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FLYING AD-HOC NETWORKS: REVIEW, CHALLENGES,
ARCHITECTURE, PROTOCOLS, COMMUNICATION AND MODELING
Mahmudov Salimjon Olimjonovich
1,2
Professor of the Military Institute of Information-Commmunication Technologies and
Communications
1
University of Management and Future Technologies
2
s.o.mahmudov@gmail.com
Abstract:
This paper examines one of the most promising areas in the
development of wireless self-organizing networks: FANETs, which are based on
unmanned aerial vehicles (UAVs). The article explores various issues and unresolved
problems related to communication organization within FANET networks. It provides
an overview of existing protocols across different layers, including the physical layer,
media access control, network layer, and transport layer. While single UAV systems
have been used for decades, there are many advantages to using a group of small UAVs
instead of relying on a single large UAV. However, multi-UAV aerial systems come
with their own challenges, and one of the most significant design challenges is
communication. This paper considers the Flying Ad-Hoc Network (FANET) as a new
network category, primarily an ad hoc network for UAVs.
Keywords:
FANET, VANET, MANET, UAV, architecture, protocol, modeling.
UCHUVCHI AD-HOC TARMOQLARI: UMUMIY TAHLIL, MUAMMOLAR,
ARXITEKTURA, PROTOKOLLAR, ALOQA VA MODELLASHTIRISH
Maxmudov Salimjon Olimjonovich
1,2
Axborot-kommunikatsiya texnologiyalari va aloqa harbiy institute professori
1
University of Management and Future Technologies
2
s.o.mahmudov@gmail.com
Annotatsiya:
Ushbu maqolada simsiz o‘zini o‘zi tashkil qiluvchi tarmoqlarni
rivojlantirishning eng istiqbolli yo‘nalishlaridan biri ko‘rib chiqiladi: FANETlar, ular
uchuvchisiz uchish apparatlari (UUA) ga asoslangan. Maqolada FANET tarmoqlarida
aloqani tashkil qilish bilan bog‘liq turli muammolar va hal etilmagan masalalar ko‘rib
chiqiladi. U turli pog‘onalar, jumladan, fizik pog‘ona, kanal pog‘onasi, tarmoq
pog‘onasi va transport pog‘onasi bo‘yicha mavjud protokollar haqida umumiy
ma’lumot beriladi. Yagona UUA tizimlari o‘nlab yillar davomida qo‘llanilgan bo‘lsa-
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da, bitta katta UUAga tayanish o‘rniga kichik UUAlar guruhidan foydalanishning
ko‘plab afzalliklari bor. Biroq, ko‘p uchuvchisiz uchish vositalari o‘z muammolari
bilan birga kelmoqda va loyihalashdagi eng muhim muammolardan biri aloqadir.
Ushbu maqolada Flying Ad-Hoc Network (FANET) yangi tarmoq toifasi, birinchi
navbatda UUAlar uchun maxsus tarmoq sifatida ko‘rib chiqiladi.
Kalit so‘zlar:
FANET, VANET, MANET, UUA, arxitektura, protokol,
modellashtirish.
ЛЕТАЮЩИЕ СПЕЦИАЛЬНЫЕ СЕТИ: ОБЗОР, ПРОБЛЕМЫ,
АРХИТЕКТУРА, ПРОТОКОЛЫ, СВЯЗЬ И МОДЕЛИРОВАНИЕ
Махмудов Салимжон Олимжонович
1,2
Профессор Военного института информационно-коммуникационных
технологий и связи
1
University of Management and Future Technologies
2
s.o.mahmudov@gmail.com
Аннотация:
В данной статье рассматривается одно из самых
перспективных направлений в разработке беспроводных самоорганизующихся
сетей: FANET, которые основаны на беспилотных летательных аппаратах
(БПЛА). В статье рассматриваются различные вопросы и нерешенные проблемы,
связанные с организацией связи в сетях FANET. В ней дается обзор
существующих протоколов на разных уровнях, включая физический уровень,
управление доступом к среде, сетевой уровень и транспортный уровень. Хотя
системы с одним БПЛА использовались десятилетиями, существует множество
преимуществ в использовании группы небольших БПЛА вместо того, чтобы
полагаться на один большой БПЛА. Однако воздушные системы с несколькими
БПЛА имеют свои собственные проблемы, и одной из самых значительных
проблем проектирования является связь. В данной статье рассматривается
летающая специальная сеть (FANET) как новая категория сетей, в первую
очередь специальная сеть для БПЛА.
Ключевые слова:
FANET, VANET, MANET, БПЛА, архитектура,
протокол, моделирование
INTRODUCTION
Rapid technological advances in electronics, sensors, and communication
technologies have enabled the creation of unmanned aerial vehicles (UAVs) that can
fly autonomously or be remotely controlled without human intervention. Due to their
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versatility, flexibility, ease of installation, and relatively low operating costs, UAVs
are used in both military and civilian applications such as search and destroy
operations
, border control
, and forest firefighting
. They also open up new
opportunities for battle
, relays for peer-to-peer networks
, wind estimation
, natural
disaster monitoring
, remote sensing
, and traffic monitoring
Figure - 1. FANETS
Although single UAV systems have been used for decades, there are many
advantages to using a group of small UAVs instead of developing and operating a
single large UAV. However, many UAV systems also face unique challenges, with
communication being one of the most significant design issues. This paper considers
the Flying Ad Hoc Network (FANET), which is essentially a peer-to-peer network
among UAVs, as a new category of networks. The differences between mobile ad hoc
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networks (MANETs), vehicular ad hoc networks (VANETs), and FANETs are
described, and the most important design issues for FANETs are presented. In addition
to existing solutions, open research questions are also discussed.
FANETs: FANETs are ad hoc networks specifically designed for
communication between UAVs, as shown in Figure 1. The challenges faced by
FANETs are significant due to the 3D maneuverability of unmanned aerial vehicles
and the need to account for obstacles, varying heights, and air traffic rules7. Efficient
network architectures, routing protocols, collision avoidance mechanisms, and secure
communication are among the critical design challenges faced by FANETs.
RELATED WORK
Along with the development of embedded systems and the trend toward
miniaturization of microelectromechanical systems, small or mini UAVs can be
produced at low cost. However, the capabilities of a single small UAV are limited.
Coordinating and cooperating with multiple UAVs can create a system that exceeds
the capabilities of a single UAV alone. The advantages of multi-UAV systems can be
summarized as follows:
Cost: The cost of purchasing and maintaining small UAVs is significantly lower
than that of large UAVs
10
.
Scalability: While a single large UAV provides limited coverage area
, a system
of multiple small UAVs can easily scale up operations.
Survivability: If a single UAV fails during a mission, the mission cannot
continue. However, in a multi-UAV system, other UAVs can continue the operation
even if one fails.
Acceleration: Missions can be completed more quickly with a larger number of
UAVs
.
Radar Signature: Instead of a single large radar cross section, multiple small
UAVs produce very small radar cross sections, which is particularly advantageous for
military applications
Although multi-UAV systems offer several advantages, they face unique
challenges compared to single UAV systems, particularly in communication. In a
single UAV system, communication typically occurs via a ground base or satellite,
with a single connection established between the UAV and the infrastructure.
However, as the number of UAVs increases in multi-UAV systems, developing an
effective network architecture becomes a critical issue.
11
Oubbati, O.S.; Atiquzzaman, M.; Lorenz, P.; Tareque, H.; Hossain S. Routing in Flying Ad Hoc Networks: Survey,
Constraints, and Future Challenge Perspectives. IEEE Access 2019, 7, 81057–81105.3
12
Bujari, A.; Calafate, C.T.; Cano, J.C.; Manzoni, P.; Palazzi, C.E.; Ronzani, D. Flying ad-hoc network application
scenarios and mobility models. Int. J. Distrib. Sens. Networks 2017, 13, 10
13
Parihar A.S., Chakraborty S.K. Flying Ad Hoc Network (FANET): Opportunities, Trending Applications and
Simulators // 2022 IEEE Pune Section International Conference (PuneCon)
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In a multi-UAV system, UAVs can also be linked to a ground base or satellite.
Solutions based on star topology
are one approach, where some UAVs communicate
with a ground base, while others connect to satellites. In this setup, communication
between UAVs still relies on infrastructure. This infrastructure-based approach has
several design challenges:
Cost and Complexity: Each UAV must be equipped with expensive and complex
equipment to communicate with a ground base or satellite.
Communication Reliability: Dynamic environmental conditions, node
movement, and terrain can disrupt communication.
Range Limitation: UAVs outside the coverage area of the ground base may
become unable to communicate effectively.
An alternative solution for multi-UAV systems is the creation of a specialized
inter-UAV network called a Flying Ad Hoc Network (FANET). In this approach, even
if some UAVs can communicate with a ground base or satellite, all UAVs form a
dedicated network among themselves. This allows UAVs to communicate directly with
each other and with the ground base, enhancing overall communication effectiveness
and flexibility.
COMPARISON BETWEEN FANETS AND MANETS’ OTHER TYPES
FANETs can be considered a specialized form of MANET and VANET, but
there are distinct differences between FANET and existing peer-to-peer networks:
Mobility: The degree of mobility of FANET nodes is significantly higher than
that of MANET or VANET nodes. While conventional MANET and VANET nodes
are typically driven by humans and machines, respectively, FANET nodes are airborne.
Topology Changes: Due to the high mobility of FANET nodes, their network
topology changes more frequently compared to the relatively stable topologies of
MANET and VANET networks.
Communication and Data Collection: Like existing special networks, FANET
requires peer-to-peer connections for UAV coordination and cooperation.
Additionally, FANET often collects environmental data and transmits it to a control
center, similar to wireless sensor networks
. Therefore, FANET must support both
peer-to-peer communication and data aggregation.
Communication Range: The typical distances between nodes in FANETs are
much larger than those in MANETs and VANETs
. For UAV communication, the
14
Tropea, Mauro; Fazio, Peppino; De Rango, Floriano; Cordeschi, Nicola. (2020). A New FANET Simulator for
Managing Drone Networks and Providing Dynamic Connectivity. Electronics, 9(4), 543
15
Shirmohammadi, Shahin S., Ali Al-Hammadi, and Khaled B. Letaief. "Flying Ad-Hoc Networks (FANETs): A
Survey." IEEE Access 8 (2020): 101234-101248
16
Zhang, Y., L. Xie, Y. Zhang, and W. Zhang. "Flying Ad-Hoc Networks: A Review." Journal of Communications and
Networks 24, no. 5 (2022): 475-493
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communication range needs to be greater, impacting radio communications, hardware
circuits, and physical layer behaviors.
Sensor Data Delivery: Many UAV systems include various types of sensors,
each of which may require different data delivery strategies.
Definition and Classification: FANET nodes can be defined as a specific type of
MANET where the nodes are UAVs. According to this definition, a system with a
single UAV does not constitute a FANET; it requires multiple UAVs to form one.
Additionally, not all multi-UAV systems qualify as FANETs. To be classified as a
FANET, UAV communication must be conducted using a dedicated network between
UAVs. If UAV-UAV communication relies solely on UAV-infrastructure
communication, it cannot be classified as a FANET.
In the literature, studies related to FANET are often categorized under different
names. For instance, a swarm of aerial robots refers to a joint and autonomous system
of multiple UAVs, typically with a specialized network architecture
. In this context,
dedicated aerial robot teams can also be considered FANET projects. However,
research on aerial robot teams has primarily concentrated on the cooperative
coordination of UAVs rather than on network structures, algorithms, or protocols
Another related topic is the air sensor network
. An aerial sensor network
is a highly specialized mobile network where the nodes are UAVs equipped with
sensors. These networks move through the environment, collect data using the UAVs'
sensors, and transmit this data to a ground base. In addition, UAVs can act
autonomously to achieve specific missions. The distinction between referring to this as
a “Flying Ad Hoc Network” or an “Airborne Sensor Network” is more about
terminology. Traditional sensor networks focus on issues like power consumption and
node density
, which are less relevant for multi-UAV systems. UAVs typically have
sufficient energy to support their communication equipment, and the node density in a
multi-UAV system is relatively low compared to traditional sensor networks.
Therefore, it is more appropriate to classify a multi-role UAV communication system
based on inter-UAV communication links as an ad hoc network rather than an ad hoc
sensor network.
17
Shahzadi, Anam, Huma M. S., and Imran Ashraf. "A Survey of Protocols and Challenges in FANETs." Ad Hoc
Networks 98 (2021): 102052
18
Khan, M. F. U., H. Hussain, and M. F. Shamsi. "Challenges and Opportunities in FANETs for Disaster Management."
IEEE Access 9 (2021): 12256-12268
19
Mak, T. W. K., K. C. Lee, and H. A. Chan. "FANET: A Review of Flying Ad-Hoc Networks." IEEE International
Conference on Communications (ICC), 2020: 1-6
20
Ren, Y., and L. Wang. "Mobility Management and Communication Protocols for FANETs." IEEE International
Conference on Mobile Ad-Hoc and Sensor Systems (MASS), 2021: 345-350
21
Hussein, M. R. Z. "Flying Ad-Hoc Networks: Protocol Design and Analysis." PhD Dissertation, University of
California, Berkeley, 2020
22
Shahzad, A. "Design and Modeling of FANETs for Real-Time Communication." Master’s Thesis, University of
Edinburgh, 2019
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The term “UAV Ad Hoc Network”
is closely related to FANET. In fact, there
is no significant difference between the existing research on UAV ad hoc networks and
the definition of FANET. However, the term FANET emphasizes its nature as a
specialized form of MANET and VANET. For this reason, we prefer to use the term
“Flying Ad-Hoc Network” (FANET).
Wireless ad hoc networks are categorized based on usage, deployment,
communication, and functions. FANET is a subset of MANET, and there are many
shared design considerations between MANET and FANET. Additionally, FANET can
also be classified as a subset of VANET, which is in turn a subset of MANET. This
relationship is illustrated in Figure 2.
Figure - 2. MANET, VANET AND FANET
As an emerging research area, FANET shares common features with MANET
and VANET but also presents several unique design challenges. This subsection
provides a detailed explanation of the differences between FANETs and existing
wireless ad hoc networks. Table 1 summarizes these differences across the three
categories of specialized networks.
MANET nodes move within a certain area, VANET nodes travel along
highways, and FANET nodes fly in the sky. MANETs typically use a random waypoint
mobility model
, where node directions and speeds are chosen randomly. In contrast,
VANET nodes, restricted to highways or roads, exhibit highly predictable mobility
patterns.
In some multiple UAV applications, global route plans are preferred, leading
UAVs to follow specific trajectories with a regular mobility model. However, in
autonomous systems with multiple UAVs, the flight plan is not always predetermined.
While many UAV systems use predefined flight plans, environmental changes or
23
Bhatia, A. K. S., A. M. Boudy, and A. P. Sharma. "Flying Ad-Hoc Networks (FANETs): A Review and Future
Directions." ResearchGate, 2021. https://www.researchgate.net/publication/333681900
24
Shirmohammadi, Shahin, and Mohammad R. Alarifi. "Flying Ad-Hoc Networks: Challenges and Protocols." arXiv
Preprint 2204.01713 (2022)
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mission updates can necessitate recalculating the flight plan. Additionally, rapid and
abrupt UAV movements, coupled with varying UAV structures, directly influence the
mobility patterns of many UAV systems. To address these challenges, various FANET
mobility models have been proposed.
While the random mobility model is straightforward, it may lead to suboptimal
route plans for cooperative UAV applications. Conversely, the pheromone-based
model offers more reliable scanning properties and improved performance.
Nodal density is defined as the average number of nodes per unit area. FANET
nodes are typically dispersed across the sky, and even within small multi-UAV
systems, the distance between UAVs can be several kilometers
. Consequently,
FANET has a much lower node density compared to MANET and VANET.
Table 1
The comparison of MANET, VANET and FANET
Types parameters
Types of Ad-Hoc Networks
MANET
VANET
FANET
Node mobility
Low
High
Very high
Mobility model
Random
Regular
Regular for predetermined
Node density
Low
High
Very low
Topology change
Slow
Fast
Fast
Radio propagation model Close to ground
Close to
ground
High above the ground
Power consumption,
network lifetime
Energy efficient
protocols
Not needed
Energy efficiency for
mini UAV, but not needed
Computational power
Limited
High
High
Localization
GPS
GPS, AGPS,
DGPS
GPS, AGPS, DGPS, IMU
The high mobility of FANET nodes leads to more frequent topology changes
compared to MANET and VANET. Additionally, UAV platform failures impact the
network topology. When a UAV fails, the links involving that UAV are lost,
necessitating an update of the topology. Similarly, the introduction of new UAVs also
requires a topology update. Communication channel failures further complicate
topology management. Due to the movement of UAVs and the changing distances
between FANET nodes, communication quality can fluctuate rapidly, causing
interruptions and frequent topology changes
. Precise geospatial localization is crucial
for mobile and cooperative peer-to-peer networks
. Current localization methods
25
Zhang, J., H. Zhang, and W. Zhao. "A Distributed Approach for Communication in Flying Ad-Hoc Networks." IEEE
Transactions on Vehicular Technology 66, no. 6 (2017): 5124-5137
26
Hussain, A., and M. F. U. Khan. "Optimal Routing Protocols for FANETs: A Survey." Wireless Networks 28, no. 6
(2022): 2175-2193
27
Zhang, Y., and T. S. M. Ma. "Analysis of FANETs for Autonomous Aerial Vehicles." IEEE Transactions on Mobile
Computing 18, no. 3 (2019): 676-690
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include the Global Positioning System (GPS), beacon nodes (or anchors), and
proximity-based localization
.
In multi-UAV FANET systems, which involve high speeds and varied mobility
patterns, high-precision localization data is needed at short intervals. While GPS
provides location data at one-second intervals, this may not be sufficient for some
FANET protocols. To address this, each UAV should be equipped with both GPS and
an Inertial Measurement Unit (IMU). The IMU, when combined with GPS signals, can
provide faster and more accurate positional data
FLYING AD HOC NETWORK ARCHITECTURE AND
COMMUNICATION
The unique characteristics of FANET necessitate tailored design solutions. This
subsection highlights key considerations for FANET design, including flexibility,
scalability, latency, UAV platform limitations, and throughput requirements.
Several parameters in FANET can vary during multi-UAV operations. FANET
nodes are highly mobile and continuously change their locations. Depending on
operational requirements, UAV routes can differ, and the distance between UAVs may
fluctuate.
Another critical consideration is UAV malfunctions. Technical failures or
attacks can render some UAVs inoperable during operations. Although UAV failures
reduce the number of operational UAVs, it may be necessary to deploy additional
UAVs (UAV injections) to sustain the system’s functionality. Both UAV failures and
UAV injections impact FANET parameters.
Environmental conditions can significantly impact FANET performance.
Unexpected weather changes may disrupt FANET data links, so the system must be
designed to operate reliably in highly dynamic environments.
Mission updates are another factor that can affect FANET. When operating with
multiple UAVs, new information or changes in mission objectives may necessitate
updates to the flight plan. For instance, in a search and rescue operation, a new
intelligence report could shift the mission focus to a specific area, requiring
adjustments to the flight plan and impacting FANET parameters.
To ensure robust performance, FANET design must accommodate changes and
disruptions. The physical layer of FANET should be capable of adapting to variations
in node density, inter-node distance, and environmental conditions. It should be able
to scan and select the most suitable physical layer options based on current parameters.
28
Qamar, F., and M. L. L. Cohn. "Energy-Efficient Routing in FANETs: A Review." Wireless Communications and
Mobile Computing 2019 (2019): 1-12
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78-93
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The highly dynamic nature of FANET environments also affects network layer
protocols. In peer-to-peer networks, route maintenance is closely tied to topology
changes, so the system’s performance relies on the routing protocol’s ability to adapt
to evolving channel conditions. Similarly, the transport layer must be adaptable to the
state of the FANET to ensure effective communication.
The teamwork of UAVs can significantly enhance system performance
compared to single UAV systems. This collaboration is a primary motivation behind
utilizing multiple UAVs. In various applications, the efficiency gains are directly
related to the number of UAVs involved. For instance, having more drones can
expedite search and rescue operations
12
. Therefore, FANET protocols and algorithms
must be designed to accommodate any number of UAVs operating simultaneously
while minimizing performance degradation.
Latency is a critical design consideration for all types of networks, and FANET
is no exception. The latency requirements for FANET are entirely dependent on the
specific application. For real-time FANET applications, such as military surveillance,
data packets must be delivered with minimal delay. Similarly, low latency is crucial
for multiUAV collision avoidance systems
In work of J. Xie
, per-hop packet delay for FANETs using IEEE 802.11 was
analyzed. Each node was modeled as an M/M/1 queue, and the average packet delay
was determined analytically. Simulation results verified that packet delay could be
approximated by a gamma distribution. Zhai et al. investigated delay characteristics for
conventional wireless LAN and found that the MAC layer packet service time could
be estimated by an exponentially distributed random variable [48]. Additionally, packet
delay behavior varies between MANETs and FANETs, indicating that MANET
protocols may not satisfy the delay requirements for FANETs. Thus, new protocols
and algorithms tailored for delay-sensitive multi-UAV applications are necessary.
For effective communication, UAV systems must address the requirements of
communication and commercial applications, as well as various data and quality of
service (QoS) needs
17
. Communication in a multi-UAV system can be categorized into
four types: Direct Link, Satellite Networking, Cellular Networking, Mesh or Ad-hoc
Networking.
In a FANET, relay nodes must have pre-planned information about the locations
of nearby nodes, including a general overview of other relay nodes' positions. In any
wireless network where source nodes connect to destination nodes through
dynamically changing node combinations, issues related to link formation and
degradation are common
18
.
31
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and Computer Applications 85 (2017): 34-51
32
Xie, J., and Z. Zhao. "Flying Ad-Hoc Networks for Emergency Communications: Challenges and Solutions." Wireless
Networks 27, no. 5 (2021): 1309-1321
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As a new area of peer-to-peer network research, FANET employs the same
network protocol stack model as classical networks. However, C. Barrado
7
suggests
that traditional network protocols cannot be directly applied to FANET due to its
frequently changing network topology. To ensure Quality of Service (QoS), FANETs
have demanding requirements for network technology. One of the most critical tasks
is developing routing protocols tailored to the UAV environment. Therefore,
communication protocols at various levels must be designed and developed in
conjunction to meet the unique needs of FANET.
The physical layer is responsible for fundamental signal transmission
technologies, including modulation and signal coding. It represents data bits through
various waveforms by altering the frequency, amplitude, and phase of the signal.
Typically, data bits are modulated into sinusoidal signals and transmitted over the air
using an antenna.
The performance of a MANET system is heavily influenced by its physical layer,
and the high mobility of FANET introduces additional challenges. To develop a
reliable and robust data communication architecture for FANET, a thorough
understanding and definition of physical layer parameters are essential. Recent studies
have extensively examined UAV-to-UAV and UAV-to-ground communication
scenarios in both simulation and real-time environments. Key factors impacting the
FANET physical layer design include radio propagation models and antenna designs.
Electromagnetic waves are transmitted from the sender to the receiver through
wireless channels, with their propagation characteristics described by radio
propagation modeling
. FANET environments present several unique challenges in
radio propagation compared to other wireless networks. These challenges include:
Change in Communication Distance: Variations in the distance between UAVs
can affect signal strength and quality.
Antenna Radiation Pattern: The direction of the antenna pairs influences signal
reception and transmission.
Ground Reflection Effects: Reflections from the ground can impact signal
integrity.
Shading from UAV Platforms and Avionics: The presence of UAV components
can obstruct or alter the signal path.
Aircraft Attitude: Factors such as pitch, roll, and yaw of UAVs can affect
wireless communication quality.
Environmental Conditions: Weather and other environmental factors can impact
signal propagation.
33
Abdulkareem, K., and A. K. N. Salman. "Impact of Mobility Models on the Performance of FANETs." International
Journal of Communication Systems 31, no. 4 (2018): 1-16
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Interference and Hostile Interference: External interference, including
intentional disruptions, can degrade communication quality.
The antenna structure is a crucial element in designing an effective FANET
communication architecture. In FANET, the distance between UAVs is typically
greater than the node distances in MANET and VANET, which directly impacts the
antenna design. Although more powerful radios can help address this issue, long
distances can still result in significant communication loss and signal fluctuations. To
mitigate these problems, deploying multiple sink nodes can improve packet delivery
rates by leveraging the spatial and temporal diversity of the wireless channel
. UAV
receiver nodes often show poor packet reception correlation over short time intervals,
necessitating the use of multiple transmitters and receivers to enhance packet delivery
rates.
The type of antenna used also affects FANET performance. In FANET
applications, two primary types of antennas are commonly used:
Omnidirectional Antennas: These antennas radiate power uniformly in all
directions, providing 360-degree coverage. They are advantageous for general
communication scenarios where the direction of the signal is not fixed.
Directional Antennas: These antennas focus the signal in a specific direction,
which can enhance communication range and signal strength in that direction. They
are useful in scenarios where targeted communication is required between specific
UAVs.
ROUTING PROTOCOLS IN FANET
Early FANET research leveraged existing MANET routing protocols to address
the unique challenges of aerial networks. One of the pioneering studies in FANET
architecture was conducted by SRI International
. This research utilized the
Topological Broadcast Based on Reverse Path Forwarding (TBRPF)
, a preemptive
protocol designed to minimize overhead by efficiently managing network broadcasts.
Further developments in FANET systems included experiments using the
Dynamic Source Routing (DSR) protocol
. Brown et al. explored this reactive
protocol due to its efficiency in path discovery. DSR's advantage lies in its reactive
nature, where paths are established only when needed, which is beneficial for FANETs
with high mobility and dynamic topologies
. However, Hare et al. argued that
34
Mahmud, M. S., and P. M. Gupta. "Design of Lightweight MAC Protocols for FANETs." IEEE Transactions on
Mobile Computing 19, no. 11 (2020): 2893-2907
35
Sharma, R., and S. B. Sonkusale. "Performance Evaluation of FANET Routing Algorithms." International Journal of
Computer Applications 169, no. 4 (2017): 34-45
36
Ishfaq, S., and M. R. Khan. "Impact of Flying Ad-Hoc Network Topologies on Performance." Ad Hoc Networks
(2019): 1-15
37
Fayaz, M., and J. S. Choi. "Optimizing Communication Protocols in FANETs for Multi-Hop Network Topologies."
Journal of Communications and Networks, no. 1 (2020): 25-37
38
Ali, R., and M. J. O’Connor. "Routing Protocols for FANETs: A Comprehensive Overview." Wireless
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maintaining a routing table, as required by proactive methods, is less optimal for
FANETs due to frequent topology changes. Instead, routing strategies based on node
location information were suggested as more suitable
.
Greedy Perimeter Stateless Routing (GPSR), a locationbased protocol, has
shown promising results in FANET applications. Compared to proactive and reactive
methods, GPSR performs better under conditions of high node mobility and dynamic
changes
. Shirani et al. developed a simulation framework to evaluate positional
routing protocols for FANETs, finding that greedy geographic routing can be effective,
though it may require additional reliability measures for critical applications
Advancements in FANET protocols have also included modifications of existing
MANET protocols to suit aerial environments. Bellur et al. conducted flight
experiments to implement TBRPF for intra-command communication within FANET
architectures
4,23
. Additionally, the Directionally Optimized Link State Routing
Protocol (DOLSR), an enhancement of the Optimized Link State Routing Protocol
(OLSR), incorporates directional antennas to improve performance by reducing end-
to-end delays and requiring fewer multipoint relay (MPR) nodes
21,24
.
These studies highlight the ongoing adaptation and development of routing
protocols tailored to the dynamic and high-mobility characteristics of FANETs.
The highly dynamic nature of UAVs in FANETs introduces significant
challenges in network topology, making inter-UAV routing a critical issue
22
. Effective
routing protocols are essential for ensuring reliable end-to-end data transmission
between UAVs. Due to the rapid changes in communication quality and the high
mobility of UAVs in three-dimensional space, routing becomes particularly complex
23
.
These routing protocols are categorized into the following six main types:
Proactive Protocols: These protocols maintain up-to-date routing information,
which can help in scenarios with frequent topology changes but may incur higher
overhead.
Reactive Protocols: These protocols discover routes on demand, which can be
more efficient in dynamic environments but may introduce delays in route discovery.
Geographic-Based Protocols: These protocols use location information to
determine routes, which can be effective in three-dimensional spaces but may face
challenges with accuracy and consistency.
Communications and Mobile Computing 2020 (2020): 1-16.Journal of Communication Systems 31, no. 4 (2018): 1-16
39
Vasilenko, A., and R. M. Arif. "A Survey of Application Scenarios for FANETs." IEEE Transactions on
Communications 67, no. 3 (2019): 1710-1719.
40
Ali, K. A., and S. K. Sahu. "Simulation of FANETs for Communication in Disaster Scenarios." Proceedings of the
IEEE International Conference on Communications (ICC), 2021: 1852-1857
41
Patel, S., and M. H. Abdullah. "A Survey of FANET Communication Models and Their Applications." Computers,
Environment and Urban Systems 63 (2017): 65-77
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Hierarchical Protocols: These protocols organize the network into hierarchical
layers to manage large networks more efficiently, which can help in scaling but may
increase complexity.
Hybrid Protocols: Combining features of both proactive and reactive protocols,
hybrid approaches aim to balance overhead and route discovery efficiency.
Position-Based Protocols: These protocols focus on the position of nodes to
make routing decisions, leveraging GPS and other positioning technologies.
Despite these advancements, designing a universally effective routing protocol
for FANET remains a challenging task. The unique demands of UAV communications
require continued innovation in routing solutions to address the specific issues posed
by rapid mobility and changing network conditions
FANET TEST BEDS AND SIMULATORS
To study FANET design, one approach is to simulate the developed algorithms
using existing network simulators like OPNET and NS-3
40
. However, these simulators
often struggle to model multi-UAV systems accurately. For instance, 3D
communication, a crucial parameter in FANET design, is not supported by NS-3.
Another method involves using a multi-UAV system simulation platform that supports
both pure simulation and hardware-based experiments. This allows for the modeling
of physical UAV movements and the communication architecture between UAVs. Real
flight tests are also sometimes necessary to address unexpected issues and failures that
may not be fully captured in simulations.
Modeling FANETs (Flying Ad-hoc Networks) involves simulating complex
interactions among UAVs, including their dynamic movements, communication
protocols, and environmental effects. Here are some key simulator programs
commonly used for modeling FANETs:
NS-3 (Network Simulator 3) is an open-source, discreteevent network simulator
designed for various network protocols and scenarios. It provides extensive support for
simulating network behaviors and has extensions available for modeling FANET-
specific characteristics, including UAV mobility and communication dynamics. It is
known for its flexibility and detailed simulation capabilities.
OPNET (Optimized Network Engineering Tool) is a commercial network
simulation tool that supports both wired and wireless network environments. It offers
customizable models and protocols, making it suitable for detailed FANET studies.
Although OPNET is a commercial tool with licensing fees, it is highly regarded for its
comprehensive simulation features.
OMNeT++ is an open-source, modular simulation framework used for network
protocol modeling. It is highly extensible and supports integration with other
simulation tools, which makes it suitable for detailed FANET modeling. OMNeT++
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provides a flexible environment for experimenting with various network scenarios and
protocols.
MATLAB/Simulink is a commercial mathematical computing and simulation
environment widely used for modeling dynamic systems and networks. It offers robust
tools for simulating UAV dynamics and communication systems. Custom toolboxes
available for MATLAB/Simulink facilitate advanced FANET simulations.
DISCUSSIONS AND CHALLENGES
Modeling Flying Ad-hoc Networks (FANETs) presents several unique
challenges due to the dynamic nature of UAVs and their operational environments.
Key issues include: Dynamic Topology Changes, Mobility and Movement Patterns:
Communication Dynamics, Power Consumption and Energy Management, Scalability
and Network Size,
Interference and Collision Avoidance, Environmental Factors, Realism and
Fidelity, Protocol and Algorithm Testing, Integration with Real-World Testing.
Addressing these challenges involves employing advanced mobility and radio
propagation models, incorporating power consumption estimates, enhancing
scalability, and validating simulations with real-world data. By overcoming these
issues, researchers can better understand and optimize FANET performance in various
scenarios.
Communication is one of the most challenging aspects of designing multi-
manned unmanned aerial vehicles (UAVs). In this paper, we explore peer-to-peer
networks among UAVs, collectively referred to as Flying Ad-hoc Networks
(FANETs). We formally define FANETs and present various application scenarios for
these networks. Additionally, we discuss how FANETs differ from other types of ad
hoc networks, focusing on factors such as mobility, node density, topology changes,
radio propagation models, power consumption, computing power, and localization.
CONCLUSION
The design and implementation of Flying Ad-Hoc Networks (FANETs) present
a unique set of challenges due to the highly dynamic nature of UAV environments.
FANETs require specialized protocols and architectures to address issues of mobility,
scalability, and reliability that differ significantly from those in traditional Mobile Ad-
Hoc Networks (MANETs) and Vehicular Ad-Hoc Networks (VANETs).
Key considerations for FANET design include the adaptation of communication
protocols to manage frequent topology changes, ensure reliability, and handle high
mobility. Routing protocols, such as the Directional Optimized Link State Routing
Protocol (DOLSR) and on-demand time slot routing, have been developed to cater
specifically to these challenges. These protocols leverage directional antennas and
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novel approaches to optimize performance, reduce latency, and improve packet
delivery.
Hierarchical and clustering approaches further enhance FANET efficiency by
addressing network scaling issues. Techniques like Mobility Prediction Clustering and
geographic clustering help maintain stability and performance in large-scale UAV
networks. Additionally, cross-layer designs, such as those integrating the IMAC-UAV
protocol with DOLSR, offer improved interoperability and adaptability across the OSI
model layers.
Simulation and real-world testing are crucial for validating FANET protocols.
While traditional network simulators provide initial insights, they often fall short in
modeling the complex 3D communication and dynamic behavior of UAVs. Thus,
advanced simulation platforms and real-flight experiments are essential for accurate
performance evaluation and the identification of unforeseen issues.
Overall, the evolution of FANET technology is driven by continuous research
and innovation in network protocols, antenna design, and simulation methods. As
FANET applications expand, especially in fields like military monitoring and search
and rescue operations, ongoing advancements will be vital for achieving reliable,
efficient, and scalable UAV communication systems.
We address key design issues for FANETs, including flexibility, scalability,
latency, limitations of UAV platforms, and throughput. Our comprehensive review
covers recent literature on FANETs and related issues using a multi-layered approach.
We also highlight open research questions and potential areas for future investigation
in.
To our knowledge, this is the first paper to consider FANETs as a distinct
category within ad hoc networks. Our primary goal is to define the multi-UAV aerial
network problem and to encourage further research into the open challenges identified
in this paper.
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