Temporal Evolution of Vehicular Network Simulators: Challenges and

Perspectives

Mauricio J. Silva

, Genilson I. Silva

, Fernando A. Teixeira

and Ricardo A. Oliveira

Universidade Federal de Ouro Preto, Ouro Preto, MG, Brazil

Instituto Federal Sudeste Minas Gerais, Barbacena, MG, Brazil

Universidade Federal de Sao Joao Del Rei, Sao Joao Del Rei, MG, Brazil

Keywords:

Vehicular Network Simulators, Trafﬁc Simulators, Mobility Models.

Abstract:

New proposals of applications and protocols for vehicular networks appear every day. Its crucial to evaluate,

test and validate these proposals on a large scale before deploying them in the real world. Simulation is by

far the preferred method by the community when conducting the evaluation. In this paper we survey the

main simulators for vehicular networks and show how they evolved over time. Thus, we provide information

that leads to an understanding of how, and how long does it take for the scientiﬁc community to absorb a new

simulator proposal. Additionally, valuable insights are presented to help researchers make better choices when

selecting the appropriate simulator to evaluate new proposals.

1 INTRODUCTION

Communication-capable vehicles allowed the emer-

gence of collaborative Intelligent Transport Systems

(ITS). The ITS comprises services developed with the

aim to improve the road safety (e.g., warning about

road conditions), reduce trafﬁc jams (e. g., intelligent

intersections) and make the travel more enjoyable

for drivers (e.g., informing about gas stations nearby,

roadside restaurants, among other things) (Fischer,

2015).

The design and evaluation of algorithms and pro-

tocols for ITS have been a constant topic of re-

search (Tornell et al., 2015). Given the unique char-

acteristics of vehicular networks, recent works have

pointed towards the emergence of new trafﬁc models

and the impact analysis of these models on the behav-

ior of proposed protocols and algorithms (Arellano

and Mahgoub, 2013; Zemouri et al., 2012). These

models must be fed by data collected in current net-

works so that it is possible to make a reasonably accu-

rate estimate of the performance of new applications.

Such an assessment is a challenge, and can usually be

done using three different methods, which are Math-

ematical Analysis, Field Operational Tests and Sim-

ulations (Guan et al., 2014; Nimje and Dorle, 2013).

Each of these methods has its advantages and disad-

vantages (summarized on Table 1), and the method to

be used should be chosen cautiously as it directly in-

ﬂuences the results (Harri et al., 2009; Eckhoff and

Sommer, 2015).

Mathematical analysis allows an analytical study

of the problem, and can provide valuable information,

allowing a better understanding of the designed sys-

tem. Statistical distributions are used to generate the

models that are necessary for the simulation. How-

ever, this method tends to simplify certain simulation

parameters such as the mobility models. Such simpli-

ﬁcations can lead to inaccurate results.

Field Operational Tests allow a better evaluation

of applications and protocols for vehicular networks.

In this type of analysis, the devices are exposed to

real environments, which can lead to unpredicted sit-

uations. The disadvantages related to this type of test

usually involve high costs in terms of time and money

as well as the difﬁculty to perform large-scale tests.

Simulations make it possible to assess the new

proposals on a large scale and at low cost. However,

similarly to what happens in the mathematical analy-

sis, complex models need to be simpliﬁed so that they

can be simulated. Again, this simpliﬁcation must be

done cautiously, as they may make the results inaccu-

rate.

Researchers prefer to evaluate their proposals

through the simulation method, which demands sim-

ulators that produce results increasingly closer to re-

ality (Harri et al., 2009). Researchers used to believe

Silva, M., Silva, G., Teixeira, F. and Oliveira, R.

Temporal Evolution of Vehicular Network Simulators: Challenges and Perspectives.

DOI: 10.5220/0006696900510060

In Proceedings of the 20th International Conference on Enterprise Information Systems (ICEIS 2018), pages 51-60

ISBN: 978-989-758-298-1

Table 1: A comparison of assessment methods for ITS applications.

Assessment method Category Advantages Drawbacks

Mathematical Analysis Valuable insights on overall be-

havior; Lower and upper bounds

of problem.

Undergo simpliﬁcations that com-

promise the results

Simulation

Ofﬂine Reproducible; Faster; Acceptable

degree of realism

Changes in one domain simula-

tor do not affect the other; nodes

mobility are immutable; scalabil-

ity by cloning

Embedded Reproducible; Reliable degree of

realism

Communication does not change

mobility and vice-versa.

Online Communication changes might

affect mobility and vice-versa;

Results more realistic; Scalable

Unreproducible; Hard to set and

parameterize;

FOTs Real scenario, generating real

data;

Not scalable; Few prototypes or

vehicles; High costs (time and

money)

that Vehicular Networks were a speciﬁc application of

Mobile Networks, and for that reason, random mod-

els have been applied to simulate the vehicles’ mobil-

ity. It was not long ago that they realized that vehic-

ular networks had their own characteristics, so spe-

ciﬁc mobility models should be proposed to represent

them. The vehicular mobility models evolved from

random models, where the mobility of all nodes was

generated in a single ﬁle that was used as input to

a network simulator (called ofﬂine), to the behavior-

based models, where network and trafﬁc simulators

interact to represent the behavior of the driver (called

online).

Unfortunately, the more realistic a simulator is,

the harder it is to use it. Because of this, one of the

biggest difﬁculties in simulating a vehicular network

is to ensure that the mobility patterns of a real en-

vironment are reproduced. According to (Sommer

and Dressler, 2008) and (Marﬁa et al., 2007), existing

simulators that use models that are able to generate

scenarios closer to the real ones, are often complex

to use. As a result, many of the new proposed pro-

tocols and algorithms are evaluated using customized

simulators, which introduce bias and compromise the

reproducibility of these algorithms by other members

of the scientiﬁc community (Mota et al., 2014).

This article provides an overview of the vehicu-

lar network simulators evolution, answering the fol-

lowing questions: how did the evolution of vehicu-

lar network simulators happened from the beginning

until now? How long does the scientiﬁc community

need to absorb a new approach for a vehicular net-

work simulator? Do we have a pragmatic solution for

vehicular network simulators? In addition to answer-

ing these questions, we will provide valuable insights

to help researchers make better choices when select-

ing the appropriate simulator to evaluate new propos-

als.

The rest of this article is organized as follows:

Section 2 presents the necessary requirements to ob-

tain better results in speciﬁc-domain simulators. In

Section 3 we survey the most used simulators to as-

sess algorithms and protocols for vehicular networks,

describing their features and drawbacks. Section 4

discusses the temporal evolution and the future chal-

lenges related to vehicular simulations. Finally, we

conclude the paper in Section 5.

2 OVERVIEW OF

DOMAIN-SPECIFIC

SIMULATORS

Vehicular networks have drawn attention from the

academia, industry and government. The goal is not

only to develop applications that contribute to a safer

transport system, but also to propose new applications

based on high quality of service. Such applications

must be tested, evaluated and validated in a controlled

environment before being deployed in the real world.

Simulation is the preferred method by the aca-

demic community to evaluate new proposals for vehi-

cle networks, because it enables scalable evaluations,

with low cost and an acceptable degree of realism.

Scalability and low cost are well deﬁned requirements

in a simulation. However, vehicular networks have

their own mobility requirements, and for a simulation

to have an acceptable degree of realism it is necessary

that the mobility models comply with such require-

ments. To resolve this issue, three approaches can be

used, which (Zemouri et al., 2012) called ofﬂine, em-

bedded and online (see Figure 1). We will use the

ICEIS 2018 - 20th International Conference on Enterprise Information Systems

same classiﬁcation because we believe that in addi-

tion to being simple, it encompasses unambiguously

all existing proposals.

In the ofﬂine approach 1(a), trace ﬁles are ex-

tracted from the real movements of vehicles, which

are either obtained from navigation systems, or gen-

erated by a vehicular mobility simulator that has real

mobility capable models. This approach allows the

nodes in the network simulator to move in accordance

with the data read from the trace ﬁles. It is called of-

ﬂine because the trace ﬁle is not modiﬁed after it has

been generated. This implies that there is no inter-

action between the mobility and network simulators.

Some simulators that are in this category are Bonn-

Motion (Aschenbruck et al., 2010), MOVE (Karnadi

et al., 2007) and VanetMobiSim (H

arri et al., 2006).

In the embedded approach 1(b) the trafﬁc and net-

work simulators are natively coupled to form a sin-

gle simulator. In fact, most of the platforms of this

category were either designed for trafﬁc simulations

or for network simulations, not both. Some modules

that would allow such platforms to be used in vehic-

ular networks were later implemented, for instance,

VISSIM that has MOVE (Karnadi et al., 2007), the

NCTUns (Wang and Chou, 2009) and VCOM (Killat

et al., 2007) as embedded modules.

The online approach 1(c) was introduced to ad-

dress the limitations of the previous two approaches.

It provides a bidirectional communication between

network simulators (developed by computer scien-

tists) and trafﬁc simulators (developed by trafﬁc en-

gineers). Thus, vehicular mobility is handled by ex-

perts in trafﬁc, and communication is handled by ex-

perts in computing. In the online approach the net-

work simulator controls the trafﬁc simulator by send-

ing commands that modify the behavior of the nodes.

The trafﬁc simulator responds to the network simula-

tor with the position of the affected node. This ap-

proach is considered, thus far, the best solution for

simulations in vehicular networks, for it allows a high

degree of realism.

Vehicular networks simulators face many chal-

lenges. Some of them are related to the communi-

cation, more speciﬁcally, physical layer, link layer,

and, in some cases, transport layer. The other chal-

lenges are related to the mobility, which is one of

the main factors that affect the assessment of proto-

cols and applications for vehicular networks. Com-

munication challenges are dealt with by the network

simulator, which must implement all models needed

to represent a real vehicle-to-vehicle communication.

Mobility challenges are dealt with by the mobility

simulator, which is responsible for all models needed

to represent a real vehicle mobility, including change

lane models, driver behavior models, trafﬁc sign mod-

els, etc. In the following sections, we present the

main simulators used by the scientiﬁc community

in each speciﬁc-domain (network and mobility), and

also show how these simulators were combined to

achieve better results.

2.1 Network Simulators

Simulations based on discrete events have become the

main method used by simulators. In this type of sim-

ulator, simulation behavior is not based on continuous

equations, but rather in discrete events distributed in

time. The most used simulators for vehicle networks

are OMNeT ++, ns-2/ns-3, Jist/Swans, GloMoSim

and GTNetS. All of them are based on discrete events

and will be discussed in greater detail below.

OMNeT++ (Varga, 2010) is a discrete event

simulator that allows communication modeling in

networks, parallel systems and distributed systems.

Available since 1997 under the GPL, it has become

one of the most used simulators by the scientiﬁc com-

munity. The OMNeT++ was designed from the be-

ginning to support large-scale simulations. For this

reason, it was built completely modular, enabling bet-

ter reuse of code and facilitating the implementa-

tion of new libraries that extend its functionality. As

an example, we can mention the INET, which is an

open-source framework that has the models to simu-

late mobile, wired and wireless networks. The OM-

NeT++ modular feature, allows each model to be im-

plemented separately and then combined to form a

protocol stack similar to the real one. INET has mod-

els of the physical layer (PPP, Ethernet and 802.11),

various communication protocols (IPV4, IPV6, TCP,

SCTP, UDP) and various application models.

The ns-2 (ns2, 2017) simulator is one of the most

popular and it is widely used by the academic com-

munity. Its ﬁrst version was launched in 1996, and

derived from its ns-1 predecessor. It includes detailed

models of a great number of TCP variations and many

applications (such as HTTP trafﬁc). It also supports

wired and wireless networks modeling. In the wire-

less networks ﬁeld, there are models for routing algo-

rithms such as AODV and DSR, as well as models for

the MAC protocol of the 802.11b protocol speciﬁca-

tion.

Scalable Wireless Ad hoc Network Simulator

(SWANS) (Barr et al., 2005) is a wireless network

simulator that can be used for sensor networks. It

was built on top of the Java in Simulation Time (JIST)

simulation platform. The JIST is general purpose en-

gine simulation based on discrete events, which was

developed in the JAVA language. This simulator is fo-

Temporal Evolution of Vehicular Network Simulators: Challenges and Perspectives

(a) Ofﬂine category (b) Embedded category (c) Online category

Figure 1: Categories of Vehicular network simulators.

cused on high performance and efﬁciency, and it sim-

ulates networks with four times higher performances

than the ns-2 networks, with the same system require-

ments and level of detail (Kargl and Schoch, 2007).

Jist/Swans was developed to meet the needs related

to simulations of wireless networks and sensor net-

works. One of the main advantages of Jist/Swans

is that it allows simulation of networks that require

large-scale tests.

GloMoSim is a modular library for parallel simu-

lation of wireless networks. This library was devel-

oped to be extended and combined and it has an API

deﬁned to each layer of the communication protocol

stack. The GloMoSim has implemented: MAC layer

of 802.11b protocol in detail; routing algorithms to

wireless networks (AODV, DSR, and some others);

transport layer protocol TCP; some application in the

application layer level. Moreover, new protocols can

be developed to extend GloMoSim capabilities. Ex-

ecuting a parallel model in GloMoSim is often trans-

parent to the user. An interface can be used to set

up the simulation parameters and also to designate a

mapping strategy for running a parallel simulation.

GTNetS is a network simulator that had its devel-

opment focused on parallelism. For this reason, it has

shown a good performance as well as good scalabil-

ity, even considering networks with millions of ele-

ments. The GTNetS was implemented in C++ using

the object-oriented paradigm, which allows an easy

extension of existing protocols. The GTNetS imple-

ments the following models: on the physical layer, it

has implemented the 802.11, for wireless simulations,

and the 802.3, for wired simulations. On the network

layer, it has implemented the routing protocols AODV

and DSR for wireless and the BGP and IEGRP for

wired simulations. On the transport layer, it has im-

plemented the TCP and UDP protocols. Finally, the

application layer includes implementations for FTP,

P2P, and client-server applications. The community

has not provided support for GTNetS since 2008.

Although all networks simulators mentioned

above have mobility models for mobile network sim-

ulations, they should not be used to simulate a vehic-

ular network. That is because the network simulators

implement random models, and as we have seen be-

fore, these models do not represent real mobility. A

good alternative is to delegate the vehicular mobility

to a trafﬁc simulator.

2.2 Trafﬁc Simulators

When vehicular networks emerged, researchers be-

lieved that it was a speciﬁc application of mobile net-

works. It means that at the beginning, vehicular net-

work protocols were evaluated using random models.

Such models worked perfectly for mobile networks,

because generally it was about networks that simu-

lated human behavior in an open ﬁeld, such as a uni-

versity campus or a conference. But it soon became

clear that the random models did not represent the ve-

hicular mobility, which produced undesirable results

when evaluating new protocols.

Since then the study of vehicular mobility has be-

come an open topic of constant research. This re-

searches resulted in an evolution from random mod-

els, where the direction, speed and origin and desti-

nation points were chosen completely randomly, to

models that extract information from actual maps and

aim to generate vehicular mobility that are closer and

closer to reality.

Thus far, several proposals of models and tools

that simulate vehicular mobility have emerged. Some

of them are based on mathematical models that simu-

late the streets as well as the driver’s behavior. Others

use maps to extract all kinds of information possible,

for instance, the limits of the streets, the number of

lanes, the direction of the tracks, the speed limits of

each vehicle category,etc. Some of the main mobility

simulators will be mentioned below.

VISSIM, proposed by (Fellendorf, 1994) in 1994,

ICEIS 2018 - 20th International Conference on Enterprise Information Systems

is a stochastic simulator of microscopic vehicular mo-

bility, meaning that it simulates the behavior of each

car individually. The quality of a trafﬁc simulator

is highly dependent on the quality of the trafﬁc ﬂow

model. For this reason, the cars in queue and the lane

change models are both part of VISSIM kernel. The

model of cars in queue describes the behavior of a

vehicle with respect to the vehicle that is in front of

it, and may include overtaking models when there is

a lane change model. Instead of using a determinis-

tic cars in queue model, VISSIM uses a model based

on a psychological study created in 1974 (Fritzsche

and Ag, 1994). This approach uses random models to

calculate the parameters that determine the potential

acceleration of each driver.

VISSIM has standardized and well-deﬁned inter-

faces that allow C-based programs to be implemented

and integrated to it. This allowed the ﬁrst Bidirec-

tionally coupled simulators to emerge (Lochert et al.,

2005) (Gorgorin et al., 2006). Bidirectionally coupled

simulators are formed by the combination of a trafﬁc

simulator with a network simulator.

Another trafﬁc simulator widely used by the aca-

demic community is the SUMO (Krajzewicz et al.,

2012). SUMO is the acronym for Simulation of Ur-

ban Mobility, and it is a platform for microscopic traf-

ﬁc simulation, intermodal and multimodal, of contin-

uous space and discrete events. The development of

SUMO started in 2001, but it was only in 2002 that it

was released under the GPL (Krajzewicz et al., 2002).

SUMO is not only a trafﬁc simulator, but rather

a set of applications that help perform and prepare

trafﬁc simulations. To allow greater ﬂexibility, var-

ious conﬁguration ﬁle formats are supported. These

ﬁles can be imported from other tools or generated by

SUMO itself. As previously mentioned, simulations

representing the real world need high quality mobility

models. For this reason, the SUMO has tools to gen-

erate the network topology, the vehicles and the trafﬁc

demand.

In SUMO, the real-world networks are repre-

sented as graphs, where nodes are the intersections

and streets are represented by edges. The intersec-

tions consist of their own position, plus informa-

tion about their shape and right-of-way rules. The

edges are one-way connections between two nodes,

and they have geometry information, the permitted

classes of vehicles and the maximum speed allowed.

Two tools can be used to generate the network topol-

ogy, which are the “netconverter” and “netgener-

ate”. The “netconverter” allows the topology to be

imported from other tools, such as VISSIM, Open-

StreetMaps, etc. The “netgenerate” allows the gen-

eration of three different types of networks, which are

manhatam grid, circular spider network and random

network, as shown in Figure 2.

Figure 2: Examples of networks generated by “netgen-

erate”, from left to right, manhatam grid, circular spider

network and random network (ﬁgure extracted from (Kra-

jzewicz et al., 2012)).

SUMO is a purely microscopic trafﬁc simulator.

Because it is multimodal, it allows not only car trafﬁc

modeling, but also the modeling of public transport

systems, rail systems and any other system that may

inﬂuence or participate in the simulation.

The VanetMobiSim (H

arri et al., 2006) is an ex-

tension of CanuMobiSim (Tian et al., 2002), a gen-

eral purpose user mobility simulator. Coded in Java,

both are platform independent and produce traces that

can be used by different network simulators such

as ns-2 (ns2, 2017), QualNet (qua, 2017) and Glo-

MoSim (Zeng et al., 1998). CanuMobiSim provides

an easily extensible architecture for mobility. How-

ever, the fact that it is designed for multiple purposes

causes the level of detail in speciﬁc scenarios to be

reduced. The VanetMobiSim therefore is a dedicated

extension for vehicle networks.

As we have seen, a critical aspect for vehicle net-

works is the need for a simulation to reﬂect, as closely

as possible, the actual behavior of vehicular trafﬁc.

When dealing with vehicular mobility models, we can

separate the scenarios in a macro and a micro views.

In the macro view, both the network topology and

its structure (number of lanes and direction) must be

taken into account. Other factors that are relevant are

the characteristics of the trafﬁc (speed limits and ve-

hicle restrictions by class), the presence of trafﬁc re-

strictions (trafﬁc signs and trafﬁc lights) and ﬁnally

the effects caused by points of interest (path between

home and job).

Micro mobility refers to the behavior of each

driver individually when interacting with other drivers

or the road infrastructure. Examples of parameters

that need to be informed to the models of micro mo-

bility are: Travel speed in different trafﬁc conditions,

deceleration and overtaking criteria, driver behavior

in the presence of intersections and trafﬁc lights and

general attitudes of the driver (which are usually re-

lated to age, sex, maturity, etc.). To model micro

mobility parameters the VanetMobSim implemented

two models that are familiar to researchers in the

ﬁeld, which are the FTM (Seskar et al., 1992) and the

Temporal Evolution of Vehicular Network Simulators: Challenges and Perspectives

IDM (Treiber et al., 2000).

STRAW (Choffnes and Bustamante, 2005)

(acronym of STreet RAndom Waypoint) was devel-

oped in 2005 and is publicly available

for download.

It was implemented as an extension of SWANS (Scal-

able Wireless Ad Hoc Network Simulator) (Barr,

2004), a Java-based, publicly available, and scalable

wireless network simulator. The STRAW extract

topology information, like road names, location, and

shapes of roads, from a TIGER data set to create

the topology of the network. According to (Rothery,

1992), the the car-following model is used to control

the nodes movement and intersection management.

As a drawback, the STRAW does not support lane

changing and not consider a vehicle’s current lane

when it attempts to make a turn.

MOVE (Karnadi et al., 2007) is implemented in

Java and runs atop of SUMO. MOVE consists of two

main components: the Map Editor and the Vehicle

Movement Editor. To build the road topology, the

Map Editor allows three possibilities, which are: (i)

the user can create the map manually, (ii) The map

can be generate automatically and; (iii) the map can

be imported from existing real maps, such as TIGER

database. The Vehicle Movement Editor allows the

user to specify the trip and the route that vehicles

should take. Then, the data is fed into SUMO to gen-

erate a mobility trace, which can be used by network

simulators such as ns-2 and QualNet to simulate a re-

alistic vehicle movement. One of the main strengths

of MOVE is the fact that it was implemented to allow

users to rapidly generate realistic mobility models for

vehicular networks.

In the previous sections, we presented the main

simulators for a speciﬁc-domains which are relevant

to vehicular networks. In the next section we will

help the reader understand how the vehicular network

researchers combined trafﬁc simulators with network

simulators to get a reliable analysis of protocols and

applications on vehicular environments.

3 VEHICULAR NETWORK

SIMULATORS

It is clear to vehicular networks researchers that nei-

ther trafﬁc simulators nor network simulators meet all

the requirements for a simulation. An alternative to

solve this problem would be to record the mobility

generated by a trafﬁc simulator in a trace ﬁle and then

use this ﬁle on the network simulator to update the

http://http://aqualab.cs.northwestern.edu/projects/111-

c3-car-to-car-cooperation-for-vehicular-ad-hoc-networks

position of the nodes. However, this approach does

not allow mobility to be inﬂuenced by the network

simulator, and therefore, does not reﬂect the topol-

ogy changes. The current state of the art is the bidi-

rectional coupling between trafﬁc and network sim-

ulators. To make it possible, both simulators run the

same simulation and exchange information on the sta-

tus of each node. Although this approach enables high

degree of realism, it requires the exchanging of a lot

of messages between the simulators, which results in

high computational cost when it is used in large sce-

narios. Characteristics and restrictions of bidirection-

ally coupled simulators are discussed below.

The MSIECV, also called VISSIM/NS-2, (Lochert

et al., 2005) is the ﬁrst simulator to propose the bidi-

rectionally coupling between trafﬁc and network sim-

ulators. The MSIECV architecture combines the ns-

2 network simulator, VISSIM trafﬁc simulator, and

the Matlab/Simulink applications simulator. A simu-

lation controller was implemented to manage the in-

teraction between all simulators. A sync class was

implemented to ensure that trafﬁc and network simu-

lators are synchronized in time during their execution.

GrooveNet (Mangharam et al., 2006), for Linux,

was developed in 2006, and it was implemented in

C++ and Qt. All types of vehicles communications

are supported, that is, vehicle-to-vehicle, and vehicle-

to-infrastructure through DSRC and 802.11. For ve-

hicular mobility, the GooveNet includes the models

for car-following, trafﬁc light, lane changing and sim-

ulated GPS. Despite the authors say that the models

were validated, they did not provide any information

about its implementation. The main characteristic

highlighted by the authors, is the ability of GrooveNet

to make hybrid simulations, including real and simu-

lated vehicles.

VanetSim (Tomandl et al., 2014) had its ﬁrst ver-

sion developed in 2008, but it was only in 2014 that

its stable version was made available. Developed in

Java, it is open source (GNU GPLv3) and can be

downloaded in http://www.vanet-simulator.org/. The

VanetSim was speciﬁcally designed to analyze at-

tacks on privacy and security. To ensure a ap-

proximated real-world simulation providing realistic

results, the VanetSim implements the state-of-the-

art micro-mobility model (Krauß, 1998) and allows

the importing of the network topology from Open-

StreetMap

(Wu et al., 2005) proposed a simulator that

combines CORSIM to control the mobility of ve-

hicles and the QualNet to model the communica-

tion between them. These two simulators were com-

bined using a distributed simulation software pack-

http://www.openstreetmap.org

ICEIS 2018 - 20th International Conference on Enterprise Information Systems

age called the Federal Simulations Development Kit

(FDK) (McLean et al., 2001). The FDK imple-

ments services deﬁned in the Interface Speciﬁcation

of High-level Architecture (Russo et al., 1995). Also,

a Communication Layer was developed to deﬁne in-

teractions between CORSIM and QualNet.

VCOM (Killat et al., 2007) is a hybrid library

which combines the micro trafﬁc simulator VIS-

SIM with a discrete event based inter-vehicle com-

munication simulator. The VCOM takes advantage

from mathematical modeling to reduce the number of

events generated by the communication. According

to the authors, this approach can overcome ns-2 by

a considerable speed-up. Also, an application mod-

ule that contains all application logic provides a well-

deﬁned interface to simplify the implementation and

evaluation of new applications.

NCTUns (Wang et al., 2007) and MoVES (Bononi

et al., 2008) are embedded simulators developed re-

spectively in 2007 and 2008, which implement their

own network and trafﬁc models. The difference be-

tween them is that MoVES was developed to be a par-

allel and distributed simulator. Another simulator that

was proposed in the same year as NCTUns is the Au-

toMesh (Vuyyuru and Oguchi, 2007). The AutoMesh

implements a custom mobility simulator, but uses ns-

2 as network simulator.

The ExNS3 (Arbabi and Weigle, 2010) is extended

from the ns-3 and implements custom trafﬁc models

to be applied in vehicular network simulations. Un-

like ExNS3, the TraNS (Pi

orkowski et al., 2008) uses

ns-2 for network simulation and SUMO for trafﬁc

simulation. A TraCI interface is used to allow data

exchange between the network and trafﬁc simulators.

Veins is a simulation framework that provides

coupling of the OMNET++ network simulator with

the SUMO trafﬁc simulator. It was initially proposed

in 2008 by (Sommer and Dressler, 2008), after the

authors discuss the development of simulators for ve-

hicular networks. But it was only in (Sommer et al.,

2011), in 2011, that it gained greater visibility in the

academic community. The coupling between the OM-

NeT++ and SUMO happens through dedicated com-

munication modules that have been implemented for

both. During the simulation, these communication

modules exchange information over TCP.

The iTETRIS (Rondinone et al., 2013) is a sim-

ulation platform developed in 2013, which is freely

available to members of the iTETRIS community. It

integrates and extends the SUMO and ns-3, which are

two open source platforms widely used for trafﬁc and

network simulations. The iTETRIS is an open source

platform, and its architecture is completely modular,

which facilitates for the community to expand it in

the future. It was designed to be aligned with inter-

national standards, more speciﬁcally, to be compat-

ible with the ETSI architecture for intelligent trans-

port systems, and it allows simulations to use either

the 802.11p (Teixeira et al., 2014) or the ETSI ITS

G5 standards.

Unlike the other bidirectionally coupled simula-

tors, the VSimRTI (Schnemann, 2011) is a runtime

simulation infrastructure that enables integration be-

tween any pair of simulators. Its goal is to make it as

easy as possible for the user to prepare and implement

a simulation. To achieve this, it uses a high-level ar-

chitecture (HLA) simulation and modeling standard

deﬁned by the IEEE (893, 2000). For immediate use,

a set of simulators is already coupled to VSimRTI,

such as the trafﬁc simulators VISSIM and SUMO,

the network simulators JIST/SWANS and OMNeT ++

and the application simulator VSimRTI APP as well

as various data analysis tools.

The OVNIS (Pign et al., 2010), proposed in 2010,

is one of the bidirectionally coupled simulator that

uses SUMO as trafﬁc simulator and ns-3 as network

simulator. Another simulator that uses SUMO and

ns-3 is the HINTS (Zemouri et al., 2012), proposed

in 2012. HINTS differs from previously mentioned

simulators by using a hybrid approach to generate ve-

hicular mobility. It manages to bring together the best

of both worlds, that is, the ﬂexibility of the online ap-

proach and low computational cost of the ofﬂine ap-

proach. The authors mention that the new approach

advances the state of the art in terms of performance

by using resources more efﬁciently, thereby reducing

the simulation time and the computational cost.

Although the bidirectionally coupled simulators

allow simulations to be performed with a high degree

of realism, some issues that are not treated by them

need to be considered. As an example, we can men-

tion the trafﬁc demand, in which unrealistic trafﬁc can

be generated if random origin and destination points

are chosen. Or, the poor quality of the maps, where

the absence or incompleteness of information can in-

ﬂuence network topology. Another factor that should

be taken into consideration is the presence of differ-

ent elements in the network. In the case of vehicu-

lar networks, future efforts should be applied to the

insertion of elements in the simulation such as Un-

manned Aerial Vehicles (UAVs), people walking and

autonomous cars.

4 DISCUSSIONS

To understand the current state and future challenges

in vehicular network simulators it is necessary to

Temporal Evolution of Vehicular Network Simulators: Challenges and Perspectives

Figure 3: Evolution timeline of vehicular network simulators.

know how the evolution happened from the begin-

ning. As we can see on Figure 3, until 2005 the appli-

cations and protocols proposed for vehicular networks

were evaluated using random models. That is because

all existing simulators until then had others purposes,

more speciﬁcally, the trafﬁc simulators were devel-

oped and used exclusively by trafﬁc engineers, and

network simulators were proposed to be applied in

other types of networks, typically, sensor networks.

This scenario changes when, in 2005, (Lochert

et al., 2005) proposed the MSIECV, which combines

VISSIM (a micro-trafﬁc simulator) with ns-2 (a net-

work simulator) to generate scenarios that are closer

to real ones. It is important to highlight that the tech-

nique proposed by (Lochert et al., 2005) comprises

the current state-of-the-art. Although MSIECV was

the ﬁrst vehicular network simulator to combine traf-

ﬁc and network simulators (online simulators), it was

only with Veins that this approach gained prominence

in the scientiﬁc community. The Veins simulator was

published three years latter by (Sommer and Dressler,

2008) and was consolidated in (Sommer et al., 2011).

We believe that one of the reasons for the lack

of attention of the scientiﬁc community with the

MSIECV, is the fact that VISSIM is a commercial

tool that is not freely available. On the other hand,

the Veins uses two freely and well-established tools

that have a bigger support of the community, which

are OMNeT++ and SUMO.

The Figure 3 also shows that most of the exist-

ing vehicular network simulators were proposed over

a period of 3 years after the ﬁrst one was proposed,

in 2005. During this period, the computer scien-

tists used to believe that implementing mathematical

models to represent vehicular mobility was the bet-

ter solution (embedded simulators). In 2008, (Som-

mer et al., 2011) turned the bidirectionally coupling

between trafﬁc and network simulators more popular,

thus gaining attention of the researchers. It is impor-

tant to notice, that the scientiﬁc community took ﬁve

year to absorb the concept of online simulators, and

three year more to start to use them.

As far as we know, after 2013 to date, there has

been no signiﬁcant effort to propose new vehicular

network simulators. We believe that this is due to the

fact that the scientiﬁc community changed focus and

concentrated their efforts on implementing new mod-

els and improving the existing ones. Some examples

of these models are: 802.11p (Eckhoff et al., 2012),

DSRC/WAVE (Eckhoff and Sommer, 2012), Obsta-

cle Shadowing (Sommer et al., 2014) and Antenna

Patterns (Eckhoff et al., 2016)

5 CONCLUSION

New proposals of applications and protocols for ve-

hicular networks appear every day. Simulation is the

preferred method by the community when conducting

the evaluation. In this context, this paper presented a

temporal evolution of vehicular applications assess-

ment from random models until the emergence and

subsequent development of vehicular network sim-

ulators. We discussed the problems related to ran-

dom models used to assess vehicular applications and

how the bidirectionally coupling of network and traf-

ﬁc simulators can solve these problems. Additionally,

we showed that the scientiﬁc community took more

than ﬁve years to consolidate and use a new simulator

paradigm.

ACKNOWLEDGEMENT

We acknowledge support from the Brazilian research

agency (CNPq), the Research Foundation of the State

of Minas Gerais (FAPEMIG), the CAPES Foundation

ICEIS 2018 - 20th International Conference on Enterprise Information Systems

and the Federal University of Ouro Preto (UFOP).

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