Integrating 5G into VANETs: Methodological Approaches and

Performance Evaluation Through Simulation

Nouri Omheni, Mazen Sboui and Faouzi Zarai

NTS’Com Research Unit, National School of Electronics and Telecommunications of Sfax, University of Sfax, Tunisia

Keywords: 5G Technology, VANETs, Intelligent Transportation Systems, end to end Latency, Packet Loss.

Abstract: The integration of 5G technology with Vehicular Ad-hoc Networks (VANETs) represents a significant

advancement in the field of Intelligent Transportation Systems (ITS). This paper explores the potential of 5G

to enhance real-time communication between vehicles and infrastructure, aiming to improve road safety and

traffic management. Using simulation tools such as OMNeT++ and SUMO, various traffic scenarios were

modeled to assess the performance of the integrated 5G-VANET system. Key performance indicators

including end to end latency, packet loss, and node acceleration behaviour were evaluated. The results indicate

that the integration of 5G reduces communication latency to below 10 milliseconds and achieves packet

delivery rates exceeding 95% in high-density traffic situations. This study demonstrates the feasibility of 5G-

enhanced VANETs, highlighting their potential to contribute to safer and smarter transportation systems.

1 INTRODUCTION

The transportation sector is undergoing a

transformative shift with the integration of emerging

communication technologies aimed at improving

road safety, reducing traffic congestion, and

supporting autonomous vehicle operations. Vehicular

Ad-hoc Networks (VANETs) (Balen, 2022), a subset

of Mobile Ad-hoc Networks (MANETs), play a

crucial role in this transformation by enabling real-

time communication between vehicles (V2V) and

between vehicles and roadside infrastructure (V2I).

VANETs allow for the exchange of critical

information such as vehicle position, speed, and

traffic conditions, which are essential for safety

applications like collision avoidance, emergency

vehicle routing, and traffic flow optimization.

However, existing communication networks such as

4G LTE are unable to fully meet the stringent

requirements of modern VANET applications,

particularly in terms of latency, bandwidth, and

connection density. The emergence of Fifth

Generation (5G) mobile networks (Banafaa, 2024) is

seen as a game-changer for vehicular communication

systems. 5G networks provide ultra-reliable low-

latency communication (uRLLC), enhanced mobile

broadband (eMBB), and massive machine-type

communication (mMTC), all of which are critical for

supporting the complex and dynamic environment of

VANETs. These features enable faster data

transmission, improved reliability, and the ability to

connect a vast number of devices, making 5G the

ideal network for supporting intelligent transportation

systems (ITS).

One of the key challenges in vehicular networks

is maintaining reliable and low-latency

communication, especially in high-speed

environments where vehicles frequently enter and

leave communication ranges. 5G's low-latency

features are designed to address this issue by enabling

real-time communication between vehicles and

infrastructure, which is vital for applications such as

cooperative collision avoidance and autonomous

driving. Moreover, 5G's ability to support dense

networks with numerous connected devices ensures

that VANETs can operate efficiently even in high-

traffic areas. As the number of vehicles on the road

continues to increase, the limitations of current

communication technologies become more apparent.

4G LTE networks, while adequate for many

applications, struggle to support the high-speed, low-

latency communication required by safety-critical

vehicular applications. The main challenges

addressed by this project are:

 Network Scalability: In dense urban

environments, the number of connected vehicles

Omheni, N., Sboui, M. and Zarai, F.

Integrating 5G into VANETs: Methodological Approaches and Performance Evaluation Through Simulation.

DOI: 10.5220/0013253900003941

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 11th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2025), pages 405-412

ISBN: 978-989-758-745-0; ISSN: 2184-495X

405

and devices places a heavy burden on the

network, leading to congestion and packet loss.

Traditional networks cannot scale effectively to

meet this demand.

 Reliability: Vehicular communication must be

reliable, even in challenging environments such

as tunnels, urban canyons, and high-speed

highways. Ensuring consistent connectivity and

data transmission in these environments is

critical.

The problem, therefore, lies in finding a way to

enhance VANET communication by leveraging 5G’s

capabilities to provide faster, more reliable data

exchange and ensure network performance, even in

the most demanding conditions.

Various approaches to enhance communication

performance and reliability in vehicular networks

have been conducted. To maintain optimal traffic

flow, a swarm intelligence-based method was

developed to support the formation and evolution of

platoons (Mushtaq, 2021). A fuzzy logic-based

weighting model was introduced to prioritize vehicles

in traffic (Abbasi, 2022), and a 3D road network

model using digital surface models was developed to

support multi-level communications (Brummer,

2019). Authors in (Irani, 2024)] present VSIM, a new

simulator for MANET and ad hoc networks that

balances user-friendliness, modularity, scalability,

and open-source accessibility, providing accurate,

reliable results validated against NS-2 and supporting

a range of routing protocols, mobility models, and

performance evaluation tools. Project (Taher, 2024)

evaluates the use of the software-defined network

(SDN) protocol with AODV and OLSR protocols in

LTE-based VANETs, demonstrating that SDN

enhances packet delivery ratio (PDR) and reduces

delivery delay by leveraging SDN controllers for

optimal routing, outperforming AODV and OLSR in

varying vehicle densities and speeds. Paper (Zanotto,

2024) extends the Veins vehicular network simulator

to incorporate 3D factors—such as building height,

obstacles, altitude, and antenna positioning

improving the accuracy of mobile network coverage

assessments with only a limited increase in simulation

time.

Our research focuses on the integration of 5G with

VANET technologies, aiming to enhance vehicular

communication systems by leveraging the advanced

capabilities of 5G networks. The study adopts the V-

model for the development and validation of the

integrated system, ensuring a structured approach that

includes rigorous testing at each phase of the project.

Through extensive simulations, this research

evaluates key performance indicators (KPIs) such as

end to end latency, packet loss, and node acceleration

behaviour, providing insights into the benefits of 5G-

enhanced VANET systems under various traffic

scenarios. To address these challenges, the project

proposes the integration of 5G networks into VANET

systems, focusing on improving the reliability and

speed of vehicular communication. The proposed

solution includes the following components:

 On-Board Units (OBUs) and Roadside Units

(RSUs): Vehicles equipped with OBUs will

communicate with RSUs using 5G-enabled

protocols, ensuring that data can be exchanged

quickly and reliably, even in high-speed

environments. These units will handle critical

data such as vehicle speed, position, and

trajectory to enable safety applications.

 uRLLC: 5G's uRLLC feature will be employed

to minimize latency, ensuring that vehicles can

share real-time data, such as accident warnings

or traffic updates, almost instantaneously. This

is essential for applications such as cooperative

adaptive cruise control (CACC), where vehicles

must react to changes in road conditions or

traffic behaviour in real time.

 Scenario Testing and Evaluation: The system

will be tested in simulated environments to

evaluate how it performs in different scenarios.

For example, high-density urban traffic,

freeway driving, and emergency vehicle routing

will be simulated to assess the system’s ability

to maintain low latency and high throughput

under various conditions.

The rest of this paper is organized as follows:

Section 2 presents the methodological approach used

in this study, detailing the V-model and justifying its

selection over other methodologies such as Scrum.

Section 3 describes the simulation setup, tools and

presents the results of the simulations, analyzing the

performance of the 5G-VANET system under

different conditions. Finally, Section 4 concludes the

paper with a summary of the findings and suggestions

for future research directions.

2 METHODOLOGY

2.1 Methodological Approach

For this project, two software development

methodologies were considered: The V-model and

Scrum. The V-model, also known as the Verification

and Validation model, is a highly structured approach

that requires sequential development. Each phase of

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406

development has a corresponding testing phase,

ensuring that any issues are identified and resolved

before moving to the next stage. This approach is

particularly well-suited for projects with stable

requirements and where quality and accuracy are

paramount. In contrast, Scrum is an Agile

methodology that focuses on iterative development,

promoting flexibility and collaboration among team

members. Scrum is better suited for projects with

evolving requirements, allowing for rapid

adjustments and continuous delivery of working

software. Given the stable and well-defined

requirements of the VANET and 5G integration, the

V-model’s sequential approach offers better accuracy

and ensures high-quality deliverables. Although

Scrum is widely used in modern software

development, its flexibility is not required for this

project, and the additional overhead of iterative

cycles would not provide significant benefits.

Therefore, the V-model was deemed more suitable

for ensuring that all critical phases were carefully

tested and validated before moving to the next.

2.2 VANET Implementation

Implementing VANETs with 5G technology comes

with its own set of challenges, scenarios, routing

protocols, and security concerns.

2.2.1 Challenges

While the integration of 5G into VANETs offers

numerous benefits, several challenges must be

addressed:

• Network Stability: Maintaining stable

connections in high-speed and rapidly changing

environments is difficult due to vehicles moving

in and out of communication range frequently.

• Data Privacy and Security: Ensuring secure

communication between vehicles and

infrastructure is crucial, as malicious attacks

could compromise safety. Security measures must

be robust enough to protect against data breaches

and unauthorized access.

• Interoperability: As different manufacturers

develop their own communication standards,

achieving interoperability between various

systems is essential for seamless integration.

2.2.2 Scenarios

Various scenarios can be simulated to evaluate the

effectiveness of VANETs in real-world conditions:

• Urban Traffic Management: Scenarios involving

dense urban environments where vehicles must

navigate through heavy traffic and communicate

with traffic signals to optimize flow.

• Emergency Response: Simulations of emergency

vehicles utilizing V2V and V2I communication to

navigate through traffic quickly, reducing

response times in critical situations.

• Highway Driving: Evaluating the performance of

vehicles in high-speed environments to ensure

that communication remains stable and reliable

during rapid vehicle movement.

3 IMPLEMENTATION AND

RESULTS

This section covers the tools, environment setup, and

results from the simulation of vehicular networks

using 5G technology. The simulation was

implemented with SUMO (SUMO, 2024) for traffic

simulation, OMNeT++ (OMNeT, 2024) for network

simulation, and Veins (VEINS, 2024)

for VANET

simulation, deployed on the Cloud.

3.1 Environment Setup and Tools

The simulation environment was built using a

combination of hardware and software tools.

Hardware included an Intel Core i5 processor with

16GB RAM. Key software components used:

 OMNeT++: A modular C++ simulation library for

building network simulators, used for handling

discrete event simulations.

 Veins: A framework for simulating VANETs,

integrated with OMNeT++ and SUMO.

 INET Framework: Used for simulating

communication networks, protocols, and

applications.

 SUMO: A traffic simulation package that provides

accurate road traffic models.

 JOSM: An application for editing and extracting

maps for use with SUMO to generate realistic traffic

models (JOSM, 2024).

3.2 Work Environment Configuration

The simulation environment required the installation

and configuration of OMNeT++ and the Veins/INET

framework. Steps involved extracting files, importing

projects into OMNeT++, and configuring various

project references. Veins and INET were crucial for

Integrating 5G into VANETs: Methodological Approaches and Performance Evaluation Through Simulation

407

simulating vehicular communication with 5G

capabilities. Veins was used to simulate vehicle

communication protocols (V2X), focusing on ultra-

reliable low-latency communication (uRLLC). INET

provided a broader range of protocols and

performance evaluation tools for testing the

throughput, latency, and packet loss in 5G networks.

3.3 AODV Integration with Veins

The simulation incorporated the AODV routing

protocol to manage vehicle-to-vehicle

communication. This required configuration of INET

and Veins to enable seamless operation in the

OMNeT++ environment. The protocol was

configured in the omnetpp.ini file, and its behaviour

was tested under various traffic and accident

scenarios. The following points show the cade

enabling the integration of AODV in routing for

nodes with :

node[*].ipv4.configurator.typename =

"HostAutoConfigurator": Specifies that IPv4

addresses will be automatically configured for each

node.

*.node[*].ipv4.configurator.interfaces =

"wlan0": Specifies the interface to be used for IPv4

configuration.

*.node[*].ipv4.configurator.mcastGroups =

"224.0.0.1": Configures multicast groups for IPv4.

Mobility:

*.node[*].mobility.typename =

"VeinsInetMobility": Configures mobility using the

Veins framework with INET mobility support.

*.manager.updateInterval = 0.1s: Sets the update

interval for the VeinsInetManager.

*.manager.host = "localhost": Specifies the host

for the manager.

*.manager.port = 9999: Specifies the port for

communication.

*.manager.autoShutdown = true: Enables

automatic shutdown of the manager.

*.manager.launchConfig =

xmldoc("square.launchd.xml"): Specifies the launch

configuration file for the manager.

*.manager.moduleType =

"vanettutorials.veins_inet.VeinsInetRoutingCar":

Specifies the module type for the manager. Vector

Recording:

**.vector-recording = true: Enables vector

recording for the simulation. Routing:

**.router = "Aodv": Specifies the routing

protocol to be used. Here, it’s set to

AODV

3.4 Collision Avoidance Simulation

An accident scenario was introduced in the

simulation, focusing on the behaviour of nodes

(vehicles) during an accident. Node 5 was set to

simulate an accident, affecting its communication and

network performance. The impact of this accident

was analyzed, particularly in terms of packet loss and

deceleration behaviour. In the accident scenario

involving Node 5, communication delays led to the

late transmission of emergency braking messages to

nearby vehicles. This delay caused increased reaction

times, resulting in near-collision events for vehicles

within a 50-meter radius. After integrating the AODV

protocol now we are looking for nodes behaviour

when an accident occurs:

Node 5 experiences an accident which impacts the

network performance and traffic flow. This scenario

is critical for evaluating the robustness and reliability

of the vehicular network under emergency conditions.

As shown in Figure 1, node 5 is depicted in red to

indicate that an accident has occurred at this location.

The red color is used to highlight the status of Node

5, making it immediately recognizable as a critical

event in the simulation. This visual representation

helps in quickly identifying nodes that are involved in

Figure 1: Node 5 has experienced an accident.

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Figure 2: RSU Communicating with nodes.

accidents, allowing for a more efficient analysis of

how such incidents impact network performance and

traffic flow

After running the simulation, the nodes turn green

due to successful communication with the RSU. The

green color indicates that the nodes are receiving

signals and exchanging data with the RSU,

confirming active participation in the vehicular

network as depicted in figure 2.

3.5 Simulation Parameters

Key simulation parameters included the playground

size, node behaviour during accidents, transmission

power, and noise floor. Table 1 outlines the key

parameters used in the simulation. The playground

size is set to 2500 meters in both the X and Y

directions and 50 meters in the Z direction. Node [5]

is configured to experience an accident starting at 73

seconds into the simulation and lasting for 50

seconds. The network configuration does not use the

service channel, and the nodes transmit with a power

of 20 milliwatts at a bit rate of 6 Mbps. The minimum

power level for the physical layer is set to -110 dBm,

and noise floor usage is enabled with a value of -98

dBm. The decider and analogue models are specified

through XML configurations.

3.6 Simulation Results

In this section, we conduct a detailed analysis of the

packet behaviour during accidents. The data reveals

that accidents significantly impact packet

transmission, resulting in increased packet loss and

fluctuations in packets sent. This analysis is crucial

for understanding and improving the robustness of

VANET systems in real-world scenarios.

3.6.1 Total Distance

The bar plot of the Figure 3 depicts the total distance

for each module. Key insights include:

Table 1: Simulation Parameters.

Parameter Value

Pla

round Size

(

)

2500m

Pla

round Size

(

)

2500m

Pla

round Size

(

)

50m

Node[5] Accident Count 1

Node[5] Accident Start 73s

Node[5] Accident Duration 50s

Use Service Channel false

Transmission Powe

20mW

Bitrate 6Mbps

Minimum Power Level -110dBm

Use Noise Floo

true

Noise Floo

-98dBm

Use Pro

ation Dela

true

 There is a noticeable variation in the total distance

covered by different nodes.

 Some nodes cover significantly higher distances,

which could indicate specific roles or activities

within the experiment that require more extensive

movement.

 nodes with lower distances might be involved in

more static or localized tasks.

 The plot allows for easy comparison across nodes,

highlighting which nodes are outliers in terms of

distance covered.

Integrating 5G into VANETs: Methodological Approaches and Performance Evaluation Through Simulation

409

Figure 3: Total distance for each module.

Figure 4: Acceleration Behaviour for nodes when the accident happens.

3.6.2 Acceleration Behaviour

Figure 4 delineates the graphical representation when

the nodes had and accident and their behaviour before

and after the accident. The behaviour of each node

can be understood through their acceleration profiles.

Nodes with high deceleration and variability, such as

Node 5, are likely facing significant obstacles or

events that force them to slow down frequently,

potentially leading to packet loss due to unstable

connectivity. In contrast, nodes with positive or stable

mean acceleration and lower variability maintain

more consistent speeds, resulting in better network

performance and fewer packet loss incidents.

3.6.3 Insights



Node 0 has the highest average acceleration and

also exhibits the highest maximum acceleration

value, indicating more significant speed changes

compared to the other nodes.

 Node 5 and Node 6 have similar mean

accelerations, which are slightly negative,

suggesting that on average, these nodes might be

decelerating slightly more often than accelerating.

 The standard deviations indicate that the

accelerations of all nodes fluctuate significantly,

with Node 5 showing the highest variability.

 The minimum acceleration values for all nodes

are quite similar, with Node 5 showing the lowest

minimum value, which indicates a significant

deceleration event.

These statistics and the visual representation

provide insights into the behaviour of the nodes

during the simulation, particularly in response to

events such as accidents. This analysis can help

understand how nodes react and adapt their speed,

which is critical for evaluating the performance and

safety of the vehicular network.

3.6.4 Overall Packet Loss

The total number of lost packets across all nodes 19.

These plots help identify specific nodes that might be

experiencing higher-than-expected packet loss,

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Figure 5: Tatal number of lost packets.

Figure 6: End to end latency variation.

making it easier to pinpoint potential issues for

further investigation or optimization. Total Lost

Packets by Node: This bar plot shows the sum of all

lost packets for each node, providing an overview of

which nodes experienced the highest and lowest total

packet loss as shown in the Figure 5.

3.6.5 Acceleration Insights

• Node 0:

– Highest average acceleration and maximum

acceleration.

– More significant speed changes compared to

other nodes.

• Nodes 5 and 6:

– Similar mean accelerations, slightly negative

(indicating slight deceleration on average).

– Node 5 has the highest variability in

acceleration.

– Node 5 also has the lowest minimum

acceleration value (significant deceleration event).

3.6.6 End-to-End Latency

In the context of this simulation scenario, we

meticulously assessed the end-to-end latency within a

5G-VANET framework where the vehicular count

fluctuated between 5 and 100 within a spatial extent

of 2500 m x 2500 m, facilitated by a single base

station (gNodeB). Each vehicle is programmed to

transmit periodic messages (CAM) comprising 1,500

bytes at a transmission rate of 6 Mbps, occurring at

an interval of 100 ms. The empirical findings

elucidate that the minimum latency consistently

Integrating 5G into VANETs: Methodological Approaches and Performance Evaluation Through Simulation

411

remains low (<8 ms) even in the presence of 100

vehicles; however, both the average and maximum

latency exhibit a considerable escalation in

correlation with an increase in vehicular density,

ultimately reaching 25 ms and 50 ms for 100 vehicles,

respectively. This phenomenon can be attributed to

network congestion and the augmented message

processing demands placed on the gNodeB. Figure 6

depicts the evolution of end to end latency depending

on the number of nodes.

3.7 Discussion

The simulation results indicate that integrating 5G

technology into VANETs offers substantial

improvements in communication performance, which

can lead to enhanced road safety and more efficient

traffic management. The reduced latency and high

packet delivery rates highlight the potential of 5G to

support real-time applications, allowing vehicles to

communicate critical information such as sudden

braking or hazardous road conditions without delay.

However, the results also suggest that there are areas

for further optimization. For instance, while the

performance metrics were strong, the system's

performance in extreme scenarios—such as during

severe weather conditions or in high-density

emergency situations—could be further investigated

to ensure robustness. The findings underline the

importance of selecting appropriate routing protocols,

such as AODV, which were shown to effectively

manage communication in dynamic environments.

Additionally, addressing security concerns is vital for

ensuring the integrity of communications,

particularly given the potential risks associated with

malicious attacks on vehicular networks. Overall, the

simulation results demonstrate the feasibility of 5G-

enhanced VANETs and their significant potential for

contributing to the development of safer and smarter

transportation systems.

4 CONCLUSION

This study has illustrated the substantial advantages

of amalgamating 5G technology with VANETs to

improve the efficacy of vehicular communication

systems. The results of the simulation studies

highlight improvements in key performance metrics

such as reduced latency, increased packet delivery

rates, which are essential for the development of

advanced safety applications. The 5G-enabled

VANET system proves capable of supporting real-

time applications crucial for collision avoidance and

traffic management, thereby contributing to enhanced

road safety. However, challenges remain, particularly

in maintaining network stability in dynamic and high-

density environments, as well as ensuring robust

security against potential threats. Future work should

focus on addressing these issues through optimized

routing protocols and enhanced security frameworks.

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