably deliver time-critical information.
The authors of [29] and [30] propose interesting schemes to
disseminate the emergency information in a certain direction
by making use of contention periods, i.e., after a message trans-
mission, all receivers wait for a certain time before forwarding
the message. Briesmeister et al. [30] favor the retransmission
of receivers located at farther distances from the sender by
the selection of shorter waiting times. Biswas et al. [29] select
random waiting times and utilize an implicit acknowledgment
scheme to cancel retransmissions from nodes closer to the
danger (where the message originated).
Our proposal for information dissemination described in
Section V makes use of the two latter principles (from [29]
and [30]) and further complements them with mechanisms that
were aimed at reducing dissemination delay and improving
reliability, particularly in high channel load conditions.
Furthermore, contrary to the aforementioned studies, we
consider probabilistic radio propagation on a per-packet basis
for the evaluation of our protocols. Although recent channel
characteristic studies such as [31]–[36] have shown that the
wireless channel for intervehicle communication at 5.9 GHz
is subject to frequency- and time-selective fading, we assume
that these effects can be taken care of by the IEEE 802.11p.
For instance, the experienced Doppler spreads up to 2 kHz,
and the root-mean-square (RMS) delay spreads around 0.8 μs,
due to multipath radio propagation are handled by an increased
guard interval of 1.6 μs between successive OFDM symbols
and an intercarrier spacing of 156.25 kHz [32], [33]. Without
these adoptions, which are part of the drafted IEEE 802.11p
Standard, the communication would be vulnerable to inter-
carrier interference and intersymbol interference. We are also
aware that multipath propagation and high vehicular mobility
cause a variation of the channel condition over time, by which,
depending on the used symbol rate and the size of a packet,
channel estimations that were performed at the beginning of
a transmission may become invalid at the end of the packet.
Although this problem is explicitly not covered by the current
IEEE 802.11p draft, various proposals on how one can over-
come this impairment exist. The approach in [32], for instance,
suggests using an advanced receiver in which time-domain
channel estimation and frequency-domain channel tracking is
performed to equalize the channel. According to the authors,
this proposal has already been implemented, is completely
IEEE 802.11p compliant, and was evaluated in more than 300
field trials. One different solution from Zhang et al. applies
differential modulation, such as differential phase-shift keying,
to mitigate the frequency-selective channel fading [37].
IV. FAIR CONGESTION CONTROL
In this section, we present the D-FPAV algorithm, which
makes use of transmit power control to achieve the following
design goals.
1) Congestion control. Limit the load on the medium pro-
duced by periodic beacon exchange.
2) Fairness. Maximize the minimum transmit power value
over all transmission power levels assigned to nodes that
form the vehicular network under Constraint 1.
3) Prioritization. Give event-driven emergency messages
higher priority compared to the priority of periodic
beacons.
As explained in the following discussion, the congestion
control requirement (Constraint 1) is applied only to beacon
messages, which is coherent with our design goal of control-
ling the channel bandwidth assigned to periodic safety-related
messages. Note that, when event-driven messages also contend
for the channel, this condition might be violated at some nodes,
which is perfectly fine, because in our proposed framework,
the entire channel bandwidth will be used in case a situation
of immediate peril is detected. With regard to Constraint 3,
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3688 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 58, NO. 7, SEPTEMBER 2009
we anticipate that prioritization is achieved through the EDCA
mechanism available in the IEEE 802.11p and by always send-
ing an event-driven emergency message using the maximum
possible transmit power.
In the following discussion, we first present some definitions
and a description of the network model. Second, we introduce
the formal definition of the beaconing problem and the designed
algorithm to solve the problem, assuming ideal conditions. Last,
we address the estimation approaches required to implement
a feasible solution for realistic environments. The resulting
tradeoffs and the corresponding performance evaluation are
presented in Section VII-B.
Assume that a set of nodes N={u1,...,u
n}moves along
a road modeled as a line2of unit length, i.e., R=[0,1]. Each
of the network nodes periodically sends a beacon with a prede-
fined beaconing frequency Fby using a certain transmit power
p[Pmin,P
max], where Pmin (Pmax)denotes the minimum
(maximum) transmit power.
Definition 1—PA: Given a set of nodes N={u1,...,u
n},
power assignment (PA) is a function that assigns to every
network node ui, with i=1,...,n,avaluePA(i)(0,1].The
power used by node uito send the beacon is PA(i)·Pmax.
Definition 2—CS Range: Given a PA and any node uiN,
the carrier-sense (CS) range of uiunder the PA, denoted
CS(PA,i), is defined as the intersection between the commonly
known CS range3of node uiat power PA(i)·Pmax and the
deployment region R. The CS range of node uiat maximum
power is denoted CSMAX(i).
Given a PA, the network load generated by the beaconing
activity under the PA is defined as follows.
Definition 3—Beaconing Load Under PA: Given a set of
nodes Nand a PA for the nodes in N, the beaconing network
load at node uiunder the PA is defined as
BL(PA,i)=|{ujN, j =i:uiCS(PA,j)}|
where CS(PA,j)is the CS range of node ujunder the PA.
Informally speaking, the beaconing load is measured in terms
of the number of nodes that contain node uiin their CS range.
In fact, under the assumptions that the beaconing frequency
is fixed to the same value for all the nodes and that beacon
messages have the same size, the observed channel load is a
function of the number of nodes in the surroundings. Note that
the aforementioned definition of beaconing load can easily be
extended to account for different beaconing frequencies in the
network and for beacon messages of different sizes.
Formally speaking, the goal of D-FPAV is to solve the
following problem in a fully distributed environment.
Definition 4—Beaconing Max-Min Tx Power Problem
(BMMTxP): Given a set of nodes N={u1,...,u
n}in R=
[0,1] and a value for the MBL, determine a PA, i.e., PA, such
that the minimum of the transmit power used by nodes for
2Modeling the road as a line is a reasonable simplification in our case,
because we assume the communication ranges of the nodes to be much larger
than the width of the road.
3The CS range, in ideal conditions, is the distance to which a node’s
transmissions can be sensed and therefore prevents other nodes from accessing
the channel at this time.
beaconing is maximized and the network load experienced at
the nodes remains below the beaconing threshold, i.e., MBL.
Formally
maxPAPA (minuiNPA (i))
subject to
BL(PA,i)MBL i∈{1,...,n}
where PA is the set of all possible PAs.
Note that solving BMMTxP addresses design goals 1 and 2
at the beginning of this section, where the MBL is used to
control the congestion generated by the beaconing activity. As
the simulation results in Section VII will show, design goal 3
can be achieved by transmitting beacons that use the transmit
power computed by D-FPAV and by transmitting event-driven
emergency messages at full power.
The proposed D-FPAV algorithm is based on the FPAV
algorithm [38], a centralized algorithm for solving BMMTxP
that assumes global knowledge (node positions). FPAV itself
is based on a “water-filling” approach [39]. Node power levels
are iteratively increased by the same amount ·Pmax, starting
from the minimum level, and this process is continued as long
as the condition on the MBL is satisfied. When the process
stops, all nodes have increased up to the same power level.
Notice that, in a previous work [38], we proposed a “second
stage” of the FPAV algorithm to achieve per-node maximality.
At the second stage, specific nodes could further increase their
transmission power until no node can increase without violating
the condition on beaconing load, which is in accordance with
the formal definition of the max-min fair allocation as in [39].
However, simulation experiments where global knowledge was
assumed showed that the second stage could only achieve a
marginal gain in scenarios with high network dynamics [38].
Because of this case and due to the higher complexity, by which
implementing per-node maximality would add to the distrib-
uted protocol, the second stage of the algorithm is not con-
sidered here.
D-FPAV is based on the following factors: 1) executing the
FPAV algorithm at each node with the information gathered
from received beacons; 2) exchanging the locally computed
transmit power control values among surrounding vehicles; and
3) selecting the minimum power level among the one locally
computed and those computed by the surrounding vehicles.
The D-FPAV algorithm is summarized in Fig. 2. A node ui
continuously collects information about the status (e.g., current
position, velocity, and direction) of all the nodes within its
CSMAX range. These nodes are the only ones that node uican
affect when sending its beacon. The communication range4
is typically smaller than the CS range; thus, a strategy based
on multihop information propagation is needed to obtain the
information from nodes outside the communication range.
Various alternatives for implementing this strategy will be
discussed later in this section. Based on the status of all nodes
within CSMAX range, node uimakes use of FPAV to compute
4Communication range is defined in this paper as the distance where the
received signal power of a transmitted message matches, on average, the
minimum power specified to successfully receive a message.
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TORRENT-MORENO et al.: VEHICLE-TO-VEHICLE COMMUNICATION: FAIR TRANSMIT POWER CONTROL 3689
Fig. 2. D-FPAV algorithm. Note that, to disseminate/collect information
to/from nodes outside the communication range, multihop communication is
involved (steps 1, 2a, and 2b).
the maximum common value Piof the transmit power for all
nodes in CSMAX(i)such that the condition on the MBL is not
violated (Step 1). Note that this computation is based only on
local information (i.e., the status of all the nodes in CSMAX(i)),
and it might globally be infeasible (i.e., it might violate the con-
dition on the MBL at some node). To account for this case, node
uidelivers the computed common power level Pito all nodes
in CSMAX(i)(see Step 2a). Meanwhile, node uicollects the
same information from the nodes ujsuch that uiCSMAX(j)
(see Step 2b). Knowing the power levels computed by the nodes
in its vicinity, node uican assign the final transmit power level,
which is set to the minimum among the value Picomputed by
the node itself and the values computed by nodes in the vicinity
(see Step 3). Setting the final power level to the minimum
possible level is necessary to guarantee the feasibility of the
computed PA.
In the Appendix, we formally prove that, under quite ideal-
ized conditions, D-FPAV solves the BMMTxP problem within
one round of communication, i.e., the time between two succes-
sive broadcast transmissions that contain a node’s local power
computation and that it has polynomial time complexity. Later
in this section and, more extensively, in Section VII, we will
show that, even in practical scenarios where these conditions
are not met and where the time interval between two broadcast
transmissions that contain a node’s local power computation is
increased, D-FPAV still performs very well.
Note that, although a perfect information accuracy from
all nodes inside CSMAX(i)is required to guarantee strict
fairness, achieving such a perfect knowledge is very difficult in
a fully distributed fast-moving scenario as given by vehicular
ad hoc networks. Furthermore, the geometric concept of a CS
range might even not apply in reality. Although the formal
treatment remains valid, even with generalized definitions of
CSMAX, the actual problem then is to estimate which nodes
can be considered to be “in” CSMAX. Hence, D-FPAV is ex-
pected to operate in situations in which nodes have incomplete
knowledge about the environment (the status of nodes within
CSMAX). Under these conditions, D-FPAV is not guaranteed
to provide strict fairness. However, as shown in the simulation
results in Section VII, D-FPAV is very effective in achieving
a very close approximation to fair power control, even if the
knowledge of the environment is inaccurate.
Another facet of the problem of estimating which nodes are
in CSMAX is given by a tradeoff between information accuracy
and additional overhead on the channel. Clearly, the only option
for acquiring the status information from nodes located outside
the communication range is to make use of a multihop strategy,
i.e., nodes retransmit the status of their neighbors. To determine
this strategy, the following design decisions have to be made:
1) how often the neighbors’ status should be forwarded; 2) what
range of neighbors must be included; and 3) which transmission
power must be used to transmit this information. We propose
to put together the Pivalues with the status information of
the corresponding nodes inside the CS(i)range5and then to
improve efficiency to piggyback this aggregated information in
beacon messages (“extended beacons”).
Now, decisions have to be made on how often the ag-
gregated information should be piggybacked in the beacons
and which transmit power should be used to send extended
beacons. In making these choices, the tradeoff exists between
additional overhead on the channel and the accuracy of the
neighbor’s status information available at the nodes. To select
the better option, we evaluate three different configurations in
Section VII: 1) Piggyback the aggregated status information to
each beacon; 2) piggyback the aggregated status information
to every fifth beacon; or 3) piggyback the aggregated status
information to every tenth beacon and transmit it with power
PA(i)(the transmit power value as computed by D-FPAV).
We considered that sending piggybacked beacons with a lower
frequency than one every ten beacons would cause D-FPAV to
deal with information that is very outdated.
Finally, the issue of how fast D-FPAV reacts to changes in the
vehicle density (and, hence, of the offered channel load) is of in-
terest. From a theoretical viewpoint, we prove in the Appendix
that D-FPAV computes an optimal solution to BMMTxP within
the time of two successive periodic beacon transmissions, i.e.,
the time of two successive broadcast transmissions that contain
a node’s local power computation. This result holds under
the assumption that the offered channel load does not change
during this time, which might not be the case in a practical
scenario. Similar to the case of imperfect knowledge of the
number of nodes within CSMAX, suboptimal transmit power
allocations will be expected in the presence of changing load
conditions, which leads to some performance degradation with
respect to optimal idealized conditions. However, the extensive
simulation results in this paper and in [41] show that, also
in practical scenarios, D-FPAV achieves a quite accurate and
stable control of the beaconing load, which indicates that the
rate of change in traffic load conditions is expected to be lower
than the frequency of information update used, supporting the
computation of the PA.
5Unlike in our previous work [40], the choice of CS(i)instead of
CSMAX(i)has been adopted to achieve a lower overhead in areas with high
load on the channel. Although a smaller amount of potential surrounding
vehicles can be discovered, a lower overhead benefits the performance of the
beaconing activity, as shown in the results in Section VII.
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3690 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 58, NO. 7, SEPTEMBER 2009
Fig. 3. Relevant area for dissemination of emergency information after an
accident detection in a highway. Cars in the opposite direction are included in
the dissemination area, because they can support the information dissemination
process.
V. D ISSEMINATION OF EMERGENCY INFORMATION
The second main goal in Section II is the dissemination
of event-driven emergency information within a geographical
area. To deliver a message6that contains information about an
existing threat, an effective strategy that offers short delay is
required.
We assume that a vehicle that detects a hazard issues an
event-driven emergency message to warn the drivers that ap-
proach the danger. The originating node, according to the
corresponding safety application, specifies the relevant area for
dissemination of the alert (dissemination area). The alert must
be distributed in the complete area, i.e., up to the border of the
dissemination area (see Fig. 3), possibly via multihop transmis-
sions, with high reliability and short delay. In this paper, we
study the case where roads do not comprise any intersection (or
highway entry/exit) and make the reasonable assumption that
the communication range of an emergency message is larger
than the road’s width. (The protocol proposed in this paper
can be extended to disseminate the emergency message in two
opposite directions and to support road junctions, e.g., with
smart strategies such as those proposed in [42] or with the use
of digital maps, which is left to our future work.)
The main purpose of our dissemination strategy is to select
the appropriate nodes to efficiently forward the message in the
direction of dissemination to cover the entire dissemination
area. The proposed strategy needs to overcome the different
challenges that exist in a vehicular environment, such as deal-
ing with uncertainties that result from node mobility, fading
phenomena, and packet collisions. Furthermore, the wireless
channel is also utilized for periodic beacon exchange; thus,
a relatively busy medium can be encountered by event-driven
emergency messages in dense vehicular traffic situations.
In previous studies [43], [44], we showed the satisfactory
performance of a forwarding strategy based on the use of the ge-
ographical positions of the nodes combined with a contention-
based approach. According to this strategy and to overcome
the uncertainties on the aforementioned message reception, an
event-driven message is transmitted in a broadcast fashion, and
all vehicles that receive it are potential forwarders. To decide
which node actually forwards the message, a contention period
is started: To favor the speed of the process, each receiving node
makes use of GNSS data to select a timeout value inversely
6Unless otherwise stated, in this section, by “message” we mean “event-
driven emergency message.”
Fig. 4. Sender perspective when utilizing the EMDV protocol.
proportional to the progressed distance in the direction of dis-
semination with respect to the actual sender. The node(s) whose
timeout fires first will rebroadcast the packet. Nodes that still
wait for their timeout to fire and that receive a rebroadcasted
packet will cancel their rebroadcast attempts. The advantages of
using a contention-based approach for forwarding is that, com-
pared to unicast-based forwarding, the probability that at least
one node forwards the message is significantly increased. There
is a chance for redundant (duplicate) rebroadcast, however,
and when appropriately controlled, these duplicates increase
robustness.
Motivated by the idealistic environments assumed to design
existing forwarding strategies and by the findings in [13] and
[44], we propose the emergency message dissemination for
vehicular environments (EMDV) strategy for the dissemination
of safety critical information. EMDV is based on the following
three design principles.
1) A contention scheme is used after the broadcast trans-
mission of the message to deal with uncertainties in
terms of reception failure caused by node mobility, fading
phenomena, and collisions.
2) To minimize the delay, the contention strategy is comple-
mented with the selection of one specific forwarder made
at transmission time, referred to as the next hop. This step
is possible due to the status information acquired from
safety beacons. The specific forwarder, in case of correct
reception, immediately forwards the message.
3) The reliability of the dissemination process is increased
by the following factors: 1) assuming a forwarding range
shorter than the communication range and 2) a controlled
message retransmission scheme within the dissemina-
tion area.
Fig. 4 shows a sketch of a sender perspective, which must
preselect a next hop among known nodes and then broad-
cast the message. The forwarding area, which is limited by
the forwarding range, identifies the area where potential next
forwarders can be located, i.e., both the preselected next hop
and the group of nodes that will start the contention period upon
the reception of the message. A forwarding range shorter than
the communication range is selected to improve the efficiency
of the process. In our previous study [44], we showed how
undesired message duplicates could be reduced in contention-
based approaches by a limitation of the “contention” range, i.e.,
the forwarding range in our case. These message duplicates
are the result of poor reception rates of broadcast messages
at distances close to the communication range, as we have
experienced in our vehicular scenarios (see Section VII-A).
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TORRENT-MORENO et al.: VEHICLE-TO-VEHICLE COMMUNICATION: FAIR TRANSMIT POWER CONTROL 3691
Fig. 5. EMDV protocol for emergency message dissemination.
EMDV is composed of four main procedures, as shown by
the pseudocode description of the protocol in Fig. 5. A node that
transmits an emergency message invokes the PrepareMessage()
procedure. This procedure first checks whether the message
has already been transmitted for the maximum number of
times (maxMessages) within the node’s forwarding area. If
not, the FindNextHop() procedure is invoked to determine the
message’s destination node. Note that this address is used only
to (possibly) select a specific forwarder and speed up message
propagation, but the message sent to the channel still has
the broadcast address specified at the link layer. This method
ensures that every node that receives an emergency message
passes it to the upper layers and that no acknowledgment is
issued for a received message. Once the message has been
transmitted, the message counter is increased, and a contention
period is started to verify that at least one neighbor forwards
the message. The FindNextHop() procedure essentially scans
the neighbor table of the sender to find (if any) the neighbor
in the sender’s forwarding area with the highest progress in the
direction of dissemination. If no neighbor in the dissemination
direction can be found or if the sender’s forwarding area is at
the border of the dissemination area (see Fig. 3), no specific
forwarder is selected, and NextHop is set to broadcastAddress.
The ReceiveMessage() procedure is invoked when a node
receives an emergency message and first ensures that the node
lies inside the dissemination area to proceed. Then, it is checked
whether the received message has been sent by a node that is
farther in the direction of dissemination and lies inside its own
forwardingArea. In this case, the message can be considered
to be a sort of “implicit ack” of message forwarding, and the
corresponding message counter is increased so that contention
for forwarding the message can be canceled if enough “implicit
acks” have already been received. If the aforementioned condi-
tions are not satisfied and the receiving node is located inside
the forwardingArea of the sender, the dissemination criteria are
used to determine whether immediate or contended forwarding
will be performed: If the receiving node is indicated as the in-
tended forwarder in the NextHop field, then the message is for-
warded with no contention by invoking the PrepareMessage()
procedure; otherwise, a contention period is started by invoking
the PrepareContention() procedure. Note that a contention will
be canceled if enough implicit acks have been received. For
this requirement to work, independent of the underlying vehicle
traffic density, i.e., in both low- and high-density scenarios, a
node will increment the corresponding message counter for its
own (re)transmission and for each (re)transmission sent by a
node inside its own forwardingArea. Therefore, the contention
will be canceled if enough (re)transmissions sent from within
its own forwardingArea have been received or if the message
has been repeated often enough by the node itself, e.g. when
there is no possible forwarder who can relay the message.
Furthermore, if the load due to periodic beaconing is controlled
and limited, the number of sufficient implicit acks is basically
a matter of desired reliability and independent of the actual
vehicle traffic density.
Finally, the protocol has to be adjusted with respect to two
specific situations. First, the contention period after delivering
the message to lower layers (PrepareMessage()) must take into
account the time that the message needs to access the channel
and transmission. To account for this case, the contention time
is set to maxContentionT ime +maxChannelAccessT ime
when flag =sent. Second, nodes located within forward-
ingRange from the border of the disseminationArea will act a
little differently, because the message must not travel farther
distances than borderDisseminationArea. Therefore, the fol-
lowing cases hold: 1) They will not select a nextHop; instead,
the broadcastAddress will be utilized, and 2) they will incre-
ment countMessages when receiving a message from any node
that is also located within forwardingRange of borderDissem-
inationArea instead of only counting the ones that come from
their forwardingArea.
In this paper, we study the performance of the protocol in
challenging saturation conditions. However, EMDV can easily
be adapted to also perform well in sparse network situations.
For instance, the case in which no vehicle is known in the
direction of dissemination can easily be addressed either by
storing the emergency message and issuing it when a beacon
from a new vehicle is received or by repeating the EMDV
contention until a predefined lifetime timer expires.
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3692 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 58, NO. 7, SEPTEMBER 2009
VI. SIMULATION MODELING AND SETUP
In the next section, we will evaluate the performance of
the two proposed protocols, i.e., D-FPAV and EMDV, with the
use of the network simulator ns-2.28. We first describe the
simulation setup, including the scenario utilized, and the con-
figuration of our proposed strategies. Special emphasis is de-
voted to the extension modules implemented into the ns-2
simulator with more accurate propagation and interference
models, realistic vehicular movement patterns, and adjustments
to model the current IEEE 802.11p specifications.
A. Network Simulator
The utilization of appropriate models and their correct con-
figuration is a critical aspect in the evaluation of wireless
communications. Furthermore (and as pointed out in existing
studies, e.g., [45]), although ns-2 [6] is a widely used network
simulator, it shows (in its standard release) insufficient accuracy
in the lower layers of its wireless modules. Thus, we have mod-
ified and extended many models of the standard distribution
of ns-2.28 to provide our simulations with a higher level of
fidelity with respect to reality and, moreover, to model the cur-
rent development status of vehicle-to-vehicle communications
technology. In the following discussion, we briefly describe the
main enhancements.
First, the interference and reception model has been extended
with cumulative noise capabilities. The original ns-2.28 code
does not keep track of all ongoing messages at a node’s
interface, i.e., it does not accumulate the power level of all
ongoing interferences. As other network simulators already
do, e.g., GloMoSim [46], we accumulate the power of all
interfering signals together with the existing background noise
(Noise) to determine whether the reception of a message is
successful. Moreover, we modified the capture feature, because
the standard distribution of ns-2.28 only allows a message to be
captured if it arrives when the channel is idle. According to the
current wireless chipsets’ capabilities [47], our implementation
also allows the successful reception of a message that arrives
during a busy period of the channel, as long as the following
inequality is satisfied during the complete reception time:7
PrI+CpTh (1)
where Pris the power of the received message, Icorresponds
to the cumulative power level of all existing interferences plus
Noise,CpTh is the capture threshold, and all powers are
expressed in decibels. To have a higher level of accuracy, we
consider all signals that arrive at the interface with a power
higher than Noise instead of discarding signals below CSTh as
in the original ns-2.28. The finite-state machine implemented
to model cumulative noise has been validated by setting up
a table of all possible combinations of triggers and condi-
tions for each state, eliminating nonfeasible combinations and
determining the finite-state machine’s transactions to the re-
7According to private conversations with the electronics company Siemens
within the NoW Project [4], a packet cannot correctly be received if it arrives
between 4 and 10 µs after the previous one due to resynchronization issues.
TAB L E I
ns-2.28 MAC AND PHY CONFIGURATION VALUES FOR SIMULATIONS
OF VEHICLE-TO-VEHICLE COMMUNICATIONS
maining ones. These modifications have recently been merged
into the official ns-2 tree and are publicly available as part of
the ns-2.33 release; thus, see either [48] or [49] for further
details.
With respect to the medium access control (MAC) layer, we
thoroughly analyzed and bug fixed the ad hoc channel access
mechanism (see [50]) according to the IEEE 802.11 standard
[51] (which is inherited by the IEEE 802.11p). With respect
to the physical (PHY) layer, ns-2.28 models a Lucent Wave-
LAN 802.11 direct-sequence-spread-spectrum radio interface.
To model a wireless access in vehicular environments OFDM
system, which operates at 5.9 GHz with 10-MHz channels, sev-
eral modifications were required according to the IEEE 802.11a
[52] Standard and the IEEE 802.11p [5] draft. Independent of
the data rate used to transmit a message payload, the preamble
and the physical-layer convergence protocol (PLCP) header are
always transmitted using the lowest data rate, i.e., 3 Mb/s. The
modulation scheme that provides 3 Mb/s is the most robust one,
i.e., binary phase-shift keying (BPSK) with the lowest coding
rate (1/2). However, note that 16 service bits of the PLCP
header are transmitted with the payload data rate instead of the
basic rate and that padding and tail bits are added to fill up the
last symbol of a message. In addition, the slot-time parameter is
adapted to support larger communication distances. Again, see
[48] and [49] for a detailed report on the implementation issues.
Table I presents the main parameters that were configured in our
version of the simulator for a data rate of 3 Mb/s, which was
used to illustrate the performance evaluation of the proposed
protocols.
In addition, a more appropriate radio propagation model
than the ones implemented in ns-2.28 has been used. Among
many radio models in the literature, the probabilistic Nakagami
distribution [53] is utilized and suggested by many authors as a
suitable model for estimating the physical fading phenomena
of mobile communication channels due to the good match
with empirical data collected from mobile communications
experiments, such as in [54]–[56]. Recently, Taliwal et al. and
Yin et al. have performed real-world tests on highways, and
they suggest the use of the Nakagami fading model for
these types of vehicular scenarios [14], [57]. Furthermore,
Taliwal et al. implemented the model into ns-2.28, which
we use in this study. The Nakagami-mmodel derives the
received signal strength from a multipath environment where
the different signal components randomly arrive because of
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TORRENT-MORENO et al.: VEHICLE-TO-VEHICLE COMMUNICATION: FAIR TRANSMIT POWER CONTROL 3693
Fig. 6. Probability of successful beacon reception (no interference from
other transmissions) with respect to the distance when no fading (two-ray
ground) and Nakagami-mfading with different intensities (m=1,3,5) is
considered. The transmit power has been set to the power required to achieve a
communication range of 500 m when considering the two-ray ground model.
the different propagation phenomena. It is used to estimate the
signal amplitude at a given distance from the transmitter as a
function of two parameters Ωand m. The following expression
describes the Nakagami probability density function of the
received signal amplitude x:
famp(x;m, Ω) = 2mm
Γ(mmx2m1expm
Ωx2
,m1
2
(2)
where Ωdefines the average received power at a specific
distance and is set to match the two-ray ground path loss of
ns-2.28 in our simulations. The mvalue identifies the fading in-
tensity, which depends on the environment, and Γis the Gamma
function. As illustrated in Fig. 6, the probability of successful
message reception is perfect up to the intended communication
range if no fading is considered. With fading, the probability of
successful message reception is already less than 100% within
the intended communication range. Moreover, the probability
of reception decreases if the fading intensity is increased. For
instance, a Nakagami m=1 distribution is equivalent to a
Rayleigh distribution and models a rough non-line-of-sight
scenario, whereas for parameters m>1, Nakagami models
an increased line-of-sight scenario. To demonstrate that our
proposals are valid over a wide range of fading intensities,
we have configured values of m∈{1,3,5}in this paper. In
our evaluation, we refer to Nakagami m=1as severe fading
conditions, to Nakagami m=3as medium fading conditions,
and to Nakagami m=5as low fading conditions. In addition,
as mentioned in Section III, we assume that OFDM receivers
can mitigate the challenges that are imposed by the time and
frequency selectivity of the wireless channel and therefore as-
sume that varying received signal strengths during the reception
of a single packet can be equalized.
Last, microscopic movement patterns validated with mea-
surements of real-world German highway traffic, which were
provided by DaimlerChrysler for the Fleetnet [58] and NoW
[4] Projects, were utilized (see [59]). The evaluated vehicular
scenarios consist of a 6-km-long bidirectional highway with
three lanes per direction. Unless otherwise stated, we utilize a
vehicular density that corresponds to an average of 11 vehicles
per kilometer in each lane, which travels at an average speed
of more than 120 km/h. Note that this scenario corresponds
to free-flow vehicular traffic, i.e., the vehicular density can be
much higher on many real highways for several hours during
the day. However, we are interested in high-speed scenarios
with high dynamics, where the utilization of high transmission
power and packet generation rates is envisioned.
B. Simulation Setup
In the simulated highway scenarios, all vehicles are equipped
with wireless communication interfaces and generate ten bea-
cons per second, which is the packet generation rate required
by many safety applications according to existing studies,
e.g., [9] or [10]. The size of each packet is configured to
500 bytes as the mean value suggested in security studies, e.g.,
[12], due to the security-related overhead (i.e., digital signa-
ture plus a certificate). The maximum communication range
for beacons is configured to 1000 m according to the IEEE
802.11p Standard, which states that vehicular communications
will occur over distances of up to 1000 m between high-speed
vehicles.
The data rate utilized is 3 Mb/s due to the robustness of
the BPSK modulation scheme (see [8]): It requires the lowest
signal-to-interference plus noise ratio (SINR) to successfully
receive a message, i.e., 5 dB. In addition, the IEEE 802.11
contention window is configured to 15 slots in our simulations.
Larger contention window values caused average channel ac-
cess times higher than 100 ms, i.e., not all generated beacons
could be transmitted to the channel.
When evaluating D-FPAV, a reference node or originator gen-
erates single-hop event-driven messages, i.e., one per second.
When evaluating EMDV, the event-driven message is destined
to a dissemination area with a segment of the highway starting
at the originator and going up to 2 km opposite the driving
direction. The originator is located around 4 km of our highway
segment, and accordingly, the 2-km-long dissemination area is
located in the middle of the 6-km scenario. All event-driven
messages, independent of whether D-FPAV is used, are sent
withaCR= 1000 m. Moreover, event-driven messages are
configured with a higher link-layer priority than beacons that
use the differentiated access categories (EDCA mechanism), as
described in the IEEE 802.11e [15].
With respect to the communication strategies, we set the
MBL of D-FPAV to two different values, i.e., 2.5 and 2 Mb/s,
to evaluate the prioritization of event-driven messages over
beacons. Note that, here, we express the MBL threshold in
terms of megabits per second instead of the number of nodes
within the CS range, as done in Section IV. However, the two
measures are equivalent when the packet generation rate and
the packet size (assumed to be the same for all the nodes) are
known. We fix each neighbor entry in the neighbor table to
15 bytes (corresponding to vehicle identifier and position) and
specify that nodes delete neighbor entries from their neighbor
table that are older than 1 s. Finally, each node will estimate
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3694 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 58, NO. 7, SEPTEMBER 2009
TAB L E I I
CONFIGURATION PARAMETERS FOR D-FPAV AND EMDV EVA L U AT I O N
its CS range for the local execution of the D-FPAV algorithm.
The CS range is given by the distance at which the average
path loss causes the signal strength to drop below the CS
threshold, i.e., the one computed using the two-ray ground
model of ns-2.28. Note that the estimated CS range is only used
to calculate the expected load in the network under a specific
PA and to determine the neighbors that will be included in
extended beacons. The propagation of a transmitted signal will
still follow the Nakagami-mfading model.
With respect to EMDV, we fix the maxContentionTime to
100 ms and the maxChannelAccessTime to 10 ms as appro-
priate values for our scenario according to a study of one-hop
broadcast communications, which is outlined at the beginning
of Section VII-A. The forwarding range is configured to three
different values, i.e., 300, 500, and 700 m, to study the tradeoff
between reliability, overhead, and delay. Last, we study the
performance of three different values for the amount of retrans-
missions (maxMessages) in a node’s forwardingArea, i.e., 1, 2,
and 3.
To obtain statistical significance, we simulate ten different
highway scenarios, with the same average vehicle density,
with ten random seeds for every selected configuration. Each
simulation consists of 11 s of simulated time, and the statistics
that correspond to the first second of simulation are not taken
into account as a transitory state. All results that were obtained
are represented with a 95% confidence interval.
The configuration details are summarized in Table II.
VII. PERFORMANCE EVA L U AT I O N
A. IEEE 802.11p One-Hop Broadcast Communication
As aforementioned, our simulation scenario consists of a
bidirectional highway where all vehicles are equipped with
wireless communication systems and periodically transmit ten
packets per second (beacons). Before evaluating the D-FPAV
and EMDV protocols, we study some aspects of the IEEE-
802.11p-based one-hop broadcast communications in vehicular
scenarios. The purpose of this evaluation is to obtain valuable
insights into the performance of vehicular networks, to corrobo-
Fig. 7. Probability of successful beacon reception with respect to the distance
to the sender for different communication ranges, i.e., different transmit powers.
In (b), the tradeoff between using higher transmit powers to reach further
distances and provide more reliable communication at close distances by
using lower transmit powers is clearly shown. (a) Highway scenario with
36 vehicles/km. (b) Highway scenario with 66 vehicles/km.
rate the performance statements of Section II, and to determine
appropriate values for the configuration of our protocols.
Fig. 7 compares the probability of successful beacon recep-
tion with respect to the distance between the sender and the
receiver for various values of CR (from 250 to 1000 m) and
two different vehicular densities. Fig. 7(a) presents the results
obtained with the lower vehicular density, i.e., 36 cars/km, and
Fig. 7(b) presents the results obtained with the higher one,
i.e., 66 cars/km. In both scenarios, we considered medium
channel-fading conditions.
In general, increasing the transmission power of one message
increases its robustness against power fluctuations and interfer-
ence; thus, it can reach farther distances.8However, increasing
the transmission power of all nodes in a network increases their
CS ranges and, therefore, the number of nodes that share the
channel at all locations.
8Note that an analysis of message reception failures and a comparison
between deterministic and probabilistic models is out of the scope of this paper.
See our previous work [60] for a detailed study.
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TORRENT-MORENO et al.: VEHICLE-TO-VEHICLE COMMUNICATION: FAIR TRANSMIT POWER CONTROL 3695
We can observe that, although the channel is not saturated,
i.e., when the amount of simultaneous transmissions from
neighboring nodes is negligible, increasing the transmission
power does not significantly decrease the reception rates at
close distances and provides improved reception rates at farther
ones. In fact, no drawback due to using higher transmission
power values can be observed in Fig. 7(a), where the lower
vehicular density is utilized.
On the other hand, CR = 750 m in Fig. 7(b) experiences
a significantly higher number of collisions at close distances
from the sender due to the higher level of interfering signals.
Moreover, the reception rates at close distances from the sender
are further reduced in the case of CR = 1000 m. The reason for
these low reception rates at close distances from the sender is
the inability of the channel access mechanism to coordinate the
high number of neighboring nodes in this scenario.
We remark that, due to the kinetic energy of moving vehicles,
reception rates at close distances are more relevant from a safety
perspective. In the case of the higher vehicular density and with
a packet generation rate of ten packets per second, a CR of,
e.g., 500 m, would be a better choice than 1000 m, although a
higher CR provides an increased probability of reception at far
distances. Therefore, the lack of node transmit power control
can result in a lower safety level due to the decreased reception
rates experienced at close distances from the transmitters.
Further analysis on different parameter configurations in this
setup assisted us in adjusting the designed protocols, as well
as the modulation and contention window value of the channel
access strategy, which are depicted in Table II.
B. D-FPAV Performance
To evaluate D-FPAV performance, we consider two main
simulation setups: 1) D-FPAV On and 2) D-FPAV Off. In the
D-FPAV Off simulations, all beacons are sent at maximum
power (CR = 1000 m), because no power control is applied.
On the other hand, in the D-FPAV On simulations, beacons are
sent using the transmit power as computed by D-FPAV. In this
set of simulations, we fix the MBL to 2.5 Mb/s. To study the
performance in different fading environments, we have config-
ured Nakagami-mto reflect severe-, medium-, and low-fading
conditions. However, we will focus on the results obtained in
medium-channel-fading conditions and complement them with
a selected set of observations obtained in severe- or low-fading
environments.
The main metrics considered to evaluate D-FPAV’s perfor-
mance are given as follows: 1) the probability of successful
reception of a beacon message with respect to the distance
and 2) the average channel access time (CAT). The CAT is
computed for all nodes in the highway, and it is used to corrob-
orate the claim that D-FPAV uniformly reduces the load on the
channel in the network, i.e., it achieves fairness. The probability
of reception is used to assess D-FPAV’s effectiveness and the
appropriate prioritization of safety-related messages (the design
goals in Section II), which is obtained by ensuring a high proba-
bility of correctly receiving beacons at close distances from the
sender and, at the same time, by increasing the probability of
successful reception of event-driven messages at all distances.
Fig. 8. Probability of successful beacon reception with respect to the distance
to the sender for different D-FPAV configurations, i.e., information exchange at
low, medium, and high frequency. The wireless channel has been configured to
reflect medium-channel fading conditions.
Before performing the aforementioned experiments, we have
to fix the estimation procedure with respect to obtaining in-
formation on which the node is residing in the “CS range.”
As indicated in Section VI-B, each node will estimate its CS
range as the distance given by the average path loss experienced
in our simulations, i.e., the one computed using the two-
ray ground model of ns-2.28. We evaluate different strategies
that D-FPAV can use to obtain the status information from
vehicles that are driven inside a node’s CS range, as described
in Section IV. Fig. 8 presents the probability of successful
reception of beacons for the different strategies and with
D-FPAV Off for comparison. These strategies are differentiated
by the generation rate of extended beacon messages that con-
tain not only the status information of the transmitter but the
positional information about its surrounding nodes as well.
Fig. 8 shows the results obtained with D-FPAV Off and
D-FPAV On using the different configurations of the protocol.
Reception rates with D-FPAV Off present low values due to the
high load that exists on the wireless medium and the resulting
packet collisions. Indeed, the high saturation on the wireless
medium causes reception rates below 60% for nodes located
at a distance of 100 m or farther. Note that the near-far effect9
of radio-wave propagation allows higher reception rates at very
close distances from the transmitter, i.e., 90% at a few meters,
and causes the strong decrease up to 150 m. By adjusting
the transmission power of all beacons, including the extended
ones, the desired results are achieved (see Fig. 8): An increased
probability of reception at close distances from the sender
for the cases where each beacon is an extended one (denoted
1over1), where every fifth beacon is sent as an extended beacon
(denoted 1over5), and every tenth beacon is sent as an extended
beacon (denoted 1over10).
Comparing the three curves in Fig. 8, we can see how sending
a lower number of extended beacons achieves higher reception
rates. Note the existing tradeoff between information accuracy
9The near–far effect refers to the significantly higher received power of
messages sent from close distances compared with messages sent from further
ones due to the strong decrease in radio wave power along the distance.
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3696 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 58, NO. 7, SEPTEMBER 2009
and the associated overhead of D-FPAV. Sending a higher
number of extended beacons offers the possibility of obtain-
ing more up-to-date information about the status information
from surrounding nodes (farther than direct communication
distances). However, the larger number of extended beacons
causes a higher amount of offered load to the channel and
consequently requires a further reduction of the transmission
power to adhere to the MBL constraint. Note that extended
beacons are significantly larger than nonextended ones. In the
case of 1over10, the introduced D-FPAV overhead, due to an
average extended beacon size of 1120.6 bytes, is already 12.4%.
This extended size corresponds to an average of 41.37 known
neighbors within the resulting CS range (448 m in this case;
CR = 356 m).
Therefore, we conclude that sending one extended beacon
every ten transmissions presents the best tradeoff between ac-
curacy and overhead among the studied options. Note that, due
to the high number of nodes within the communication range of
each other, the same information is repeated by several nodes.
Thus, extending one beacon every ten provides a sufficient
degree of neighbor table accuracy at a lower price than 1over5
in terms of overhead. Thus, in the following discussion, we
adopt the 1over10 strategy in the D-FPAV design.
The observations are also valid if we consider more or less
severe fading of the wireless channel. As illustrated in Fig. 9(a),
the probability of successful message reception increases at
close distances and decreases at far distances if D-FPAV is
not used and fading is severe. Due to the greater variation of
the received signal strengths, which causes a reduced number
of interferences from far distances, the chance to successfully
decode a message is increased for close distances. At the same
time, the probability is decreased for far distances. Severe
fading can, therefore, also be shown as a natural way of
performing congestion control. The same phenomenon does
not occur if D-FPAV is enabled, as shown in Fig. 9(b). The
interference level is already controlled and limited by the
D-FPAV algorithm; thus, the probability of successful message
reception decreases over all distances if the fading intensity
increases. Nevertheless, comparing the curves in Fig. 9(a) and
(b), the benefit of transmission power control also clearly exists
in severe fading environments.
Fig. 10(a) and (b) present the probability of successful packet
reception with respect to the distance of beacons and single-hop
event-driven messages with D-FPAV On and Off under severe-
and medium-fading channel conditions. As aforementioned, not
using power adjustment results in a high load experienced on
the channel, which, in turn, causes a high number of packet
collisions and low reception rates. Note that, if beacons and
event-driven messages are sent with the same transmission
power (D-FPAV Off), event-driven messages achieve higher
reception rates at closer distances. As explained in a previous
work [16], a prioritized channel access category decreases the
probability of experiencing collisions with neighboring nodes.
Using the D-FPAV mechanism and setting the MBL parame-
ter to 2.5 Mb/s results in an average reduction of the beacon’s
transmission power from 19 dBm to 4.9 dBm, which decreases
the communication range from 1000 m to an average of 356 m.
Therefore, the CS range is reduced to 448 m, which, according
Fig. 9. Probability of successful beacon reception with respect to the distance
for D-FPAV On/Off and different fading intensities of the Nakagami-mdistri-
bution. (a) D-FPAV Off. (b) D-FPAV On 1over10.
to an average of 66 cars/km, corresponds to an average of
59.13 vehicles within the CS. Note that 59.13 vehicles corre-
spond to an offered load of 2.36 Mb/s, which is less than the
MBL threshold (2.5 Mb/s) due to the conservative approach
of D-FPAV, i.e., the minimum of the PA values received from
nodes within CSMAX is selected (see Section IV).
To evaluate the saturation on the channel, we also computed
the average channel busy time ratio. As intended, the reduction
of the transmission power decreases the average-channel busy-
time ratio experienced by all nodes in the highway from about
86.2% with D-FPAV Off to 62.2% with D-FPAV On, i.e., a 24%
decrease.
The resulting power adjustment allows D-FPAV to fulfill its
design goal of ensuring high message reception rates at close
distances from the sender, which corresponds to the safety
distance of a vehicle.10 As outlined in Section II, achieving a
10The safety distance of a vehicle commonly refers to the distance that a
driver needs to stop the vehicle completely, and it is approximately calculated
(in meters) as half of the value of the speed (in kilometers per hour). For
example, a car that is drive at 120 km/h has a safety distance of approximately
60 m.
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TORRENT-MORENO et al.: VEHICLE-TO-VEHICLE COMMUNICATION: FAIR TRANSMIT POWER CONTROL 3697
Fig. 10. Probability of successful reception of beacons and one-hop event-
driven messages (without retransmission) with respect to the distance to the
transmitter, with D-FPAV On/Off and MBL =2.5Mb/s. (a) Severe fading with
Nakagami m=1. (b) Medium fading with Nakagami m=3.
good estimation of the close environment is critical to identify
dangerous situations. In our scenario with a medium fading
intensity [see Fig. 10(b)], the beacons’ probability of successful
reception presents higher values up to distances of 160 m with
D-FPAV On, e.g., an increase of 41.7% at 100 m (from 54.0%
with D-FPAV Off to 76.5% with D-FPAV On). In addition,
we can observe a significant increase in the reception rates
experienced by each transmission of an event-driven message at
all distances with D-FPAV On. Experiencing a lower load on the
medium allows event-driven messages, which are not restricted
in terms of transmission power, to achieve improved reception
rates not only at close distances (e.g., a 78.6% increase at
100 m from 55.7% to 99.6%) but also at further ones (e.g.,
192.2% increase at 500 m from 24.3% to 71.0%). The price
to pay for these improvements is the lower reception rates of
beacons for distances farther than 160 m, which, however, are
distances where the information conveyed by beacons is less
relevant compared with the “closer beacons” and emergency
messages. In an environment with higher fading intensity [see
Fig. 10(a)], the difference between D-FPAV On and Off is, as
expected, slightly reduced for beacon messages. However, the
Fig. 11. Average channel access time experienced by beacons with and
without D-FPAV and an MBL =2.5Mb/s. (a) Average channel access time
experienced by each node with respect to its position on the highway.
(b) Probability of experiencing a channel access time.
probability of reception of emergency messages still signifi-
cantly benefits from the load reduction of D-FPAV.
To evaluate the fairness of the algorithm, we study the aver-
age channel access time of all nodes on the highway. Fig. 11(a)
illustrates the results obtained with medium-fading conditions,
where each vehicle is represented with its middle position11
during the simulation run. In this case, the results of only one
scenario are presented such that we will not average out dif-
ferent vehicular densities in different segments of our highway.
We omit a detailed presentation of CAT results in severe- and
low-fading channel conditions, because the difference from the
medium-fading condition is only marginal.
We can observe how the average channel access time has
been reduced from 17.5 ms to 1.1 ms when using D-FPAV.
Furthermore, if no power control is applied, nodes can expe-
rience considerably different values of CAT, which range from
about 13 ms to 22 ms [see Fig. 11(b)]. The CAT reflects the
amount of load on the channel at that particular location; thus,
11We compute the middle position of a vehicle as the middle point between
its position at the beginning of the simulation and its position at the end.
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3698 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 58, NO. 7, SEPTEMBER 2009
the results obtained with D-FPAV Off show that different nodes
have different opportunities of sending and correctly receiving
messages, impairing fairness. On the other hand, when D-FPAV
is active, all the nodes experience similar CAT values, which
range between 0.9 and 1.3 ms. Therefore, similar opportunities
of sending and correctly receiving safety messages are expe-
rienced by the nodes in the network. In other words, D-FPAV
achieves its design goal, i.e., fairness.
C. EMDV Performance
Let us now evaluate the performance of the EMDV protocol
when operating with D-FPAV Off and in synergy with the
D-FPAV protocol. We recall that, in the investigated scenario,
the reference node generates an emergency message that has
to be delivered within the relevant area for dissemination. In
our case, the dissemination area is 2 km long and lies in the
middle of our highway segment. In addition, three different
values of the maxMessages parameter are studied (1, 2, and 3),
as well as three values of the forwardingRange (300, 500, and
700 m). Unless otherwise stated, the utilized forwardingRange
will be the middle value, i.e., 500 m. Again, we will focus on
the result obtained with medium-channel-fading conditions and
subsequently complement them with the results from severe-
and low-fading conditions.
Fig. 12(a) presents the probability that the emergency infor-
mation is successfully received by vehicles located inside the
dissemination area when maxMessages =1. With D-FPAV
Off, we observe a reception rate of 90.9% averaged over the
dissemination area. The use of the D-FPAV protocol increases
the emergency information reception rates up to an average of
99.9%. The result shows the dependency of the success of the
dissemination strategy on the channel load conditions.
Fig. 12(b) shows the probability of reception in the dis-
semination area obtained when setting maxMessages =2.
Note how the curve that presents the reception rates obtained
with D-FPAV Off is increased with respect to the values in
Fig. 12(a) when maxMessages =1, i.e., from 90.9% to a
99.1% on average. To achieve a 100% probability of reception
within the dissemination area with D-FPAV Off, maxMessages
must be set to three repetitions [see Table III(b)]. In fact, we
discovered that, due to the high load on the channel, several
dissemination processes did not succeed at all, because the
initial transmission of the originator was not received by at
least one of the intended receivers. As intended, allowing more
message repetitions within a node’s forwardingRange enhances
the reliability of the protocol and resolves that problem, but at
the cost of an increased overhead.
In Table III(b), we also present the average number of
retransmissions caused by the EMDV protocol, i.e., the number
of times that the emergency message is transmitted by any node
within the dissemination area. Observe how, with D-FPAV Off
and Nakagami m=3, increasing maxMessages from1to2
causes the emergency message to increase from 39.5 to 67.1
retransmissions per dissemination process and to 87.6 in the
case of allowing three repetitions.
When using D-FPAV, the most efficient EMDV choice is
to configure maxMessages =1, because it already reaches
Fig. 12. Probability of information delivery inside the dissemination area with
respect to the distance from the message originator (with multihop retransmis-
sions) with D-FPAV On/Off and MBL =2.5Mb/s. (a) maxM essages =1.
(b) maxMessages =2.
TABLE III
AVERAGES OF THE PROBABILITY OF RECEPTION AND THE AMOUNT OF
RETRANSMISSIONS EXPERIENCED WITHIN THE DISSEMINATION AREA
WITH A f orwardingRange = 500 mAND FOR THREE VALUES OF
maxMessages,1,2,AND 3. (a) AVERAGES FOR SEVERE FADING WITH
NAKAGAMI m=1.(b)AVERAGES FOR MEDIUM FADING WITH m=3.
(c) AVERAGES FOR LOW FADING WITH m=5
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TORRENT-MORENO et al.: VEHICLE-TO-VEHICLE COMMUNICATION: FAIR TRANSMIT POWER CONTROL 3699
Fig. 13. Probability of successful reception of beacons with respect to the
distance, with D-FPAV On and MBL =2.5Mb/s and while disseminating
emergency information for three values of maxMessages, i.e., 1, 2 and 3.
a 99.9% delivery rate while requiring fewer messages, on
average, than the other choices, i.e., 8.1 and 16.3 messages
fewer than configuring maxMessages to 2 and 3, respectively.
However, it is the responsibility of the application designer
to define the requirements for communication protocols, i.e.,
maxMessages 3 may be preferred due to the 100% reception
rates achieved with both D-FPAV On and Off.
If we consider different fading intensities, we observe that
a low-fading intensity, i.e., Nakagami m=5, presents equiv-
alent results to Nakagami m=3 in terms of probability of
reception and the number of retransmissions [see Table III(c)].
On the contrary, in more severe-fading environments such as
Nakagami m=1, the dissemination reliability for D-FPAV Off
and maxMessages =1is higher compared with the medium-
fading condition. Here, the interference level and the chance of
a packet to collide is decreased, resulting in an improved dis-
semination process and, thus, in a higher probability of recep-
tion within the dissemination area. However, due to the higher
fading and the lower probability of successfully receiving a
message at farther distances, the number of retransmissions is
slightly increased.
Furthermore, we analyzed the effect that event-driven mes-
sages have on beacon reception rates during the complete sim-
ulation. Fig. 13 presents the probability of successful reception
of beacons sent by the reference node for the three values of
maxMessages and for the case where no EMDV process is
started, always with D-FPAV On. The difference between the
three curves stays below 2% for all distances, showing the low
impact that the emergency dissemination process has on the re-
ception rates of periodic messages. In fact, a simple calculation
of the additional load required by each dissemination process
shows that, with the configured emergency message size of
500 bytes and a retransmission count between 15 and 30 [see
Table III], in each dissemination process, only up to 15 Kbytes
of data will be sent in total. Because the total amount of data
is transmitted within about 20 ms and taking into account some
degree of spatial reuse (due to the large dissemination area), a
“peak bandwidth utilization” of 375 Kbytes/s can be expected
for the dissemination process.
Fig. 14. Reception delay of the emergency information inside the dissem-
ination area with respect to the distance from the message originator (with
multihop retransmissions) with D-FPAV On/Off, and MBL =2.5Mb/s.
In the following discussion, we focus on the performance of
maxMessages =1, which provides a 99.9% of delivery ratio
with D-FPAV On and utilizes the least amount of overhead.
Fig. 14 shows the average delay experienced by the nodes in the
dissemination area12 until receiving the emergency information
with respect to the distance to the originator. Both curves
present higher values for increasing distances, as expected due
to the multihop dissemination approach. In the case of D-FPAV
Off, event-driven messages are disseminated up to 2 km, with
an average delay below 250 ms. This delay should be compared
to the estimated driver reaction time, which is on the order
of 700 ms and higher [61]. Furthermore, in case D-FPAV is
utilized, the delay experienced significantly falls, i.e., from
52.3 ms to 4.7 ms for close distances to the sender and from
235 ms to 20 ms in the case of a vehicle located 2 km away
from the originator. With respect to different fading intensities,
we observed different delays only for the D-FPAV Off case,
i.e., for m=1, the average delay went up to 290 ms, and for
m=5, it went down to 226 ms. The delays when using
D-FPAV On only slightly changed by 1 ms.
Another relevant parameter for safety is the maximum delay
experienced by the information that will be delivered. To ac-
count for this instance, we measured the maximum time of all
simulated scenarios that a node located at 2 km of the originator,
i.e., at the other edge of the relevant area, has to wait until re-
ceiving the emergency information. According to the results ob-
tained, the maximum delay experienced in the case of D-FPAV
Off is 924 ms, whereas in the case of D-FPAV On, it is only
80 ms. Note the difference of 844 ms between both cases,
which is a significant value compared with the aforementioned
driver reaction time. Finally, we study the performance re-
sults obtained with a forwardingRange of 300 and 700 m
[see Table IV]. Let us first focus on the values obtained
with D-FPAV Off, where the channel load is not controlled.
We can observe how the reception rates vary between 91%
and 93% in all cases. With respect to the delay and amount
12Only the set of vehicles that receive the emergency message can be taken
into account.
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3700 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 58, NO. 7, SEPTEMBER 2009
TAB L E I V
AVERAGES OF THE PROBABILITY OF RECEPTION,THE AVERAGE DELAY,AND THE AMOUNT OF RETRANSMISSIONS EXPERIENCED WITHIN THE
DISSEMINATION AREA FOR THREE VALUES OF THE forwardingRange: 300, 500, AND 700 m
of retransmissions, configuring a forwardingRange of 300 m
presents the worst results due to the limitation of the num-
ber of possible forwarding nodes and a highly saturated
wireless environment. The cases of 500 and 700 m do not
present significant differences due to the low message re-
ception probability between both distances, as illustrated in
Fig. 10(b) (“Event-driven D-FPAV Off”). When D-FPAV is
active, on the other hand, the three values of forwardingRange
achieve almost a perfect message reception within the dissem-
ination area. In a controlled wireless channel, as expected,
allowing a larger forwardingRange allows for reaching the
2-km distance with fewer hops, thus presenting a shorter aver-
age delay, i.e., from 27 ms in case of a 300-m forwardingRange
to 17 ms in case of 700 m. Furthermore, we can observe a
higher robustness of the shortest forwardingRange (300 m),
which presents the smallest number of retransmissions. Forcing
shorter wireless hops decreases the probability of generating
message duplicates due to the high probability of message re-
ception up to the 300-m distance when the load is under control
(see the “Event-driven D-FPAV On” curve in Fig. 10). The price
to pay, as aforementioned, is a slower dissemination speed.
D. Choice of MBL Value
Finally, we evaluated the prioritization effect that a different
choice of the MBL value has on both types of messages and,
therefore, on the performance of our protocols. We simulated
the same scenario as in the previous section, with an MBL set to
2 Mb/s, and describe the results that were obtained as follows.
A smaller MBL value further restricts the transmission power
utilized for beacons, i.e., it achieves a more strict prioritization
of event-driven messages over periodic messages.
In the case of configuring MBL =2.0Mb/s (instead of
2.5 b/s), the average transmission range of beacons is decreased
to 346 m (instead of 356 m) and the channel busy time ratio
to 54.4% (instead of 62.2%). Fig. 15 presents the effect of
configuring the MBL with the two selected values on the
reception rates of single-hop messages. We can observe how,
with MBL =2.0Mb/s, event-driven messages benefit from a
lower load on the medium, which increases their probability
of being successfully received over the distance. Note how this
difference is more noticeable at far distances, i.e., from 500 m,
due to the lower number of collisions that result from hidden
terminals.13
With a lower MBL, the EMDV protocol achieves a more
efficient performance due to the lower channel load and the
13In [60], we showed that the impact of hidden nodes in a broadcast
environment increases with the distance to the transmitter. This effect occurs
due to the capability of wireless interfaces to successfully receive a signal in
the presence of an interference if the latter one is weak enough, i.e., the capture
feature.
Fig. 15. Probability of successful reception with respect to the distance of
beacons and one-hop event-driven messages (without retransmission) with
D-FPAV On for MBL =2.0Mb/s and MBL =2.5Mb/s.
increased event-driven messages’ reception rates. The proba-
bility of information reception with maxMessages =1 and
forwardingRange = 500 m obtains 99.9% along the dissem-
ination area, which is similar to MBL =2.5Mb/s. However, a
lower load on the channel results in a smaller channel access
time for event-driven messages in every hop, which results in
an average delay of 8 ms to deliver the emergency information
at 2 km from the information originator, which is less than
half the time compared with an MBL =2.5Mb/s. Furthermore,
the number of messages sent per dissemination process is also
lower with MBL =2.0Mb/s, i.e., 15.4 instead of 16.2.
VIII. SUMMARY AND CONCLUSION
This paper has been based on the assumption that vehicular
networks will use the IEEE 802.11p or an 802.11 variant and
that market penetration will be high. active-safety communica-
tion will consist of two types of messages: 1) periodic beacon
messages and 2) event-driven emergence messages. We have
shown that channel saturation can “easily” occur due to the load
caused by beacon message transmissions. Simply increasing the
rate or power will just make the channel conditions worse. In
these “uncontrolled” saturated channel conditions, both types
of messages might not be received where they are needed and,
thus, will not contribute to the original goal of improving road
traffic safety.
To satisfy the requirements of active-safety communication
in vehicular networks also under these “stressed” conditions,
we have proposed two communication strategies that can
separately be used but show synergistic effects when combined.
On one hand, we have proposed the D-FPAV algorithm to
limit the beaconing load on the channel below a predefined
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TORRENT-MORENO et al.: VEHICLE-TO-VEHICLE COMMUNICATION: FAIR TRANSMIT POWER CONTROL 3701
threshold while ensuring a high probability of beacon reception
at close distances from the sender. On the other hand, we
have proposed the EMDV protocol to disseminate emergency
information within a geographical area.
D-FPAV is a transmit power control approach based on a
strict fairness criterion that can maximize the minimum value
over all transmission power levels assigned to nodes that form
the vehicular network under a given constraint on the MBL. As
a distributed algorithm, with D-FPAV, each node Vneeds as
input the number of nodes that can sense node V’s transmis-
sion. When this information is available, it can be proven that
D-FPAV provides an optimal PA. Under realistic assumptions,
the required input has to be estimated. We have shown, through
a simulative investigation, that the estimation procedure is
sufficiently accurate and with low communication overhead.
The simulation results show that D-FPAV works very well in
realistic vehicular environments.
The EMDV approach provides for robust and effective infor-
mation dissemination of emergency information. For EMDV,
we made use of the idea of contention-based forwarding that
can very well deal with the unreliability of the channel and
with node mobility. For the reduction of the dissemination
delay, we also made use of beacon information and of classical
position-based forwarding techniques in combination with the
contention-based approach.
Synergy is gained when using both protocols together, be-
cause D-FPAV can ensure that the channel load, in particular
the channel busy time, is kept at a level where EMDV (or other
dissemination protocols) can successfully operate.
The performance of the proposed protocols has been ana-
lyzed via simulation. For a proper evaluation, we extended the
network simulator ns-2 with more accurate reception, interfer-
ence, and propagation models and adjusted it to the current
version of the IEEE 802.11p draft. Furthermore, realistic vehic-
ular movement patterns that correspond to fast-moving German
highway scenarios were utilized.
According to the results obtained, our suite of protocols
presents a viable solution for improving active-safety-related
communications in IEEE 802.11-based vehicular networks.
When we consider the two proposed approaches together with
the formal treatment and with the simulative performance
evaluation as the key contributions, we also see the results
obtained for “plain” IEEE 802.11p-based systems as a valuable
contribution for understanding the performance limits of a
system without power or rate control.
For our future work, many issues need to be further investi-
gated. We will explore in detail the complete parameter space
of EMDV in accordance with the environment and will address
complex scenarios with intersections and with multiple infor-
mation originators. Eventually, power and rate control should
jointly be treated for optimum performance of the vehicle-to-
vehicle communication system, particularly in traffic scenarios
with a very high vehicle density, e.g. in traffic-jam situations.
In such scenarios, for instance, it might be better to first reduce
the beacon rate (because the velocity of the vehicles and the
rate of topology changes are relatively small) and then reduce
the transmission power to control the load on the wireless
channel.
APPENDIX
D-FPAV ANALYSIS
Theorem 1: Assume that the CS ranges of the nodes are
symmetric, i.e., uiCSMAX(j)ujCSMAX (i), and that
the number of nodes within range CSMAX of each network
node does not change. Then, the D-FPAV algorithm computes
an optimal solution to BMMTxP within the time of two suc-
cessive periodic beacon transmissions, i.e., the time of two
successive broadcast transmissions that contain a node’s local
power computation.
Proof: First, we have to show that, under the theorem
assumptions, the PA computed by D-FPAV after one round
of communication is a feasible solution to BMMTxP. Assume
the contrary, i.e., assume that there exists node uisuch that
BL(PA,i)>MBL, where PA is the power assignment com-
puted by D-FPAV. This condition means that node uihas many
interferers, all of which are located in CSMAX(i)(assuming
symmetric CS ranges). Let uj,...,u
j+h,forsomeh>0,be
these interferers and let PAibe the PA computed by node uifor
all the nodes in CSMAX(i). In Step 1 of D-FPAV, uicomputes
an optimal solution PAito BMMTxP restricted to CSMAX(i).
Assuming symmetric CS ranges, this solution includes a power
setting for the interferers uj,...,u
j+h, and this power setting is
such that BL(PAi,i)MBL. In Step 2 of D-FPAV, the power
setting PAiis disseminated to all nodes in CSMAX(i), which
includes all the interferers uj,...,u
j+h. Hence, each of the
interferers receives from node uia power setting PAisuch that
the condition on the beaconing load is not violated at node ui.
Because the final power setting of the interferers is at most PAi
(this instance follows from the minimum operation executed
in Step 3 of D-FPAV) and the number of nodes within range
CSMAX from uiare not changed and assuming a monotonic
CS range,14 we have that the beaconing load at node uicannot
exceed the MBL threshold, which leads to a contradiction. This
result proves that the PA computed by D-FPAV is a feasible
solution to BMMTxP.
Let us now prove that the computed PA is optimal. Let PA
be the power assignment computed by D-FPAV after all power
computations by surrounding nodes have been received, and let
pmin be the minimum of the node power levels in PA. Assume
that PA is not optimal, i.e., there exists another feasible solution
PAto BMMTxP such that the minimum of the node power
levels in PAis p>p
min. Without loss of generality, assume
that PAsets the power level of all nodes in the network to
p.PA
is feasible; thus, we have that BL(PA ,i)MBLi
1,...,n. Hence, given the monotonicity of the CS range, every
node uiin the network computes a power setting Pipin
Step 1 of D-FPAV (this instance follows from the water-filling
principle used in the FPAV algorithm). The solution computed
at Stage 1 by each node is at least p; thus, each node receives
power values at least as large as pin Step 2b of the algorithm.
Hence, the final power setting of every node in the network, as
computed by D-FPAV in Step 3, is at least p>p
min, which
contradicts our initial assumption that the minimum of the
14Under this assumption, an increase in transmit power can only increase the
CS range.
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3702 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 58, NO. 7, SEPTEMBER 2009
power levels computed by D-FPAV was pmin.Itfollowsthat
the solution computed by D-FPAV is optimal, and the theorem
is proven.
Theorem 2: The D-FPAV algorithm has O(n)message
complexity.
The straightforward proof of the theorem is omitted.
ACKNOWLEDGMENT
The authors would like to thank the anonymous reviewers for
their valuable comments and insights that greatly improved the
quality of this paper, K. P. Laberteaux for his insights and very
helpful discussions, and K. Tschira Stiftung and INIT GmbH
for the research group on Traffic Telematics.
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Marc Torrent-Moreno received the M.Sc. degree
in telecommunications engineering from the Poly-
technic University of Catalonia, Barcelona, Spain,
and the Ph.D. degree in computer science from the
University of Karlsruhe, Karlsruhe, Germany.
Since 2001, he has participated in several research
projects concerning mobile networks as part of dif-
ferent companies and universities such as British
Telecom; NEC Deutschland, Düsseldorf, Germany;
DaimlerChrysler Research and Technology North
America, Palo Alto, CA, the University of California,
Berkeley; and the University of Karlsruhe. In 2007, he made a step toward prod-
uct development and market analysis by joining A2C (a joint venture between
Ficosa International, S.A. and Grupo Mecanica del Vuelo Sistemas, S.A.),
where he has been the Business Development and Project Manager, working
on the development of innovative telematic solutions for the automotive market.
He has contributed more than 20 papers and has reviewed for several conference
proceedings and journals.
Dr. Torrent-Moreno has been on the organizing committees of the 2005 and
2006 Third ACM International Workshop on Vehicular Ad Hoc Networks.
Jens Mittag (S’08) received the Diploma degree in
computer science from the University of Karlsruhe,
Karlsruhe, Germany. He is currently working toward
the Ph.D. degree with the Decentralized Systems
and Network Services Research Group, Karlsruhe
Institute of Technology (KIT).
Before joining KIT as a student assistant in 2005,
he was with the Process and Data Management,
Engineering Research Group, Research Center for
Information Technology, Karlsruhe. His research in-
terests include mobile networks, simulation environ-
ments, and, more recently, the modeling of wireless lower physical layers.
Paolo Santi received the Laurea and Ph.D. degrees
in computer science from the University of Pisa,
Pisa, Italy, in 1994 and 2000, respectively.
Since 2001, he has been a Researcher with the
Instituto di Informatica e Telematica del CNR, Pisa.
During his career, he visited the Georgia Institute of
Technology, Atlanta, in 2001 and Carnegie Mellon
University, Pittsburgh, PA, in 2003.He has contribut-
ed more than 45 papers and one book on wireless
ad hoc and sensor networking. His research interests
include fault-tolerant computing in multiprocessor
systems (during his Ph.D. studies) and, more recently, the investigation of
fundamental properties of wireless multihop networks such as connectivity,
topology control, lifetime, capacity, mobility modeling, and cooperation issues.
Dr. Santi is a Senior Member of the Association for Computing Machinery
(ACM) and the ACM SIGMOBILE. He is an Associate Editor for the IEEE
TRANSACTIONS ON MOBILE COMPUTING. He was a General Cochair of the
Fourth and Fifth ACM International Workshop on Vehicular Ad Hoc Networks
and a Technical Program Cochair of the Fourth IEEE Workshop on Wireless
Mesh Networks. He is involved in the organizational and technical program
committee of several conferences in the field.
Hannes Hartenstein (M’01) received the Diploma
degree in mathematics and the Ph.D. degree in com-
puter science from the Albert Ludwigs University,
Freiburg, Germany.
He is a Full Professor of Decentralized Systems
and Network Services with the Karlsruhe Institute
of Technology (KIT), Karlsruhe, Germany, and a
Director of the KIT Steinbuch Centre for Computing.
Before joining the University of Karlsruhe, he was a
Senior Research Staff Member with NEC Europe. He
was involved in the FleetNet—Internet on the Road
(2000–2003) and NOW: Network on Wheels (2004–2008) Projects, which
were funded in part by the German Ministry of Education and Research. He
is currently actively participating in the European Union Seventh Framework
Programme project PRE-DRIVE-C2X. His research interests include mobile
networks, virtual networks, and information technology management.
Dr. Hartenstein has been a Technical Program Committee Cochair and Gen-
eral Chair of various highly selective ACM and IEEE international workshops
and symposia on vehicular communications.
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... For example, Yu and Biswas [5] proposed a unique dedicated short-range communication (DSRC)-based medium access control (MAC) framework for intervehicle communication (IVC). Furthermore, the distributed power control method is suggested to control a load of periodic messages on the channel [6]. Bi et al. [7] introduced a cross-layer broadcast protocol for efficient and dependable message delivery in IVC systems. ...
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... When detecting a collision, the UE transmits a To increase network stability, in [21], the author describes how UE C, located between UE A and UE B broadcasting basic safety messages (BSMs), detects a collision. [21] uses two fixed transmit powers P 0 and P 1 , unlike in [22], [23], which adjusts the transmit power according to the local vehicle density. UE A and UE B randomly transmit packets with P 0 or P 1 power, where P 1 > P 0 . ...
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This paper proposes an evolved wireless ad hoc network (WANET) in which user equipment (UE) can manage radio resources by itself without the control by infrastructure. There are two independent channels in the proposed WANET, one is a data channel, the other is a tone channel for resource management. UEs allocate, occupy, and return resources by using tones in the tone channel. Although UEs do not exchange channel information with each other, they are able to occupy a clear one-hop area without a hidden node problem by setting the data communication area and the tone communication area differently. A UE occupying a resource performs a real time collision check on the occupied resource, and if a collision is detected, it is able to re-occupy a resource without collision. In this paper, we present the operating principle of the proposed WANET, and design its communication area, and analyze the average resource occupation time. Since the proposed WANET operates in a general communication environment that is not limited to special assumptions, it supports periodic and aperiodic communication and broadcast links and unicast links without hidden node problem, and could be utilized to various communication fields such as drones, vehicles, ships, and smartphones.
Chapter
This research presents a method based on deep learning network architecture for image classification problems for a communication system between autonomous vehicles. Vehicular communication is a research direction of data transmission between autonomous vehicles based on a communication system called a vehicular optical communication camera (VOCC). Thanks to the VOCC system, the vehicles transfer information about the position, direction, speed, and future behavior. In addition, the power of the deep learning network-based proposed algorithm improves the VOCC system to gain the accuracy of image processing from the acquisition of signals from autonomous vehicles. Experimental results show that the proposed algorithm achieves high performance on image signals with difficult conditions.
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In vehicular ad hoc networks, a technique known as multi-node message transmission through VANETs is utilized to inform all vehicles in an emergency area. However, sending multi-node warning message notifications outside a vehicle’s transmission range has three significant drawbacks: detecting an accident based on sensors, sending emergency message notifications to a nearby ambulance or petrol vehicle, and rerouting vehicles after an accident has occurred; people can re-navigate their destination. Hence, this paper proposes a prototyping design for emergency message notifications between vehicles in vehicular ad hoc networks. The proposed research is based on a prototype implementation and investigates the transmission of emergency messages from vehicles to vehicles and infrastructures.KeywordsVehicular networksRoutingVehicle to infrastructureIntelligent transportation systemVehicle to vehicle
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Technology advancements have become the focal point for bettering one’s quality of life. VANETs (Vehicular Ad Hoc Networks) have risen to prominence in the research community as a promising topic for a variety of purposes, including infotainment, safety, and traffic management. VANETs architecture includes for Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication. Safety s are required To avoid numerous road accidents, effective V2V communication. Vehicles communicate using two messages: beacons and event-driven using Dedicated Short Range Communication (DSRC). As the number of vehicles in a network increases, it causes network congestion because of the dissemination of safety messages. The proposed scheme analyzes congestion detection schemes followed by congestion control to mitigate network congestion. The proposed scheme adopts a priority model and adjusts the transmission rate of the beacon messages using the Tabu-search algorithm to control the network congestion. In terms of Packet Delivery Ratio (PDR) and End to End (E2E) delay, the proposed scheme’s performance is compared to those of state-of-the-art methods.
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Vehicular Ad hoc Networks (VANETs) are the special types of Mobile Ad hoc Networks (MANETs) designed to assist drivers in emergency situations and improve the overall driving experience. The VANET is an infrastructure-less environment with communication-enabled highly mobile vehicular nodes and has capabilities of sending and receiving various messages using Dedicated Short Range Communication (DSRC). VANET uses IEEE 802.11p standard for Wireless Access in Vehicular Environment (WAVE) for sharing traffic and safety information. VANET is playing a crucial role in Intelligent Transport Systems (ITS) by avoiding accidents and reducing pollution to achieve accident-free and pollution-less motorways. Highly mobile vehicular nodes and rapidly changing network topology throws many challenges in the dissemination of critical messages in VANET. Improper routing in emergency and Safety applications will result in congestion and degradation of network performance and QOS. This survey paper presents an overview of VANET with the help of VANET communications, characteristics, and applications of VANET. This article discusses the problem of congestion and reviews the researcher’s contribution to solving congestion problems as it plays a major role in the Quality of Service (QOS) of VANET.KeywordsQuality of Service (QOS)Vehicular Ad hoc Network (VANET)Intelligent Transport Systems (ITS)RoutingCongestion control
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VANETs (vehicular ad hoc networks) are emerging as a new network environment for intelligent transportation systems. Many of the applications built for VANETs will depend on the data push communication model, where information is disseminated to a group of vehicles. In this paper, we present a formal model of data dissemination in VANETs and study how VANET characteristics, specifically the bidirectional mobility on well defined paths, affects the performance of data dissemination. We study the data push model in the context of TrafficView, a system we have implemented to disseminate information about the vehicles on the road. Traffic data could be disseminated using vehicles moving on the same direction, vehicles moving in the opposite direction, or vehicles moving in both directions. Our analysis as well as simulation results show that dissemination using only vehicles in the opposite direction increases the data dissemination performance significantly.
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