Available via license: CC BY 4.0
Content may be subject to copyright.
Journal of the
B
razilian Computer Society
Weber et al. Journal of the Brazilian Computer Society (2021) 27:8
https://doi.org/10.1186/s13173-021-00113-x
RESEARCH Open Access
VANET simulators: an updated review
Julia Silva Weber* †, Miguel Neves†and Tiago Ferreto†
*Correspondence:
julia.weber@acad.pucrs.br
†Julia Silva Weber, Miguel Neves
and Tiago Ferreto contributed
equally to this work.
School of Technology, Pontifical
Catholic University of Rio Grande do
Sul (PUCRS), Ipiranga, Porto Alegre,
BR
Abstract
Research on VANETs (vehicular ad hoc networks) date back to the beginning of the
2000s. The possibility of enabling communication between vehicles through a wireless
network stimulated the creation of new protocols, devices, and diverse utilization
scenarios. Due to the intrinsic difficulties of using a real testbed to evaluate these
research contributions, several simulators were developed at the time. Recently, with
the advent of autonomous vehicles and the emergence of novel technologies (e.g., 5G
and edge computing), new research challenges on VANETs are coming into sight.
Therefore, revisiting VANET simulators is required to identify if they are still capable of
evaluating these new scenarios. This paper presents an updated review of VANET
simulators, showing their current state and capabilities to assess novel scenarios in
VANET research. Based on this analysis, we identify open research challenges that
should be addressed in current and future VANET simulators.
Keywords: Vehicular ad hoc network, Simulation, SDN, Edge computing, 5G, Security
Introduction
Intelligent vehicles are a developing technology with promising future. However, to guar-
antee such technology to be safe, vehicles need to be able to communicate with each
other and exchange information in real time. VANETs (vehicular ad hoc networks) were
created to fulfill this necessity. VANETs are a special class of MANET (mobile ad hoc net-
work) with predefined routes [1]. It allows vehicles to share information such as location,
telemetry data, and safety warnings. VANET aims to ensure safe driving by improving
the traffic flow and therefore significantly reducing car accidents. This is possible by
providing appropriate information to the driver or to the vehicle.
Due to the easiness of embedding computers in vehicles, it is not far fetched to imagine
in the forthcoming years that most of the vehicles will be equipped with an on-board
wireless device (OBU), GPS (Global Positioning System), EDR (event data recorder), and
a multitude of sensors. However, it is important to notice that, since VANET aims on
ensuring the safety of its users on the road, any delayed communication or defective level
of implementation may affect people lives. Therefore, any feature provided by a VANET
protocol must be properly tested and validated.
Simulators present a valuable tool for testing VANETs at low cost and without risking
theusers.However,simulatorsmustbeabletomodelthenoveltechnologiespenetrating
the VANET space (e.g., SDN, edge computing,and 5G) and provide support for safety and
© The Author(s). 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate
credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were
made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless
indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your
intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly
from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Weber et al. Journal of the Brazilian Computer Society (2021) 27:8 Page 2 of 31
security mechanisms in order to be useful and convey credible results. In other words,
while simulators are a great tool for VANETs, they ought to improve to better support its
evolution. Surveys on VANET simulators date back to the early 2010s [2–5]. They present
an analysis over diverse VANET tools evaluating their accuracy as a mobility simulator,
network simulator, and how these building blocks mend together. The emergence of new
network technologies, such as 5G, SDN, and edge computing, and the reappearance of
VANET research due to the investments on autonomous vehicles urge a reassessment of
VANETsimulatorsandtheirsupportforthesenewfeatures.
In this paper, we provide an updated review on VANET simulators, presenting their
main features and current support for novel technologies. In addition, we also analyze
their footings for modeling important safety and security issues (and associated coun-
termeasures) that have motivated extensive research in VANETs over the last years. To
the best of our knowledge, this is the first study providing a detailed view on the afore-
mentioned aspects in VANET simulators, and we hope it can motivate the community to
develop further tools that assist VANETs towards widespread adoption. To take the first
steps in that direction, we also list and discuss many research challenges we found during
our investigations, which range from performance issues to lack of support for current
standards. Overall, our main findings are as follows:
•Although many tools for simulating VANETs are available, most of them are
outdated and not maintained anymore.
•Among the currently maintained tools (open-source and commercial), Veins [6]is
currently the simulator with best support for modeling novel technologies and
safety/security issues in VANETs. For example, the simulator contains pre-built
extensions for modeling 5G networks, signal interference/attenuation, and privacy
solutions.
•Despite the recent advances, current VANET simulators still lack support for more
realistic models. For instance, none of them provide a full-stack implementation of
the main security standards for VANETs (e.g., IEEE 1609.2) or offer any mechanism
for systematically modeling faulty nodes (e.g., an unreliable RSU).
The remainder of this paper is organized as follows: The “VANET over vie w”section
presents an overview of VANETs. The “Simulators” section describes the main build-
ing blocks of VANET simulators while the “VAN ET s imulators ” section shows an
in-depth study of current tools. The “Support for novel technologies” section analyzes
the support of selected simulators to novel technologies while the “Safety, security, and
privacy functionalities” section does a similar analysis for safety and security issues.
The “Research directions” section discusses some open research challenges. Finally, the
“Conclusion” section concludes the paper.
VANET overview
VANET characteristics
As a collection of interconnected vehicles, VANETs present some unique characteris-
tics not seen in other types of MANETs (e.g., smartphone-based ad hoc networks).
First, deploying a VANET is usually expensive due to the fact that each VANET node
(i.e., a vehicle) must contain a rich set of sensors (e.g., LIDAR and proximity sensors)
as well as computation and communication resources (e.g., processors, memory, and
Weber et al. Journal of the Brazilian Computer Society (2021) 27:8 Page 3 of 31
communication antennas) to analyze and exchange information [4]. Moreover, VANETs
tend to primarily use short-distance communication (i.e., messages are typically sent
when vehicles are close to each other), relegating long-range signals to some special sce-
narios (e.g., when vehicles need to communicate with road-side units in less populated
areas). The life span of a VANET link is short as it is highly affected by the movement of
vehicles. As a consequence, the network topology tends to change often and thus impose
strict latency and bandwidth requirements for applications [7]. VANETs also have pre-
dictable mobility patterns as node movements are constrained by the road topology, and
node locations must be very precise as any vaguely estimated vehicle location can put
human lives in danger (e.g., by causing two vehicles to collide). Finally, VANETs have no
issues with respect to power constraints as vehicles have the ability to provide a continu-
ous source of power via long life batteries [8]. These characteristics enable VANETs to be
used in a wide range of applications, including safe driving, improving passenger comfort
and enhancing traffic efficiency [9].
VANET architecture
Vehicles participating in a VANET are equipped with a set of wireless sensors and On
Board Units (OBUs). Those units allow wireless communication between the vehicles and
their environment. These devices make each vehicle to act as packet sender, receiver, and
router. It enables the vehicles to send and receive messages to other vehicles or Road Side
Units (RSUs) within their reach via wireless medium [10]. The RSU, normally fixed along
the road side, is equipped with one network device for DSRC (Dedicated Short Range
Communication) based on IEEE 802.11p radio technology [11]andcanalsobeequipped
with other network devices for the purpose of communicating within the network infras-
tructure [4]. All vehicles move freely on the road network and mainly communicate within
each other or with RSUs, as can be seen in Fig. 1.
Fig. 1 Example of OBU and RSU at work. RSU work as information source and provides internet connectivity
to the OBUs
Weber et al. Journal of the Brazilian Computer Society (2021) 27:8 Page 4 of 31
Vehicles can communicate directly with each other using DSRC in a single or multi-
hop way. The communication mode is either V2V (vehicle-to-vehicle), V2I (vehicle-to-
infrastructure), or hybrid [12],ascanbeseeninFig.2. These vehicular communication
configurations rely heavily on the acquisition of accurate and up-to-date kinematic data
from both the vehicles and the surrounding environment with the aid of positioning
systems and intelligent wireless communication protocols.
Simulators
Deploying and testing VANETs involve high cost and intensive labor. As an alternative
solution, simulation is a useful and less expensive substitute prior to actual implementa-
tion. In order to achieve good results from VANET simulation, it is essential to generate
accurate models, which is a non-trivial task given the complexities of the VANET infras-
tructure (e.g., simulators need to model both mobility patterns and communication
protocols). In this section, we describe the main building blocks of current VANET
simulators, namely their mobility and network components.
Mobility simulators. A critical aspect in a simulation study of VANETs is the need
for a mobility model that closely reflects the real behavior of vehicles in traffic. Mobility
simulators are mainly used to generate the movement of vehicles pattern under a certain
trace. When dealing with vehicular mobility modeling, we distinguish between macro-
mobility and micro-mobility descriptions [13]. For macro-mobility, simulators need to
consider all the macroscopic aspects that influence vehicular traffic: the road topology,
car movement constraints, speed limits, number of lanes, safety rules, and traffic signs
governing the crossing rules at intersections.
Micro-mobility, on the other hand, refers to the drivers’ individual behavior,when inter-
acting with other drivers or with the road infrastructure: traveling speed in different
traffic conditions, acceleration, deceleration and overtaking criteria, behavior in the pres-
ence of road intersections and traffic signs, general driving attitude related to driver’s
age, gender, or mood. An ideal VANET simulation should consider both macro- and
Fig. 2 Communication types in VANET
Weber et al. Journal of the Brazilian Computer Society (2021) 27:8 Page 5 of 31
micro-mobility descriptions to be trustworthy. Examples of mobility simulators include
SUMO [14], VISSIM [15], SimMobility [16], PARAMICS [17], and CORSIM [18].
Network simulators. Anetworksimulatorisusedtosimulatetheexchangeofmes-
sages among connected nodes. In the case of a VANET, this usually includes vehicles
and RSUs and mostly involves wireless communications. Ideally, all components of the
communication system (e.g., the whole protocol stack) must be modeled, and eventu-
ally, the simulation also includes other relevant metrics (e.g., signal to noise ratio, packet
error rates) [19]. The network model describes both the network components and events.
Nodes, routers, switches, and links are examples of components. Events, on their turn,
can include data transmissions and packet errors.
For a given simulation scenario, the output from a network simulator usually includes
network level metrics, link metrics, and device metrics. Trace files also use to be avail-
able. Such files record each event that occurred in the simulation and can be processed
for further analyses. Most network simulators available are based on discrete-event sim-
ulation [5]. In this approach, a list of “pending events” is stored, and then processed in
order at each simulation step. Some events may trigger new ones. For example, the arrival
of a packet at a node may trigger the sending of a new packet. Examples of network simu-
lators available (some of them widely used in VANETs) include OMNeT++ [20], OPNET
[21], JiST/SWANS [22], NS3 [23], and NS2 [24].
VANET simulators
VANET simulators are the combination of network and mobility simulators [5]. Network
simulators are responsible for modeling communication protocols and the exchange of
messages, while mobility simulators are in control of the movement of each node, i.e., its
mobility. In this section, we describe the main VANET simulators found in the literature,
focusing on both their architecture and functionalities. We based our search on popu-
lar research databases and search engines (IEEE Xplorer, ACM Digital Library, Science
Direct, Google Scholar, among others) and considered papers that propose a simulator
or a comparative study of VANET simulators. In addition, we also carefully checked their
citations. In total, we reviewed more than 100 papers during this process. We also used
Google Search Engine to find proprietary VANET simulators that are not necessarily used
by the academia.
Tabl e 1summarizes the simulators we found. Although most are open-source, we were
also able to find some proprietary simulators. We focus our analysis on simulators that
present a release after 2015 (highlighted with a gray background in Table 1). We consider
that outdated tools are probably discontinued and thus have a high probability of not
supporting the latest advances in VANET research, which is one of our analysis criteria in
this paper. We refer to [4,5] for a detailed analysis of older simulators.
NetSim
NetSim is a commercial discrete event simulator covering a broad range of wired, wireless,
mobile, and sensor networks. The simulator offers three types of licences: pro, standard,
and academic, where only the first two provide support for VANET simulations. To sim-
ulate VANETs, Netsim interfaces with SUMO. The former handles the WAVE standard
for wireless communication between vehicles, while the latter takes care of modelling
road traffic conditions. NetSim provides a set of network performance metrics, link and
Weber et al. Journal of the Brazilian Computer Society (2021) 27:8 Page 6 of 31
Table 1 List of VANET simulators. The ones in gray (last release after 2015) are covered in detail in this
work
Simulator Last release License Network simulator Mobility simulator
NetSim 2021 proprietary own SUMO
Veins 2020 open-source OMNeT++ SUMO
Eclipse MOSAIC12020 open-source NS-3, OMNeT++, SUMO and VISSIM
SND and Eclipse MOSAIC Cell
EstiNet 2020 proprietary own own
ezCar2X 2020 proprietary NS-3 SUMO
VENTOS 2018 open-source OMNeT++ SUMO
VANETsim 2017 open-source own own
GrooveNet 2013 open-source NS-2 own
VNS 2012 open-source NS-3, OMNet++ own
iTETRIS 2010 open-source NS-3 SUMO
NCTUns 2010 proprietary NS-2 own
CityMob 2009 open-source NS-2 own
TraNS 2009 open-source NS-2 SUMO
FreeSim 2008 open-source NS-3 own
STRAW 2007 open-source JiST/SWAN own
VanetMobiSim 2007 open-source NS-2 CanuMobiSim
application throughput plots. Metrics will vary depending upon the type of network simu-
lated. Using packet trace and event trace, users can log details of each packet, as it flows in
the network. Figure 3presents a simplified version of the NetSim architecture. Each type
of network corresponds to a component of the simulator with its respective communica-
tion protocols. Netsim provides a reasonable number of components and the possibility
to connect real hardware running live applications to the simulation.
Within Netsim VANET modules, one of them worth taking note is RF Propagation
Models. This module includes Path Loss, Shadowing Model, and Fading Model, which
are essential to predict the signal loss or signal encounters inside a building or densely
Fig. 3 Architecture of the NetSim platform
Weber et al. Journal of the Brazilian Computer Society (2021) 27:8 Page 7 of 31
populated areas over distance. This module assists in making the simulation more realis-
tic, as in a real scenario VANET will most likely encounter many obstacles (e.g., buildings,
heavy traffic) for it signal communication. According to the architecture of Netsim, it
allows adding new components to fit the user’s needs and the appearance of new tech-
nologies. Technologies such as W-LAN, cognitive-radio, LTE, MANET, Military-radio,
IoT, VANET, Software-Defined Networking (SDN), and satellite communications have
been implemented on the simulator over the years. The SDN module, in particular, sup-
ports various networking commands to control simulation, routing, access control, etc.,
which can be executed on the controller command line during the simulation.
Veins
Vei ns [6] is an open source framework for running vehicular network simulations. It is
based on OMNeT++ and SUMO. Figure 4shows the different modules that form the
Veins architecture. Overall, the simulator instantiates an OMNeT++ node for each vehicle
present in the simulation and then pairs node movements with movements of vehicles
in the road traffic simulator (i.e., SUMO). In this case, both the network and mobility
simulations can run in parallel. This is possible due to a bidirectional coupling achieved
by a standardized connection protocol, the Traffic Control Interface (TraCI) [26]. TraCI
enables OMNeT++ and SUMO to exchange messages (e.g., containing mobility traces)
while the simulation runs, as part of TCP connections [27].
The simulator includes many extensions (currently, more than 17 [28]) that allow mod-
eling different protocol stacks (e.g., IEEE 802.11p [29], ETSI ITS-G5 [30]) as well as
applications (e.g., car platooning [31]). In summary, Veins is designed to serve as an
execution environment for user-written programs, which facilitates modeling new envi-
ronments and applications. As a disadvantage, it needs both SUMO and OMNeT++ to
run correctly in order to obtain precise results. Any bug in one of those can cause Veins
to give unreliable results. Veins can run on Linux, Windows, and Mac OS.
Eclipse MOSAIC
Eclipse MOSAIC, formerly known as V2X Simulation Runtime Infrastructure (VSim-
RTI) [32], is an open-source, multi-scale and multi-domain simulation framework for the
assessment of new solutions for connected and automated mobility. Eclipse MOSAIC
Fig. 4 Architecture of the Veins platform. Adapted from [25]
Weber et al. Journal of the Brazilian Computer Society (2021) 27:8 Page 8 of 31
main objective is to provide users with the flexibility to perform various V2X simulations
with their own choice of simulators. To guarantee that, Eclipse MOSAIC couples different
simulators for a more realistic presentation of vehicle traffic, emissions, and wireless com-
munication. Examples of simulators currently supported by Eclipse MOSAIC are SUMO
and PHABMACS for traffic simulation; NS3, OMNET++, and SNS for communication
simulation; and Eclipse MOSAIC Application for application simulation. Other simula-
tors and analysis tools (especially those from third-parties) can also be easily integrated,
ascanbeseeninFig.5.
To integrate the simulators to Eclipse MOSAIC, there are three core elements needed
in the runtime infrastructure. The Federation Management is responsible to connect each
participating simulator with the runtime infrastructure. A federate consists of an original
simulator and two connectors, one to receive data from the runtime infrastructure and
the other one to send data to it. The Time Management is necessary for coordinating
the simulation and synchronizing participating federates. It assures that each federate
processes its events in correct order. The Interaction Management enables the exchange
of data among federates through a publish-subscribe paradigm.
According to the architecture of Eclipse MOSAIC, one of its unique features is the pos-
sibility to visualize data in several ways. The same scenario can be evaluated in different
visualization tools that can be connected to a running simulation. Some of these tools are
WebSocket Visualizer, Integrated Test and Evaluation Framework (ITEF), and PHABMap
(for 3D visualization).
EstiNet
EstiNet [34] is a commercial network simulator and emulator with high time fidelity.
EstiNet uses an innovative methodology, called kernel re-entering [35], to combine the
advantages of both the simulation and emulation approaches. Basically, the kernel re-
entering methodology uses tunnel network interfaces to automatically intercept the
packets exchanged by two real applications and redirect them into the EstiNet simulation
engine, as shown in Fig. 6.
VANET is an optional module add-on to EstiNet. To simulate vehicular traffic, EstiNet
supports a road-building function, in which a road network can be built from scratch or
Fig. 5 Architecture of the Eclipse MOSAIC framework. Adapted from [33]
Weber et al. Journal of the Brazilian Computer Society (2021) 27:8 Page 9 of 31
Fig. 6 Simulation architecture of EstiNet, host-to-host case. Adapted from [34]
by importing a roadmap file. EstiNet possess their own mobility simulator that allows for
basic vehicle and human driving behavior, such as car following, lane changing, overtak-
ing, and compliance with traffic light signals. As for protocols, IEEE 802.11p, IEEE 1609.3,
and IEEE 1609.4 are supported by EstiNet.
EstiNet provides OBU and RSU abstractions in VANET simulation. To simulate OBUs,
EstiNet provides different OBU communication interfaces. Each communication inter-
face represents a specific communication behavior, such as agent-based vehicles (IEEE
802.11p/1609) and module-based vehicles (IEEE 802.11p) [36]. This feature in EstiNet
allows for better vehicle driving intelligence implementation, as the user has more free-
dom to implement OBUs communication and behavior according to their needs. In
the case of RSU, EstiNet provides two specific communications protocol stacks: IEEE
802.11p/1609 and IEEE 802.3.
ezCar2X
ezCar2X [37] is a modular software framework for rapid prototyping of cooperative ITS
applications and novel communication protocols. Currently, ezCar2X simulator is pro-
prietary, but planned to be made open-source in the near future [38]. It was created
for Car2X communication [39] with the objective of facilitating vehicle manufacturers,
suppliers, and road infrastructure operators to implement new applications and evaluate
them in a simulation environment. ezCar2X is implemented in C++ with specific opti-
mizations for efficient use of system resources. It also includes SUMO, which can be
coupledwithothersimulatorsusingitsTraCIAPI[26].
ezCar2X architecture is based on the European Telecommunication Standards Institute
(ETSI) architecture for Intelligent Transport Systems (ITS) stations [40], which defines
Weber et al. Journal of the Brazilian Computer Society (2021) 27:8 Page 10 of 31
Fig. 7 Architecture of the ezCar2X framework. Adapted from [37]
access, network, facility, management, and security layers, as can be seen in Fig. 7.Regard-
ing ezCar2X modules, its Core module provides a logging system as well as an event
scheduler for asynchronous tasks and simple timeout realization. It also has modules to
Access and Network that support ITS-G5 and 3G/4G, GeoNetworking, and the Basic
Transport Protocol (BTP) according to ETSI standards. A security module provides an
implementation of a network security entity to sign and encrypt transmitted messages as
well as validate and decrypt received ones.
ezCar2X simulator can be executed on both Linux and Windows.
VENTOS
VENTOS [41] is an open-source simulator designed for analyzing vehicular network
applications (e.g., collaborative driving, automated cruise control, and platooning). Sim-
ilar to Veins, it also uses SUMO and OMNET++ for mobility and network modeling,
respectively. However, unlike its counterparts, VENTOS has many prebuilt modules
that facilitate simulating complex application scenarios. For example, the simulator
offers implementations of traffic signal control (TSC) algorithms, as well as platoon
management operations (e.g., merge, split, entry and leave).
VENTOS has two special modules that simplify the process of generating traffic
demands at a microscopic level: addNode and trafficControl. The former allows users to
easily add both fixed and mobile nodes to the simulation, while the latter enables control-
ling vehicular traffic by changing its speed or specifying platooning maneuvers. Figure 8
shows VENTOS architecture. The simulator can be expanded to interact with real OBUs
and RSUs in a hardware-in-the loop (HIL) scenario [41]. In this case, each physical device
connected to the computer has a corresponding virtual node in the simulation, and any
action performed on the physical device is reflected on its alias and vice-versa.
The machine running VENTOS can be connected to the hardware device via an Eth-
ernet port and communicate with it through SSH connections. The simulator requires
support for a small control program (responsible for managing data and control instruc-
tions) to be run on the device though, which may hinder its integration with some
Weber et al. Journal of the Brazilian Computer Society (2021) 27:8 Page 11 of 31
Fig. 8 Architecture of the VENTOS platform. Adapted from [42]
boards. Similar to Veins, VENTOS also depends on both SUMO and OMNeT++ working
smoothly to get correct results. The simulator can run on Linux, Windows, and Mac OS
based platforms.
VANETsim
VANETsim [43] is an event-driven simulator particularly designed to investigate security
and privacy issues in vehicular communications. It allows analyzing attacks and coun-
termeasures from an application perspective, e.g., by creating an attack and measuring
its impact on different types of vehicles [44]. VANETsim architecture contains four main
components: a Graphical User Interface (GUI), the Scenario Creator, the Simulation Core,
and the Post Processing Engine, as can be seen in Fig. 9.
The GUI provides a graphical map editor that allows users to create and manipulate
road maps. Maps can be either created from scratch or imported from OpenStreetMap
[45]. It is possible to change imported maps and store them as XML files, which facili-
tates interoperability with other tools (e.g., TraNS [46], VanetMobiSim [47]). VANETsim
interface aims to be accessible for users, with features to interact with it on-the-fly (i.e.,
while the simulator is running). It can also indicate the transmission range of vehicles, a
functionality that can be activated on demand.
The Scenario Creator offers to users the ability to design a set of experiments and store
their configuration in XML files. This feature facilitates the reproducibility of experi-
ments as configurations can be shared online. The Simulation Core carries out the actual
simulation. It coordinates the traffic map, network infrastructure, and all security and
privacy modules specified. Finally, the Post Processing Engine processes generated logs
Weber et al. Journal of the Brazilian Computer Society (2021) 27:8 Page 12 of 31
Fig. 9 Architecture of the VANETsim platform. Adapted from [43]
for exhibiting relevant events/metrics (e.g., showing compromised pseudonyms after an
attack).
VANETsim comes with a few predefined security and privacy modules, which imple-
ment concepts such as silent periods [48]andMixZones[49]. Vehicles navigate to
individually determined destinations routed by the A* algorithm [50], and communica-
tion among vehicles can contain two types of messages: beacons, which broadcast regular
information (e.g., position, speed), and special-purpose messages, transmitted whenever
a relevant event (e.g., an emergency vehicle approaching) occurs. VANETsim project was
closed in April of 2017. However, despite the tool not being updated anymore, the sim-
ulator site [51] still features its documentation, downloadable content, and a very easy
to follow guide about how to use the simulator. VANETsim is available for the Windows
operating system.
Support for novel technologies
The number of new technologies penetrating the VANET space has been growing fast
over the last years [52–54]. From autonomous vehicles (a.k.a. self-driving cars) to 5G, they
play a crucial role to improve bandwidth, latency, and reliability of VANET applications,
which enables their adoption in production environments. In this section, we analyze the
current support of VANET simulators for various technologies.
Tabl e 2summarizes our results.
Software-Defined Networking (SDN).
SDN is a suitable solution for dealing with dynamic network environments, especially
those with a large number of connected devices and heterogeneous applications [55].
Weber et al. Journal of the Brazilian Computer Society (2021) 27:8 Page 13 of 31
Table 2 Support for novel technologies in current VANET simulators
Technology NetsSim Veins Eclipse MOSAIC EstiNet exCar2X VENTOS VANETsim
SDN X X X
Edge computing X X X
5G X X X X X
Self-driving cars X X X X
Unmanned aerial X X
vehicles (UAVs)
Interestingly, this is exactly the case for vehicular networks. SDNs are characterized by
a separation between control and data planes, and the existence of a logically central-
ized network controller that coordinates the whole network operation [56]. Recently,
researchers have focused on the integration of SDN and VANETs in the so called
Software-Defined Vehicular Networks, which have spawn many initiatives in the area and
made support for SDN in VANET simulators a must [57].
However, few simulators provide explicit support for SDN or are used in researches
involving SDN and VANETs. NetSim version 11 provides a module that supports SDN
and is OpenFlow compatible, thus making the use of such technology easy to be imple-
mented with different types of network such as Internetworks, IoT, MANETs, VANETs,
and LTE. In the case of VANETs, the RU is equipped with an SDN controller. In [58], the
objective is to study the internal structure of network equipment models of the OmNET
++ modeling system, as well as create alternative models that take into account all the
features of various software-defined equipment implementations. In this paper, NetSim
is one of the simulators used to improve the simulation accuracy in terms of packet
processing delay parameters.
When considering the Veins simulator, there are more papers related to SDN, such as
[59–61]. In [59], SDN is used to provide a secure platform for VANET communication by
creating a framework minimizing storage load, communication load, and response time.
VeinsisconfiguredwithOpenFlow[62] to support SDN controllers which interact with
RSUs to control the entire network. Differently, in [60], SDN is used to offer an energy-
efficient multicast routing protocol. In this case, Veins incorporate POX [63]tosimulate
SDN controllers. The SDN controller assists in sending data from the source to the set
of target nodes in different locations. For example, when a vehicle exits the range from a
formed VANET and enters another one. In [61], Veins is once again combined with Open-
Flow to obtain reliable and fast emergency message dissemination in low RSU density
areas.
Eclipse MOSAIC is a very powerful simulator used to model and assess new solutions
for Cooperative ITS Systems. It can integrate several simulators which are individually
used to model vehicular environment, communication environment, and social appli-
cation environment. However, we could not find any significant research on SDN and
VANETs using this simulator.
EstiNet provides an OpenFlow module as an original part of the simulator [64]. With
this SDN module, a simulated OpenFlow-enabled Ethernet switch can support in-band
control plane or out-of-band control plane through which is controlled by a single or
multiple controllers. With EstiNet version 9, a VANET module was incorporated on the
Weber et al. Journal of the Brazilian Computer Society (2021) 27:8 Page 14 of 31
simulator. EstiNet provides a good potential to make realistic scenarios using SDN, as
presented in [65].
ezCar2x provides the key components needed to rapidly create prototype applications
for networking vehicles. Due to a lack of access to this commercial tool, it is more difficult
to identify if ezCar2x supports SDN technology. We also could not find papers using
ezCar2x to test SDN scenarios.
VENTOS has many practical applications, ranging from studying platoon management
[66] to studying security vulnerabilities of VANET-assisted cooperative driving; however,
there are no researches related to SDN that use the simulator. Nevertheless, VENTOS
should also be capable to use OpenFlow as OMNet++ supports [67].
VANETsim documentation does not present any information regarding SDN sup-
port. Besides, it carries all its components inbuilt, which makes it harder to extend the
simulator to support novel technologies [43].
Edge computing. Edge computing is one of the approaches for controlling the huge
volume of data being exchanged in VANETs. Existing solutions, such as cellular net-
works, RSUs, and mobile cloud computing, are far from perfect because these are highly
dependent on centralized architecture and bear the cost of additional infrastructure
deployment. Edge computing platforms, on the other hand, show the potential to replace
RSUs as they support services and applications using extensively distributed deployments
[68]. Edge computing is a topic that is being well explored in VANET research.
NetSim provides an IoT module, which has been used to simulate fog computing
applications [69], and a cellular network module [70]. Both modules could be used in a
edge computing scenario. It would enable the simulation of multi-access edge computing
(MEC), which applies cloud computing technology to provide an application deployment
platform closer to the end users. However, there are no public projects or researches that
use Netsim to further evaluate edge computing. On the same note, Eclipse MOSAIC and
EstiNet do not present significant contributions in this field.
Veins displays a number of researches on the topic [71–73]. Veins includes two-ray path
models [74], which are applied in [71] to test a vehicular edge computing architecture.
This paper uses vehicles as support infrastructure to form edge nodes to efficiently alle-
viate the bandwidth congestion by using both vehicles and road side units. In [72], edge
services are co-located with RSUs in order to augment contextual information in real-
time. In other words, in order to improve the offloading efficiency, the authors propose
a new vehicle-to everything communication by adding a microcell in an RSU as an edge
server that communicates with a macrocell server before reaching a cloud data center. In
[73], the authors consider the task offloading among vehicles and propose a solution that
enables vehicles to learn the offloading delay performance of their neighboring vehicles
while offloading computation tasks. Veins uses an autonomous vehicular edge (AVE) [75]
framework to enable V2V and V2I offloading.
ezCar2X is used by a project called Car2MEC [76]. The project aims to improve
connectivity, especially for delay-sensitive traffic safety applications, by using local mes-
sage distribution and processing based on MEC to improve communication latency for
short-range information exchange via cellular communication. In [77], a MEC-enabled
cooperative Collision AVoidance (CAV) is created to anticipate the detection and local-
ization of road hazards by extending vehicles perception range beyond the capabilities of
their own sensors. The CAV service is a software application that runs on MEC servers
Weber et al. Journal of the Brazilian Computer Society (2021) 27:8 Page 15 of 31
allocated at the roadside and at mobile network infrastructures. The CAV service receives
ETSI ITS-G5 standard-compliant messages transmitted by vehicles: periodic cooperative
awareness messages, which include the position, velocity, and direction of the vehicle,
and event-triggered environmental notification messages, which include the position of
detected road hazards.
VENTOS main features focus on car-following models and dynamic traffic routing. As
such, edge can be applied to secure traffic monitoring [78]. In this case, while VENTOS is
used to create diverse road conditions, the centralized server (cloud) and the edge server
are simulated in separate workstations. Basically, the edge server is associated to a certain
region and is responsible to analyze traffic events (e.g., accident, congestion). With that
in mind, it is possible to use the edge to assist in platooning, as the edge layer collects
information about the platoon through beacon messages and observations from other
connected entities. This is the case in [79], where the edge server is connected to RSUs
and cellular Base Stations (BS) over broadband connections.
VANETsim focus on the implementation of security and privacy concepts, being mostly
oriented towards the performance study of communication protocols on vehicular net-
works. This characteristic makes it harder to simulate newer technologies, due to the
lack of detailed edge/network infrastructure implementation for application life cycle
management, or detailed mobility models for realistic urban scenarios [80].
5G. 5G is meant to deliver high bandwidth and ultra-low latency network connec-
tivity by using millimeter waves [81]. Comparing with 4G, 5G offers greater coverage,
accessibility, and higher network density. Moreover, not only 5G presents a new access
technology, but it also aims to provide a unifying platform that leverages the existing
techniques offering a diverse set of services to customers. One of the services that would
benefit from 5G is vehicles communication. With the current vehicular communication
standards (IEEE802.11p) [82], there is no guarantee of service delivery in large-scale net-
work deployments. With 5G, existing investments can be leveraged and its capabilities
extended to guarantee a better performance for vehicular communications. For example,
currently, 4G LTE [83] provides support for the integration of Wi-Fi and the unlicensed
spectrum. With 5G, the capability will be extended to include 3G, 4G, Wi-Fi, ZigBee, and
Bluetooth. This feature will enable vehicles and passengers to connect with the most suit-
able network to support the specific requirements of safety, non-safety, and infotainment
applications [81].
NetSim provides a whole library to simulate the 5G NR standard [84]. The library is
based on the 3GPP38 (3rd Generation Partnership Project) series [85]andenablesthe
simulation of different devices (e.g., user equipment and next generation base stations) as
well as protocol specifications (e.g., Packet Data Convergence Protocol, Radio Link Con-
trol). NetSim 5G library can also interface with the proprietary TCP/IP stack deployed in
the simulator and provide simulation capabilities for 5G across all layers of the network
stack. As an example on using NetSim to couple 5G and VANET simulations, the authors
in [86] combined both capabilities to analyze different routing protocols for cooperative
collision warning in underground mining scenarios.
Veinssupports5GthroughtheINETframework[87]. The framework, which is an
open-source library for OMNeT++, provides models for the TCP/IP stack as well as the
3GPP standard via Simu5G (an extension of SimuLTE) [88]. The works of Chekired et al.
[89], Zhang et al. [90], and Huang et al. [91] provide good examples on how to couple
Weber et al. Journal of the Brazilian Computer Society (2021) 27:8 Page 16 of 31
5G, SDN, and edge computing simulations in Veins. Eclipse MOSAIC can also support
5G simulation through Simu5G, as the tool can include OMNeT++. As an example on
coupling Eclipse MOSAIC and 5G, the authors in [92] use VsimRTI (the predecessor of
Eclipse MOSAIC) to test their proposed routing algorithm in a 5G-VMesh network (i.e.,
a hybrid network combining the features of VANET, mesh and 5G infrastructures).
EstiNet supports 5G network simulation by integrating the 5G core network stack
deployed by the free5GC alliance [93]. The stack includes abstract modules to simulate
simplified Radio Access Network (RAN) attributes and User Equipment (UE) behaviors
[94].Theworkof[95] claims that if VANET is made capable of relaying 5G, each car
could act as a mobile cell tower in a downtown area. EstiNet is then used to compare
their performance in urban dense regions that have better conditions like slow-moving
and densely packed vehicle traffic. ezCar2X, according to the official documentation [37],
only allows for 3G/4G connections with its access module.
5G technology can be implemented in VENTOS, however not to the same depth as in
Veins. The study [96] uses VENTOS to simulate 5G communication between the platoon-
ing to implement platoon maneuvers (e.g., merge, split, and lane change). 5G can be used
in VENTOS by applying Simu5G [97].
Self-driving cars. Autonomous vehicles (AVs) are defined as vehicles that have the
capability of sensing and navigating their environment without or with minimum human
input. However there are different levels of automation which classify autonomous vehi-
cles. Currently, there are 6 levels of automation, with level 0 being no automation and level
5 being full automation. What lies in between those levels are different driving modes that
increasingly aim for full automation [98]. For example, level 1 consists of minor driver
assistance with a high degree of human input, while level 4, high automation, the driv-
ing system does most of the driving tasks with little human input. Regardless of the level
(excepting level 0), AVs can benefit VANET by improving network capacity, keeping traf-
fic flow steady, and taking into account faster responses and more tightly spaced vehicles
[99]. In regard to adding AVs to VANET simulation, no significant change would have to
be done to emulate a self-driving car. All simulators are already capable of supporting AVs
to some capacity. The changes more relevant to properly accommodate AVS are related
to mobility simulators. In this case, the different levels of automation need to be taken
into consideration for the simulation vehicles to behave accordingly to each level. This
can be accomplished by applying different percentage of both conventional cars and full
automation AVs. Basically, the parameters of each vehicle need to considered different
longitudinal movement, acceleration, deceleration, and gap acceptance. The mobility sim-
ulator, however, needs to consider both macro and micro models to properly simulate AVs
behavior, something that can be accomplished in SUMO [99]. To be able to simulate AVs,
SUMO is modified to permit USARsim [100] to be integrated with its architecture. For
simplification, SUMO simulates the traffic while USARsim controls the robotic simulator
that is responsible for the autonomous driver agent. For further details, all modifications
and methodology are explained in [101]. With SUMO being able to accommodate AVs in
their mobility simulator, Veins, VENTOS Eclipse MOSAIC, and ezcar2X are able to sup-
port AVs experiments, as it can be seen in the works of [102–104]forVeins,[102,104]for
VENTOS, [105] for Eclipse MOSAIC, and finally [106] for ezcar2X.
Unmanned aerial vehicles (UAVs). Unmanned aerial vehicles (UAVs), due to their abil-
ity to move in three dimensional space and reach high altitudes, present great flexibility
Weber et al. Journal of the Brazilian Computer Society (2021) 27:8 Page 17 of 31
in creating a networked environment. Due to their mobility, UAVs can be deployed as
mobile infrastructure elements to provide service to vehicles. For example, in vehicu-
lar communication scenarios, there can be some cases where direct multi-hop car-to-car
communications are not reliable at ground level, such as a rural area with lots of hills. The
possible solution for cases, like the one mentioned, would be to deploy the UAVs to for-
ward the information related to car-to-car communication, acting as information relays
[107]. As UAVs can adjust their position dynamically if they want to offer the best signal
coverage to ground vehicles, this technology could greatly benefit VANETs.
Simulators usually deal with a 2D approach [2]. Yet, with technologies such as UAVs,
it is important to consider the effects of a three-dimensional scenario and how it could
improve VANET simulation. However, in order to achieve so, the simulation framework
has to be extended. Generating 3D road networks and providing an extension to 3D net-
work simulation would be the basic requirements to simulate three-dimensional vehicular
networks [108]. Veins, while not presenting any current module for 3D support, is able to
create 3D scenarios with the inclusion of Digital Elevation Models (DEMs) [109], three-
dimensional antenna patterns , and environmental diffraction [108]. In addition, [107]
presents a 3D mobility model algorithm as a module for Veins, specifically allowing UAV
communications in a 3D environment. Fundamentally, UAV movement is computed in
OMNeT++, while the signal strength is affected by the elevation obtained from DEM. As
for signal blockage, Veins utilize a path loss model from the Two-Ray Interference module.
NetSim can be used to simulate unmanned aerial vehicle (UAV) communication [110].
It allows co-simulation of UAV flight dynamics and UAV-BS network communication.
Basically, it involves interfacing NetSim and UAV toolbox of MATLAB [111]. For each
drone/UE in NetSim, an UAV is instantiated in MATLAB. MATLAB then calculates the
flight path and passes the mobility information to NetSim. However, while VANET is not
directly correlated to UAV communication, NetSim provides the source-code for user
modification, which could be used to future experimentation in VANET cases.
Both Netsim and Eclipse MOSAIC, despite having useful elements that seem promising
for combining UAV communication with VANET, present a lack of research in this area.
Although Netsim is capable of simulating VANET and co-simulating UAV, no research
combining both elements using this simulator was found. Eclipse MOSAIC, in contrast,
has a 3D visualization tool. This tool is based on the PHABMACS vehicle simulator and
uses the same 3D engine and models to visualize vehicle movements and various events
which occur during the simulation. Due to the main characteristic of Eclipse MOSAIC
that enables combining simulators, future works for visualization of 3D UAVs with Veins
and Eclipse MOSAIC are a possibility. However, Eclipse MOSAIC does not possess any
module to test UAVs experiments, and no further studies about the use of UAVs in Eclipse
MOSAIC were found.
We could not find any module or extendable framework to support 3D environments
in VANETsim, VENTOS, EtiNet, and ezCar2X.
Safety, security, and privacy functionalities
As important as modeling novel technologies, VANET simulators need to provide sup-
port for testing safety, security, and privacy issues (and associated countermeasures) in
vehicular networks. In this section, we analyze which functionalities they provide for
reaching each of these goals.
Weber et al. Journal of the Brazilian Computer Society (2021) 27:8 Page 18 of 31
Safety
One of the main motivations behind VANET development is to improve road safety.
As a result, significant effort has been made to create high-performance applications
that utilize connected vehicles to exchange safety messages (e.g., collision avoidance and
hazardous spot detection) [112,113]. At the same time, VANETs themselves have to
meet stringent reliability requirements to properly deliver their critical services, which
demands fault tolerance techniques (e.g., heartbeats, information redundancy) to be
applied [114]. In this section, we start by analyzing the support of current VANET sim-
ulators for testing road safety applications as well as VANET safety techniques. We base
our discussion on the reviews provided by [115–117], although their focus is on VANET
safety and reliability issues in general, not on the simulator themselves.
Road traffic safety. VANETs can increase road safety by sharing information about
both vehicles (e.g., position, speed and direction) and traffic conditions (e.g., accidents,
jams, aquaplaning) as beacons. Safety messages, in this case, are the key information to
avoid accidents. However, collisions can occur when safety messages and transmission of
packets are improperly broadcast from multiple vehicles. The study of [118] deals with the
exact issue as it proposes a Novel Segment based on safety message broadcasting in Clus-
ter (NSSC). Basically, the VANET simulator incorporates three successive processes that
are cluster formation, collision avoidance, and safety message broadcasting. The latter
focus on mitigating broadcast storms, so safety messages are not lost. On a different note
about safety, [119] considered that context awareness in vehicular re-routing is essen-
tial, since drivers can have different preferences when choosing their routes. Safety is
one of the parameters that is considered (e.g., route passes through a good neighborhood
have the less street bump). In this work, the simulator is used to create a realistic traf-
fic mobility and to implement a non-deterministic multi-objective re-routing approach
to reduce public safety risks. Lastly, the study [120]proposesaneffectivedelay-aware
packet forwarding (DAPF) for safe and efficient driving in vehicular networks. In this
work, the VANET simulator is used to provide a more realistic environment compared to
the mathematical analysis.
VANE T saf ety. VANETs themselves are susceptible to failures which is particularly
dangerous for critical applications. As a consequence, ensuring VANETs can operate
safely is of utmost importance for their widespread adoption. According to Dharmaraja
et al. [116], the reliability of a VANET depends on how reliable are both its end nodes
(OBUs, RSUs, etc.) and communication channels. Nodes can fail due to a broad range of
reasons, both at hardware and software levels. Hardware faults can include power out-
ages, damaged sensors, and/or malfunctioning antennas, while software faults typically
include protocol, firmware, and operating system bugs [121].
Usually, node failures manifest as silent entities, either permanently or in tran-
sient/intermittent intervals depending on the fault type. For example, a system crash may
stop the node from working permanently, while a malformed packet will simply forbid it
from properly communicating the associated message. VANET nodes are also subject to
Byzantine faults, in which a node forwards correct but misleading (e.g., false) information
to a peer [122].
Similar to nodes, links are also subject to different types of faults in VANETs. Accord-
ing to Albano et al. [117], two of the most common types of link faults are interference
and signal attenuation. The former frequently results from congestion and/or ground
Weber et al. Journal of the Brazilian Computer Society (2021) 27:8 Page 19 of 31
reflection [123], while the latter is typically a consequence of buildings close to the road
or vehicles moving away (e.g., exiting a freeway) [124].
Tabl e 3summarizes the current support of VANET simulators to different types of
faults. As can be seen, VANETsim does not provide any feature for simulating faults,
though some functionalities (e.g., silent periods) could be adapted to model limited types
of node and link failures [43]. Unfortunately, that may require generating a large amount
of code, as the simulator was not designed to support generic applications. NetSim
possesses an RF Propagation module which includes path loss, shadowing, and fading
models [69]. Together, these models enable the simulation of a rich set of interference and
attenuation link faults.
Veins is able to simulate Byzantine faults through its Framework for Misbehavior Detec-
tion (F2MD) [125]. This framework provides a solution for simulating malfunctioning
nodes that produce erroneous information (e.g., inaccurate position, velocity, and accel-
eration for vehicles), as well as misbehavior detection algorithms (e.g., based on local
plausibility checks) [126]. The simulator also carries two modules, Obstacle Shadowing
[127], and Vehicle Obstacle Shadowing [128], which can capture the effect of build-
ings and vehicles, respectively, on the quality of data transmissions. Together with the
Two-Ray Interference module [129], which simulates signal interference due to ground
reflection, these modules can be used to induce link failures in a simulation.
There is no current support for fault injection in Eclipse MOSAIC. The closest it gets, to
test similar fault tolerance scenarios, is through the application simulator. The application
simulator in Eclipse MOSAIC provides the capability to model the application logic for
different simulation units (e.g., vehicles, Road Side Units (RSUs), traffic lights, and others),
as well as possible interaction attempts between the units via different communication
links [33].
EstiNet does not have any apparent support for fault injection or any modules that can
be used to facilitate testing of fault tolerance. It is worth noting that EstiNet does allow for
different settings and definition of vehicle driving behavior [64]. The user could indirectly
cause a car accident to test the safety mechanisms of VANET. This is possible in EstiNet,
since the user can implement a car profile method, which enables each vehicle to possess
an unique driving behavior.
ezCar2X has a Bus module that supports basic serial interfaces (RS232, USB) and con-
troller area networks [37]. It is mostly used to integrate sensors and actuators within a
vehicle or along the road. However, an additional abstraction layer is provided by the
Sensor module to enable reuse of algorithms based on sensor input across different equip-
ment types. The library provides several basic types, such as position, speed, acceleration,
and object detection (radar or laser scanner). Furthermore, self-description of a specific
sensor adaptation provides properties, like accuracy, that vary among different devices of
the same category. However, there is no publication indicating this feature can be used to
inject faults in OBUs.
VENTOS enables the detection of bad behavior in small and specific scenarios by imple-
menting extensions such as another programming language [79] or software environment
[130]. In [131], time series analysis and misbehavior detection schemes are implemented
in Python [131] and integrated with VENTOS. [130] uses VENTOS to collect the speed
value observations (e.g., exchanged beacon messages), while those observations are ana-
lyzed with R [132] separately. By analyzing the data, it is possible to detect abnormal
Weber et al. Journal of the Brazilian Computer Society (2021) 27:8 Page 20 of 31
Table 3 Current support for fault injection in VANET simulators
VANET component Fault type Example NetSim Veins Eclipse MOSAIC EstiNet ezCar2X VENTOS VANETsim
Node Software Malformed packet,
(e.g., OBU, RSU) OS bug
Hardware Power outage,
malfunctioning
antenna
Byzantine False messages X X
Link Interference Congestion, ground X X X
reflection
Attenuation Building shadowing,
vehicle moving away X X X
Weber et al. Journal of the Brazilian Computer Society (2021) 27:8 Page 21 of 31
behavior. In both cases, the simulation scenario is based on platooning and the misbe-
havior detection is based on the input of vehicle’s speed acceleration. Lastly, VENTOS,
although not possessing any modules for transmission interference, does possess the abil-
ity to change the Transmission Power and Data Rate of each V2V or V2I communication
[25]. Basically, while having more limited options, such as not being able to create obsta-
cles to interfere with the transmission, it is still possible to variate the transmission range
to create faulty links communication.
Fault tolerance. Although many fault tolerance mechanisms have been proposed for
VANETs, almost none of them were implemented on top of the simulators studied in this
paper (an exception is the work at [133]). Moreover, we were not able to find enough
information about the support for fault tolerance on the simulators’ documentation. For
this reason, we avoid making a detailed assessment on the support of each simulator for
fault tolerance techniques, and leave that as a future work. We refer the interested reader
to the work of Almeida et al. [115] for more information about fault tolerance in VANETs
and how these mechanisms are evaluated.
Security and privacy
VANET security greatly depends on the security of the exchanged messages (i.e., the deliv-
ery of messages should be secure and fast). In this sense, the exchanged messages must
be not modified or captured by any malicious party. Although the use of simulators alone
cannot solve any of these issues, they could be used to further explore security concerns
in order to find possible solutions without putting any human life at risk.
Tabl e 4shows diverse security mechanism and their relation to the studied VANET
simulators. The table is organized as follows: it establishes what security service these
mechanisms belong to, and whether VANET simulators have the means to include them
in their simulation. We base our discussion on the works in [12,134,135], which report
general security and privacy issues in VANETs.
Confidentiality. Responsible for preventing data from being accessed by unauthorized
nodes, confidentiality is essential to VANETs as it determines a set of rules or a promise
that limits access to classified information [12]. Support for confidentiality services vary
according to the simulator. NetSim, being a commercial simulator, does not disclose the
details of its cryptographic support. However, it allows Key Management Scheme based
on symmetric and asymmetric encryption to be used on its simulations, as seen in the
work [136]. Veins, VENTOS, and Eclipse MOSAIC do not provide confidentiality mecha-
nisms by default, but allow their usage by means of importing cryptographic libraries (e.g.,
Crypto++ [137]) to their network simulator, OMNeT++. ezCar2X has a security module
[37] that provides implementation of a network security entity for signing and encrypting
messages to transmit, as well as validating and decrypting received messages. Although
VANETsim aims at providing easy support for testing novel security and privacy mecha-
nisms, no real encryption of messages is performed (or supported). Instead, each message
contains a boolean attribute that indicates whether it is “encrypted” or not. VANETsim
assumes that adversaries cannot break the “employed” cryptographic primitives [43].
Integrity. EnsuresthatthedataexchangedinaVANETisnotalteredbyunauthorized
third parties. This service is usually associated to mechanisms such as message authen-
tication codes and digital signatures, which prevent attacks such as message tampering,
forgery or replay. Similarly to confidentiality, Veins, VENTOS, and Eclipse MOSAIC do
Weber et al. Journal of the Brazilian Computer Society (2021) 27:8 Page 22 of 31
Table 4 Current support of VANET simulators to different security services
Attribute Example mechanism NetSim Veins Eclipse MOSAIC EstiNet ezCar2X VENTOS VANETsim
Confidentiality Symmetric and
asymmetric cryptography X X X X X
Integrity Message Authentication code,
Digital signature X X X X
Availability Watchdog,
Redundancy X X X X
Authentication Position verification,
Analyze signal strength,
Dual Authentication X X X X X X X
Non-repudiation ID-Based cryptosystem X
Privacy Silent periods,
Mix Zones, X X
Periodical pseudonym change
Weber et al. Journal of the Brazilian Computer Society (2021) 27:8 Page 23 of 31
not have native support for integrity services, but allow them to be included as third-party
libraries. ezCar2X security module also allows the management of available certificates
and regular pseudonym updates. VANETsim, on its turn, adopts the concept of detecting
false data in VANETs by identifying its associated malicious actor (e.g., an inside attacker)
through an intrusion detection system (IDS). In this case, the IDS monitors application
layer data (e.g., position and time) to perform a context verification. VANETsim offers a
general-purpose IDS module that can be configured with different rule sets depending on
the particular application [43].
Availability. Availability in VANETS should be assured both in the communication
channel and in participating nodes. Although being an important security attribute, it is
one of the lessen researched topics [138], as the solutions for it often falls into the fault tol-
erance field. It is possible to implement some security mechanism to test, to some extent,
availability issues on VANET. In the case of implementing a watchdog on the ad hoc net-
work, it is made possible for Veins, VENTOS ,and Eclipse MOSAIC by incorporating this
mechanism on the Network simulator [139,140]. Basically, this watchdog defines one set
of nodes as monitor nodes. Those nodes then check they neighborhood nodes. Redun-
dancy is also a mechanism that can be implemented in different ways, such as equipment
redundancy , for instance by adding more sensors to OBUs. Redundancy can also be used
in other security mechanisms, as it can reduce attacker influence, since fewer redundant
copies of attacker-influenced information will be received [141]. EstiNet allows the users
to modify their OBUs when creating their communication behavior [64], which also con-
sists of changing the number of sensors in each OBU. As another example, redundancy
can be used in implementing new protocols for VANET, such as the work of Achour [142]
where a novel density based dissemination protocol called “Redundancy Based Protocol -
RBP” is created. Considering how redundancy would affect the simulations, Veins, VEN-
TOS, and Eclipse MOSAIC would be ideal to test protocols that use redundancy, as it just
needs to be implemented in the network simulator.
Authentication. In addition to message/data authentication (which is closely related
to integrity), VANETs also require authentication of origin to be provided. Authentica-
tion mechanisms must be designed to protect VANET nodes from impersonation attacks.
ID-Based cryptography is one of the main mechanisms to solve authentication issues
[135]. The main idea is to use any known information which represent the identity of the
user for the purpose of verifying the digital signature. This public information could be
email address, network address, user name, or any combination of these identities. When
considering simulators, Veins is the only one that supports such mechanism [143]. The
main advantage of using the ID-Based cryptosystem is that it uses pseudonym genera-
tion, which can be changed as required for security purposes. ID-based techniques could
be an efficient replacement for the PKI technique in VANET environments, since it is not
required to store, fetch, and verify the public key certificates of some road safety scenarios
by a trusted third party.
Non-repudiation. The main goal of non-repudiation is to forbid an entity (e.g., a
car) from being able to deny an action. Non-repudiation of Origin (NRO) and Non-
repudiation of Receipt (NRR) are the most common examples in computer networks,
yet both services are different by nature and so are their implementing mechanisms in
VANETs. While NRR has not been extensively explored in VANETs, NRO is traditionally
implemented in VANETs using digital signatures [144]. In the case of each simulator, the
Weber et al. Journal of the Brazilian Computer Society (2021) 27:8 Page 24 of 31
network simulation, OMNET++, VANETsim, NetSim, EstiNet, and ezCar2X inbuilt net-
work simulators [37,43,64,69], respectively, would be the ones responsible with ensuring
digital signature. In the case of OMNET++, it is possible to deal with digital signatures and
even modify them to better satisfy VANET characteristics [145]. Non-repudiation is also
needed for the sender in V2V warnings and beacons. In this way, if a vehicle sends some
malicious data, there will be a proof that could be employed for liability purposes [144]. In
this case, it is confirmed that Veins [146], VENTOS [147], and VANETsim [148]atleast
guarantee non-repudiation of origin, so wrong warning messages can be undoubtedly
linked to the sending node.
Privacy. Privacy is one of the most important requirements for VANET security [135].
Malicious attacks to security do not only aim to potentially cause harm to the traffic, but
also to obtain personal information about the user. It is responsible for hiding the identity
of the user against unauthorized nodes using temporary and anonymous keys. Other than
personal data, privacy should also guarantee location privacy, so no attacker can track the
trajectory of any node.
Veins, on the other hand, has a privacy extension (PREXT [149]) which comes equipped
with a diverse set of predefined privacy schemes (e.g., periodical pseudonym change,
PeriodicalPC, cooperative pseudonym change based on the number of neighbors, CPN
[150], and context aware privacy scheme - CAPS [151]). PREXT supports simulating an
adversary who aims at tracking vehicles by eavesdropping beacon messages. Although
PREXT does not represent a significant overhead for simulations involving low vehicle
densities, simulations can be up to 30% slower when the module is active depending
on the adopted privacy scheme. VANETsim implements a number of privacy con-
cepts for VANETs, including MixZones [49], SilentPeriods [48,152], SLOW [153], and
ProMix [154], which provide unlinkable pseudonym switchover via radio silence and/or
encryption. It can also be used to simulate attacks against these concepts through the
addition of new adversary modules [43]. The remaining studied simulators, VENTOS,
NetSim, Eclipse MOSAIC, EstiNet, and ezCar2X, do not currently support any pri-
vacy service [41] or due to their commercial nature, makes it difficult to investigate
further.
Research directions
Although current VANET simulators have a great number of functionalities, we were able
to identify important issues with respect to their support for novel technologies, as well as
safety and security mechanisms. Solving these issues turns out to be interesting research
directions that we believe could be explored by the community to develop better VANET
simulation tools. In this section, we describe some of these issues.
Implementation of security standards. Over the last years, there has been a con-
siderable amount of effort to develop security standards for intelligent transportation
systems, and in particular VANETs. As a result, we currently have two major standards:
IEEE 1609.2 [155] (USA) and ETSI ITS Security Standards [156–161](Europe).Although
Veins and VENTOS support implementing parts of the proposed standards (e.g., the
recommended cryptographic algorithms) using library extensions, none of the VANET
simulators we found is currently compliant to them. Ultimately, this forbids researchers
and practitioners from comparing their novel security proposals with the status quo.We
believe that extending the current simulators to support the proposed security standards
Weber et al. Journal of the Brazilian Computer Society (2021) 27:8 Page 25 of 31
is not a trivial task and will require certain systems and programming research to produce
correct and efficient artefacts.
Systematic fault injection. Fault injection consists in the observation of the system
behavior in response of deliberately introduced faults [162]. It is adopted to perform either
robustness testing or dependability evaluation. It allows testing the fault coverage of fault
tolerance strategies implemented in a system. It also can determine the possible ways a
system fails in presence of rare or unexpected faults (e.g., transient and permanent faults,
arbitrary faults, hardware faults, and data faults) [163]. It is generally applied during the
development of a system. However, each system relies in its own ad hoc injection solu-
tion and no general tools for fault injection on VANET has been proposed to date. An
appropriate fault injection tool could be used to verify and validate VANET operation in
the presence of possible faults without having to wait for those faults to occur in a real
scenario. This could lead to more reliable systems and also to dependability benchmarks
that could be used to compare the safety of different solutions [164].
Real-time simulations. The coupling of real-time systems with non-real-time event-
based simulation in the so called Hardware-in-the-loop (HIL) scenario brings new
challenges. In particular, current simulators cannot meet performance constraints of
hardware prototypes when simulating a whole network with many vehicles, mainly due
to resource limitations [165]. Some workarounds, such as the Ego Vehicle Interface (EVI)
[166], have been proposed in order to reduce the complexity of the simulation and thus
making it run faster. However, these workarounds usually do not take into account the
extra performance overhead resulting from the simulation of security mechanisms such
as cryptographic procedures [167,168], which can negatively impact the behavior of
real VANET components. Investigating the coupling of VANET simulators and hardware
devices in the presence of security primitives is an interesting research direction.
Model inaccuracies. The quality of a VANET simulation strongly depends on the accu-
racy of the underlying models. Noticeably, the degree of realism has increased over the
last years, and some simulators now include modules that incorporate signal attenua-
tion, different antenna patterns, and environmental diffraction. Nevertheless, the advent
of novel (computation and communication) technologies, and their increasing adoption
in vehicular networks, poses a constant challenge to produce accurate simulations. For
example, 5G networks are more susceptible to weather conditions than its predecessors,
edge computing requires more processing capacity on end hosts, and unmanned aerial
vehicles heavily rely on 3D scenarios for moving and interacting with other nodes. In this
sense, extending current VANET simulators to encompass these new conditions could be
an important feature for current and future simulators.
Automated testing. As Table 1shows, the number of simulators currently maintained
(or recently proposed) is small compared to the overall number of VANET simulators, and
this is actually a trend we observed in our investigations (i.e., new functionalities tend to
be added as extensions to current open-source simulators rather than triggering the cre-
ation of a new one). That makes sense from the perspective in which current simulators
(e.g., Veins) offer a plethora of basic functionalities (e.g., different communication stan-
dards and routing protocols) that can be easily used off-the-shelf by new modules, but at
the same time brings new challenges in terms of program performance and engineering.
For example, it becomes harder to cope with development bugs such as the ones reported
in [169]and[170], when one needs to run and test a larger code base. In this sense,
Weber et al. Journal of the Brazilian Computer Society (2021) 27:8 Page 26 of 31
developing automated context-driven testing tools for VANET simulators is an interest-
ing research direction that can help developers to debug and deploy new functionalities
faster.
Conclusion
The increasing popularity and attention to VANETs has prompted researchers to develop
accurate and realistic simulation tools. In this work, we extensively studied the current
state of VANET simulators, specially from the perspective of their support for novel tech-
nologies as well as safety and security mechanisms. When comparing the simulators,
Veins seems to be the one with best support for these features at the moment of writing
this paper. Finally, we also identified a number of challenges that should be addressed in
ordertoimprovethequalityofVANETsimulations.
Acknowledgements
This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nivel Superior – Brasil (CAPES) –
Finance Code 001
Authors’ contributions
All authors contributed to the writing of this article and read and approved the final manuscript.
Funding
This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nivel Superior – Brasil (CAPES) –
Finance Code 001
Availability of data and materials
Not applicable.
Declarations
Competing interests
The authors declare that they have no competing interests.
Received: 10 July 2020 Accepted: 20 April 2021
References
1. Qu F, Wu Z, Wang F-Y, Cho W (2015) A security and privacy review of VANETs. IEEE Trans Intell Transp Syst
16(6):2985–2996
2. Martinez FJ, Toh CK, Cano J-C, Calafate CT, Manzoni P (2011) A survey and comparative study of simulators for
vehicular ad hoc networks (VANETs). Wirel Commun Mob Comput 11(7):813–828
3. Spaho E, Barolli L, Mino G, Xhafa F, Kolici V (2011) Vanet simulators: a survey on mobility and routing protocols. In:
2011 International Conference on Broadband and Wireless Computing, Communication and Applications. IEEE.
pp 1–10
4. Al-Sultan S, Al-Doori MM, Al-Bayatti AH, Zedan H (2014) A comprehensive survey on vehicular ad hoc network. J
Netw Comput Appl 37:380–392
5. Mussa SAB, Manaf M, Ghafoor KZ, Doukha Z (2015) Simulation tools for vehicular ad hoc networks: A comparison
study and future perspectives. In: 2015 International Conference on Wireless Networks and Mobile
Communications (WINCOM). IEEE. pp 1–8
6. Sommer C, Eckhoff D, Brummer A, Buse DS, Hagenauer F, Joerer S, Segata M (2019) Veins: The open source
vehicular network simulation framework. In: Recent Advances in Network Simulation. Springer. pp 215–252
7. Lee KC, Lee U, Gerla M (2010) Survey of routing protocols in vehicular ad hoc networks. In: Advances in Vehicular
Ad-hoc Networks: Developments and Challenges. IGI Global. pp 149–170
8. Jakubiak J, Koucheryavy Y (2008) State of the art and research challenges for VANETs. In: 2008 5th IEEE Consumer
Communications and Networking Conference. IEEE. pp 912–916
9. Luckshetty A, Dontal S, Tangade S, Manvi SS (2016) A survey: comparative study of applications, attacks, security
and privacy in VANETs. In: 2016 International Conference on Communication and Signal Processing (ICCSP). IEEE.
pp 1594–1598
10. Eze EC, Zhang S, Liu E (2014) Vehicular ad hoc networks (VANETs): Current state, challenges, potentials and way
forward. In: 2014 20th International Conference on Automation and Computing. IEEE. pp 176–181
11. Jiang D, Delgrossi L (2008) IEEE 802.11 p: Towards an international standard for wireless access in vehicular
environments. In: VTC Spring 2008-IEEE Vehicular Technology Conference. IEEE. pp 2036–2040
12. Hasrouny H, Samhat AE, Bassil C, Laouiti A (2017) Vanet security challenges and solutions: a survey. Veh Commun
7:7–20
Weber et al. Journal of the Brazilian Computer Society (2021) 27:8 Page 27 of 31
13. Fiore M, Harri J, Filali F, Bonnet C (2007) Vehicular mobility simulation for VANETs. In: 40th Annual Simulation
Symposium (ANSS’07). IEEE. pp 301–309
14. Lim KG, Lee CH, Chin RKY, Yeo KB, Teo KTK (2017) SUMO enhancement for vehicular ad hoc network (VANET)
simulation. In: 2017 IEEE 2nd International Conference on Automatic Control and Intelligent Systems (I2CACIS).
IEEE. pp 86–91
15. Fellendorf M, Vortisch P (2010) Microscopic traffic flow simulator VISSIM. In: Fundamentals of Traffic Simulation.
Springer. pp 63–93
16. Azevedo CL, Deshmukh NM, Marimuthu B, Oh S, Marczuk K, Soh H, Basak K, Toledo T, Peh L-S, Ben-Akiva ME (2017)
Simmobility short-term: An integrated microscopic mobility simulator. Transp Res Rec 2622(1):13–23
17. Cameron GD, Duncan GI (1996) Paramics–parallel microscopic simulation of road traffic. J Supercomput 10(1):25–53
18. Halati A, Lieu H, Walker S (1997) CORSIM-corridor traffic simulation model. In: Traffic Congestion and Traffic Safety
in the 21st Century: Challenges, Innovations, and OpportunitiesUrban Transportation Division, ASCE; Highway
Division, ASCE; Federal Highway Administration, USDOT; and National Highway Traffic Safety Administration, USDOT
19. Korkalainen M, Sallinen M, Kärkkäinen N, Tukeva P (2009) Survey of wireless sensor networks simulation tools for
demanding applications. In: 2009 Fifth International Conference on Networking and Services. IEEE. pp 102–106
20. Varga A (2010) Omnet++. In: Modeling and Tools for Network Simulation. Springer. pp 35–59
21. OPNET Projects team (2015) OPNET - Optimin Network Performance. https://opnetprojects.com/. Accessed 5 Feb
2021
22. Barr R, Haas ZJ, Van Renesse R (2005) Jist/swans. Wireless networks laboratory, Cornell University. http://jist.ece.
905cornell.edu. Accessed 6 Aug 2020
23. Carneiro G (2010) NS-3: Network simulator 3. In: UTM Lab Meeting April Vol. 20. pp 4–5
24. Issariyakul T, Hossain E (2009) Introduction to network simulator 2 (NS2). In: Introduction to Network Simulator NS2.
Springer. pp 1–18
25. Amoozadeh,M.,b.VENTOS Manual. https://veins.car2x.org/documentation/
26. Wegener A, Piórkowski M, Raya M, Hellbrück H, Fischer S, Hubaux J-P (2008) TraCI: an interface for coupling road
traffic and network simulators. In: Proceedings of the 11th Communications and Networking Simulation
Symposium. pp 155–163
27. Sommer C, German R, Dressler F (2010) Bidirectionally coupled network and road traffic simulation for improved
IVC analysis. IEEE Trans Mob Comput 10(1):3–15
28. Sommer C (2006) Veins - Vehicles Network Simulation. https://veins.car2x.org/. Accessed 6 Aug 2020
29. Eckhoff D, Sommer C, Dressler F (2012) On the necessity of accurate IEEE 802.11 p models for IVC protocol
simulation. In: 2012 IEEE 75th Vehicular Technology Conference (VTC Spring). IEEE. pp 1–5
30. Riebl R, Günther H-J, Facchi C, Wolf L (2015) Artery: Extending veins for VANET applications. In: 2015 International
Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS). IEEE. pp 450–456
31. Segata M, Joerer S, Bloessl B, Sommer C, Dressler F, Cigno RL (2014) Plexe: A platooning extension for veins. In: 2014
IEEE Vehicular Networking Conference (VNC). IEE. pp 53–60
32. Schünemann B (2011) V2X simulation runtime infrastructure VSimRTI: an assessment tool to design smart traffic
management systems. Comput Netw 55(14):3189–3198
33. DCAITI (2006) Eclipse MOSAIC - Smart Mobility Simulation. https://www.dcaiti.tu-berlin.de/research/simulation/.
Accessed 5 Feb 2021
34. Wang S-Y, Chou C-L, Yang C-M (2013) EstiNet openflow network simulator and emulator. IEEE Commun Mag
51(9):110–117
35. Wang SY, Kung H (1999) A simple methodology for constructing extensible and high-fidelity TCP/IP network
simulators. In: IEEE INFOCOM’99. Conference on Computer Communications. Proceedings. Eighteenth Annual
Joint Conference of the IEEE Computer and Communications Societies. The Future Is Now (Cat. No. 99CH36320).
IEEE Vol. 3. pp 1134–1143
36. Ullah K (2016) On the use of opportunistic vehicular communication for roadside services advertisement and
discovery. PhD thesis. Universidade de São Paulo, São Paulo
37. Roscher K, Bittl S, Gonzalez A, Myrtus M, Jiru J (2014) ezCar2X: rapid-prototyping of communication technologies
and cooperative ITS applications on real targets and inside simulation environments. In: 11th Conference Wireless
Communication and Information. pp 51–62
38. Jiru J (2021) Fraunhofer-Instituts für Kognitive Systeme IKS. https://www.ezcar2x.fraunhofer.de/en.html. Accessed 5
Mar 2021
39. Schweppe H, Roudier Y, Weyl B, Apvrille L, Scheuermann D (2011) Car2x communication: securing the last meter-a
cost-effective approach for ensuring trust in car2x applications using in-vehicle symmetric cryptography. In: 2011
IEEE Vehicular Technology Conference (VTC Fall). IEEE. pp 1–5
40. Festag A, Hess S (2009) ETSI technical committee ITS: news from european standardization for intelligent transport
systems (ITS)-[global communications newsletter]. IEEE Commun Mag 47(6):1–4
41. Amoozadeh M, Ching B, Chuah C-N, Ghosal D, Zhang HM (2019) VENTOS: Vehicular network open simulator with
hardware-in-the-loop support. Procedia Comput Sci 151:61–68
42. Sommer C (2017) Veins User Manual documentation. https://goo.gl/rLdn2v. Accessed 6 Aug 2020
43. Tomandl A, Herrmann D, Fuchs K-P, Federrath H, Scheuer F (2014) VANETsim: an open source simulator for security
and privacy concepts in VANETs. In: 2014 International Conference on High Performance Computing & Simulation
(HPCS). IEEE. pp 543–550
44. Sliman A, Madi K, Khadour A, Maala B, Ahmad AS (2017) Fabrication attack effect on medical applications based on
Q4 VANETs. Int J Comput Sci Trends Technol (IJCST) 5(2):1–4
45. Haklay M, Weber P (2008) Openstreetmap: User-generated street maps. IEEE Pervasive Comput 7(4):12–18
46. Piorkowski M, Raya M, Lugo AL, Papadimitratos P, Grossglauser M, Hubaux J-P (2008) TraNS: realistic joint traffic and
network simulator for VANETs. ACM SIGMOBILE Mob Comput Commun Rev 12(1):31–33
47. Härri J, Filali F, Bonnet C, Fiore M (2006) VanetMobiSim: generating realistic mobility patterns for VANETs. In:
Proceedings of the 3rd International Workshop on Vehicular Ad Hoc Networks. ACM. pp 96–97
Weber et al. Journal of the Brazilian Computer Society (2021) 27:8 Page 28 of 31
48. Huang L, Matsuura K, Yamane H, Sezaki K (2005) Enhancing wireless location privacy using silent period. In: IEEE
Wireless Communications and Networking Conference, 2005. IEEE Vol. 2. pp 1187–1192
49. Beresford AR, Stajano F (2004) Mix zones: User privacy in location-aware services. In: IEEE Annual Conference on
Pervasive Computing and Communications Workshops, 2004. Proceedings of the Second. IEEE. pp 127–131
50. Stentz A (1997) Optimal and efficient path planning for partially known environments. In: Intelligent Unmanned
Ground Vehicles. Springer. pp 203–220
51. Tomandl A, Scheuer F, Gruber B, Federrath H (2013) VANETsim- VANET simulator. https://svs.informatik.uni-
hamburg.de/vanet/. Accessed 3 Aug 2020
52. NEC (2020) Smart Transportation Systems Will Be the Central Pillar of the Smart City. https://www.nec.com/en/
global/insights/article/2020022504/index.html. Acessed 3 Jan 2021
53. Ge X, Li Z, Li S (2017) 5G software defined vehicular networks. IEEE Commun Mag 55(7):87–93
54. Hussein A, Elhajj IH, Chehab A, Kayssi A (2017) SDN VANETs in 5G: An architecture for resilient security services. In:
2017 Fourth International Conference on Software Defined Systems (SDS). IEEE. pp 67–74
55. Wang W, Chen Y, Zhang Q, Jiang T (2016) A software-defined wireless networking enabled spectrum management
architecture. IEEE Commun Mag 54(1):33–39
56. Farhady H, Lee H, Nakao A (2015) Software-defined networking: a survey. Comput Netw 81:79–95
57. Jaballah WB, Conti M, Lal C (2019) A survey on software-defined VANETs: benefits, challenges, and future directions.
arXiv preprint arXiv:1904.04577:1–17
58. Ushakova M, Ushakov Y, Bolodurina I, Parfenov D, Legashev L, Shukhman A (2020) Research of productivity of
software configurable infrastructure in vanet networks on the basis of models of hybrid data transmission devices.
In: 2020 International Scientific and Technical Conference Modern Computer Network Technologies (MoNeTeC).
IEEE. pp 1–9
59. Arif M, Wang G, Balas VE, Geman O, Castiglione A, Chen J (2020) Sdn based communications privacy-preserving
architecture for vanets using fog computing. Veh Commun 26:100265
60. Kadhim AJ, Seno SAH (2019) Energy-efficient multicast routing protocol based on SDN and fog computing for
vehicular networks. Ad Hoc Netw 84:68–81
61. Zhu W, Gao D, Zhao W, Zhang H, Chiang H-P (2018) SDN-enabled hybrid emergency message transmission
architecture in internet-of-vehicles. Enterp Inf Syst 12(4):471–491
62. McKeown N, Anderson T, Balakrishnan H, Parulkar G, Peterson L, Rexford J, Shenker S, Turner J (2008) Openflow:
enabling innovation in campus networks. ACM SIGCOMM Comput Commun Rev 38(2):69–74
63. Murphy (2012) POX- networking software platform. https://github.com/noxrepo/pox. Accessed 19 Oct 2020
64. EstiNet Technologies Inc (2015) USER INTERFACE (GUI) MANUAL GRAPHIC VANET MODULE OF ESTINET
SIMULATOR 9.0. http://www.estinet.com/ns/wp-content/uploads/2015/12/EstiNet_9.
0_VANET_GUI_Manual_20150303.00.pdf. Accessed 15 Jan 2021
65. Manzanares-Lopez P, Malgosa-Sanahuja J, Muñoz-Gea JP (2018) A Software-Defined Networking Framework to
Provide Dynamic QoS Management in IEEE 802.11 Networks. Sensors 18(7):2247
66. Amoozadeh M, Deng H, Chuah C-N, Zhang HM, Ghosal D (2015) Platoon management with cooperative adaptive
cruise control enabled by VANET. Veh Commun 2(2):110–123
67. OMNeT++ Projects (2020) SOFTWARE DEFINED NETWORKING PROJECTS USING OMNET++ SIMULATOR. https://
omnet-manual.com/omnetsdn- projects/. Accessed 23 Jan 2021
68. Garg S, Singh A, Kaur K, Aujla GS, Batra S, Kumar N, Obaidat MS (2019) Edge computing-based security framework
for big data analytics in VANETs. IEEE Network 33(2):72–81
69. Tayal A (2018) Fog computing in IoT. https://www.tetcos.com/file-exchange.html. Acessed 22 Feb 2021
70. TETCOS (2019) Cellular Network User Manual. https://www.tetcos.com/downloads/v12.2/NetSim_User_Manual.
pdf. Accessed 22 Feb 2021
71. Boukerche A, Soto V (2020) An efficient mobility-oriented retrieval protocol for computation offloading in vehicular
edge multi-access network. IEEE Trans Intell Transp Syst 21(6):2675–2688
72. Zhou P, Braud T, Zavodovski A, Liu Z, Chen X, Hui P, Kangasharju J (2020) Edge-facilitated augmented vision in
vehicle-to-everything networks. IEEE Trans Veh Technol 69(10):12187–12201
73. Sun Y, Guo X, Song J, Zhou S, Jiang Z, Liu X, Niu Z (2019) Adaptive learning-based task offloading for vehicular edge
computing systems. IEEE Trans Veh Technol 68(4):3061–3074
74. Sommer C, Joerer S, Dressler F (2012) On the applicability of two-ray path loss models for vehicular network
simulation. In: 2012 IEEE Vehicular Networking Conference (VNC). IEEE. pp 64–69
75. Feng J, Liu Z, Wu C, Ji Y (2017) AVE: Autonomous vehicular edge computing framework with ACO-based
scheduling. IEEE Trans Veh Technol 66(12):10660–10675
76. Guderitz A (2020) Enhanced Traffic Safety with LTE and Mobile. Edge Computing. https://www.iks.fraunhofer.de/
en/projects/car2mec.html. Accessed 22 Feb 2021
77. Olmos AG, Vazquez-Gallego F, Sedar R, Samoladas V, Mira F, Alonso-Zarate J (2019) An automotive cooperative
collision avoidance service based on mobile edge computing. In: International Conference on Ad-Hoc Networks
and Wireless. Springer. pp 601–607
78. Roy A, Madria S (2019) Secured traffic monitoring in VANET. arXiv preprint arXiv:1909.1005:1–12
79. Kan X, Ganlath A, Ucar S, Han K, Tiwari P, Karydis K (2019) Edge assisted misbehavior detection for platoons. In: 2019
IEEE Vehicular Networking Conference (VNC). IEEE. pp 1–4
80. Gilly K, Alcaraz S, Aknin N, Filiposka S, Mishev A (2020) Modelling edge computing in urban mobility simulation
scenarios. In: 2020 IFIP Networking Conference (Networking). IEEE. pp 539–543
81. Shah SAA, Ahmed E, Imran M, Zeadally S (2018) 5G for vehicular communications. IEEE Commun Mag 56(1):111–117
82. Uzcátegui RA, De Sucre AJ, Acosta-Marum G (2009) Wave: A tutorial. IEEE Commun Mag 47(5):126–133
83. Dahlman E, Parkvall S, Skold J (2013) 4G: LTE/LTE-advanced for Mobile Broadband. Academic press
84. TETCOS (2013) NetSim 5G NR Technologies. https://www.tetcos.com/5g.html. Accessed 14 Feb 2021
85. 3rd Generation Partnership Project (1998) Mobile Broadband Standard. https://www.3gpp.org/about-3gpp.
Accessed 6 Mar 2021
Weber et al. Journal of the Brazilian Computer Society (2021) 27:8 Page 29 of 31
86. Chehri A, Chehri H, Hakim N, Saadane R (2020) Realistic 5.9 GHz DSRC vehicle-to-vehicle wireless communication
protocols for cooperative collision warning in underground mining. In: Smart Transportation Systems 2020.
Springer. pp 133–141
87. Veins_INET Subproject. https://veins.car2x.org/documentation/modules/#veins_inet. Accessed on: 15 August 2020
88. Virdis A, Nardini G (2020) 5G New Radio User Plane Simulation Model for INET and OMNeT++. http://simu5g.org/.
Accessed 20 Aug 2020
89. Chekired DA, Togou MA, Khoukhi L, Ksentini A (2019) 5G-slicing-enabled scalable SDN core network: Toward an
ultra-low latency of autonomous driving service. IEEE J Sel Areas Commun 37(8):1769–1782
90. Zhang J, Zhong H, Cui J, Tian M, Xu Y, Liu L (2020) Edge computing-based privacy-preserving authentication
framework and protocol for 5G-enabled vehicular networks. IEEE Trans Veh Technol 69(7):7940–7954
91. Huang J, Qian Y, Hu RQ (2020) Secure and efficient privacy-preserving authentication scheme for 5G software
defined vehicular networks. IEEE Trans Veh Technol 69(8):8542–8554
92. Dharanyadevi P, Venkatalakshmi K (2016) Proficient routing by adroit algorithm in 5G-Cloud-VMesh network.
EURASIP J Wirel Commun Netw 2016(1):1–11
93. Chen H (2019) free5GC. https://www.free5gc.org/. Accessed 11 Mar 2021
94. EstiNet Technologies Inc (2013) EstiNet 11. https://www.estinet.com/ns/?page_id=21140. Accessed 6 Mar 2021
95. Puttagunta H, Agrawal DP (2020) Performance of 802.11 P in VANET at 5G Frequencies for Different Channel
Models. In: 2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference
(IEMCON). IEEE. pp 0468–0472
96. Ibrahim A, Math CB, Goswami D, Basten T, Li H (2018) Co-simulation framework for control, communication and
traffic for vehicle platoons. In: 2018 21st Euromicro Conference on Digital System Design (DSD). IEEE. pp 352–356
97. 5G New Radio User Plane Simulation Model for INET and OMNeT++. http://simu5g.org/
98. Shadrin SS, Ivanova AA (2019) Taxonomy and definitions for terms related to driving automation systems for
on-road motor vehicles. In: Avtomobil’. Doroga. Infrastruktura Vol 3 Issue 21. p 10
99. Lu Q, Tettamanti T, Hörcher D, Varga I (2020) The impact of autonomous vehicles on urban traffic network capacity:
an experimental analysis by microscopic traffic simulation. Transp Lett 12(8):540–549
100. Carpin S, Lewis M, Wang J, Balakirsky S, Scrapper C (2007) USARSim: a robot simulator for research and education.
In: Proceedings 2007 IEEE International Conference on Robotics and Automation. IEEE. pp 1400–1405
101. Pereira JL, Rossetti RJ (2012) An integrated architecture for autonomous vehicles simulation. In: Proceedings of the
27th Annual ACM Symposium on Applied Computing. pp 286–292
102. Alotibi F, Abdelhakim M (2020) Anomaly Detection for Cooperative Adaptive Cruise Control in Autonomous
Vehicles Using Statistical Learning and Kinematic Model. In: IEEE Transactions on Intelligent Transportation
Systems. pp 1–11. https://doi.org/10.1109/TITS.2020.2983392
103. Li Y, Liu Q (2020) Intersection management for autonomous vehicles with vehicle-to-infrastructure
communication. PLoS ONE 15(7):0235644
104. Teixeira M, d’Orey PM, Kokkinogenis Z (2020) Simulating collective decision-making for autonomous vehicles
coordination enabled by vehicular networks: A computational social choice perspective. Simul Model Pract Theory
98:101983
105. Zehe D, Nair S, Knoll A, Eckhoff D (2017) Towards citymos: a coupled city-scale mobility simulation framework. 5th
GI/ITG KuVS Fachgespräch Inter-Vehicle Communication 2017:03
106. Roscher K, Maierbacher G (2016) Reliable message forwarding in VANETs for delay-sensitive applications. In: 2016
International Symposium on Wireless Communication Systems (ISWCS). IEEE. pp 199–203
107. Hadiwardoyo SA, Dricot J-M, Calafate CT, Cano J-C, Hernández-Orallo E, Manzoni P (2020) UAV Mobility model for
dynamic UAV-to-car communications in 3D environments. Ad Hoc Netw 107:102193
108. Brummer A, German R, Djanatliev A (2018) On the necessity of three-dimensional considerations in vehicular
network simulation. In: 2018 14th Annual Conference on Wireless On-demand Network Systems and Services
(WONS). IEEE. pp 75–82
109. Montgomery DR, Foufoula-Georgiou E (1993) Channel network source representation using digital elevation
models. Water Resour Res 29(12):3925–3934
110. NetSim Applications Unmanned Aerial Vehicle (UAV) Communication. https://www.tetcos.com/uav-drone-
communication.html. Accessed on: 18 February 2021
111. Higham DJ, Higham NJ (2016) MATLAB Guide. SIAM - Society for Industrial and Applied Mathematics, Philadelphia.
pp 1–502
112. Oliveira R, Montez C, Boukerche A, Wangham MS (2017) Reliable data dissemination protocol for VANET traffic
safety applications. Ad Hoc Netw 63:30–44
113. Bello Tambawal A, Md Noor R, Salleh R, Chembe C, Oche M (2019) Enhanced weight-based clustering algorithm to
provide reliable delivery for VANET safety applications. PLoS ONE 14(4):0214664
114. Sutagundar AV, Kalyani T (2017) Fault tolerance in VANET’s. In: 2017 International Conference On Smart
Technologies For Smart Nation (SmartTechCon). IEEE. pp 1039–1043
115. Almeida J, Rufino J, Alam M, Ferreira J (2019) A survey on fault tolerance techniques for wireless vehicular networks.
Electronics 8(11):1358
116. Dharmaraja S, Vinayak R, Trivedi KS (2016) Reliability and survivability of vehicular ad hoc networks: An analytical
approach. Reliab Eng Syst Saf 153:28–38
117. Albano WA, Nogueira M, de Souza JN (2015) A taxonomy for resilience in vehicular ad hoc networks. IEEE Lat Am
Trans 13(1):228–234
118. Alkhalifa IS, Almogren AS (2020) NSSC: Novel segment based safety message broadcasting in cluster-based
vehicular sensor network. IEEE Access 8:34299–34312
119. de Souza AM, Braun T, Botega LC, Villas LA, Loureiro AA (2019) Safe and sound: Driver safety-aware vehicle
re-routing based on spatiotemporal information. IEEE Trans Intell Transp Syst 21(9):3973–3989
120. Shahwani H, Mugabarigira BA, Shen Y, Jeong JP, Shin J (2020) DAPF: Delay-aware packet forwarding for driving
safety and efficiency in vehicular networks. IET Communications 14(9):1404–1411
Weber et al. Journal of the Brazilian Computer Society (2021) 27:8 Page 30 of 31
121. Offor P (2012) Vehicle Ad Hoc Network (VANET): Safety Benefits and Security Challenges. SSRN Electron J. https://
doi.org/10.2139/ssrn.2206077.http://ssrn.com/abstract=2206077. Accessed 8 Mar 2021
122. Liu H, Lin C-W, Kang E, Shiraishi S, Blough DM (2019) A byzantine-tolerant distributed consensus algorithm for
connected vehicles using proof-of-eligibility. In: Proceedings of the 22nd International ACM Conference on
Modeling, Analysis and Simulation of Wireless and Mobile Systems. pp 225–234
123. Sommer C, Dressler F (2011) Using the right two-ray model? A measurement based evaluation of PHY models in
VANETs. In: Proc. ACM MobiCom. pp 1–3
124. Sommer C, Eckhoff D, Dressler F (2013) IVC in cities: Signal attenuation by buildings and how parked cars can
improve the situation. IEEE Trans Mob Comput 13(8):1733–1745
125. SystemXIRT (2019) F2MD: Framework For Misbehavior Detection. https://www.irt-systemx.fr/. Accessed 3 Mar 2021
126. Kamel J, Ansari MR, Petit J, Kaiser A, Jemaa IB, Urien P (2020) Simulation framework for misbehavior detection in
vehicular networks. IEEE Trans Veh Technol 69(6):6631–6643. https://doi.org/10.1109/TVT.2020.2984878
127. Sommer C, Eckhoff D, German R, Dressler F (2011) A computationally inexpensive empirical model of IEEE 802.11 p
radio shadowing in urban environments. In: 2011 Eighth International Conference on Wireless On-demand
Network Systems and Services. IEEE. pp 84–90
128. Sommer C, Joerer S, Segata M, Tonguz OK, Cigno RL, Dressler F (2014) How shadowing hurts vehicular
communications and how dynamic beaconing can help. IEEE Trans Mob Comput 14(7):1411–1421
129. Sommer C, Joerer S, Dressler F (2012) On the applicability of two-ray path loss models for vehicular network
simulation. In: 2012 IEEE Vehicular Networking Conference (VNC). pp 64–69. https://doi.org/10.1109/VNC.2012.
6407446
130. Ucar S, Ergen SC, Ozkasap O (2017) Data-driven abnormal behavior detection for autonomous platoon. In: 2017
IEEE Vehicular Networking Conference (VNC). IEEE. pp 69–72
131. Oliphant TE (2007) Python for scientific computing. Comput Sci Eng 9(3):10–20
132. R Core Team (2013) R: A language and environment for statistical computing. R Foundation for Statistical
Computing, Vienna. https://www.Rproject.org/. Accessed 3 Mar 2021
133. Hasan S (2020) Fail-operational and fail-safe vehicle platooning in the presence of transient communication errors.
PhD thesis, Mälardalen University, Västerås and Eskilstuna
134. Tanwar S, Vora J, Tyagi S, Kumar N, Obaidat MS (2018) A systematic review on security issues in vehicular ad hoc
network. Secur Priv 1(5):39
135. Bariah L, Shehada D, Salahat E, Yeun CY (2015) Recent advances in VANET security: a survey. In: 2015 IEEE 82nd
Vehicular Technology Conference (VTC2015-Fall). IEEE. pp 1–7
136. Alaya B, SELLAMI L (2021) Clustering method and symmetric/asymmetric cryptography scheme adapted to
securing urban VANET networks. J Inf Secur Appl 58:102779
137. Walton J (2012) Data Security with Crypto++. O’Reilly Media, Inc, Newton
138. Singh V, Mahajan K (2016) Vanet and its security issues-a review. Int J Comput Sci Eng 4(10):59–64
139. Bakar KAA, Irvine J (2010) A scheme for detecting selfish nodes in MANETs using OMNET++. In: 2010 6th
International Conference on Wireless and Mobile Communications. IEEE. pp 410–414
140. Hortelano J, Ruiz JC, Manzoni P (2010) Evaluating the usefulness of watchdogs for intrusion detection in VANETs. In:
2010 IEEE International Conference on Communications Workshops. IEEE. pp 1–5
141. Dietzel S, Gürtler J, Kargl F (2016) A resilient in-network aggregation mechanism for VANETs based on
dissemination redundancy. Ad Hoc Netw 37:101–109
142. Achour I, Bejaoui T, Busson A, Tabbane S (2015) A redundancy-based protocol for safety message dissemination in
vehicular ad hoc networks. In: 2015 IEEE 82nd Vehicular Technology Conference (VTC2015-Fall). IEEE. pp 1–6
143. Khacheba I, Yagoubi MB, Lagraa N, Lakas A (2018) CLPS: context-based location privacy scheme for VANETs. Int J Ad
Hoc Ubiquit Comput 29(1-2):141–159
144. De Fuentes JM, González-Tablas AI, Ribagorda A (2011) Overview of security issues in vehicular ad-hoc networks. In:
Handbook of Research on Mobility and Computing: Evolving Technologies and Ubiquitous Impacts. IGI global.
pp 894–911
145. Soliman JN, Mageed TA, El-Hennawy HM (2017) Digital signature and authentication mechanisms using new
customized hash function for cognitive radio networks. In: 2017 12th International Conference on Computer
Engineering and Systems (ICCES). IEEE. pp 175–181
146. Liu Y, Wang Y, Chang G (2017) Efficient privacy-preserving dual authentication and key agreement scheme for
secure V2V communications in an IoV paradigm. IEEE Trans Intell Transp Syst 18(10):2740–2749
147. Singh PK, Tabjul GS, Imran M, Nandi SK, Nandi S (2018) Impact of security attacks on cooperative driving use case:
CACC platooning. In: TENCON 2018-2018 IEEE Region 10 Conference. IEEE. pp 138–143
148. Mohan AP, Elshakankiri M (2019) Enhanced priority-based routing protocol (EPRP) for inter-vehicular
communication. In: International Conference on Computing. Springer. pp 325–337
149. Emara K (2016) Poster: Prext: Privacy extension for veins vanet simulator. In: 2016 IEEE Vehicular Networking
Conference (VNC). IEEE. pp 1–2
150. Pan Y, Li J (2013) Cooperative pseudonym change scheme based on the number of neighbors in vanets. J Netw
Comput Appl 36(6):1599–1609
151. Emara K, Woerndl W, Schlichter J (2015) CAPS: Context-aware privacy scheme for VANET safety applications. In:
Proceedings of the 8th ACM Conference on Security & Privacy in Wireless and Mobile Networks. pp 1–12
152. Huang L, Yamane H, Matsuura K, Sezaki K (2006) Silent cascade: enhancing location privacy without communication
qos degradation. In: International Conference on Security in Pervasive Computing. Springer. pp 165–180
153. Buttyán L, Holczer T, Weimerskirch A, Whyte W (2009) Slow: A practical pseudonym changing scheme for location
privacy in vanets. In: 2009 IEEE Vehicular Networking Conference (VNC). IEEE. pp 1–8
154. Scheuer F, Fuchs K-P, Federrath H (2011) A safety-preserving mix zone for vanets. In: International Conference on
Trust, Privacy and Security in Digital Business. Springer. pp 37–48
Weber et al. Journal of the Brazilian Computer Society (2021) 27:8 Page 31 of 31
155. IEEE (2016) IEEE standard for wireless access in vehicular environments-security services for applications and
management messages. Std 1609.2-2016 (Revision of IEEE Std 1609.2-2013):1–240. https://doi.org/10.1109/
IEEESTD.2016.7426684. Accessed 8 Mar 2021
156. ETSI TS (2010) 102 731 v1.1.1 - Intelligent Transport Systems (ITS); Security; Security Services and Architecture.
Standard, TC C-ITS. https://www.etsi.org/deliver/etsi_ts/102700_102799/102731/01.01.01_60/ts_102731v010101p.
pdf. Accessed 8 Mar 2021
157. ETSI TS (2012) ETSI TS 102 941 v1.1.1 - Intelligent Transport Systems (ITS); Security; Trust and Privacy Management,
Standard, TC C-ITS. https://www.etsi.org/deliver/etsi_ts/102900_102999/102941/01.01.01_60/ts_102941v010101p.
pdf. Accessed 8 Mar 2021
158. ETSI TS (2019) 102 941 v1.3.1 - Intelligent Transport Systems (ITS); Security; Trust and Privacy Management,
Standard, TC C-ITS. https://standards.iteh.ai/catalog/standards/etsi/9ba459d3-be83- 4b0d-82ca-28644f10d731/
etsi-ts- 102-941-v1- 3-1-2019-02. Accessed 8 Mar 2021
159. ETSI TS (2015) 103 097 v1.2.1 - Intelligent Transport Systems (ITS); Security; Security Header and Certificate Formats.
Standard, TC C-ITS. https://www.etsi.org/deliver/etsi_ts/103000_103099/103097/01.03.01_60/ts_103097v010301p.
pdf. Accessed 8 Mar 2021
160. ETSI TS (2016) 102 940 v1.2.1 - Intelligent Transport Systems (ITS); Security; ITS Communications Security
Architecture and Security Management. Standard, TC CITS. https://www.etsi.org/deliver/etsi_ts/102900_102999/
102940/01.02.01_60/ts_102940v010201p.pdf. Accessed 8 Mar 2021
161. ETSI TS (2017) 102 893 v1.2.1 Intelligent Transport Systems (ITS); Security; Threat, Vulnerability and Risk Analysis
(TVRA). Standard, TC C-ITS. https://www.etsi.org/deliver/etsi_tr/102800_102899/102893/01.02.01_60/
tr_102893v010201p.pdf. Accessed 8 Mar 2021
162. Cinque M, Cotroneo D, Di Martino C, Russo S, Testa A (2009) Avr-inject: A tool for injecting faults in wireless sensor
nodes. In: 2009 IEEE International Symposium on Parallel & Distributed Processing. IEEE. pp 1–8
163. Sailhan F, Delot T, Pathak A, Puech A, Roy M (2010) Fault injection and monitoring for dependability analysis of
wireless sensor-actuators networks. In: 4th Workshop Gestion des Données dans les Systèmes d’Information
Pervasifs (GEDSIP) in Cunjunction with INFORSID. Citeseer
164. Raposo D, Rodrigues A, Silva JS, Boavida F (2017) A taxonomy of faults for wireless sensor networks. J Netw Syst
Manag 25(3):591–611
165. Buse DS, Dressler F (2019) Towards real-time interactive V2X simulation. In: 2019 IEEE Vehicular Networking
Conference (VNC). IEEE. pp 1–8
166. Buse DS, Schettler M, Kothe N, Reinold P, Sommer C, Dressler F (2018) Bridging worlds: Integrating
hardware-in-the-loop testing with large-scale VANET simulation. In: 2018 14th Annual Conference on Wireless
On-demand Network Systems and Services (WONS). IEEE. pp 33–36
167. Riebl R, Monz M, Varga S, Janicke H, Maglaras L, Al-Bayatti AH, Facchi C (2016) Improved security performance for
vanet simulations. In: 4th IFAC Symposium on Telematics Applications
168. Baee MAR, Simpson L, Foo E, Pieprzyk J (2019) Broadcast Authentication in Latency-Critical Applications: On the
Efficiency of IEEE 1609.2. IEEE Trans Veh Technol 68(12):11577–11587
169. Sommer C (2020) Veins 5.1 Changelog. https://veins.car2x.org/download/#changelog. Accessed 8 Mar 2020
170. DCAITI (2020) Eclipse MOSAIC Download Area. https://www.dcaiti.tu-berlin.de/research/simulation/download/.
Accessed 8 Mar 2020
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.