Cross-Network Information Dissemination in

VANETs

G. Ferrari and S. Busanelli

Wireless Ad-hoc and Sensor Networks Lab

Department of Information Engineering

University of Parma

I-43124 Parma, Italy

http://wasnlab.tlc.unipr.it/

N. Iotti

Guglielmo Srl

Strada Parma 35/D5

43010 Pilastro di Langhirano (Parma)

Italy

http://www.guglielmo.biz/

Y. Kaplan

Cellint Traffic Solutions Ltd

1 Bat Sheva Rd.,POB 1300

Lod 71100

Israel

http://www.cellint.com/

Abstract—In this work, we provide an overview of an innova-tive approach for effective cross-network information dissemina-tion, with applications to Vehicular Ad-hoc Networks (VANETs).In particular, we describe the main approach followed in anon-going bilateral Italy-Israel project (“Cross-Network EffectiveTraffic Alert Dissemination,” X-NETAD). The X-NETAD projectleverages on the spontaneous formation of WiFi local VANETs,with direct connections between neighboring vehicles, in order todisseminate, very quickly and inexpensively, traffic alerts receivedfrom the UMTS network.

I. INTRODUCTION

Real time traffic alerting is a key issue in efficient trans-

portation systems, for several reasons: from the perspective

of road operators, efficient traffic alerting allows congestion

reduction and smoother traffic flow; from the perspective of

drivers, the availability of reliable and updated information on

traffic incidents means time and gas saving, increased safety

and less stress; from the economic perspective, real time traffic

alerting will save time and gas and decrease CO2 emissions.

There are numerous systems that monitor traffic and detect

traffic congestion and traffic incidents, based on road sensors,

police reports, Global Positioning System (GPS) and cellular-

based traffic data collection, and other means.

Cellint Traffic Solutions Ltd has developed an efficient and

accurate real time traffic detection systems (named Traffic-

Sense), based mainly on data extraction from cellular net-

works [1]. TrafficSense relies on the existence of a cellular

network over the road areas to be monitored and is based on

the fact that, with extremely high probability, there is (at least)

a cellular phone inside each vehicle. Cellular phones are also a

good way to disseminate traffic information and supply drivers

with real time traffic alerts in their neighborhood (neighbor-

hood can be defined as a circle of 20-50 km around the current

vehicle location). However, traffic alerts dissemination using

cellular phones have some inherent limitations. Using cellular

phones continuously for real time traffic alerts on the cellular

network (i.e., cellular Internet) has relatively high cost and

consumes a large amount of energy from the mobile phone.

Approximate location is required in order to receive relevant

traffic alerts. Location can be supplied by GPS. Currently, less

than 20% of the phones have a built-in GPS and, in addition,

phones that have GPS are often located inside the vehicle so

that the GPS does not have line of sight to the satellites and

cannot determine the location.

Nowadays, most of the vehicles available on the market are

provided by sensorial, cognitive, and communication skills.

In particular, leveraging on Inter-Vehicular Communications

(IVCs)—a set of technologies that gives networking capabili-

ties to the vehicles—vehicles can create decentralized and self-

organized vehicular networks, commonly denoted as Vehicular

Ad-hoc NETworks (VANETs), involving either vehicles and/or

fixed network nodes (e.g., road side units). VANETs have

a few unique characteristics: (i) the availability of virtually

unlimited energetic and computational resources (in each

vehicle); (ii) very dynamic network topologies, due to the

high average speed of the vehicles; (iii) nodes’ movements

constrained by the underlying road topology; (iv) broadcast

communications protocols, used as truly information-bearing

protocols (especially in multihop communication scenarios)

and not only as auxiliary supporting tools.

In recent years, many broadcast protocols for VANETs have

been proposed by the research community.

In [2], one can find a possible classification of broadcast

protocols. In [3], the authors focus, from a theoretical per-

spective, on efficient broadcasting for mobile ad hoc networks.

Position-based broadcast protocols (see [4] and references

therein) exploit the knowledge of some geographical character-

istics of the network, to improve the retransmission efficiency.

For example, the Emergency Message Dissemination for Ve-

hicular environments (EMDV) protocol achieves remarkable

performance exploiting pure geographical information and

information on the local network topology [5]. The major

drawback shared by all position-based protocols, however,

is the need for information on the network topology and

the geographical characteristics of the environment where the

nodes are located [5].

Since collecting this information may be very difficult,

several alternative broadcasting protocols have been recently

proposed with the goal of achieving the same performance

level of position-based protocols without the need for ma-

jor information exchange. The Urban Multihop Broadcast

(UMB) [6], the Smart Broadcast (SB) [7], and the Binary

2011 11th International Conference on ITS Telecommunications

978-1-61284-671-2/11/$26.00 ©2011 IEEE 351

Partition Assisted Protocol (BPAB) [8], are good examples

of threshold-based protocols, based on the idea of partitioning

the transmission range of the source in distinct regions.

It can be shown that the so-called probabilistic broadcasting

protocols, in which nodes rebroadcast according to a certain

probability assignment function, can achieve a performance

level comparable with which guaranteed by threshold-based

protocols, but with a lower complexity [9]–[11].

The problem which arises is how to properly assign the

rebroadcast probability to each node. Intuitively, the farthest

node in the range of the transmitter should be given the

responsibility of rebroadcasting the packet, as this will yield

the highest forward progress. Thus, the rebroadcast probability

should be a function of the distance between the receiver

and the transmitter. In other words, the farther the receiving

node is from the transmitting one, the higher its associated

rebroadcast probability should be. A simple function, which

is commonly used for assigning the rebroadcast probability, is

of the form d/z, where d is the distance between receiving

and transmitting nodes, and z is the transmission range. With

this assignment, the rebroadcast probability is an increasing

linear function of the distance d [10], [12].

In this paper, we describe the innovative cross-network

approach, for effective information dissemination in vehicular

environments, proposed in the on-going Italy-Israel “Cross-

Network Effective Traffic Alerts Dissemination” (X-NETAD)

project [13]. The goal of the X-NETAD project is the design

and implementation of low cost, efficient and ubiquitous

traffic alerts dissemination to drivers. In particular, is envisions

the formation of ephemeral local VANETs, formed by one

“primary” vehicle surrounded by “secondary” vehicles. The

former vehicle acts as a gateway for traffic alerts coming

from the UMTS network, which are broadcasted to the latter

vehicles by means of WiFi broadcast communications.

II. X-NETAD: THE IDEA

As of today, smartphones are gaining more and more

popularity. This new class of personal device assistants are

typically equipped with a dual interface, for cellular networks

(typically 3G UMTS networks) and WiFi networks. Therefore,

in the near future each vehicle will likely contain at least one

of these devices. The key idea of the X-NETAD approach

is that of leveraging on the spontaneous formation of WiFi

local VANETs, with direct connections between neighboring

vehicles, in order to disseminate traffic alerts and GPS lo-

cation in the VANETs very quickly and inexpensively. More

precisely, the X-NETAD system will be designed in such a

way that, at any given time, only a small percentage (e.g.,

10%) of the vehicles (i.e., the smartphones inside the vehicles)

will be directly connected to the traffic alerts dissemination

system using cellular Internet over UMTS (i.e., to the Traf-

ficSense system). These (primary) vehicles will then dissemi-

nate the same information, through WiFi communications, to

the other (secondary) vehicles, which cannot receive directly

UMTS traffic alerts, in their VANETs. To this end, a very

efficient broadcast technique for information dissemination

in VANETs, denoted as Irresponsible Forwarding (IF), has

recently been proposed [14]. IF assigns to each vehicle an

optimized rebroadcast probability, which takes into account the

surrounding vehicle spatial density and can be easily tuned:

this guarantees fast information dissemination even in the

presence of high vehicular traffic. The IF protocol does not

require the exchange of additional messages, therefore, it could

easily replace the legacy flooding protocol that is still used in

many projects related to the the Car 2 Car Communications

Consortium (C2C-CC) [15]. For instance, the geo-broadcasting

protocols defined within the GeoNet project (e.g., GeoBroad-

cast and TopoBroadcast) rely on the flooding protocol for

realizing multihop broadcast communications [16].

The X-NETAD project aims at designing and implementing

the proposed innovative traffic alerts dissemination system

based on a cross-network (cellular and WiFi) software appli-

cation (to be developed by Guglielmo and the University of

Parma), easily downloadable and installable on dual-interface

(UMTS and WiFi) smartphones. An illustrative representa-

tion of the cross-network traffic alert dissemination system

envisioned by the X-NETAD project is shown in Fig. 1.

The innovative software application will allow these devices

to receive traffic alerts from the UMTS network, through a

cellular Internet-based traffic alerts dissemination system (to

be developed by Cellint), and quickly broadcast it (through

the use of the IF protocol) to all vehicles of its VANET. For

safety considerations, the traffic alerts will be disseminated

as audio messages rather than visual data, so that the drivers

attention will not be distracted by looking at its smartphone

screen. Besides extending the coverage and lowering the

operational costs of traffic alerts dissemination, the X-NETAD

project approach will also lead to mutual UMTS-WiFi network

optimization. For example, the vehicle spatial density can

be estimated analyzing the data collected by the traffic data

collection system (namely TrafficSense, that collects data from

the UMTS cellular network) and used by the IF protocol (in

WiFi VANETs) to optimize communications therein. On the

other hand, vehicles in a VANET that have a functional GPS

will broadcast (through WiFi communications) their locations

using the IF protocol so that the (primary) smartphone that

receives the traffic alerts from the cellular Internet (UMTS)

will know its approximate location, even without having a

functional GPS, and will disseminate only relevant traffic

alerts.

III. CELLULAR BASED TRAFFIC CONDITION ESTIMATION

The traffic information disseminated by X-NETAD relies on

a proprietary technology developed by Cellint Traffic Solutions

Ltd and implemented in the TrafficSense system [1]. Traffic-

Sense measures traffic data by analyzing the movement of

anonymous cellular phones in vehicles. TrafficSense measures

the exact travel-time of each anonymous vehicle over small

road intervals, every few hundred meters. By analyzing and

aggregating all the travel time samples over the coverage area

TrafficSense provides complete real-time traffic data for all

types of roads. The output data can then be extrapolated to

352

UMTS Traffic Alert

WiFi IF-based Alert Broadcast

WiFi IF-based Alert BroadcastWiFi IF-based Alert Broadcast

Fig. 1. Illustrative X-NETAD traffic alerts dissemination scheme.

Fig. 2. TrafficSense system overview.

generate traffic predictions according to historical patterns and

the current road network status.

Cellint’s high accuracy traffic data is a result of its detec-

tion technology: unlike most cellular-based traffic suppliers,

Cellint does not use cell tower locations for triangulation.

The triangulation method is not accurate enough due to

signal obstructions, reflections and multipath and thus provides

poor traffic data. Instead, Cellint creates a reference database

by driving the roads and creating cellular signaling maps

named “road signatures” from ground truth drives. The road

signatures keep the exact locations of events on the cellular

network. This provides orders of magnitude better location

information, especially in urban areas, resulting in accurate

traffic information and real time detection of any change in

traffic. Cellints TrafficSense is the only cellular based traffic

detection system that was successfully tested against road

sensors by Departments of Transportation in Europe and the

USA. An illustrative representation of the TrafficSense system

architecture is shown in Fig. 2.

IV. EFFICIENT INFORMATION DISSEMINATION IN

VANETS THROUGH IRRESPONSIBLE FORWARDING

In this section, we describe the basic idea of the probabilistic

dissemination protocol to be used in each VANET to propagate

Fig. 3. A typical linear network topology of a VANET.

traffic alerts from the primary user (which acts as local VANET

source) to all users forming the VANET.

A. Reference Scenario

Fig. 3 shows the linear network topology of reference.

We consider a one-dimensional wireless network with N(receiving) nodes, under the assumption that the road’s width

is considerably smaller than the transmission range of the

vehicles. Each node is uniquely identified by the indices

j ∈ {1, 2, . . . , N}. The source node, denoted as node 0, is

placed at the left end of the network.

The following preliminary assumptions are introduced to

derive simple, yet significant, insights. The inter-vehicle spac-

ing is exponentially distributed with mean 1/ρs, where ρsis the vehicle spatial density (dimension: [veh/m]). In other

words, the nodes’ positions are generated according to a

one-dimensional Poisson distribution with parameter ρs—the

validity of this assumption is confirmed by empirical traffic

data [17]. Each vehicle has a fixed transmission range, de-

noted as z (dimension: [m])—the transmission range depends,

obviously, on the transmit power. Each vehicle is equipped

with a Global Positioning System (GPS) receiver. As a result,

each vehicle knows its own position at any given time. The

network size (the line length) is set to L (dimension: [m]). For

generality, we denote as normalized network size the positive

real number ℓnorm , L/z, and we observe that ρsz (dimen-

sion: [veh]) represents the average number of vehicles within

a transmission range.

Each topology realization, consistent with the previous

assumptions, is obtained by iteratively generating the positions

of consecutive nodes (with exponentially distributed distance

353

between a pair of consecutive nodes). The generation process

stops as soon as d0,N+1 > L, where d0,N+1 is the (positive)

distance between the source node and the N + 1-th node.

Therefore, N is the random variable denoting the index of

the last generated node. In general, dj,k denotes the distance

between the two nodes j and k (j, k ∈ {1, . . . , N}). In order

to guarantee the significance of every scenario, we impose that

the 1-st node has to lie in the interval [0, z], i.e., d0,1 < z, so

that N > 1. Apparently, these requirements break the Pois-

sonianity of nodes’ position process. However, conditionally

on d0,1, the remaining nodes in the set {2, . . . , N} are still

distributed according to a Poisson point process of parameter

ρs. Considering that1 E[d0,1] ≃ 1/ρs, it is accurate to assume

that the number of nodes, denoted as N ′ , N − 1, in the

network region [d0,1, L] (i.e., after the first node) has a Poisson

distribution with parameter ρs(L − 1/ρs) = ρsL− 1.

On the basis of the previous assumptions, we want to

preliminary investigate the network connectivity (which does

not depend on the chosen broadcasting protocol). To this

end, we introduce the concept of last reachable node (iden-

tified by the index nreach). Given that there are N nodes

in the network, for every pair of consecutive nodes, say

(j, j + 1), j ∈ [1, 2, . . . , N − 1], since the distribution of

dj,j+1 is well approximated by an exponential distribution

with parameter ρs, there is (approximately) a probability

e−ρsz that dj,j+1 > z. We denote as j∗ the minimum node

index such that dj∗,j∗+1 > z. Clearly, if j∗ exists, than the

(j∗ + 1)-th node is unreachable from any node j < j∗ + 1.

Therefore, the network is said topologically disconnected and

nreach = j∗. Conversely, if j∗ does not exist, then the network

is topologically connected and nreach = N . Obviously, the

average value of the ratio nreach/N is a meaningful metric to

evaluate the connectivity level of the network.

In Fig. 4, E [nreach/N ] is shown as a function of the product

ρsz, considering both simulation and analytical results. More

precisely, the simulation results are obtained by averaging, for

each value of ρsz, the values of the ratio nreach/N obtained

over 1000 network topologies generated according to the

previous assumptions.2 Recalling that N ′ = N − 1 is approx-

imately Poisson(ρsL− 1), the average value E [nreach/N ] can

be analytically approximated as follows:

E

[

nreach

1 +N ′

]

=

∞∑

j=0

E

[

nreach

1 +N ′|N ′ = j

]

P{N ′ = j}

≃

∞∑

j=0

1

1 + jE [nreach |N

′ = j ](ρsL− 1)j e−(ρsL−1)

j!. (1)

Given that N ′ = j, nreach can assume values between 1 (if

only the first node is connected) and j + 1 (if all nodes are

1Given that d0,1 < z, it follows that d0,1 has the truncated exponential

probability density function fd0,1 (τ) = ρse−ρsτ

1−e−ρsz[U(τ) − U(τ − z)],

where U(·) is the unit step function. It can be shown that E[d0,1] =1/ρs − z e−ρsz/(1 − e−ρsz) is well approximated by 1/ρs, i.e., by theaverage value of a (non-truncated) exponential distribution.

2Note that the assumption that d0,1 < z, i.e., N ≥ 1, makes the chosenmetric meaningful.

0

0.2

0.4

0.6

0.8

1

1 5 10 15 20

ρs z [veh]

Anal.Sim.

Fig. 4. The average ratio E[nreach/N ] as a function of ρsz.

connected). More precisely

P{nreach = ℓ|N ′ = j} ≃

{

(1− PD)ℓPD 1 ≤ ℓ ≤ j − 1

(1− PD)j ℓ = j

where PD = e−ρsz is the probability that two consecutive

nodes are disconnected, i.e., that the distance between them is

longer than z. Therefore,

E [nreach |N′ = j ] ≃

j−1∑

ℓ=1

ℓ(1− PD)ℓPD + j(1− PD)

j (2)

and, inserting (2) into (1), the desired average value can be

approximated. As one can see from the results in Fig. 4, the

proposed analytical approximation is very accurate. It can be

observed that E [nreach/N ] is an increasing function of ρszthat approaches 1 for ρsz ≥ 12 veh. In other words, for ρsz ≥12 veh the network is completely connected.

B. Probability Assignment Function

Let us consider a vehicle, at a generic distance d from the

source node (positioned at the origin of the horizontal axis),

within the transmission range of the source. In Fig. 3, Nz

denotes the number of nodes within the transmission range

of the source, i.e., d ∈ {d1, d2, . . . , dz}. According to the

idea of the IF protocol, the vehicle should rebroadcast the

packet only if the probability of finding another vehicle in

the consecutive interval of length z − d is low; otherwise,

it should not. More specifically, when a vehicle receives a

packet, it compares its position with that of the transmitter

and computes its rebroadcast probability as follows:

p = exp

{

−ρs(z − d)

c

}

(3)

where d is the distance between the vehicle and the transmitter

and c ≥ 1 is a coefficient which can be selected to “shape”

the probability of rebroadcasting. The higher the value of c,the higher the probability of rebroadcasting at any position d.

In the particular case with c = 1, the rebroadcast probability

reduces to the probability that there is no vehicle in the interval

of length z − d.

354

0

0.2

0.4

0.6

0.8

1

0 50 100 150 200

p

d [m]

ρs=0.01 veh/m, c=1ρs=0.05 veh/m, c=1ρs=0.01 veh/m, c=3ρs=0.05 veh/m, c=3

Fig. 5. Rebroadcast probability, as a function of the distance, with z =200 m, c ∈ {1, 3}, and ρsz ∈ {0.01, 0.05} veh/m.

In Fig. 5, the rebroadcast probability is shown, as a function

of the distance between the receiver and the transmitter, for

various values of c and ρsz. This figure can be interpreted as

follows. When d is small, the value of p is also small because

the receiving vehicle is very close to the transmitter and,

therefore, it should let the other node take the responsibility

of rebroadcasting. When d becomes larger, the value of pbecomes larger and approaches 1 when the node is at the

edge of the transmission range. Note that with the probability

assignment in (3), the node spatial density is also taken into

account. When the network is sparse, the overall rebroadcast

probability should be high (e.g., even if the receiving node is

relatively close to the transmitter) in order to ensure complete

reachability. As observed in Fig. 5, when ρsz decreases from

0.05 veh/m to 0.01 veh/m, the overall forwarding probability

also increases. In addition, the coefficient c is also effective at

shaping the rebroadcast probability, as the overall rebroadcast

probability can be increased by increasing the value of c.

C. IF in IEEE 802.11 Networks

Since communications are broadcast, correct packet re-

ception cannot be acknowledged. Therefore, the packets are

never retransmitted and the Contention Window (CW) of

the backoff procedure of the Carrier Sense Multiple Access

with Collision Avoidance (CSMA/CA) medium access control

(MAC) protocol is constant and equal to the value specified

by the parameter CWmin of the IEEE 802.11 standard [18].

Moreover, it is well known that broadcast communications

cannot exploit the Ready-To-Send/Clear-To-Send (RTS/CTS)

mechanism foreseen by the IEEE 802.11 standard. Hence, they

suffer from the hidden terminal problem, which inevitably

reduces the number of packets being able to propagate to the

farthest reachable node of the network. Note that the DCF

mechanism of the IEEE 802.11 standard obviously introduces

an additional channel access delay, proportional to the traffic

load and to the parameter CWmin, thus increasing the overall

TABLE IMAIN IEEE 802.11 NETWORK SIMULATION PARAMETERS FOR IF.

c {1, 5}λ {0.1, 100} pck/s

ρs 0.01 veh/m

z {100, 300, 500, 750, 1000, 1500, 2000} m

ℓnorm 8Packet Size 105 bytes

Carrier Freq. 2.4 GHz

Data rate 1 Mbps

CWMIN 31

0

0.02

0.04

0.06

0.08

0.1

0 5 10 15 20

D[s

]

ρs z [veh]

IF, c=1, λ=0.1 pck/sIF, c=1, λ=100.0 pck/s

IF, c=5, λ=0.1 pck/sIF, c=5, λ=100.0 pck/s

flood, λ=0.1 pck/sflood, λ=100.0 pck/s

c=1

c=5flood

Fig. 6. D as a function of ρsz, obtained for λ = 100 pck/s and λ =0.1 pck/s. The curves are obtained for the IF protocol with c = 1, c = 5 andfor the flooding protocol (flood).

delay. We remark that while the traffic load is ineffective in

an ideal (collision-free) scenario, in an IEEE 802.11 network

scenario it will have a relevant impact.

The IF protocol is “inserted” on top of the IEEE 802.11

model presented in Network Simulator 2 (ns-2.34 [19]). Since

this work does not focus on physical layer issues, we adopt a

simple Friis free-space propagation model [20]. The relevant

parameters of the IEEE 802.11 network and of the IF protocol

are listed in Table I. In particular, we underline the choice of

a small packet size (105 bytes) to avoid fragmentation at the

MAC layer. Finally, in order to have a performance bench-

mark, we have also carried out simulations of the classical

flooding protocol, whose forwarding policy is trivial: every

node rebroadcasts any fresh packet with probability equal to

1 (i.e., always). All the results presented are accurate within

±5% of the values shown with 95% confidence.

In Fig. 6, D is shown as a function of ρsz. One can observe

that in the saturation region (high values of ρsz) the flooding

protocol (denoted as “flood” in the figure) is very sensitive to

the traffic load, as shown by the “explosion” of the delay in the

case with λ = 100 pck/s. Conversely, all the IF curves exhibit

an “attractive” behavior, since they are slightly increasing in

the region with low network connectivity (small values ρsz),

while they become approximately constant in scenario with

355

high connectivity (ρsz ≈ 20). We stress the fact that all IF

schemes (with c = 1 and c = 5) have approximately the same

delay in the saturation region, whereas there is a gap for small

values of ρsz. This behavior is mostly due to the CSMA/CA

mechanism. In fact, in the poorly connected region the packets

experience, on average, a small number of hops. In addition,

the CSMA/CA mechanism has an impact mostly in the very

first hops experienced by the packet, when the traffic load is

more intense due to the proximity to the source.

D. On the Impact of Node Mobility

Since we have considered broadcasting applications where

a single information packet is transmitted by the source, it can

be immediately concluded that the mobility of the vehicles has

an impact only if the mobility level is sufficient to generate

relevant network topology variations during the propagation

time, throughout the network, of the information packet.

According to the results shown in Fig. 6, it can be concluded

that the maximum delay in the considered network schemes

is Dmax = 0.04 s. Consider a uni-directional highway traffic

with maximum allowed vehicle speed denoted as vmax and

with maximum speed difference, between any pair of ran-

domly selected nodes, denoted as ∆v. Considering vmax =50 m/s (180 km/h) and (as a worst case speed variability level)

∆v = vmax = 50 m/s, the pair of vehicles corresponding

to ∆v move away from each other, with respect to their

initial positions, of roughly 2 m during the end-to-end travel

time of the information packet, i.e., Dmax = 0.04 s. This

is a negligible displacement, with respect to the considered

minimum value of the transmission range z (100 m), and,

therefore, the performance analysis carried out remains basi-

cally unchanged—simulations results with the just described

mobility distribution do not show any noticeable variation.

We remark that if the broadcasting application involves the

transmission of an information flow, such as in a session with

hundreds of information packets to be distributed, then the

impact of mobility has to be carefully taken into account. In

this case, denoting as Npck−flow the number of packets to

be transmitted by the source and neglecting the propagation

time in each transmission, it can be concluded that the total

transmission time is approximately equal to Npck−flow Dmax.

Therefore, in this case the performance analysis carried out in

this work still applies provided that the following conditions

satisfied:

Npck−flow Dmax vmax ≪ z

where we have implicitly considered the worst-case pair of

mobile nodes with speed difference ∆v given by vmax.

V. CONCLUSION

In this paper, we have presented an overview of the research

activities carried out in the on-going Italy-Israel X-NETAD

project [13]. The goal of the project is that of designing

an efficient cross-network vehicular information dissemination

system. The key idea is that of receiving traffic alerts from the

UMTS network and broadcast them very rapidly to all vehicles

in the neighborhood of the receiver (which acts as information

gateway). Preliminary results are very encouraging.

ACKNOWLEDGMENT

Part of this work is carried out under the one-year project

“Cross-Network Effective Traffic Alerts Dissemination” (X-

NETAD, Eureka Label E! 6252 [13]), sponsored by the

Ministry of Foreign Affairs (Italy) and The Israeli Industry

Center for R&D (Israel) under the “Israel-Italy Joint Inno-

vation Program for Industrial, Scientific and Technological

Cooperation in R&D.”

REFERENCES

[1] Cellint TrafficSense, “http://cellint.com/traffic data/traffic system.html.”[2] S. Ni, Y. Tseng, Y. Chen, and J. Sheu, “The broadcast storm problem

in a mobile ad hoc network,” in Proc. ACM Conf. on Mobile Comput.

and Networking (MOBICOM), Seattle, WA, USA, August 1999, pp.151–162.

[3] M. Khabbazian and V. K. Bhargava, “Efficient broadcasting in mobile adhoc networks,” IEEE Trans. Mobile Comput., vol. 8, no. 2, pp. 231–245,February 2009.

[4] M. Kihl, M. Sichitiu, and H. P. Joshi, “Design and evaluation of twogeocast protocols for vehicular ad-hoc networks,” Journal of Internet

Engineering, Klidarithmos Press, vol. 2, no. 1, pp. 127–135, June 2008.[5] M. Torrent-Moreno, J. Mittag, P. Santi, and H. Hartenstein, “Vehicle-to-

vehicle communication: Fair transmit power control for safety-criticalinformation,” IEEE Trans. Veh. Technol., vol. 58, no. 7, pp. 3684–3707,September 2009.

[6] G. Korkmaz, E. Ekici, and F. Ozguner, “Black-burst-based multihopbroadcast protocols for vehicular networks,” IEEE Trans. Veh. Technol.,vol. 56, no. 5, pp. 3159–3167, September 2007.

[7] E. Fasolo, A. Zanella, and M. Zorzi, “An effective broadcast scheme foralert message propagation in vehicular ad hoc networks,” in Proc. IEEE

International Conf. on Commun. (ICC), vol. 9, Istanbul, Turkey, June2006, pp. 3960 –3965.

[8] J. Sahoo, E. Wu, P. Sahu, and M. Gerla, “BPAB: Binary partition assistedemergency broadcast protocol for vehicular ad hoc networks,” in Proc.

IEEE Intl. Conf. on Computer Comm. and Networks (ICCCN), SanFrancisco, CA, USA, August 2009, pp. 1–6.

[9] Z. J. Haas, J. Y. Halpern, and L. Li, “Gossip-based ad hoc routing,”IEEE/ACM Trans. Networking, vol. 14, no. 3, pp. 479–491, June 2006.

[10] N. Wisitpongphan, O. Tonguz, J. Parikh, P. Mudalige, F. Bai, andV. Sadekar, “Broadcast storm mitigation techniques in vehicular ad hocnetworks,” IEEE Wireless Comm. Mag., vol. 14, no. 6, pp. 84–94, 2007.

[11] A. M. Hanashi, A. Siddique, I. Awan, and M. Woodward, “Performanceevaluation of dynamic probabilistic broadcasting for flooding in mobilead hoc networks,” Simulation Modelling Practice and Theory, Elsevier,vol. 17, no. 2, pp. 364 – 375, February 2009.

[12] Q. Zhang and D. P. Agrawal, “Dynamic probabilistic broadcastingin MANETs,” Journal of Parallel and Distributed Computing, SI on

Theoretical/Algorithmic aspects of Wireless Networks, Elsevier, vol. 2,no. 65, pp. 220–233, February 2005.

[13] “Eureka project 6252 X-NETAD,” http://www.eurekanetwork.org/project/-/id/6252.

[14] S. Busanelli, G. Ferrari, and S. Panichpapiboon, “Efficient broadcast-ing in IEEE 802.11 networks through irresponsible forwarding,” inProc. IEEE Global Telecommun. Conf. (GLOBECOM), Honolulu, HA,USA, December 2009.

[15] “Car-to-Car Communication Consortium,” http://www.car-2-car.org.[16] “Final GeoNet specification, GeoNet deliverable D2.2,” January 2010,

http://www.geonet-project.eu/.[17] N. Wisitpongphan, F. Bai, P. Mudalige, V. Sadekar, and O. K. Tonguz,

“Routing in sparse vehicular ad hoc wireless networks,” IEEE J. Select.

Areas Commun., vol. 25, no. 8, pp. 1538–1556, October 2007.[18] Insitute of Electrical and Electronics Engineers, “IEEE Std 802.11TM-

2007. Part 11: Wireless LAN Medium Access Control (MAC) andPhysical Layer (PHY) specifications,” 2007.

[19] “Network Simulator 2 (ns-2),” http://isi.edu/nsnam/ns/.[20] T. S. Rappaport, Wireless Communications. Principles & Practice, 2nd

Edition. Upper Saddle River, NJ, USA: Prentice-Hall, 2002.

356