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Integrating Safety in VANETs: A
Taxonomy and Systematic Review of
VEINS Models
ZEYAD GHALEB AL-MEKHLAFI1, MAHMOOD A. AL-SHAREEDA2, BADIEA
ABDULKAREM MOHAMMED3, ABDULAZIZ M. ALAYBA1, AHMED M. SHAMSAN
SALEH4, HAMAD A AL-RESHIDI5, KHALIL ALMEKHLAFI6
1Department of Information and Computer Science, College of Computer Science and Engineering, University of Ha’il, Ha’il 81481, Saudi Arabia
2Department of Communication Engineering, Iraq University College (IUC), Basra, Iraq.
3Department of Computer Engineering, College of Computer Science and Engineering, University of Ha’il, Ha’il 81481, Saudi Arabia
4Department of Information Technology University of Tabuk, Tabuk, Saudi Arabia.
5Department of Instructional Technology, College of education, University of Ha’il, KSA.
6Taibah University, CBA-Yanbu, Al Madinah, KSA.
Corresponding author: Mahmood A. Al-Shareeda (e-mail: alshareeda022@gmail.com).
ABSTRACT Vehicular Ad-Hoc Networks (VANETs) play an essential role in road safety through
Vehicle-to-Vehicle Communications and Vehicle-to-Infrastructure communications. In this paper, we offer
a survey of the state-of-the-art literature about VEINS tool set which is an extraordinary vehicle and
network simulation framework for VANET researchers. This paper identifies and classifies the existing
research into a comprehensive taxonomy that we called Applications, Solutions and Networks so as to
provide an organized survey of safety-related VEINS-based literature. This review can be used by the
research community to understand where gaps exist in the literature of particular real-world applicability or
integration with emerging technologies, as well as socio-economic factors associated with deployment of
VEINS-based safety applications. In addition, we evaluate the VEINS framework including what it provides
(e.g., better simulation accuracy), safety testing is more comprehensive as well as other technologies that are
used may have like in our case 5G and AI. We also refer to its affordability, growth-ability and adaptiveness
along with real-time data analytics commented the author. The constraints of VEINS are also discussed,
for instance the bridge from simulation to reality, computational complexity and problems with regard to
emerging technologies integration. We discuss future research directions to further enrich the potential
of VEINS by incorporating Beyond-5G technologies, cutting-edge AI algorithms, blockchain (BC) for
communication security and reliability, semi-virtual/hybrid simulation environments and a wider range of
V2X communications. It includes case studies and applications which illustrate how general safety scenarios
(e.g., collision avoidance, emergency vehicle prioritization), road hazard detection can be simulated using
VEINS highlighting the importance of this approach for practicing engineers. In all, the wide-ranging review
would be useful to researchers and practitioners working toward ensuring a secure and efficient vehicular
network design.
INDEX TERMS VEINS Framework, Real-World Applicability, Vehicular Ad-Hoc Networks (VANETs),
Simulation Accuracy, Safety Applications, 5G and AI Integration, Emerging Technologies in VANETs.
I. INTRODUCTION
The majority of company R&D establishments started in-
vestigating VANETs (Vehicular Ad-hoc Networks) in 2000
[1]–[3]. At first, VANET is used to make roads safer and
to cut down on traffic accidents and delays [4]–[6]. Its
original scope was limited to the essential features provided
by VANET architecture, but now it encompasses integrated
services made possible by a wide range of technologies [7].
In a typical VANET, the Trusted Authority (TA), Road-
side Units (RSUs), and Onboard Units (OBUs) serve as the
primary nodes, as shown in Figure 1. The TA is a reliable
part of VANETs that oversees the entire system and ensures
that all other parts have their settings kept up to date. While
RSUs are installed along the roadside to serve as wireless
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Al-Shareeda et al. et al.: Safety-based VEINS model in vehicular ad-hoc networks (VANET)
infrastructure linking automobiles to TA. Each vehicle is
equipped with an OBU, a wireless device that processes,
sends, and receives messages (such as road condition, road
status and etc.) between other cars.
FIGURE 1: VANET Architecture.
The trouble with VANET is that its topology is constantly
susceptible to security attacks, leading to safety leakage.
Since installing a VANET requires a lot of time and money,
simulations are commonly used instead. Furthermore, it is
possible that results from testing VANETs under these set-
tings will not be accurate. Previous research has shown
that the mobility models significantly affect how closely
simulation results need to reflect real-world values. The
many VANET simulators fall into one of three broad cate-
gories: mobility simulators (like sumo, netstream, and straw);
network generators (like ns2, ns3, and OMNeT++); and
integrated frameworks (like mobireal, trans, and VEINS).
Unfortunately, a universal VANET simulator does not exist.
Researchers typically merged existing network simulators
with existing mobility simulators. One example of such a
system that is gaining popularity is VEINS [8]. Veins integers
the well-known OMNET++ [9] with SUMO [10] network
simulator.
OMNeT++ is widely recognised as one of the most accu-
rate network simulators. The incredibly portable SUMO road
traffic simulator accounts for granular vehicle behaviours
like speed, acceleration, location in relation to the road, and
route details on the map like arrival and departure times. In
view of the network’s robustness, portability, and movability
paradigm, the Veins simulator appears to be vastly superior.
A. COMPARISON WITH EXISTING STUDIES
Literature Comparison: In this part, we review our previous
work with [11]–[13] of VANETs and VEINS based research.
Fu et al. [11] focused on VEINS-based applications like col-
lision avoidance but does not integrate advanced technologies
like 5G or AI. Noori et al. [12] simulated particular collision
avoidance cases but does not generalize to other safety cases.
[13] a detailed survey on security issues in VANETs with a
particular emphasis on research works based-on VEINS plat-
form. The paper is devoted to revealing the most important
aspects of cybersecurity and underlines some security issues.
such as privacy, authentication, trust management and also a
few methods for how it could be solved namely blockchain
technology an AI-ML grade algorithms. Although we have
made much progress in simulating vehicular networks with,
for example, the tool VEINS; many are still limited: scalabil-
ity and real-world applicability to include 5G-AI-Blockchain
integration. We address these gaps in this study by present-
ing a full taxonomy that categorizes VEINS-based works
under three main categories: Applications, Solutions, and
Networks. Moreover, we provide new perspectives on the
incorporation of state-of-art technologies for improved ve-
hicular network simulators. Table 1 provided below indicates
the unique aspects of our work in relation to existing studies.
TABLE 1: Comparison of our work with existing studies
Study Focus Limitations Novel
Contributions of
This Work
[11] VEINS-
based safety
applications in
VANETs
Limited
integration
of advanced
technologies
like 5G, AI, and
blockchain
Comprehensive
integration of
5G, AI, and
blockchain for
enhanced safety
applications
[12] VEINS-based
collision
avoidance
simulations
Focused on spe-
cific safety sce-
narios, lacks gen-
eral applicability
Generalized
framework
covering a
wide range of
safety scenarios,
including
collision
avoidance and
hazard detection
[13] Focuses
on threats,
vulnerabilities,
and solutions in
VANET security
using VEINS
Narrow focus on
security aspects,
lacking coverage
of broader appli-
cations and solu-
tions in VEINS-
based research
Broad coverage
of applications,
solutions,
and networks
in VEINS,
addressing
mobility,
communication,
and technology
integration
This
Work
Comprehensive
review and
taxonomy of
VEINS-based
research
Fragmented and
limited in scope
Novel taxonomy,
identification of
research gaps,
and future
directions
for VEINS-
based research,
integrating
advanced
technologies
Comparison Between This Study and "A Systematic Lit-
erature Review on Security of Vehicular Ad-Hoc Network
(VANET) Based on VEINS Framework" Paper: However,
the focus of our work is more comprehensive by including
additional applications and solutions in VANETs — not only
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Al-Shareeda et al. et al.: Safety-based VEINS model in vehicular ad-hoc networks (VANET)
security aspects (VEINS), but also network routing strategies
using artificial mobility models within mobility simulators
aside from reinforcing sense-making enabling integration
with current-future technologies like 5G, AI or blockchain.
The Securing VANETs with a Systematic Surveys of
Threat and Solution Technologies: also provides insight on
security requirements namely privacy, authentication & trust
management as well as reliability and revocation in relation
to the reviewed works based on VEINS. The catch is that
the study focuses so closely on security challenges to restrict
it primarily into certain scenario in which security would
seem relevant. On the other hand, our work is extended with
more than just security as we present a multidimensional
study of network performance and scalability issues along
side real-time communication in vehicular networks using
cutting edge solutions to answer how can modern Vehicular
Communication Challenges be addressed.
B. TAXONOMY OF VEINS-BASED VANET RESEARCH
The developed taxonomy provided in this paper is able to
classify the existing VEINS-based research works into differ-
ent areas, which provides a structured view for safety applica-
tions studied under Vehicular Ad-Hoc Networks (VANETs).
We fulfil this missing need in classification scheme by
providing a detailed categorisation to structure and support
analysis of VEINS based studies.
•Applications: This is a category under safety-critical
where use cases such as collision avoidance, emergency
vehicle prioritization and road hazard detection. This
area involves running realistic safety scenarios across
the different levels to evaluate how well, and with what
confidence, it is working. VEINS is ideally suited for
this use-case by making it possible to test different
safety solutions under various traffic conditions and ap-
plication scenarios. Further examples of studies in this
category present the simulation for Collision Avoidance
System, Emergency Vehicle Prioritization Protocol and
Road Hazard Detection Mechanism.
•Solutions: The challenges in this category are traffic
congestion, resource allocation and intersection man-
agement. Studies in this field apply VEINS for simula-
tion purposes to analyze approaches that improve traffic
conditions, reduce waiting times and the stability of
vehicular networks. The main emphasis here is to play
a role in the improvement of total network efficiency,
congestion reduction and smoother traffic operation. For
instance, congestion control mechanisms or dynamic
resource allocation strategies play crucial roles in en-
suring efficient road traffic management and should be
carefully adapted so that the environment can actively
operate to avoid congestions.
•Networks: This category is related to VANETs technical
dimensions that comprises data dissemination strate-
gies, routing protocols, Medium Access Control (MAC)
and physical layer protocols. VEINS, which is the most
widely used based of research to evolve more efficient,
reliable and scalable from vehicular communication net-
works. Examples of this work include techniques for op-
timizing data routing and enhancing MAC performance,
as well as the development of more efficient physical
layer protocols. The first category is fundamental and
centered on the technical property of VANETs, which
should be guaranteed to implement safety applications
or traffic control applications shown in the previous
categories.
Introducing a structured way of mapping and reviewing
VEINS research, the taxonomy can help researchers recog-
nize key missing evidence in existing literature and propose
possible avenues for future investigation. The taxonomy also
outlines the changing purpose of VEINS by virtue of techno-
logical advancements like 5G and AI that were not investi-
gated deeply before, simulating real-life cases. This taxon-
omy provides a consolidated view of the multiple aspects
of VANET research and promotes holistic comprehension
on existing state-of-the-art work and future directions for
enhancement.
C. CONTRIBUTION
Recent scholarly works have investigated the limitations and
challenges that prohibit the complete implementation of the
VEINS-based safety of VANET, and have proposed solu-
tions. In the context of the VEINS framework, studies on
the safety of VANET are ongoing and varied. This article
contributes in four ways:
•Comprehensive Literature Review and Taxonomy De-
velopment: In this paper, we also provide a compre-
hensive literature review on the VEINS framework in
the context of Vehicular Ad-Hoc Networks (VANETs)
particularly for safety applications. It somehow orga-
nizes the existing research work into a comprehensive
taxonomy, which is further fragmented in terms of Ap-
plications, Solutions and Networks to give an organized
glimpse over different domains.
•Identification of Research Gaps and Evaluation of
VEINS Framework: The study also lists major research
gaps in the existing literature, especially regarding real-
world deployment opportunities, interoperability with
new technologies and socio-economic drivers shaping
VEINS-based safety applications for VANETs. It ex-
amines the VEINS framework discussing much-needed
benefits as improvement in simulating realism, discrimi-
nation of safe offerings by conducting a complete safety
evaluation, incorporation with forthcoming technolo-
gies like 5G and AI; seeking inexpensive development
plus assessment costings linked to corresponding lo-
cated realizations for developed vehicles resulting im-
proved simulation capabilities, scalability as well anal-
ysis over big data.
•Discussion on Limitations and Future Research Direc-
tions: In this paper, the limitations of VEINS have been
identified as gap between real-world and simulation
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Al-Shareeda et al. et al.: Safety-based VEINS model in vehicular ad-hoc networks (VANET)
conditions, computation complexity, lack in socio eco-
nomic / human factor consideration within simulations,
heterogeneity concerns towards compatibility with other
technologies including an aggressive need for data accu-
racy availability. It also provides possible directions for
future research to advance the capabilities of the VEINS
framework, including interoperability with Beyond-5G
(B5G) technologies, advanced AI and machine learn-
ing algorithms as well blockchain for secure vehicular
communications systems, semi-virtual and hybrid simu-
lation environments; in addition to a large-scale Vehicle-
to-Everything (V2X)-based traffic system.
•Case Studies and Practical Applications: Case studies
and applications showing how the VEINS framework
can be used to simulate different safety scenarios (e.g.,
collision avoidance, emergency vehicle prioritization,
road hazard detection) are detailed in this paper. To
demonstrate the real-world relevance and impact of its
innovative approach, I provide examples about how
this new framework can be useful to researchers and
practitioners in practice.
D. ORGANIZATION
The following are the article’s six sections: The safety of
VANET system within the VEINS model is presented in
Section I. In terms of study objectives, data sources, and data
extraction procedures, we detail everything in Section II. A
full taxonomy of the research area’s flora and fauna is in-
cluded. The conclusions of this article, including a summary
table and statistical analysis of the entire set of publications
reviewed, are presented in the section titled Section III. In
Section IV, we break down the total number of journals and
the number of databases used to choose articles for each
subcategory. The VEINS framework is thoroughly discussed
in Section V. Dissection if this paper is presented in Section
VI. This work is then concluded in Section VII.
II. METHOD
Here, "VEINS framework and its utilisation in VANET" was
the primary term in the original database. The search did not
turn up any relevant articles discussing the safety measures
taken by the VANET-assisted VEINS method. In addition,
the research was limited to papers written in English that
discuss the VEINS method used in vehicular networks.
A. SOURCES OF INFORMATION
This paper’s literature review is based on a search of three
databases according to the PRISMA 2020 guidelines: (1)
IEEE ® Digital Library, a database with a large collection of
papers in electrical and electronic engineering and computer
science; (2) ScienceDirect, a database with a large value of
scientific studies; and (3) Scopus, a database with a wide
range of scientific techniques in many disciplines. These
three registries explore numerous related methods, including
the VANET-established VEINS method. The following is the
URL to the electronic database we used:
•IEEE Xplore® Digital Library (http://ieeexplore.ieee.org;
accessed on: 5 MAY 2022).
•Science Direct (http://www.sciencedirect.com; accessed
on: 5 MAY 2022).
•Scopus Database (http://www.scopus.com; accessed on:
5 MAY 2022).
B. STUDY SELECTION PROCEDURE
Finding sources of applicable research was the first step
in the study selection process, followed by three rounds
of screening and filtering according to the PRISMA 2020
guidelines. All items not pertinent to the ANET-established
VEINS method were weeded out in the initial round of
screening and filtering. The second version used abstract and
title analysis to weed out irrelevant items. The last step was
a thorough screening and investigation of the full-text docu-
ments. Authors consistently applied the same set of eligibility
criteria throughout all iterations. The final group chosen was
thus connected to the VANET-based VEINS framework in
some way.
C. SEARCH
In March of 2022, we used the search functions on Sci-
enceDirect, Scopus and IEEE Xplore to compile our re-
sults according to the PRISMA 2020 guidelines. Dr. Abeer
Abdullah Alsadhan who make research via Scopus and
ScienceDirect, while the Mahmood A. Al-Shareeda who
perform research article from IEEE Xplore. With those
authors, the paper is selected and studied. To resolve the
differences in the plan, we presented the work to two ex-
pert arbitrators: Murtadha A. Alazzawi was Department of
Computer Techniques Engineering, Imam Al-Kadhum Col-
lege (IKC), Baghdad 10001, Iraq, while the second referee
was Badiea Abdulkrem Mohammed who Department of
Computer Engineering, College of Computer Science and
Engineering, University of Ha’il, Ha’il 81481, Saudi Ara-
bia. Identifying VANET-related research studies, such as
’Vehicular Ad-hoc Networks’, a set of keywords was used,
including, ’VEINS OMNeTPP’, ’vehicles in network simula-
tion’, ’VEINS’ ’VEINS/OMNeTPP’, ’OMNeT++/VEINS’,
’OMNeT++ VEINS’, ’VEINS simulator’, ’VEINS frame-
work’ and ’VEINS model’ in various combinations and
combined with the ’OR’ and ’AND’ operators, followed by
’Vehicular Ad hoc Network’ ,’Vehicular Ad-hoc Network’
OR ’VANET’. Figure 2 shows the query text. The powerful
search tools of the database systems allowed us to exclude
irrelevant short articles, essays, and book chapters. We also
looked into the most recent research on the article’s topic, the
skyrocketing popularity of the VEINS/OMNeT++ method in
vehicular systems.
D. ELIGIBILITY CRITERIA
The papers that satisfied our standard presented in Figure
2 were contained. The initial objective was to classify the
research involved in VEINS-established studies for VANET
into one of three broad categories. It was extrapolated freely
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Al-Shareeda et al. et al.: Safety-based VEINS model in vehicular ad-hoc networks (VANET)
FIGURE 2: Study Selection Flowchart with Search Terms and Eligibility Requirements.
from the ante-survey literature. After three rounds of filtering
and screening, only papers that meet the requirements remain
in the first round. Studies for VANET that did not use VEINS,
articles focusing on security or services, and articles written
in languages other than English were all disqualified. Articles
that did not meet the requirements for use in VEINS were
therefore not included.
E. DATA COLLECTION PROCESS
All of the articles that made the cut were sorted into Excel
and PowerPoint files according to their initial category for
easy reference during the screening and filtering processes.
The authors took the time to read each article’s full text.
So, the proposed taxonomy executes a classification of all
the articles based on the vast comments and highlights on
the connected articles. In addition, taxonomy was utilised to
organise the thousands of highlighted passages and handwrit-
ten notes. The taxonomy proposed various classes and sub-
divisions, with the core classes being Application, Solution,
and Network. Authors’ preferred writing styles were used to
categorise texts. In order to give scholars a complete picture
of how various studies and uses have evolved, they read and
analysed every article in every database available to them.
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Al-Shareeda et al. et al.: Safety-based VEINS model in vehicular ad-hoc networks (VANET)
III. RESULTS AND STATISTICAL INFORMATION OF
ARTICLES
After searching 640 publications using the first query, we
got the following results: 70 from IEEE Explore, 179 from
ScienceDirect, and 391 from Scopus. In this study, we used
the filtered papers published between 2011 and 2022 to create
three distinct categories. Only 68 of the 640 articles were
unique across the three databases. After reading the abstracts
and titles, we eliminated another 199 papers, leaving us with
373. Finally, the full-text scan omitted 93 articles, leaving a
final set of 280 articles related in some way to the application
of the VEINS framework in VANET technology. The major
research avenues into the VEINS framework and their gen-
eral application in VANET are displayed in a taxonomy as
shown in Figure 3. The overall scope of the topic is covered
by this classification. The taxonomy provides a framework
for imagining a variety of classes and subclasses.
FIGURE 3: A VEINS-Based Literature Taxonomy for
VANET Safety.
First, there are VEINS framework applications concerned
with VANET safety (23 out of 121 total articles). The second
group consists of pieces that focus on the answer (25/121
total). The majority of the articles in this section (73 out of
121) focus on the network.
Categorizing the reviewed articles by which taxonomy
items they have been assigned to as per our detailed tax-
onomy development previously in this study Taxonomy -
Applications, Solutions and Networks, as shom in Table ??.
The specific themes within each category are also further
broken down to produce an easy-to-follow, structured review
of multiple aspects relating to VEINS-based research as well
as the prevalent focus points and trends of interest among
particular subcategories.
•Applications: In this category the focus is to use VEINS
for safety- critical applications like collision avoidance,
emergency vehicle prioritization and road hazard detec-
tion.
•Solutions: Solutions are then evaluated, for example by
utilizing VEINS to test solutions regarding vehicular
network challenges (e.g. traffic jams; resource alloca-
tion and intersection control). This will give researchers
the opportunity to test these solutions in normal traffic
and under real communication conditions.
•Networks: in this category, network layer simulation
models such as VEINS can be used to evaluate various
routing protocols and data dissemination strategies or
MAC layer optimizations that are proposed for commu-
nication performance improvement of VANET.
TABLE 2: VEINS usage in Applications, Solutions, and
Networks
Category Description Examples of VEINS Use
Applications In vehicular networks,
VEINS is used to simulate
many safety-critical
applications like collision
avoidance, emergency
vehicle prioritisation and
road hazard detection.
Emulating V2V communi-
cations for collision avoid-
ance, emergency vehicle
preemption in traffic, road
hazard warning dissemina-
tion·
Solutions VEINS provides a way
for evaluating solutions to
these problems in vehic-
ular networks like traf-
fic congestion, resource al-
location and intersection
management.
V2X Communication: —
Testing Traffic Manage-
ment Solutions, Conges-
tion Control Mechanisms
and Dynamic Resources
Allocation.
Networks In VEINS to model
the network layer, such
as routing protocols
and data dissemination
strategies and MAC layer
optimization for vehicular
networks.
Simulating routing proto-
cols (AODV, DSR), eval-
uating data dissemination
schemes for VANET; opti-
mizing MAC protocols for
real-time V2G/VGV com-
munication
A. APPLICATION
This stage takes a closer look at one of the subcategories in
Applications on our taxonomy. This with a look at the kind of
safety application in VANET which VEINS can be employed
for: risk prediction, road secureness making sure etc. This
overview, therefore, is key to giving insights on the practical
applications of VEINS and types safety scenarios which have
been solved through simulation in it.
•Risk Assessment: By obtaining local data coming from
infrastructures or surrounding vehicles, a global risk
indicator was proposed in Highly automated driving
(HAD) [14]. To satisfy completeness, reliability, and
higher robustness, the fuzzy risk-based decision model
was proposed by [15]. The Emergency Vehicles EV
warning system was outlined and designed based on V2I
communication to reduce the risks of accidents [16].
•Road Safety: By using two mechanisms of game-
theoretical, a decentralized stochastic solution was pro-
posed to address the data dissemination problem [17].
An Adaptive Neuro-Fuzzy Inference System (ANFIS)
applied was propped for obtaining a forecast pattern of
security index [18]. A cooperative overtaking assistance
persistent system was proposed to better avert the pos-
sibility of collisions during overtaking maneuvers [19].
The performance of the Vehicle-to-UAV (V2U) com-
munication was investigated to identify the traffic flow
requirements over the road network for turning frac-
tion estimation and traffic counting [20]. A Cooperative
Road Hazard Detection Persistent System (CopRoad-
HazDPS) was proposed to identify a road hazard based
on V2V, and V2I communications [21]. The Dynamic
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Traffic Assignment problem (DTA) was presented to
dynamically distribute vehicles efficiently on the road
network [22].
•Emergency Alert: Under terms of particular conditions
of road traffic in Zilina city, the performance of L2
broadcasting and L3 routing was evaluated to trans-
mit emergency warning messages [23]. A Dynamic
Partitioning Scheme (DPS) scheme was analyzed and
implemented in both light traffic, and dense scenarios
[24]. The response time of the emergency vehicles was
decreased by changing the status of the traffic lights with
using communication technologies [25]. To deal with
passengers under conditions of abnormal health, a co-
operative health intelligent emergency response system
was proposed to reduce the receiving the initial emer-
gency treatment time [26]. An automatic emergency
corridor framework was proposed to deal with such
emergency situations for speedy clearance of emergency
vehicles [27]. By using several sensors, a new approach
was designed to collect data during vehicle trips for
emergency situations or advising dangerous [28]. Based
on the situation, the utilization of a decision-making
module was explored for accident situations which pro-
cesses information [29]. A congestion control approach
was proposed to use the number of detected collisions as
a metric for controlling the beacon creation frequency
and thus minimize the congestion effect [30]. The dy-
namic backbone-assisted MAC (DBA-MAC) scheme
was proposed to support efficient and fast multi-hop
broadcast communication in VANETs [31]. A Enhanced
Priority VANET Scheme (EPVS) was proposed accord-
ing to data type and reliable distance range [32].
•Person Protection: In order to bidirectional dependency
between mobility and communication, OMNeT++ and
Vadere were connected to updates in communication
cause to update in mobility, and vice versa [33].
Ambulance-to-Vehicle communication (A2V) was pro-
posed to implement communication between ambu-
lances and other non-emergency for enhancing res-
cue missions [34]. A Hybrid Pseudonyms Distribution
Method (HPDM) was proposed to protect the location
privacy of drivers based not only on RSUs but also
on vehicles for performing the pseudonyms distribution
[35]. Prior to the maneuver beginning, the overtaking
assistant was proposed to predict whether a collision
would occur and warns the driver [36].
B. SOLUTION
This step deals with the Solutions subcategory of this cate-
gory where we have discussed how different problem state-
ments in VANETs has been addressed using VEINS. It
encompasses solutions for intersection management, traffic
congestion and resource allocation (e.g. parking spaces).
From these solutions, we can see what VEINS gives for
future analysis of the work and thus may help researchers
to improve traffic management and safety in vehicular net-
works.
•Intersection Management: The performance of a dis-
tributed traffic control approach was introduced to an-
alyze the dynamics, and the efficiency of the recently
proposed traffic control protocol [37]. In order to en-
hance safety at intersections, the utilization of real-
time databases and Vehicle-To-Vehicle (V2V) commu-
nication were investigated for reducing the calculation
time, and mitigating collision risks [38]. A decentral-
ized congestion control protocol was presented to track
accuracies needed by Intelligent Transportation System
(ITS) applications [39]. The propagation of a count
request message-based algorithm for counting vehicles
stopped at a traffic light [40].
•Traffic Congestion: In order to decrease travel time
and reduce the overall emissions of CO2, a traffic
congestion detection system was discussed in terms
of the evaluation and simulation by [41]. Vehicular
traffic congestion was identified and reduced by [42].
To quantify and detect the traffic congestion level, an
algorithm was designed in a completely distributed way
to enable each vehicle in the network [43]. According
to vehicle-to-RSU (V2R) and vehicle-to-vehicle (V2V)
communications, two novel approaches were proposed
to identify a more suitable candidate for traffic man-
agement [44]. A security protocol and a high-level
system architecture were specifically designed to detect
congestion according to vehicle-to-infrastructure (V2I)
type communication [45]. In the transport system, a
TRAFFIC solution was proposed to estimate the level
of congestion for maximizing the vehicle traffic flow
[46]. In an urban smart city environment, an electric
vehicle charging management and a novel dynamic
traffic congestion pricing system was proposed for the
internet of vehicles by [47]. A vehicle-to-vehicle con-
gestion avoidance mechanism was introduced to reroute
vehicles when detecting levels of real-time congestion
to minimize their trip times [48]. According to the
map of the city is categorized into a servers hierarchy,
a Distributed Infrastructure-Free Traffic Optimization
System (DIFTOS) was proposed in urban environments
[49]. A Multi-metric Power Control (MPC) approach
was proposed to handle the trade-off between control-
ling channel congestion and providing sufficient cov-
erage [50]. An algorithm was proposed in order to
resolve congestion and car CO2 emission issues by
executing the re-routing process [51]. A scalable and
novel dynamic route planning approach was proposed
to utilize information of real-time traffic for updating
vehicle routes [52]. Cognitive radio technology was
presented and simulated for achieving the throughput
demands of vehicular communications [53]. An INCI-
DEnT solution was proposed to detect, disseminate and
control congested roads to reduce the average trip time
[54]. A solution was proposed to detect and control con-
VOLUME 4, 2016 7
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Al-Shareeda et al. et al.: Safety-based VEINS model in vehicular ad-hoc networks (VANET)
gested roads by decreasing fuel consumption, average
trip time, and CO2 emissions according to inter-vehicle
communication [55]. A new method was proposed to
assign a Current Traveling Time (CTT) for each city’s
street to find the fastest route from origin to destination
in VANETs communication [56].
•Resource Allocation: The resource allocation scheme
was presented by highlighting the presence of severe
impairments, and initial results for platooning appli-
cations [57]. Besides, without supporting the external
infrastructure in the vehicular network, resource sharing
was facilitated through mobile cloud [58]. A relevance-
aware resource allocation mechanism was proposed
under congested channel conditions for decentralized
vehicular networks [59]. Based on a multi-hop fashion,
a proximity-based broadcast service was designed in
network-controlled device-to-device (D2D) communi-
cations [60]. Without supporting the external infrastruc-
ture in a vehicular cloud, an allocation resource and
efficient search protocol was proposed by [61].
C. NETWORK
Finally the last step reviews all potential Articles that were
collected during Networks: research in VANET technical
aspects such as Physical Layer, MAC Protocols, Routing
protocols (or Data Dissemination), Congestion Control. The
review helps to understand the network technologies and
protocols at work in the underlying vehicle-to-vehicle com-
munication employed by VEINS for optimal data exchange.
•Physical Layer: Based on the key communication pa-
rameters in terms of the power of transmitting and the
frequency of beacon, the packet loss, and the end-to-end
delay were studied as the two major factors affecting
the performance of the mobile nodes [62]. An RA-
TDMAp protocol was proposed to put together prop-
erties of CSMA/CA and TDMA-based overlay proto-
cols for Vehicles Platooning [63]. The two Markovian
models were proposed for analyzing the performance
of IEEE 802.11p EDCA mechanism for entertainment
applications of vehicle-to-vehicle (V2V) [64]. In rural
and urban environments, a Line-of-Sight (LOS) prob-
ability model was proposed by [65]. A hybrid han-
dover algorithm was proposed to combine the Long-
Term Evolution (LTE) networks and multihop clustering
based on IEEE 802.11p for satisfying a low end-to-
end delay and high data packet delivery ratio (PDR)
[66]. The beaconing performance of IEEE 802.11p was
analyzed for the use of control channel by [67]. To
improve Received Signal Strength (RSS) for VANET, a
supplementary lower frequency, e.g., around 700 MHz
was demonstrated by [68]. Based on the channel load
and the vehicle traffic, CCH time interval and the con-
tention window size were tuned adaptively by [69].
The Vehicular Cyber-Physical System (VCPS) perspec-
tive was investigated by [70]. The two-ray interference
model was implemented by [71].
•Medium Access Control (MAC) Protocol: A Car-
rier Sense Multiple Access with Collision Avoidance
(CSMA/CA) and two TDMA-based overlay protocols
were taken to carry out extensive simulations with
transmit power, occupied lane numbers, and sizes of
the varying platoon for deducing empirical models
[72]. By using an application-oriented metric, the MAC
protocol responsiveness was evaluated and presented
to limit the semi-persistent collision time in a large-
scale system-level simulation [73]. Scheduling based
on the TDMA scheme was proposed for infrastructure
communications that dynamically reallocates unused
TDMA slots [74]. In highway and urban environments,
the MAC protocol based on congestion behavior of
1609.4/IEEE802.11p was presented by varying vehi-
cle density [75]. A beacon messages-based schedul-
ing algorithm was proposed by adjusting road traffic
based on the CCH interval and scheduling the priorities-
based safety messages [76]. With CCH interval set-
tings and various vehicle densities, an evaluation of the
802.11p MAC protocol was proposed by [77]. Based
on load conditions and vehicle density, an extended
MAC protocol was proposed to dynamically adjust the
length of SCH, and CCH intervals [78]. A connection-
level scheduling algorithm was presented to schedule
the start sending time of each transmission [79]. The
MAC protocol was developed in an urban environment
[80]. According to real-time communication traffic con-
ditions, adaptive multi-channel assignment based on
safety communication was proposed to allow flexible
multi-channel usage [81].
•Routing Protocol: A modified hybrid routing scheme
was proposed by combing a greedy forwarding system
with Advanced Greedy Hybrid Bio-Inspired (AGHBI)
to enhance the performance of the system [82]. The
multi-objective Harris hawks optimization algorithm
based on a 2-Hop routing algorithm was proposed for
choosing the best path among vehicles [83]. A routing
method was proposed for revealing the relationship be-
tween whole node density and network connectivity and
realizing interconnections between the source and des-
tination vehicles in an urban scene [84]. An Enhanced
Greedy Perimeter Stateless Routing (GPSR) protocol
was proposed by introducing a multipath feature for de-
creasing packet loss [85]. To reduce stoppage time and
average delay waiting in VANET, an efficient routing
algorithm was optimized by [86]. A Mobility Prediction
Based Routing Protocol (MPBRP) was developed by
utilizing the user’s intention gathered from the posi-
tioning systems for path recovery, packet transmission
and neighborhood detection [87]. A novel probabilistic
multimetric routing protocol (ProMRP) was proposed
to obtain a suitable performance in terms of average
end-to-end packet delay and the average percentage of
packet losses for VANETs [88]. Without any changing
on the value of beaconing frequency, the position of
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Al-Shareeda et al. et al.: Safety-based VEINS model in vehicular ad-hoc networks (VANET)
nodes and the operation of the routing protocol were
improved for selecting the next forwarding node [89].
Based on active multicasting for efficient multime-
dia dissemination, a multicriteria adaptive opportunistic
tree cast routing protocol (MAOTRP) was proposed
to adapt the route selection mechanism in vehicle-to-
vehicle telescreen (VVT) [90]. By using buses as the
major relay to deliver messages, a bus-trajectory-based
street-centric (BTSC) routing algorithm was proposed
by using two novel concepts, i.e., path consistency
probability and the street consistency probability [91].
A vehicle rerouting mechanism was proposed for ac-
cident situations via scenario simulations of VANET
[92]. A unicast routing protocol based on the quality of
services was proposed by using an artificial bee colony
algorithm, and clustering algorithm for vehicular net-
works [93]. During previous route discovery, a beacon
information independent geographic routing algorithm
was presented to reduce the broadcast’s numbers for
forwarding the data packets [94]. In terms of network
performance and network reliability, an efficient rout-
ing protocol GeoWave was presented to minimize the
Carbon Dioxide (CO) and address the traffic blockage
in high traffic density of urban area scenarios [95].
According to the contention-based geographic routing
protocol, a Dual-Mode Optimum Distance (DMOD)
routing protocol was presented for serving applications
of unicast messaging [96]. By utilizing entropy change
and selective flow sampling, a novel approach was
proposed to perform real-time detection of node mis-
behavior [97]. Geographical routing protocols based on
two communication modes were introduced to attain an
anticipated charging slot reservation for VANETs [98].
•Data Dissemination: A realistic mobility model based
on Bus dissemination was proposed by utilizing a bi-
directional coupled technique for service discovery pro-
tocol that uses the networks of public bus [99]. An
emergency packets dissemination scheme was proposed
by vehicles fitted with both cellular LTE wireless and
DSRC abilities [100]. For different types of channel
conditions, an reliable emergency message dissemina-
tion (REMD) scheme satisfies predefined reliability for
message dissemination while achieving delay require-
ments [101]. For efficient information dissemination,
counter-based, probability-based and simple flooding
techniques were implemented to need in broadcasting
techniques to cater to the network required [102]. In
the case of normal and hazardous traffic messages, a
forwarding scheme was proposed to improve the irre-
sponsible forwarding probability by using the adaptive
broadcast range for specific applications [103]. The se-
lective forwarding mechanism-based data dissemination
technique was proposed by [104]. A data transmission
control system was proposed for Spatio-temporal data
(STD) retention in low vehicle density environments
[105]. A fully distributed protocol was proposed for
dissemination of collection and query of reply messages
carrying data collected from vehicles moving in a given
destination region in an urban scenario [106]. A lo-
cal knowledge-based data dissemination protocol was
proposed for achieving fairness with Nash Bargaining
concepts from game theory [107]. An infrastructure-
less Geocast protocol was proposed to forward mes-
sages only to vehicles in the relevance zone with a
lower overhead cost [108]. A DISCOVER protocol was
proposed to collect and disseminate the interest data
in a massive city area timely and efficiently by utiliz-
ing an individual network structure [109]. According
to traffic regime estimation, a multi-hop-based data
dissemination approach was provided to offer scalable
broadcast without further communication cost [110].
A data dissemination protocol based on the Markov
chain was proposed by [111]. Under sparse and dense
networks, a Data dissemination pRotocol In VEhicular
networks (DRIVE) was proposed to deliver messages
[112]. In an efficient way, An Adaptive Data Dissemina-
tion Protocol (AddP) was proposed to provide reliability
to message dissemination [113]. A map splitting-based
data dissemination protocol was introduced by [114].
According to communication metrics and vehicular traf-
fic, the influence of road traffic warning message dis-
semination strategies was evaluated by [115]. A Road-
Casting Protocol scheme was proposed to distribute the
contents of high-definition video [116]. By utilizing
only local one-hop neighbor information, an Adaptive
Beacon-based Data Dissemination (ABDDis) scheme
was proposed [117]. A Content-Centric Networking
(CCN)-based vehicle-to-vehicle (V2V) communication
scheme was proposed on top of DSRC [118]. For giving
a higher priority inside special forwarding zones, a new
broadcast suppression mechanism based on geographic
was proposed to rebroadcast to nodes [119]. A passive
data dissemination approach based distributed system
was implemented by [120]. A CarAgent was presented
as a message exchange protocol for collecting and dis-
seminating floating vehicular data [121]. A two-purpose
structure was presented to simultaneously identify the
path of shortest travel time for efficient packet dis-
semination, and a fast-moving vehicle [122]. Virtual
backbone based on minimum stable CDS was proposed
to ensure guaranteed fast and efficient broadcasting
[123]. Based on dissemination protocol, an effective
mobile content delivery solution was proposed for Con-
tent Delivery Network (CDN) [124]. To address the
broadcast storm and network partition problems, a novel
story–carry–forward (SCF) scheme was proposed [125].
A Precise Point Positioning (PPP) based self-sufficient
Cooperative Positioning (CP) system was introduced
without requiring infrastructure [126].
•Congestion Control: According to a density estimation
derived from the driving speed of a vehicle, a new
distributed congestion control algorithm was proposed
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Al-Shareeda et al. et al.: Safety-based VEINS model in vehicular ad-hoc networks (VANET)
for manipulating the transmission power [127]. A De-
centralized Congestion Control (DCC) reactive control
approach were investigating to control channel load
[128]. According to the current communication traffic
condition, an adaptive multichannel approach was pro-
posed to allow flexible multichannel usage [129]. A dis-
tributed cross-layer congestion protocol was presented
to combine and map onto congestion levels [130]. For
traffic congestion control of low communication over-
head, a DisTraC protocol was proposed to minimize
the vehicle’s average travel time [131]. According to
selfish players requesting high data transmission rates,
a non-cooperative game approach was proposed to in-
crease the network performance, and reliability [132].
Based on the concept of contention window and hybrid
power control, a new congestion control approach was
proposed to ensure safe and reliable communications
within the vehicle network [133]. Based on collective
perception for sharing sensor data, A decentralized con-
gestion control investigated the effect in dense traffic
scenarios [134].
IV. DISTRIBUTION RESULTS
Here, you can see how the taxonomy’s subcategories and ar-
ticle sources affect the distribution of articles across different
topics.
A. DISTRIBUTION BY DATABASE SOURCE
This step includes an examination of the distribution in
database sources containing articles screened. Looking at the
counts of papers downloaded from IEEE Xplore, Science
Direct and Scopus we learn about both how much research
is out there as well as what each database contributes to the
combined knowledge pool on VEINS-based VANET safety
applications.
Figure 4 reveals that numerous scholarly publications can
be found in the three databases. The essay divides every-
thing down the middle into three sections: the applications,
the solutions, and the networks. The IEEE Explore team
read and analysed seventeen papers on the topic of security.
Twenty-nine of the papers that were analysed may be found
on ScienceDirect. Seventy-five articles were analysed from
Scopus. As shown in Figure 4, the total result of studied
articles from Scopus is 75, which decreases by 75−17
75 ≈78%
and 75−29
75 ≈62% receptively, against IEEE Explore and
ScienceDirect database sources.
B. DISTRIBUTION BY SUBCATEGORIES IN THE
TAXONOMY
The percentage of studies represented by their subheadings
in this work’s taxonomy is displayed here. Figure 5 displays
the distribution of subcategories in the taxonomy based on
the searched database sources. The term in the taxonomy
is broken down into three main categories: applications,
solutions, and networks for security. Each main category has
several more specific ones. Future research in this area can
FIGURE 4: Number of Studied Articles in Varying Taxon-
omy Based on Database Source.
be organised into these categories. This means that many
researchers may use these studies as a springboard for their
own work.
FIGURE 5: Distribution of Subcategories in the Taxonomy.
A total of 23 articles from the application were analysed
for their potential impact on safety. This number includes 3
risk assessment articles, 6 road safety articles, 10 emergency
alert articles, and 4 articles aimed at protecting individual
users. Four papers on intersection management, sixteen on
traffic congestion, and five on resource allocation make up
the twenty-five articles examined from the solution. There
were a total of 73 articles from the network that were
analysed; 10 pertained to the physical layer, 10 pertained
to the media access control protocol, 17 pertained to the
routing protocol, 28 pertained to the data dissemination, and
8 pertained to congestion control.
V. VEINS
Researchers can use VEINS [135] for OMNeT++ to run
simulations that incorporate communicating road vehicles as
either the principal subject of the investigation (as in the case
of VANETs) or as a component (as in the case of ITS). As
an open-source project, it can be downloaded, altered, and
used without cost. With the release of VEINS 5.2, the model
library now includes a full suite of simulators for analysing
WLAN connections between stationary infrastructure and
10 VOLUME 4, 2016
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Al-Shareeda et al. et al.: Safety-based VEINS model in vehicular ad-hoc networks (VANET)
moving vehicles, such as trucks and autos. To this end,
VEINS employs a sophisticated model of the entities found in
the IEEE 802.11 MAC layer, which is used by specifications
like the IEEE Wireless Access in Vehicular Environments
(WAVE) [136]. Veins’ adaptability stems from its modular
design, which makes it suitable for simulating a wide range
of communication and transportation technologies, such as
mobile broadband of Long Term Evolution (LTE) [137] and
Visible Light Communication (VLC) [138].
A. HISTORY OF VEINS
The INET Framework version 20-10-2006 add-on VEINS
saw its initial public release in the early part of 2006. Due
to limitations in the quality of wireless channel modelling at
the time, VEINS was adapted to be an extension of MiXiM
for its 1.0 release. After then, VEINS 2.0 was built from the
ground up to take use of newer, more advanced paradigms
like WAVE, IEEE 1609.4, and IEEE 802.11p. As more effort
was done to rebuild and rewrite the channel models, VEINS
3.0, which was kept compatible with mixed simulations em-
ploying paradigms from the INET framework, developed into
a valid fork of MiXiM. Up until the 6.0 release, VEINS had
been steadily streamlined and more of the aforementioned
paradigms relating to contacting street cars were added. This
version is compatible with SUMO 1.8.0 and OMNeT++ 6 (up
to the most recent 6.0 pre15 version). There is an online list
of all compatible devices in http://veins.car2x.org/.
B. DESIGN AND ARCHITECTURE WITH TWO-WAY
COUPLING
While it could be expected that VEINS would include spe-
cialised mobility variants of road vehicles, this is not the
case. Instead, it relies on simulations tied to a dedicated, inde-
pendently running traffic simulator. Veins can take advantage
of the years of research and development that have resulted
in totally functional instruments for simulating road traffic.
To complement the Simulation of Urban MObility (SUMO)
traffic simulator, VEINS was developed [139]. SUMO can
model large-scale urban and intercity road systems, including
motorways. They allow riders to simulate the experience of
riding a train, bicycle, scooter, or even a car or truck. SUMO
accepts input from a wide range of mobility models, has a
number of available intersection controllers, and can process
data from several different road network formats.
C. THE MAC AND PHY LAYER
One of the most important parts of Veins is the complex
model it provides of low-level IVC. Most existing IVC pro-
grammes and infrastructures require examination. That’s why
it’s important to do a detailed simulation at the packet level
using accurate representations of the technology being tested
[140]. When talking about transport networks, IEEE WAVE
(or ETSI ITS-G5 in Europe) is often brought up as the tech-
nology of interest. The IEEE 1609.4 specification for multi-
channel operations with the IEEE 802.11p MAC and PHY is
the backbone of this collection of standards. Veins prioritises
the lower layers because they are responsible for channel
access and packet transport even though any of the other
standards and implementations can be used [141]. Additional
protocol layers from the several ITS protocol stacks in use
across the world can be modelled with additional simulation
tools (not provided by Veins but freely available online) in
order to provide a more accurate global picture such as ARIB
T-109 [142]) can be built upon this base.
Any node, be it a car, a roadside device, a pedestrian,
or a cyclist using wireless communications, needs at least
an 802.11p Network Interface Card (NIC) to communicate
with other nodes. This NIC, a composite model consisting
of the MAC and PHY layers, has an immediate connection to
higher-level abstractions. This results in a simple APP-MAC-
PHY architecture for each node in Veins. The veinsmobility
module is in charge of updating the vehicle’s position. A
roadside unit’s mobility would be represented by the static
value "textit":"BaseMobility." OMNeT++ is designed such
that any two linked modules can exchange data with one
another. Plain text messages and (encapsulated) packets of
any specific message format (such as Wave Short Messages
or Wave Service Advertisements, for example) descend from
cMessage* and can be used here. "Regular" communications
within a node are transmitted to higher or lower levels,
whereas "control" messages instruct the receiving layer to
perform some action. Depending on the kind, the receiving
layer will carry out a certain action. The physical layer is
solely connected to the MAC layer and to the outside world.
1) MAC and Upper Layers
The simulation’s MAC layer needs to be as accurate a rep-
resentation of the simulated system as possible [140], [143].
Specification of IEEE 802.11p MAC and IEEE 1609.Veins
incorporates IEEE 802.11e Enhanced Distributed Channel
Access (EDCA) with four distinct access classes and features
four layers that allow for multi-channel operation, channel
switching, the transmission of broadcast and unicast mes-
sages [136]. The curious should check out IEEE 802.11e
and IEEE 802.11p [136], [140], [144] as well as the actual
standardisation publications [145] and [146] in order to give
a complete account of what’s going on. Researchers are able
to conduct a wide range of simulation studies, including
comparisons of wireless network performance, thanks to
the granularity of Veins’ MAC and PHY layer architecture
[147], investigating the potential of a wireless network for
enhancing vehicle cooperation and safety [148], or using a
simulated platoon analysis [149].
•Transmitting a Packet: The WaveShortMessage channel
information and user priority information that will be
translated to an EDCA queue are required to be sent
down to the MAC layer from higher layers (such as the
application layer).
•Receiving a Packet: The MAC layer’s function in re-
ceiving packets is relatively straightforward but crucial.
If the PHY layer transmits a Mac80211Pkt, the MAC
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Al-Shareeda et al. et al.: Safety-based VEINS model in vehicular ad-hoc networks (VANET)
will check if the destination address is the layer two
broadcast address or if it matches its own MAC address.
2) The Wireless Channel and Physical Layer
The advantage of using packet-level simulator is being able to
(almost) realistically identify if each packet may be received
successfully. The position of the transmitter and receiver
in space, the characteristics of the antennas, the presence
of obstructions in the line of sight, and interference from
other transmitting nodes are some of the variables affect-
ing packet decoding. While Carrier Sense Multiple Access
with Collision Avoidance (CSMA/CA) greatly minimizes the
possibility of two neighboring nodes sending simultaneously
(i.e., they can hear each other), it does not provide a fix for
the concealed terminal issue [150]. Veins are able to record
all of these impacts. We shall describe the PHY’s functions
in this section.
•Analogue Models: To be capable to hand send data to
the recipient nodes, the OMNeT++ connection manager
keeps track of a connectivity map. A copy of the trans-
mitted packet is sent to every node that lies within the
configurable interference range of a broadcasting node.
Therefore, it is the node’s job to determine if this packet
was successfully received. The definition of an inter-
ference range, which establishes an artificial domain
beyond which no radio broadcast requires to be regarded
as interfering, is solely a matter of optimization.
•The Decider: The airframe is given to the Decider, an
external class that identifies if packets can be properly
decoded after all loss models have been applied. This
data does not have ability to set the channel to busy if the
received power is less than the configurable Clear Chan-
nel Assessment (CCA) sensitivity. No warning will be
sent to the MAC layer. The decider determines whether
the node is currently transmitting or receiving another
packet if the packet is greater than the CCA threshold.
Both times, the packet won’t be able to be decoded.
D. VEINS WORKS
In this subsection, we use VEINS (Vehicles In Network
Simulation), an open-source simulation framework that was
specially developed for simulating vehicular ad-hoc networks
(also known as inter-vehicle communication systems). By
combining the SUMO (Simulation of Urban MObility) traffic
simulator and the OMNeT++ network simulator, Veins offers
a realistic simulation environment that models both vehicular
mobility and vehicle-to-vehicle or infrastructure-based com-
munication.
1) Architecture of VEINS
VEINS is developed upon the modular architecture, consist-
ing of two major simulators that are coupled to offer extensi-
ble among functional simulator. The architecture utilizes the
well known SUMO and OMNeT++ simulation environments
to focus on specific aspects of vehicular simulation:
a: SUMO (Simulation of Urban Mobility)
SUMO is a free and open traffic simulator, it allows the
simulation of micro-simulation models in road networks. It
gives direct access to vehicle locations, traffic conditions and
actual trajectories of single vehicles. The flexibility which
SUMO provides is indispensable to present real world traffic,
ranging from urban intersections up until highways with a
high detail of complexity. Features of SUMO include:
•Lane changing behavior: SUMO provides vehicle’s lane
change in real time by understanding the traffic density,
road conditions and signalling system.
•Speed Control: SUMO calculates the road conditions
and speed limit for vehicles, as well as when they should
be moving forward (e.g., karma) or staying in place.
•Route Planning and Decision Making: SUMO has an
improved routing algorithm, that calculates route dy-
namically depending upon real time traffic details, road
closures and signal plans.
It provides a realistic mobility model for tracking vehicular
movement (e.g. cars, buses and trucks) which can be used as
input to vehicle communication systems in network simula-
tors such OMNeT++ through its SUMO simulation module.
b: OMNeT++
OMNeT++ is a component-based, modular C++ simulation
library and framework, primarily for building network sim-
ulators. VEINS environment: Communication between vehi-
cles (V2V) and between vehicles and roadside infrastructure
(V2I) is managed by OMNeT++. IEEE 802.11p is a vehicular
communication standard which have been accepted by afflu-
ence line of researches due to its potential capability to offer
low-latency vehicle-to-vehicle communications from above
set of wireless nodes. OMNeT++ features
•Message Transmission and Reception: OMNeT++ is
used for simulating the message transmission between
vehicles, as well as communication process between
infrastructures. Such messages may carry information
about the position of vehicles, traffic announcements or
instructions for switching traffic signals in intelligent
transport management systems.
•In particular, OMNet++ includes aspects of signal
strength, fading and interference in vehicular commu-
nication which gives a good clue on how this type of
network works.
•Vehicular Communication Protocols: different commu-
nication protocols and scenarios, such as unicast, multi-
cast and broadcast communications required for optimal
V2V (vehicle-to- vehicle) come up with V2I interac-
tions can be simulated.
The combination of these two simulators allows VEINS to
provide a simulator which on one side synchronizes network
and vehicular movement simulations, offering a complete
tool for the experimentation in different scenarios with ITS.
12 VOLUME 4, 2016
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Al-Shareeda et al. et al.: Safety-based VEINS model in vehicular ad-hoc networks (VANET)
2) Working Mechanism
VEINS uses a dual communication layer between SUMO
and OMNeT++ to properly couple the two frameworks. This
integration makes possible the co-simulation of mobility
(movement) and communication in vehicular networks, with
each simulator affecting the other.
a: Data Flow from SUMO to OMNeT++
The vehicle mobility data is reported by SUMO to OMNeT
+++ which uses this information for simulating network
communications. OMNeT++ updates the position, speed and
direction of each vehicle at every time step which can be
used to calculate signal strengths and interference levels as
well chances a communication success between vehicles. For
instance:
•Information Flow: When a vehicle approaches an event
in the traffic system, SUMO sends location updated
to OMNeT++, which subsequently broadcasts hazard
warning message through V2V channel for wider dis-
semination among surrounding vehicles.
•Route selection: As vehicles traverse, the position and
communication range of other interacting vehicle or in-
frastructure is calculated on OMNeT++ to depict signal
strengthening.
b: Data Flow from OMNeT++ to SUMO
For the other direction, OMNeT++ can decide on vehicle
behavior in SUMO via network communications. This could
include rerouting a vehicle around an accident or traffic
congestion, slowing the speed limit on certain roads based
upon channelized message from 511 signals indicating immi-
nent warning and changing how some vehicles operate given
information broadcast to those within receiving range. This
leads to a dynamic interplay between the two simulators:
•Route Modifications: Vehicles can modify their routes
based on real-time traffic data obtained from communi-
cation through OMNeT++ which guide them to avoid
congestion and accidents.
•Speed changes: communication of road information
related to speed or traffic lights may vary the vehicle
speed, lane and intersection priority.
It is crucial that the synchronization mechanism between
SUMO and OMNeT++ in interacting over VANETs for
simulating such complex real-time vehicle mobility-network
communications interoperations.
3) Key Components of VEINS
It depends on the following key elements of the VEINS
framework, which allow realistic simulation of vehicular
networks.
a: Mobility Models
The SUMO database has advanced mobility models that
show the vehicular movements as in real-life. They were
traffic flow models that provide predictions on how vehicles
would behave—such as speed, acceleration, lane changes and
routes taken. The accurate simulation of vehicle movement is
required to get a realistic traffic environment, based on which
communication models in OMNeT ++ can be created. Key
features include:
•Traffic Signals and Intersections:SUMO enables to
model complex traffic environment with intersec-
tions,traffic lights (signals),stop signs simulating the
behavior of vehicles in urban areas.
•Real time Traffic Flow: SUMO is capable of modifying
vehicle paths based on real time traffic, and simulate
over congestion and rerouting.
b: Network Models
This is also a modeling framework that describes the network
layer, simulating how vehicles communicate not only with
other cars but also roadside infrastructure as well. These
network models include:
•With the support of IEEE 802.11p, OMNeT ++ can
simulate a MAC protocol that is used for vehicular com-
munication to provide low-latency and high-reliability
adaptations based on its specifications. Which enables
to simulate safety-critical applications like collision-
warning and emergency-braking alarms.
•Routing Algorithms: OMNeT++ can simulate different
routing algorithms employed in vehicular ad-hoc net-
works (VANETs) which determine the way messages
are distributed, as per requirement
c: Coupling Module
VEINS uses a coupling module that ties the SUMO and
OMNeT++ simulations together. This guarantees that alter-
native vehicle mobility data from SUMO continues to get
propagated into OMNeT++, and also means that network
communication events in OMNeT++ impact the on-board
vehicular behaviors in SUMO. This very tight bidirectional
coupling is what permits VEINS to simulating complex ve-
hicular communication scenarios with a high level of fidelity
4) Advantages and Applications
Due to the modular architecture of VEINS has strong flex-
ibility, scalability and is applicable to a broad spectrum of
vehicular network scenario. In other words: It is a Physical
Hardware in the Loop simulator, which can not only simulate
real vehicle mobility but also perform network communica-
tion with minimal delay; this facilitates for seamless develop-
ment and testing of several vehicular networking applications
such as :
•Collision Avoidance Systems: VEINS is a popular
framework mostly for congestion avoidance systems by
V2V communication. SUMO provides real-time vehicle
position data, allowing OMNeT++ to simulate the way
vehicles would communicate these safety messages (for
example, about a nearby car or sudden stop) in order to
be aware of them and avoid colliding.
VOLUME 4, 2016 13
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Al-Shareeda et al. et al.: Safety-based VEINS model in vehicular ad-hoc networks (VANET)
TABLE 3: Comparison of VEINS with other tools
Feature/Tool VEINS NS-3 OMNeT++ Alone SUMO Alone
Mobility Model-
ing
Lane-changing, speed control and
route planning at the limit (SUMO
leader in advanced mobility model-
ing)
Simplemobility along with easy
mobility models, does not simulate
intricate traffic behaviors
No Mobility support is
built in, it has to use exter-
nal tool such as SUMO for
mobility modeling
Microscopic traffic simu-
lation with lane changing,
speed control and traffic
lights support
Network
Communication
V2V and V2I communication de-
tail simulation with UMLF net-
work middle-ware (supports IEEE
802.11p) using OMNeT++
Vehicular networks: it focuses on
the communication layer which
support various protocols, from all
networking stack levels.
Supports Full
Communication Protocol
Simulation, Including
VANET But Without
Mobility Model
Lack of Inbuilt Network
Communication
Real-Time Syn-
chronization
Real-time sync in both sides for
traffic mobility and network com-
munication
Lack of real-time synchronization
between behaviour and communi-
cation efforts
It does not provide any
real-time synchronization
with traffic mobility simu-
lators.
Simulates only traffic, no
communication modeling
and synchronization with
network simulators
Use Case The best for modeling the move-
ment and communication relation-
ship, e.g. Conflict avoidance service
or emergency vehicle prioritization)
Suited for network-centric simula-
tions (e.g., test of networking pro-
tocols), while without complex mo-
bility scenarios.
Good for network protocol
simulation but a poor man
mobility interaction devel-
opment
Ideal for traffic flow anal-
ysis and planning applica-
tions, yet not feasible or
realistic enough to model
vehicular communication.
Scalability Extremely Scalable Multi Large-
scale Vehicular Networks Thus
Supporting Complex Traffic and
Communication Simultaneously
great for large networks, but not as
good in terms of simulating mobil-
ity.
It is scalable for network
protocols, but mobility as-
pects are missing and it
can be too expensive to
simulate large traffic.
Can be scaled to cover
large traffic scenarios, but
does not provide inbuilt
network communication
Flexibility Extremely flexible with modular
SUMO and OMNeT++ integration,
can be tailored for case-specific re-
quirements
Good for the developers working
on network protocols but limited in
getting real-world traffic dynamics
incorporated
General network
simulation flexibility,
but no built-in traffic (use
other tools)
Generalizable to traffic
modelling, but unable
to simulate vehicular
communication
Integration with
Emerging Tech-
nologies
Integrates with 5G, AI and
blockchain for advanced V2X
support
Enables integration with sophisti-
cated networking technologies, but
implies very little actual real-time
mobility interactions.
Advanced networking
technologies support
Nothing integrated with
traffic simulators
Neglects networking tech-
nologies, focuses entirely
on traffic dynamics
•Priority emergency vehicle: For emergency vehicles
such as those in the second picture, VEINS can simulate
how they communicate with traffic lights and other
vehicles to get out of way. In OMNeT++ we use network
models like this to actually test protocols that would
give priority to emergency vehicles and hence be able
respond more quickly for the safety of everyone on road.
•Road Hazard Detection: VEINS is a framework de-
veloped to simulate the propagation of hazard warn-
ings throughout a vehicular network. VEINS: It mod-
els how vehicles detect and report hazards (accidents,
road closures, weather conditions etc. . . ) which helps
researchers to evaluate the effectiveness of V2V / V21
communication systems in increasing safety on roads.
VEINS has the capability to simulate big vehicular net-
works along with its modular flexible architecture, making
it an essential tool for researchers and developers alike on
advanced vehicular communication systems (VCSs) which
is core component of Intelligent Transport Systems(ITS), as
well smart city applications.
E. COMPARISON WITH OTHER TOOLS
In this section, we compare VEINS with the other widely
used simulation tools including NS-3 and OMNeT++ when
not coupled with SUMO as well select a stand-alone SUMO
solution, as shown in Table 3. You can foil the simulated
networks using these tools, but they have strengths and
weaknesses in network simulation especially on vehicular-
focused scenarios. VEINS is unique in that it bi-directionally
integrates traffic mobility (using SUMO) and network com-
munication (using OMNeT++), thus making them more ap-
propriate to simulate real-world vehicular networking scenar-
ios interleaving both, whose movement can largely depend on
communications.
•VEINS vs. NS-3: NS-3 is a well-known (network)
simulator as it can be used to model different kind
of network protocols, among them those for vehicular
communication. However, it does not have high ad-
vanced mobility models but supports basic MOBILITY.
It restricts the use in situations that need realistic traffic
behavior simulations. VEINS on the other hand uses
SUMO to simulate traffic at a detailed level, thus it is
more compliant for projects which require both vehicle
movement and communication.
•VEINS vs. OMNeT++ Alone: OMNeT++ itself enables
powerfull simulation of network protocol, but not of-
fers mobility modeling by default. VEINS combines
OMNeT++ with SUMO, and provides a full simulation
environment in which vehicles move in the road network
while on-board communication algorithms are executed
over 802.11p (inter-car) as well as other IEEE chan-
nel models for data transmission. Such real-time syn-
chronization is required (e.g., collision avoidance and
emergency vehicle prioritization, which need precise
prediction on the future vehicular behavior).
•VEINS vs. SUMO Alone: It is an advanced traffic
14 VOLUME 4, 2016
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content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3476512
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Al-Shareeda et al. et al.: Safety-based VEINS model in vehicular ad-hoc networks (VANET)
simulator in the world but has no network communica-
tion functionality built into it. It is largely a tool used
in traffic flow studies and transportation planning. In
contrast, VEINS combines SUMO and OMNeT++ to
simulate vehicular communications (V2V/V2I) together
with detailed traffic behaviour. This renders VEINS a
more rounded solution for vehicular network research.
VI. DISCUSSION
Results-including insights into scientific experiments and
identified research trends-are discussed in the "Discus-
sion" section, based on a comprehensive survey of VEINS-
centric literature pertaining to Vehicular Ad-Hoc Networks
(VANETs). This subsection describes practical implications
of the VEINS framework about vehicular safety improve-
ment including identified benefits and limitations during this
study. In this paper, we discuss how VEINS assists with
the development and validation process for safety-critical
applications as well as the issues involved in simulating real-
world conditions. We also suggest innovative ways as possi-
ble future research to resolve these limitations and boost the
functionality of VEINS framework. Through the absorption
of such technologies and solving for these gaps, VEINS has
strategies to achieve its vision - progression in intelligent
transportation systems with a higher degree of road safety.
The objective of this discussion is to discuss a comprehensive
study on. the current state of VEINS, possible enhancements
and overall relative impact it has made in safety applications
for VANET when compared side by side.
A. HANDLING CONNECTIVITY IN HIGH-SPEED VANETS
This is indeed a challenging method in VANETs especially
when the vehicles are moving very fast at high speed. But
there are several methods to deal with this problem that pro-
vides reliable communication even at highmp/s, fast-moving
environments.
•Store-and-Forward Mechanism: An example solution is
to use a store-and-forward mechanism where messages
are briefly stored at intermediate nodes (i.e., road-side-
units or other vehicles) until the connection becomes
available, and forwarded afterwards. This means that
both parties do not have to be online at the same time for
messages to get through, and where say a message need
got delayed due momentary connectivity fluctuation.
•Trigger and Message Redundancy, Mutihop communi-
cation: Since direct communication between two vehi-
cles may be short-lived, VANETs could benefit from
long multi-hop dissemination and message redundancy
to disseminate information into the screen. In multi-
hop communication, a message is propagated across
intermediate vehicles and relayed until the information
can reach wherever they need to go then even if the
source vehicle finds itself out of range.
•5G, and Beyond-5G Technologies: 5G and Beyond-
5G (B5G) technologies allows ultra-reliable low-latency
communication (URLLC), which is crucial in the case
of integration with VANETs. In particular, these tech-
nologies are purpose-built to support fast-moving vehic-
ular environments by enabling faster and more reliable
wireless communication with low latency so that the
losing signal is less common.
•Handover is proactive and edge computing: Proactive
Handover Strategies: VANETs can provision forthcom-
ing handovers in advance, i.e., when it knows that a
vehicle is going to move out of the coverage area then
along with its movement prediction it gives overcharge
also so distant end becomes at ease after routing. In
addition, to allow for processing and communication
of critical data closer to the vehicle (i.w. not all real-
time continuous connections), there is edge computing
available.
•Mobility-Aware Protocols: We’re building mobility-
aware communication protocols that can take vehicle
speed and location into account. This standard method
helps the system predict and adapt to rapid changes
in connectivity, thus reducing any short-term negatives
which can come with high-speed movement.
Justification: Although real-time continuously connection is
the best approach, these mechanisms promote VANET com-
munication in a reasonable manner when both parties are
not always connected. VANETs leverage a mix of store-and-
forward, multi-hop communication, 5G technologies and
mobility-aware protocols to address the challenges generated
by high vehicle speeds.
B. BENEFITS OF THE VEINS FRAMEWORK FOR
SAFETY APPLICATIONS IN VANETS
In this paper, we use the VEINS framework to implement
and test safety applications in vehicular ad-hoc networks
(VANETs) due its wide range of advantages. Such benefits
augment the safety measures in terms of accuracy, effec-
tiveness and efficiency which are desired properties making
VEINS a useful tool for research purposes as well as by
the community. As shown in Figure 6, in the following
we present some of their main benefits if used for safety
applications in VANETs.
FIGURE 6: Key benefits of the VEINS framework for safety
applications in VANETs.
VOLUME 4, 2016 15
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Al-Shareeda et al. et al.: Safety-based VEINS model in vehicular ad-hoc networks (VANET)
1) Enhanced Simulation Accuracy
As a main advantage of the VEINS framework, it offers ad-
vanced precision for simulating Vehicular ad-hoc Networks
(VANETs). VEINS links the OMNeT++ network simulator
and the SUMO road traffic simulation providing a detailed
and realistic modeling of vehicular behavior as well as net-
working aspects. This high degree of accuracy is significant
for the creation and validation of safety-critical applications,
as simulated test scenarios need to reflect real world con-
ditions. Realistic models are needed in order to discover
possible safety issues and check the efficiency of safety
precautions before trying them out for real. VEINS is so
accurate in recreating the dynamics of vehicular movements
and communication protocols that it enables researchers to
obtain profound understandings on safety applications work-
ing under a diversity of conditions, this makes for sounder
results.
A first and foremost feature of the VEINS framework is
its high fidelity for simulation of Vehicular ad-hoc networks
(VANETs) VEINS combines the OMNeT++ network sim-
ulator with SUMO and provides realistic models for both
vehicle dynamics (Ackerman steering) as well as wireless
communication using IEEE 802.11p which is typically used
in Vehicular Ad-hoc Networks. This realistic environment is
essential for creating and validating safety functions, because
only in this way can the simulated maneuvers simulate reality
very accurately. For instance, VEINS can properly reproduce
the dynamics of vehicles at intersections and a realistic
human drivers behind different traffic control systems. This
level of accuracy permits researchers to validate its collision
avoidance systems in practical environments so that the tech-
niques can work well when driven on real roads.
2) Comprehensive Safety Testing
A broad range of safety scenarios (e.g., collision avoidance,
emergency vehicle prioritization and road hazard detection)
can be tested in detail using VEINS. Virtual Network In-
terface VEINS allows developers to assess the performance
and efficacy of safety applications in various challenging
scenarios by reproducing varying traffic conditions, behavior
of vehicles and communication protocols. This is something
unseen in the physical world but essential for uncovering
vulnerabilities, and optimizing safety system designs prior
to their deployment on public roads. For example, VEINS
has been used to replicate congested traffic conditions in
cities and replicating poor weather events so how safety
applications can reduce these risks is better understood by
researchers. VEINS enables virtually unlimited safety testing
to ensure the applications are robust and can handle real-
world challenges with ease.
VEINS supports a plethora of safety scenarios for thor-
ough tests that include collision avoidance, emergency vehi-
cle prioritization and road hazard detection. VEINS provides
the opportunity to fast prototype applications, it gives a
platform in which for researchers new safety mechanisms
can be evaluated based on Hard scenarios under different
traffic layouts, vehicle behavior and communication proto-
cols. VEINS is capable of simulating the traffic conditions in
high-density urban areas at peak times, to validate about how
a system would handle congestion while fetching priority
for emergency vehicles. Agrim also used VEINS to model
different weather conditions (e. g., heavy rain or fog) with the
purpose of simulating road hazard detection systems in other
simulation scenarios similar as well. This extensive testing
capability provides insights into possible vulnerabilities and
allows safety system design to be optimized in advance of
field implementation.
3) Integration with Advanced Technologies
The VEINS Framework also offers a tremendous advantage
that it can work with the innovative technologies like 5G, ar-
tificial intelligence and blockchain. By coupling VEINS with
5G networks it can improve the reliability and performance
of vehicle-to-vehicle (V2V) and vehicle -to-infrastructure
V2I communications, which in turn will be responded faster
by safety-critical scenarios. Safety applications can be fur-
ther enhanced by utilizing AI algorithms to enable real-time
analysis of data and improve decision making. At the same
time, blockchain can provide an additional level of trustwor-
thiness and security to communication mechanisms proofing
safety messages against manipulations. With this integration,
VEINS can be used not just for traditional safety applications
but also new-media heavy news ways of technically interest-
ing in-vehicle network research and development work.
The VEINS framework also has other important advan-
tages: the compatibility with new technologies-such as 5G,
AI and blockchain-which makes it very attractive. The com-
bination of VEINS with 5G support will play a significant
role in improving the reliability and speed of V2V & V2I
communications further improved, allowing for quicker re-
sponse times during safety-critical situations. For instance,
VEINS can simulate the coordination of emergency braking
across multiple cars using 5G-enabled V2V communication
to prevent pile-up accidents. Furthermore, AI algorithms
within can further enhance safety applications decision-
making ability by processing huge volumes of data on the fly.
In another possible example, AI could forecast where a car is
headed and-by all means with some slight margin of error-
rightfully pump the brakes to get it outta dodge. This allows
blockchain technology to be able to provide an added layer of
security as well as trust in the communication process which
should guarantee that safety messages can only come from
a source with integrity and authenticity; applications such
autonomous vehicle coordination cannot exist without this
extremely important feature.
4) Cost-Effective Development and Testing
VEINS is a highly cost-efficient way to develop and try out
safety applications for VANETs. With USB device VM driver
and simulation-based traffic tracking, VEINS saved a large
number of expensive, time-consuming field tests by offering
good opportunities for simulating complicated scenarios with
16 VOLUME 4, 2016
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content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3476512
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Al-Shareeda et al. et al.: Safety-based VEINS model in vehicular ad-hoc networks (VANET)
TABLE 4: Benefits of the VEINS Framework for Safety Applications in VANETs
Benefit Description Example
Enhanced Simu-
lation Accuracy
Combines OMNeT++ and SUMO for highly accurate simula-
tions Improved realism of vehicle behavior Refined network
interactions.
Simulating vehicle movements at intersections to validate col-
lision avoidance systems in real-world settings.
Comprehensive
Safety Testing
Allows for testing a wide range of safety scenarios under
many different conditions, giving rise to vulnerabilities and
opportunities with making the most out of existing security
technology.
This involves high-density urban traffic at peak hours and
emergency vehicle prioritization systems being tested.
Integration
with Advanced
Technologies
Suitable for emerging new technologies > 5G, AI, and
blockchain Improved communication reliability Improving
decision-making Optimised security of safety applications.
This task aims to execute a simulation that involves V2V
communication for emergency at noon coordination of the
braking function (connected by 5G) among multiple vehicles.
Cost-Effective
Development and
Testing
Creates a virtual environment for exhaustively simulating sce-
narios with complex traffic scenes, which replaces the need for
expensive and time-consuming road tests as well as validates
in an efficient manner.
Fake rural environment (with no traffic) and the other in a Mad
Max-like urban center to test safety applications.
Scalability and
Flexibility
Capacity to simulate thousands of vehicles from customizable
large-scale networks, tailored for your research needs that
accommodate complexities and variabilities only observable in
real-world vehicular networks.
Including new communication protocols or designing a spe-
cific custom vehicle behavior models for VANET studies.
Real-Time Data
Analysis
Enables real-time vehicular and network data monitoring for
situated safety applications to provide safe response to chang-
ing road conditions.
Traffic intersection traffic flux monitoring that would adjust
the timing of signals on a real-time basis to allow emergency
vehicles pass CLEARLY some milliseconds earlier.
network interactions. This also reduces the total development
costs, but also increases testing and validation speed. It
allows researchers to refine their designs rapidly through
the testing of various configurations and scenarios without
the costs or challenges posed by real-world experimentation.
Such affordability would be of interest, in particular to aca-
demic institutions and smaller teams for research who lack
the resources otherwise to contribute much beyond original
data (thus avoid meaningful replication as well) when tack-
ling improvements on vehicular safety.
VEINS is a free solution to develop and test safety ap-
plications in VANETs, especially for the European roadside
structures. VEINS: It reduces the demand of costly and time-
consuming field tests by a virtual environment to simulate
challenging traffic scenarios as well as network interactions.
This reduces the cost of development even further, and de-
creases testing time as well. For instance, through VEINS it
is possible to replicate a myriad of traffic situations such as
empty rural roads all the way up to dense urban centers. Such
flexibility supports the fast iterate-and-optimize development
of safety applications while decreasing expensive road test-
ing and accelerating successful solution deployment.
5) Scalability and Flexibility
This is also befitting a safety application research - the
scalability and flexibility available with VEINS framework
provides an edge. VEINS is able to simulate large-scale net-
works of typically tens or even hundresd of vehicles, making
it suitable for evaluating urban traffic scenarios and also in
the case where we need road-level data, correspondingly
simulating a much greater highway networks. We built a
modular architecture so that our researchers could person-
alise & extend the framework for their specific research.
VEINS can support a wide range of research activities, be
it integrating new communication protocols, testing novel
safety algorithms or modeling different types of vehicles.
Such scalability that VEINS can entertain makes it capable
of dealing with the complexity and variation in real-world
vehicular networks, which is hardcoded utility for holistic
safety studies.
Safety Application Research: scalability & flexibility of
the VEINS framework are major plus points for researchers
working on safety applications. VEINS can also simulate
thousands of vehicle in large-scale networks, and is there-
fore adequate to study urban traffic scenarios as well as
large highway systems. A modular architecture to allow
researchers to modify, and extend the framework for their
specific research requirements. This includes adding new
communication protocols researchers have been pioneered,
or developing custom vehicle behavior models to focus on
particular aspects of VANETs. VEINS is this scalable, which
means that it can cope with the complexity and variability of
real-world vehicular networks offering unique opportunities
to an extensive safety analysis. Moreover, VEINS can be
easily extended to simulate a variety of vehicles ranging from
passenger cars over trucks and buses up various other types
needed for testing many different aspects of vehicle safety
applications.
6) Real-Time Data Analysis
VEINS enables real-time logging and evaluation of traffic
and network information, which is fundamental prior to
the design responsive safety applications. Basically, VEINS
allows researchers to capture and analyze data on vehicle
movements as well as communication delays currently fac-
ing the network performance for safety issues discovering
general solutions in real-time. This responsiveness helps to
ensure that safety applications are both useful and capable
of adapting with evolving driving conditions across the road-
way. You get the picture, including fine-tuning the timing of
traffic lights for emergency vehicles or rerouting cars due
to an accident in real time. This immediate data process
VOLUME 4, 2016 17
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content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3476512
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Al-Shareeda et al. et al.: Safety-based VEINS model in vehicular ad-hoc networks (VANET)
and reply facility makes safety applications more efficient in
vehicular networks, leading to a safer operation.
Such tools are essential for the rapid prototyping of in-
telligent safety applications that depend on both vehicular
and network data,whereas VEINS is particularly designed to
support real-time monitoring andreplay analysis. Using data
on vehicle movements, communication delays and network
performance collected during simulation, VEINS allows re-
searchers to spot possible safety challenges and adapt their
solutions accordingly in the real-time. VEINS can be used
to monitor traffic flow at intersections and allow them adjust
the traffic lights on-demand for emergency vehicles. There is
a further capability to analyse communication delays in order
to ensure that safety messages are delivered promptly and
reliably. This functionality guarantees that safety applications
not only works but also adapts to the continuously changing
road conditions making them more robust and reliable.
7) Summarizing
Table 4 summarizes the benefits of VEINS along with exam-
ple, it explains in appropriate manner will help to understand
how these varies are affecting and using them VEINS im-
proving vehicular safety in VANETs. To summarize, VEINS
framework has many advantages for safety application devel-
opment and evaluation in VANETs. The simulation software
can simulate a variety of prediction methods, be more com-
prehensive in testing, integrated with advanced technologies
and carry out large-scale work at low cost as well scaling
up quickly to meet peak demand while also easy being
used for real-time data analysis by professionals or academic
researchers etc. This work is a major contribution to the suc-
cessful development of ITS and smart city applications. Ur-
ban planners might use VEINS to design traffic management
systems that reduce congestion and improve safety, while
automotive manufacturers can develop and test advanced
driver-assistance systems (ADAS) targeting the same goal
of increased vehicle safety. These applications showcase the
wide range of potential and impact that VEINS framework
has, in terms increasing vehicular safety as well as efficiency.
C. LIMITATIONS OF USING VEINS FOR SAFETY
APPLICATIONS IN VANETS
The VEINS framework provides a variety of advantages
and hurdles for enhancing as well as screening the stability
applications inside Vehicular Ad-hoc Networks (VANETs)
- open-source or exclusive VANET simulator. Awareness of
these limitations is instrumental in overcoming obstacles or
improving research design and its impact, as shown in Figure
7.
1) Simulation vs. Real-World Conditions
An inherent gap between simulation environments and real-
world conditions is one of the major drawbacks for VEINS.
While the simulations achieved via VEINS are very precise,
they cannot mirror all dimensions of a real-world traffic.
While simulations can help us grasp a significant portion of
FIGURE 7: Limitations of the VEINS framework for safety
applications in VANETs.
the issue, many aspects like human behaviour for example or
ambient condition etc. are outside any feasible simulation and
have potential to disrupt significantly safety performance.
Real-world driver reactions to emergencies can vary greatly,
a variability that may not be conveyed in the more controlled
setting of simulation. This gap can result in discrepancies
between simulated and real world performace.
2) Computational Complexity
The highly detailed models provided by VEINS, even though
accurate representations of the physical world-make it in-
feasible to model more complex scenarios and are compu-
tationally expensive. Simulating large fleets with thousands
of vehicles and intricate network interplays at a systemic
level can be expensive to implement, in terms of both time
and resources. This could potentially limit the extensive sim-
ulations that can be performed, particularly for researchers
with few computational resources. For instance, simulating
dense urban traffic with real-time communication nodes and
a high number of vehicles could use up much processing
power memory that would slow the simulation down to be
more pragmatically not suitable for extensive testing.
3) Limited Socio-Economic and Human Factors
The main effort in VEINS has been directed towards the
communication and traffic management sides of vehicular
networks. While this is the case it does not take into account
socio-economic and human factors that are equally important
for successful deployment and use of safety applications.
User acceptance, regulatory framework and economic incen-
tives can be decisive factors for the successful deployment
of security mechanisms. This would include, for example,
the drivability of new safety technologies and how they are
designed to be used by drivers - this type design scrutiny is
not typical in VEINS simulations.
4) Integration Challenges with Emerging Technologies
Although VEINS is tech-agnostic and can be mixed with
new technologies such as 5G, AI (artificial intelligence) or
blockchain the integration of those technologies in them
simulation environment may become a hard task. A lot of
effort and a high expertise are needed to actually verify
that all these different technologies can seamlessly interact
18 VOLUME 4, 2016
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3476512
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
Al-Shareeda et al. et al.: Safety-based VEINS model in vehicular ad-hoc networks (VANET)
with one another in order to accurately model the sort of
behavior we would expect. For instance, implementation of
AI-based decision-making algorithms together with VEINS
simulations might require significant customization and opti-
mization to correctly model the interaction between vehicles
and infrastructure in a real-world scenario.
5) Data Accuracy and Availability
VEINS simulations are accurate only to the extent that input
data is well validated and available. Especially if informa-
tion concerning traffic patterns, vehicle behaviors or com-
munication protocols of the system in use are inaccurate
or incomplete might provide misleading results and hence
diminishing the trust in simulation outcomes. Simulation
based on obsolete or incomplete vehicular behavior models
and traffic datasets can produce inaccurate rendering of real-
time traffic situations, resulting in less-than-efficient safe
application development.
6) Summary
Table 5 provides a concise summary of the limitations associ-
ated with using the VEINS framework for safety applications
in VANETs. While VEINS is a powerful tool for developing
and testing safety applications in VANETs, researchers must
be aware of its limitations. Addressing these limitations,
such as the gap between simulations and real-world condi-
tions, computational complexity, and integration challenges,
is essential for maximizing the effectiveness of VEINS-
based studies. By considering these factors, researchers can
improve the reliability and applicability of their findings,
contributing to the advancement of safer vehicular networks.
D. FUTURE DIRECTION
The VEINS framework allows for the simulation and test-
ing of safety applications in vehicular ad-hoc networks
(VANET). In light of the rapid evolution in technology,
there are several future directions that hold promises to
make VEINS capabilities and effectiveness even better. Fur-
ther enhanced by integration of 5G and Beyond-5G (B5G)
technologies, as well as augmented with artificial intelli-
gence (AI)/machine learning(ML), offering advanced pre-
diction/warning/prescription capabilities & real time optimi-
sation. Vehicular communications security can be height-
ened by blockchain technology. Semi-virtual and hybrid
simulation environments which run with a combination of
real-world data and hardware-in-the-loop (HIL) testing will
narrow the gap between virtual reality scenarios, as well
as actual conditions. Finally, broader Vehicle-to-Everything
(V2X) communication will make roads safer for all users and
integration with smart city deployments will improve urban
mobility whilst creating a culture of safety. By incorporating
environmental surroundings, such as live weather and air
quality into simulations it provides much-needed context for
safety applications adding further robustness in creating safer
vehicles; And the ability to simulate test various ADAS fea-
tures can validate their algorithms thus reducing hardware-
TABLE 5: Limitations of Using VEINS for Safety Applica-
tions in VANETs
Limitation Description
Simulation vs.
Real-World
Conditions
VEINS provides highly accurate simulations but can-
not perfectly replicate the complexity and unpre-
dictability of real-world traffic scenarios. Factors such
as human behavior, environmental conditions, and
unforeseen events may not be fully captured.
Computational
Complexity
Detailed modeling capabilities of VEINS result in
high computational complexity. Running large-scale
simulations with thousands of vehicles and complex
network interactions can be resource-intensive and
time-consuming, limiting the ability to conduct exten-
sive simulations.
Limited Socio-
Economic and
Human Factors
VEINS primarily focuses on technical aspects of ve-
hicular networks, not accounting for socio-economic
and human factors crucial for the successful deploy-
ment and adoption of safety applications. Factors like
user acceptance, regulatory policies, and economic
incentives are not inherently included.
Integration
Challenges
with Emerging
Technologies
Integrating emerging technologies like 5G, AI, and
blockchain into the VEINS simulation environment
can be complex and challenging. Ensuring seamless
interaction between different technologies and accu-
rately modeling their combined effects requires sub-
stantial effort and expertise.
Data Accuracy
and Availability
The accuracy of VEINS simulations heavily depends
on the quality and availability of input data. Inac-
curate or incomplete data on traffic patterns, vehicle
behaviors, or communication protocols can lead to
misleading results and reduce the reliability of the
simulation outcomes.
in-the-loop testing iterations making them more reliable. In
the next few years, they will support the construction of safe
and efficient intelligent vehicular networks.
The VEINS framework provides a strong emerging tool for
facilirating the simulation and testing of safety applications
in VANETs. It interfaces with the OMNeT++ network simu-
lator and integrates the SUMO road traffic simulation to offer
a truthful vehicular mobility model allowing for realistic net-
working intersctions as well. This integration creates realistic
tests for different safety scenarios leading to a more robust
and efficient testing of safety applications. Future paths
for VEINS include combining trends like 5G, AI&Ml, and
blockchain more effectively improving scalability & versa-
tility embedding world data from real worlds/environmental
settings. These improvements will strengthen the VEINS
capability to enable the design of reliable and efficient safety
applications in ITSs, but also smart cities.
1) Integration with 5G and Beyond 5G (B5G) Technologies
•Description: The advent of 5G technology brings ultra-
low latency, high data rates, and enhanced connectivity,
which can significantly improve vehicular communica-
tions. By integrating VEINS with 5G and upcoming
B5G technologies, simulations can more accurately re-
flect real-world conditions and support the development
of safety applications that rely on fast and reliable data
transmission.
•Example: Testing vehicle-to-vehicle (V2V) communi-
cation for collision avoidance systems in high-speed
VOLUME 4, 2016 19
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3476512
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
Al-Shareeda et al. et al.: Safety-based VEINS model in vehicular ad-hoc networks (VANET)
TABLE 6: Top 8 Future Directions for Using VEINS Framework for Safety Applications in VANETs
Future Work Description Example Strengths Challenges
Integration with
5G and B5G
Technologies
Enhance vehicular
communications with
ultra-low latency and high
data rates.
Testing V2V communication for
collision avoidance in high-speed
scenarios using 5G networks.
Improves communication
reliability and latency.
Requires significant modifications
to VEINS and consistent 5G cover-
age.
AI and ML Inte-
gration
Use AI/ML for real-time
optimization and predic-
tive capabilities.
Using ML models to predict traffic
congestion and reroute vehicles dy-
namically.
Enhances predictive
capabilities and decision-
making processes.
High computational requirements
and ensuring robustness in diverse
conditions.
Blockchain
for Enhanced
Security
Provide secure
and tamper-proof
communication.
Implementing a blockchain-based
system to authenticate and verify
messages between vehicles and in-
frastructure.
Ensures data integrity and
security.
Computational overhead and la-
tency issues, extensive changes to
simulation architecture.
Hybrid
Simulation
Environments
Combine VEINS with
real-world data and
HIL testing for accurate
validation.
Integrating real-world traffic data
into VEINS simulations.
Bridges gap between
simulations and real-world
conditions.
Resource-intensive and complex to
set up.
Vehicle-to-
Everything
(V2X)
Communication
Include interactions with
pedestrians, cyclists, and
other road users.
Simulating pedestrian warning sys-
tems at crosswalks using V2X com-
munication.
Improves safety for all
road users.
Significant updates to VEINS re-
quired, ensuring accurate simula-
tion of diverse interactions.
Smart City Inte-
gration
Integrate with smart city
infrastructure for urban
mobility.
Testing an adaptive traffic light sys-
tem that adjusts timings based on
real-time traffic flow.
Enhances urban mobility
and safety.
Complex and data-intensive mod-
eling, coordination with real-world
smart city initiatives.
Environmental
Impact Analysis
Consider environmental
factors like weather and
air quality.
Simulating effects of heavy rain or
fog on vehicle sensors and commu-
nication systems.
Leads to more robust
and reliable safety
applications.
Accurate environmental modeling
is challenging, requires additional
data integration.
ADAS Testing Simulate and test ADAS
features like lane-keeping
assist and automatic emer-
gency braking.
Testing lane-keeping assist systems
under different road conditions.
Refines ADAS algorithms
and improves reliability.
Detailed and extensive scenario
modeling needed, ensuring simula-
tion accuracy.
scenarios using 5G networks.
2) Artificial Intelligence (AI) and Machine Learning (ML)
Integration
•Description: AI and ML algorithms can process vast
amounts of data to identify patterns, predict poten-
tial hazards, and optimize system performance in real-
time. Integrating these technologies into VEINS can
enhance the predictive capabilities and decision-making
processes of safety applications.
•Example: Using ML models within VEINS to predict
traffic congestion and reroute vehicles dynamically to
avoid accidents.
3) Blockchain for Enhanced Security
•Description: Blockchain technology offers a decentral-
ized and secure method for managing vehicular commu-
nications, ensuring that data cannot be tampered with.
Integrating blockchain into VEINS can provide a secure
communication framework for safety applications, pre-
venting unauthorized access and ensuring data integrity.
•Example: Implementing a blockchain-based system in
VEINS to authenticate and verify messages between
vehicles and infrastructure, enhancing the security of
emergency communication systems.
4) Hybrid Simulation Environments
•Description: Combining VEINS with real-world data
and hardware-in-the-loop (HIL) testing creates a hybrid
simulation environment that bridges the gap between
virtual and actual testing conditions. This approach
allows for more accurate validation and refinement of
safety applications.
•Example: Integrating real-world traffic data into VEINS
simulations to test the effectiveness of traffic manage-
ment systems under realistic conditions.
5) Vehicle-to-Everything (V2X) Communication
•Description: V2X communication extends beyond V2V
and vehicle-to-infrastructure (V2I) to include interac-
tions with pedestrians, cyclists, and other road users. In-
corporating comprehensive V2X protocols into VEINS
can help develop safety applications that ensure the
safety of all road users.
•Example: Simulating pedestrian warning systems that
alert drivers to the presence of pedestrians at crosswalks
using V2X communication in VEINS.
6) Smart City Integration
•Description: As cities become smarter, integrating
VEINS with smart city infrastructure allows for the test-
ing and development of safety applications that interact
with urban mobility systems. This can include adaptive
traffic lights, connected public transportation, and smart
road signs.
•Example: Testing an adaptive traffic light system in
VEINS that adjusts signal timings based on real-time
traffic flow and vehicle density to reduce accidents and
improve traffic efficiency.
20 VOLUME 4, 2016
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content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3476512
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
Al-Shareeda et al. et al.: Safety-based VEINS model in vehicular ad-hoc networks (VANET)
7) Environmental Impact Analysis
•Description: Environmental factors such as weather
conditions and air quality can significantly impact vehi-
cle performance and safety. Incorporating these factors
into VEINS simulations allows for a more comprehen-
sive assessment of safety applications under various
environmental conditions.
•Example: Simulating the effects of heavy rain or fog on
the performance of vehicle sensors and communication
systems to develop robust safety applications.
8) Advanced Driver Assistance Systems (ADAS) Testing
•Description: ADAS features such as lane-keeping as-
sist, adaptive cruise control, and automatic emergency
braking are critical for vehicle safety. Using VEINS to
simulate and test these features can help refine their
algorithms and improve their reliability under different
traffic scenarios.
•Example: Testing the performance of lane-keeping as-
sist systems in VEINS under different road conditions
and traffic densities.
9) Summary
Table 6 provides a concise overview of the top 8 future
works, describing their strengths and challenges along with
examples to illustrate each point. Future work for utiliz-
ing the VEINS framework in VANETs to improve safety
applications There are many different advantages to each
direction, ranging from improved communication reliability
and advanced predictive capabilities, even better safety all the
way around. These innovations also present challenges: they
demand intensive computation; they need multiple integra-
tions points to other systems (B2 or A geospatial databases);
and, you have to model right down in fine detail. To meet
these challenges, it will take thoughtful planning and signif-
icant resources as well as coordination amongst researchers,
industry professionals and policymakers. This will help the
VEINS in striving to develop safer and efficient vehicular
networks even after facing these challenges.
VII. CONCLUSION
In this paper, a detailed survey of the role of VEINS in safety
enhancement on Vehicular Ad-Hoc Networks (VANETs) is
presented. Using a systematic review of existing literature,
we created a detailed taxonomy grouping the research into
three core areas: Application; Solution; and Network. Such
a structured approach in this paper showcases the capabil-
ity of VEINS to tackle various security challenges within
VANET’s, and re-enforces its position as an effective sim-
ulation toolkit.
The VEINS framework, one the other hand provides
higher level of simulation accuracy as it is a complete co-
simulation environment that integrates the OMNeT++ net-
work simulator with SUMO road traffic simulator. Such a
combination is required for accurate modeling of vehicular
behavior and communication protocols that are fundamental
in developing safety-critical applications. VEINS accommo-
dates comprehensive safety testing on a range of situations,
including collision avoidance (CARMA), emergency vehicle
prioritization and road hazard finding These features exist to
allow secure applications to be thoroughly vetted and fine-
tuned prior deployment in real-world conditions.
Although VEINS [7] is advantageous from several aspects,
it also suffers a number of disadvantages that one would
want to work on. The space between simulation and reality is
another big challenge, it’s hard to simulate those cases with
many human interactions or environment features. BodyThe
high computational cost of fine-grained simulations may also
slow down extensive testing, particularly if the researchers
lack sufficient resources. Another issue is the lack of support
for modern software features in VEINS and especially when
it comes to integrating new technologies like 5G, AI or even
Blockchain into your simulation which would require a lot of
customization by someone who has programming skills.
In addition, one of the directions for follow-up research
is to support the VEINS by properly integrating Beyond-
5G (B5G) technologies and advanced AI-based algorithms
regarding machine learning. Such improvements that should
help to do accelerate real-time data analysis and decision-
making for safety use cases. The Blockchain provides an at-
tractive method for securing information exchanges to ensure
that safety messages are only provided based on protocols
which dictate their integrity and authenticity. Additionally,
semi-virtual and hybrid simulation environments under de-
velopment can help connect the gap between full-scale vir-
tual representation of autonomous systems simulations with
real-world conditions to enable more relevant tests.
Full power Vehicle-to-Everything (V2X) communication
will help enhance road safety for all users, and we have
also developed plans to integrate VEINS with smart city
deployments. For safety applications, VEINS also supports
the integration of real-world environmental data (E.g. actual
weather and air quality conditions). The integration will
allow those same urban planners and carmakers to develop
and test advanced driver-assistance systems (ADAS) as well
as manipulate traffic for the goal of safer, more efficient
vehicular networks.
Finally, the VEINS framework can be used to facilitate
research on safety applications in VANETs. VEINS can make
a substantial contribution to the advancement of intelligent
transportation systems and smart city projects, which will
help improve road safety and urban mobility if its limitations
are addressed while heading in future researches.
FUNDING STATEMENT
This research has been funded by the Scientific Research
Deanship at the University of Ha’il - Saudi Arabia through
project number «RG-23117»
ACKNOWLEDGEMENT
We would like to acknowledge the Scientific Research Dean-
ship at the University of Ha’il, Saudi Arabia, for funding this
VOLUME 4, 2016 21
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3476512
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
Al-Shareeda et al. et al.: Safety-based VEINS model in vehicular ad-hoc networks (VANET)
research through project number «RG-23117»
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ZEYAD GHALEB AL-MEKHLAFI Dr. Zeyad
Ghaleb Al-Mekhlafi received the B.Sc. degree in
computer science from the University of Science
and Technology, Yemen, in 2002, the M.Sc. degree
in computer science from the Department of Com-
munication Technology and Network, Universiti
National Malaysia (UKM), in 2011, and the Ph.D.
degree from the Department of Communication
Technology and Network, Faculty of Computer
Science and Information Technology, Universiti
Putra Malaysia, in 2018. He is currently an Associate Professor in the
Faculty of Computer Science and Engineering, University of Ha’il, Saudi
Arabia. His current research interests include wireless sensor networks, en-
ergy management and control for wireless networks, time synchronization,
bio-inspired mechanisms, and emerging wireless technologies standard.
VOLUME 4, 2016 25
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3476512
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
Al-Shareeda et al. et al.: Safety-based VEINS model in vehicular ad-hoc networks (VANET)
MAHMOOD A. AL-SHAREEDA received his
B.S degree in from communication Engineering
in Iraq University College and MSc in Infor-
mation Technology from Islamic University of
Lebanon (IUL) in 2018. He obtained Ph.D. in Ad-
vanced Computer Network from University Sains
Malaysia (USM). He was worked as a postdoc-
toral fellowship at National Advanced IPv6 Centre
(NAv6), Universiti Sains Malaysia. He is currently
assistance professor at Communication Engineer-
ing, Iraq University College (IUC). His current research interests include
network monitoring, Internet of Things (IoT), Vehicular Ad hoc Network
(VANET) security and IPv6 security.
BADIEA ABDULKAREM MOHAMMED Badiea
Abdulkarem Mohammed received his BSc in
Computer Science from Babylon University, Iraq
in 2002, M.Tech in Computer Science from Uni-
versity of Hyderabad, India in 2007 and PhD from
Universiti Sains Malaysia, Malaysia in 2018. He is
currently an Associate Professor in the College of
Computer Science and Engineering at University
of Hail, KSA. He is permanently Assistant Profes-
sor at Hodeidah University, Yemen. His research
focuses on Wireless Networks, Mobile Networks, Vehicle networks, WSN,
Cybersecurity, and Image Processing. He is an IEEE member, Member,
IAENG member, and ASR member. In his research area, he has published
many papers in reputed journals and conferences.
ABDULAZIZ M. ALAYBA Abdulaziz M. Alayba
was born in Ha’il, Saudi Arabia, in 1987. He
earned his B.S. degree in Computer Education
from the University of Ha’il, followed by an M.S.
degree in Computer Science from Coventry Uni-
versity, UK, in 2012. Subsequently, he completed
his Ph.D. in Artificial Intelligence and Natural
Language Processing from Coventry University,
UK, in 2019. Presently, he serves as an Assistant
Professor of Computer Science at the College of
Computer Science and Engineering, University of Ha’il, Saudi Arabia. His
research interests encompass data science, machine learning (specifically
Neural Networks and Deep Learning), natural language processing, and the
internet of things. Additionally, he contributes as a reviewer for various
esteemed international journals and conferences.
AHMED M. SHAMSAN SALEH received the
B.Sc. degree in computer science from the Univer-
sity of Baghdad, Iraq, in 2001, the M.Sc. degree in
computer science from the Department of Com-
munication Technology and Network, Universiti
Putra Malaysia, in 2008, and the Ph.D. degree in
communications and networks engineering from
the Department of Computer and Communication
Systems Engineering, Universiti Putra Malaysia,
in 2012. He is currently a Lecturer with the Uni-
versity of Tabuk, where he is also an Assistance Professor with the Faculty
of Computer Science. His main research interests include wireless sensor
and adhoc networks, energy management and control for wireless networks,
routing technologies, bio-inspired mechanisms, and emerging wireless tech-
nologies standard.
HAMAD A AL-RESHIDI HAMAD A AL-
RESHIDI received the B.Sc. degree in applied
science from the KSA , in 1997, the M.Sc. degree
in instructional Technology from KSU in 2005 ,
and the Ph.D. degree in Instructional Technology
from IIUM Malaysia, in 2012. He is currently a
full professor in education college at University of
Hail. His main research interests includes Artifi-
cial intelligent, smart cities, e-learning -Learning
technology and learning robotics.
KHALIL ALMEKHLAFI Khalil Almekhlafi is cur-
rently President of e-learning Unit in College
of Business Administration - Yanbu Taibah Uni-
versity. He is also lecturing information systems
and electronic commerce (e-commerce)/electronic
business (e-business) courses at Taibah University,
Saudi Arabia Al Madinah, Yanbu. He holds PhD in
E-commerce& Logistics Management, Master of
Computer Applied Technology, and Bachelor de-
grees of Statistics & Informatics. His research in-
terests are in e-commerce/e-business, Knowledge Support Systems, Context-
aware mobile systems. Since July 2018. Khalil Almekhlafi has been ap-
pointed as Distinguished Professor at Police Academy, Yemen. He has been
appointed as Distinguished Professor at College of Business Administration
- Yanbu, Taibah University, Saudi Arabia.
26 VOLUME 4, 2016
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3476512
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
