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All Sciences Proceedings
http://as-proceeding.com/
1st International Conference on Recent
and Innovative Results in Engineering
and Technology
August 16-18, 2023 : Konya, Turkey
https://as-
proceeding.com/index.php/icriret
© 2023 Published by All Sciences Proceedings
247
Efficient Content Delivery in Urban Vehicular Networks: A Hybrid RSU-
UAV Framework
Muhammad Ismail1*, Syed Luqman Shah2, Fazal Muhammad1, Zeeshan Shafiq3, Abdullah Abdullah4,
Jamal Hussaın Arman1
1Department of Electrical Engineering, University of Engineering and Technology Mardan, 23200 Mardan, Pakistan
2 Telecommunication and Networking (TeleCoN) Research Center, GIK Institute of Engineering Sciences and Technology,
Topi 23640, Pakistan
3National Center of Artificial Intelligence (NCAI), University of Engineering and Technology Peshawar, Pakistan
4Chung Ang university Seoul South Korea
*(Corresponding Author: m.ismail012018@gmail.com )
Abstract – Vehicular networks offer many advantages in smart transportation systems, even when dealing
with occasional disruptions in regular networks and relying on Road Side Units (RSUs) to share data. For
a smooth flow of information to and from smart vehicles in transportation systems, it is crucial to seamlessly
switch communication from RSUs to Unmanned Aerial Vehicles (UAVs) when a smart vehicle goes
beyond RSU coverage. However, transitioning smart vehicles from RSUs to UAVs in smart transportation
presents a significant challenge. This is because the remaining content must be efficiently delivered by
UAVs to vehicles to ensure smooth and efficient data transmission. To address this challenge, this study
proposes an advanced vehicular network design that divides the responsibility of content delivery between
RSUs and UAVs. In this research article, we propose a cooperative approach that unites UAVs and RSUs
to enhance content delivery, employing diverse strategies that may overlap or not within the context of
smart transportation. The research establishes a robust network structure, clearly outlining the roles of RSUs
and UAVs in content delivery. By maintaining a balanced utilization of communication channels between
RSUs and UAVs, resources are allocated evenly, ensuring efficient content delivery. We evaluate the
effectiveness of these strategies, both overlapping and non-overlapping, and their impact on data rates,
throughput, and overall network performance through extensive simulations. The results reveal that our
coordinated non-overlapping content delivery scheme yields higher individual RSU-UAV throughput and
the sum of which is equal to the total required content size and required throughput.
Keywords – Vehicular Network, Content Delivery, Interference Management, Channel Allocation, Resource Management.
I. INTRODUCTION
In the realm of modern urban living, smart
transportation systems play a crucial role,
seamlessly integrated into the everyday lives of
people. As urban areas see an increase in population,
there is a simultaneous rise in motor vehicles,
leading to various negative impacts like noise,
traffic congestion, roadside safety concerns,
pollution, and more. This situation calls for effective
solutions. Addressing these challenges within smart
transportation hinges on a fundamental principle:
establishing smooth communication among
vehicles navigating the roads. As vehicles traverse
diverse environments, a seamless shift between
Road Side Units (RSUs) and Unmanned Aerial
Vehicles (UAVs) becomes not just a convenience
but a vital aspect to disentangle these intricate issues
[1]. Amid this complexity, the synergy between
RSUs and UAVs emerges as a beacon of potential,
offering a promising path for content distribution in
smart transportation systems [2]. However, this
seamless integration brings along its own set of
challenges, particularly in interference management
and channel allocation [3]. A pivotal moment arises
when a vehicle, initially relying on an RSU for
content, and then transition occurs to from RSU to
UAV. Ensuring the consistent flow of content relies
248
on a smooth transition from RSU to UAV, further
complicated by RSUs initially providing half of the
content. This necessitates subsequent UAV
transmission, a refined orchestration of data transfer
that lies at the core of this intricate network
choreography.
Our research centers on crafting an advanced
vehicular network architecture that intricately
manages the fluid allocation of content delivery
responsibilities between RSUs and UAVs, aimed at
tackling this intricate challenge of smooth content
delivery to and from smart vehicles. Our objective
revolves around establishing a dynamic
environment where RSUs and UAVs
collaboratively guarantee seamless and
uninterrupted content delivery to vehicles
navigating congested urban domains. This endeavor
is realized through strategic interference control
tactics and meticulous channel allocation protocols
[4]. Our proposed strategy champions equitable
resource utilization, optimizing content distribution
efficiency by designating half of the available
communication channels to RSUs and the
remaining half to UAVs.
In this study, our primary emphasis lies in
enhancing content delivery efficiency within smart
transportation systems. We have conducted an
analysis of how RSUs and UAVs synergistically
collaborate to effectively harness available
resources and address the challenge of UAV
transitioning from RSU to UAV domains without
disrupting ongoing communication streams. Our
objective revolves around assessing the efficacy of
these strategies, encompassing both overlapping
and non-overlapping scenarios, and gauging their
implications on data rates, throughput, and the
holistic network performance, leveraging extensive
simulation techniques. The proposed system model
capitalizes on the combined strengths of RSUs and
UAVs, culminating in a seamless and highly
efficient content delivery paradigm.
The subsequent sections of this study are
organized as follows: In Sec. II, we present the
relevant prior research. Sec. III outlines the
proposed system model, encompassing
methodologies for channel allocation mechanisms
and interference management strategies. Our
obtained results, expounded in Sec. IV, precede the
paper's conclusion in Sec. V.
II. RELATED WORK
Significant research focus has been given to the
landscape of content distribution within vehicular
networks, including a variety of aspects including
RSUs, UAVs, interference management, channel
allocation, and resource optimisation. Researchers
have investigated creative strategies to increase the
effectiveness of data dissemination while assuring
continuous connectivity and better user experiences.
The crucial function of RSUs in vehicular networks
has been the subject of numerous research. Brown
et al. [5] proposed a seamless content distribution
system utilising RSUs and UAVs to accommodate
various vehicle densities.
UAVs have attracted a lot of interest for content
delivery in difficult terrain because to their dynamic
mobility and flexible coverage. Martinez et al.'s [6]
investigation into the use of UAVs for effective
content distribution showed how they may expand
network coverage and connectivity. A dynamic
UAV coverage strategy was also established by
Thompson et al. [7] to guarantee seamless content
distribution within vehicle networks.
In RSU-UAV collaborative contexts,
interference management continues to be of
paramount importance. In these networks, Davis et
al.'s [8] developed interference mitigation
approaches that emphasised improving data
dependability and lowering signal contention. This
is in line with the requirement to maximise channel
allocation in order to reduce interference and
promote effective communication. In their
optimised channel allocation algorithms for RSUs
and UAVs, White et al. [9] emphasised the
importance of balanced resource utilisation.
Resource allocation, a crucial component of
content distribution, has drawn interest in order to
guarantee the best possible use of available network
resources. In order to improve resource allocation
and network performance, Johnson et al. [10]
looked at resource allocation strategies for RSU-
UAV communication in urban environments.
Nevertheless, despite these efforts, there are gaps in
the literature that call for additional research.
While specific content delivery issues have been
addressed in previous works, a comprehensive
strategy that integrates RSUs and UAVs while
regulating interference, allocating channels, and
optimising resources has not yet been thoroughly
investigated. By outlining a comprehensive system
that seamlessly transfers content delivery
249
responsibilities between RSUs and UAVs, this
article intends to close these gaps and ensure
uninterrupted data dissemination inside crowded
traffic networks. A innovative method that
optimises the distribution of communication
channels while preserving seamless connectivity is
required due to the complex interactions between
RSUs and UAVs as well as the difficulties
associated with interference management and
channel allocation.
III. SYSTEM MODEL
In this section, we provide a clear picture of the
proposed coordinated system model, i.e.,
distribution of smart vehicles, UAVs, and RSUs.
Following that, we delve into the mathematical
modelling of the network parameters under the
proposed scenario.
A. Vehicular Network Setup
In this section, we present coordinated system
model considered in this research work that employs
a dynamic strategy to enhance content distribution
efficiency within vehicular networks. Our approach
revolves around the seamless integration of RSUs
and UAVs to ensure ehanced utilization of resources
and facilitate effective content dissemination.
İn the proposed coordinated system model we
distribute the smart vehicles evenly, with half
assigned to RSUs and the other half to UAVs for
content dissemination, as depicted in Fig. 1. Smart
vehicles possess the capability to seamlessly
transition between RSUs and UAVs, or vice versa,
as they navigate the network. This smooth transition
ensures a continuous and effective distribution of
content, accommodating the dynamic mobility
patterns of vehicles.
In cases where a vehicle initially receives half of
its content from an RSU and subsequently enters the
coverage area of a UAV, the UAV dynamically
assumes responsibility for delivering the remaining
half of the content. This intelligent handover
mechanism enhances content delivery efficiency by
capitalizing on the strengths of both RSUs and
UAVs.
The essence of our dynamic and intelligent
resource allocation strategy lies in enhancing
throughput and content distribution efficiency. This
technique maximizes the utility of available RSUs
Fig. 1. Coordinated content delivery in vehicular network.
250
and UAVs, resulting in an elevated level of content
delivery performance across the network.
B. Mathematical Modeling
Assume that a scheduling parameter for the
resources of the RSU and UAV is represented by
v,n
t
V
and
,
t
wvn
respectively is:
RSU1 whenvehiclev serveby
,0 otherewise
t
Vvn
=
(1)
1 vehicle v served byUAV
,0 otherwise
t
wvn
=
(2)
Next, the resources for uplink from vehicle v
to the UAV for content n are as follows:
1 vehicle v is sending to UAV
=
,0 otherwise
n
tvn
(3)
The data rate for downloading content n to vehicle v
is:
v,n
,n 0
t t A D
v T if T t T
tv R v R v
DRv otherwise
→ → →
=
→
U
(4)
Where
A
TRv→
and
D
Rv
T→
is the arrival and
departure time to RSU coverage area and
T
is the
time slot. In the same way when content n is
uploaded to UAV through vehicle v is:
v,n
,n 0
t t A D
D T if T t T
tv U v U v
DvU otherwise
→ → →
=
→
U
(5)
Then the data rate for using the UAV to serve
vehicle v is:
v,n
0
t t A D
w T if T t T
tU v U v U v
DUv otherwise
→ → →
=
→
(6)
After that, the vehicle v is only deemed to have
received all of the content n it required from the
RSU, UAV, or both, as indicated below:
1 ( (D )
v,n ,n v,n
0 0 0
),
0 otherwise
N T N t
C Z C
n R v
n t n
t
S D v V
v U v
→
= = =
= +
→
(7)
IV. RESULTS AND ANALYSIS
A. Coordinated Content delivery and throughput
without overlapping between RSU and UAV
Fig. 2. Coordinated content delivery without overlapping
between RSU and UAV
Fig. 3. Coordinated throughput without overlapping between
RSU and UAV
The proposed coordinated content delivery
method, shown in Fig. 2, is intended to reduce
interference while optimising resource distribution
between RSUs and UAVs in the context of a
crowded vehicular network. With the help of our
planned strategy, a vehicle is effortlessly handed off
from RSU to UAV while still getting 5.2 units of the
entire content size from RSU. The UAV then
dynamically transmits the remaining 2.5 units of
content. The fact that the overall amount of content
given by RSU and UAV precisely matches the total
amount of content which is 7.7 highlights the lack
of overlap in their delivery methods. As shown in
Fig. 3, the throughput that RSU and UAV were able
251
to accomplish as a result, without any overlap,
demonstrates the effectiveness of our interference
control, channel allocation, and resource allocation
techniques, providing successful content delivery in
crowded vehicular networks. Fig. 3 displays the
comparable throughput without overlap provided by
RSU and UAV.
B. Content delivery and throughput with
overlapping between RSU and UAV
Fig. 4. Content delivery with overlapping between RSU and
UAV
Fig. 5. Throughput with overlapping between RSU and UAV
Results shown in Fig. 4 in the context of content
distribution with overlap between RSU and UAV
show an intriguing phenomena. It is obvious that the
entire amount of content delivered exceeds the sum
of the individual amounts of content delivered by
RSU and UAV. This finding emphasises how well
resources are used by demonstrating non overlap
between RSU and UAV as discussed earliar in our
coordinated methodology in sub-heading A of result
section.. Additionally, the matching throughput, as
shown in Fig. 5, supports the equitable content
delivery by both RSU and UAV but not equall to the
total required content and required throughput,
highlighting the effective use of our coordinated
methodology as discussed in sub-heading A of
result section. This result demonstrates the potential
for intelligently regulating the interaction between
RSU and UAV to optimise content delivery in
vehicle networks.
V. CONCLUSION AND FUTURE WORK
In conclusion, our study offers a tactical strategy
utilising RSUs and UAVs to overcome the
difficulties of content distribution in crowded
vehicle networks. We maximise the efficiency of
content distribution through efficient interference
control, channel allocation, and resource
distribution. Our method guarantees a smooth
handoff between RSUs and UAVs in the absence of
overlap, delivering complete required content and
throughput. In contrast, when taking into account
overlap, the combined content delivery by RSU and
UAV follows the required content and throughput.
By enabling intelligent cooperation between RSUs
and UAVs in urban settings, our study lays the
groundwork for improving content delivery in
vehicle networks.
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