Autonomous UAV Networks Based on Artificial Intelligence

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International Journal of Research Publication and Reviews, Vol 5, no 4, pp 6709-6723 April 2024
International Journal of Research Publication and Reviews
Journal homepage: www.ijrpr.com ISSN 2582-7421
Autonomous UAV Networks Based on Artificial Intelligence
Alireza Gholami
RL Innovation Inc, MN, 55387, Waconia, 1262 Kinder Drive, United States
ARGholami982@gmail.com
A B S T R A C T
UAV developments in recent years have ensured that UAVs would be an inherent part of the upcoming networking and communication systems. Several studies
have recommended UAV-assisted solutions to improve conventional networks’ performance, offering more coverage and capacity to the consumers. However, the
comprehensive study on the AI-based autonomous UAV network design subfields remains to be fully established. This study provides a systematic analysis of the
AI-based autonomous UAV networks. This analysis was carried out on a sample of more than 100 published articles on UAVs, focusing on the autonomous
capability classification, network resource provision, network planning and selection, multiple access and routing protocols, power control, and power consumption
strategies in UAV networks. It could be ascertained from the review and analysis of the previously existing literature on UAV networking that AI-based UAVs are
a profitable and technologically sound choice for future networks. This shall aid in the swift designing of the cost-effective choices, while also deploying them as
the next generation of autonomous networks. Finally, we have also identified the research issue for future research programming in the UAV networks. It is also
hoped that this review shall encourage more research projects for the construction of low-cost and energy-efficient future UAV autonomous networks.
Keywords: Artificial intelligence; autonomous UAV; AI-based UAV networks
1. Introduction
The burgeoning interest in unmanned aerial vehicle (UAV) [109] networks has recently captivated the network research community. Various studies
have focused on the development and performance evaluation of UAV networks [1,2]. These networks are primarily employed to navigate challenging
terrains that traditional communication networks cannot cover. UAV networks excel in delivering superior performance through optimized topology,
spectral efficiency, and situational awareness. With minimal human oversight required in UAV network operations, researchers are increasingly turning
to artificial intelligence (AI) to efficiently manage these networks, developing various applications through machine learning for both scholarly and
commercial use.
The central question addressed in this review is: How can a comprehensive literature survey on AI-driven autonomous UAV networks be structured? In
response, we have reviewed over 100 papers on UAVs that explore autonomous characteristics, network planning, resource management, access
protocols, and energy efficiency. We discuss AI-based UAV networks in the following sections.
1.1. AI-Based UAV Networks
AI is employed to create methods that emulate or surpass human cognitive abilities, crucially learning and adapting [3]. Given their dynamic nature and
complex challenges, UAV networks are ideal candidates for AI application. This ongoing research spans several critical areas including security, network
architecture, and applications essential for advanced UAV network deployment.
1.1.1. Security and Privacy Concerns
Security and privacy are significant concerns in wireless networks, especially those like UAV networks with constantly changing topologies. Numerous
studies address these issues using AI strategies, with some proposing AI-based solutions for cyber and physical threats using convolutional and recurrent
neural networks to analyze high-risk areas and UAV motion [4]. An intriguing study demonstrated how a UAV swarm could maintain terrestrial coverage
effectively through local communications while mitigating cybersecurity risks [5].
1.1.2. UAV Network Design Challenges
UAV networks, unique in their attributes, require bespoke solutions unlike those applicable to other networks such as MANETs and VANETs. A novel
autonomous flock control technique has been proposed to support UAV swarms by maintaining an energy-efficient topology using principles from

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Reynolds’ Boid model [7]. Other wireless challenges tackled include enhancing reliability, reducing latency, and improving handover and path planning
through various AI applications [4].
1.1.3. Localization and Trajectory
Challenges in localization and trajectory are prominent when dealing with UAVs. A recent innovation introduced AI-enabled trajectory planning using a
quantum mechanism, proving more effective than traditional Q-learning approaches [9]. An intelligent localization method for UAV swarms has also
been developed, which minimizes localization errors and speeds up network convergence by implementing an energy-efficient routing algorithm [10].
1.1.4. General Applications
Research into UAV networks extends to their integration with cellular and vehicular networks, coverage of high-risk zones, and spectrum utilization.
Notably, UAV networks have been shown to enhance VANET performance, with novel routing protocols improving vehicular reliability and aiding in
the detection of malicious vehicles [11]. The results indicate a significant enhancement in identifying harmful vehicular activities
1.2. Overview of Recent Literature Reviews
The field of autonomous UAV networks has seen a proliferation of innovative technological approaches recently, prompting extensive reviews of existing
literature to comprehensively understand these advancements. This section covers some of the most pertinent surveys recently conducted in this area.
UAV coverage remains a critical area of study. Several technologies have been introduced to enhance the coverage capabilities of UAV networks. A
comprehensive review [12] discusses various challenges related to UAV network coverage, categorizing these challenges and considering multiple
constraints. The review emphasizes the urgent need for more detailed research to explore coverage issues within and between UAV networks as well as
to address latency and reliability concerns.
A 2015 survey [13] explores the distinctive attributes of UAV networks that differentiate them from conventional ad hoc, vehicular, and other wireless
networks. This survey examines the challenges across different network layers—physical, data link, network, and transport—specific to UAVs,
characterized by their dynamic nature and intermittent connections. The findings advocate for cross-layer design approaches tailored to the unique
requirements of UAV networks, particularly in dynamics and energy efficiency.
Channel modelling for UAV networks is thoroughly examined in another study [14], where the authors delve into the specific requirements for effective
network performance and design, particularly focusing on low-altitude platforms. This survey investigates cost-effective and low-power solutions for
channel propagation both in the air and to the ground.
The integration of 5G millimetre wave technology with UAV networks is the focus of another survey [15], which proposes a novel classification of recent
developments into seven categories, ranging from antenna technologies to performance evaluations. This survey pinpoints numerous emerging research
opportunities and practical considerations to enhance the efficacy of UAV networks for both industrial and academic purposes.
A recent survey [19] provides an in-depth review of UAV network applications, offering a detailed classification of these networks into nine categories
and discussing the three levels of autonomy that define their operations. This paper also reviews current challenges and future trends.
From a security perspective, a survey [20] explores critical issues like sensor faults, unreliable communications, and potential data breaches in UAV
networks, suggesting adaptations of techniques from mobile ad-hoc and vehicular networks to address these concerns.
The use of machine learning techniques in UAV networks is comprehensively reviewed in surveys conducted in 2019 [21] and more recently [22]. These
reviews classify ML strategies and discuss their implementation to optimize UAV network performance, especially when integrated with mobile-edge
computing for enhanced reliability and efficiency.
Lastly, a study [23] details the methodologies for developing UAV network prototypes and experimental setups, covering all stages from aircraft selection
to communication protocols, and suggests the future integration of machine learning and 5G technologies to advance UAV communication systems.
Table 1 summarizes these related surveys.
Table 1. Summary of Recent Literature Reviews
Survey Scope
AI-
Inspired?
UAV Features Addressed
Limitations
Reference
Cooperative UAVs, system
deployment
Yes
Coverage, deployment, and nodes
used
Obstacles in coverage are not
considered
[12]
Various UAV networks, routing
Yes
Topology, mobility, reliability,
and energy efficiency
System optimization has not been
explored
[13]

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UAV channel modeling, low
altitude
Yes
Channel measurement and
characteristics, fading
UAVs in dense urban areas are
not explored
[14]
UAV-assisted and 5G mm wave
communications
No
UAV as aerial access, relay, and
backhaul
Antenna design, channel
modeling, and performance
assessment
[15]
Routing protocols for UAV
networks
No
Topology, position, and cluster-
based routings
UAV routing such as link
disconnection has not been
explored
[10]
Integration of UAV and cellular
networks
Yes
UAV categorization,
standardization, aerial channel
modeling, and security
UAV antenna design has not been
explored
[17]
UAV software-defined network
(SDN) and network function
virtualization (NFV)
Yes
SDN, NFV, cellular
communication, routing, and
monitoring
Wireless power transfer has not
been addressed
[18]
Applications of multiple UAV
systems
Yes
Coordination, cooperation, system
autonomy
Multiple UAV systems have not
been explored
[19]
Safety, privacy, and security issues
of UAVs
No
Sensor-based attacks, GPS
jamming, spoofing, and multi-
UAV-based security
UAV privacy and security have
not been addressed well
[20]
Machine learning for UAV
communications
Yes
Channel modeling, positioning,
resource management
UAVs for vehicular networks not
addressed
[21]
UAV-centric machine learning
Yes
Cooperation trajectory planning,
channel modeling, mobile-edge
computing
Traffic dynamics and channel
conditions not explored
[22]
UAV prototyping and experiments
No
Cellular UAVs, interference
mitigation
Path planning optimization not
explored
[23]
UAV Channel modeling, link
budget
No
Two-ray fast fading, Rician fading,
Rayleigh fading
UAV with satellite not explored
[24]
1.3. Main Contributions
This paper's primary contributions are outlined as follows:
- We perform a detailed review and analysis of over 100 research articles from academic journals and conference proceedings concerning UAVs.
- We organize the existing studies on UAVs by their autonomous characteristics, concentrating on the examination of network resource management,
multiple access and routing protocols, and power control and energy efficiency, which are crucial for developing and implementing future autonomous
UAV systems.
- We pinpoint and elaborate on potential open research topics such as UAV network coverage, MAC protocol formulation, AI algorithm development,
and issues related to security, safety, and privacy management.
1.4. Paper Structure
The layout of this paper is depicted in Figure 1. Section 1 introduces AI-based UAV networks and summarizes previous surveys on the subject, including
a highlight of the main contributions. Section 2 covers the foundational background of the survey, discussing autonomous UAV networks, their
communication, computation, control, channel modeling, and interference management. The review of autonomous UAV features, resource management,
network planning, multiple access and routing protocols, power control, and energy efficiency is detailed in Section 3. Section 4 is dedicated to the
exploration of security, safety, and privacy management, including aspects of physical layer security. The hallenges and future research directions are
explored in Section 5, with the paper concluding in Section 6.

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Figure 1. Structure and organization of this paper.
2. Background and Preliminaries
Unmanned Aerial Vehicle (UAV) networks present a novel communication approach distinct from traditional methods. This section outlines the
foundational concepts and initial considerations in the development of UAV networks.
2.1. Traditional versus Autonomous UAV Networks
According to Business Insider [25], the drone industry is projected to reach USD 63.6 billion by 2025. The origins of UAVs trace back to World War I
in 1917 [26], where challenges in accuracy, privacy, and communication rendered UAVs unreliable for extensive use. Over time, with progressive
research, more dependable solutions for UAV networks have been developed. This discussion, however, focuses on contemporary applications of UAV
networks implemented in recent years. UAVs are increasingly utilized in fields such as telecommunications, surveillance, security, and military operations
due to their cost-effectiveness and adaptability. These networks are particularly advantageous in complex environments like riverbanks, mountainous
areas, and forests. The aerodynamics of UAVs is crucial for their operation. UAVs are primarily categorized into fixed-wing UAVs (Figure 2a) and multi-
rotor UAVs (Figure 2b) [27]. Multi-rotor UAVs, also known as rotary wing UAVs, rely on several rotors to create lift through vertical thrust, typically
featuring 2 to 8 motors. These UAVs consume a significant amount of energy due to their vertical take-off and landing capabilities. Conversely, fixed-
wing UAVs are more energy-efficient, utilizing gliding mechanisms to conserve energy and generally requiring runways for take-off and landing.
Contemporary UAV networks often employ both types of UAVs to leverage their respective advantages.
Figure 2. Commonly used UAV. (a) Fixed-wing UAV [29] and (b) multi-rotors UAV [30].
2.2. Communication, Computation, and Control
Unmanned Aerial Vehicles (UAVs) have significant utility in various daily applications. The idea of UAV cooperation has been introduced to augment
the functionalities of individual UAVs, sparking numerous studies on the potential benefits of UAV networks.
Reference [31] introduces a decentralized predictive control model applied to a group of UAVs. Simulations indicate that this model is more
computationally efficient than centralized methods.

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Research by Bellingham, J. et al. [32] develops a control system to enhance the coordination of multiple UAVs, tackling critical issues like trajectory
optimization, resource distribution, and objective setting. Their strategy integrates these elements to improve the management of the UAV group,
facilitating cooperative pathfinding and multitasking.
The potential for conflicts within UAV groups is addressed in a study presented in [33], which offers a model to detect and resolve such conflicts. This
model uses intermediate waypoints and velocity controls, and is shown to be both scalable and rapidly implementable through simulations and
experiments. Another investigation [34] suggests using thermal lift to extend the endurance of cooperative UAVs, demonstrating substantial potential
improvements.
2.3. Channel Modelling
Channel modeling is essential for the integration of UAVs into the aerospace sector. UAVs are predicted to become commonplace in the near future,
making the study of their channel use crucial. A thorough analysis conducted in 2019 [24] breaks down UAV channel utilization into three types: air-to-
ground, air-to-air, and ground-to-ground, discussing aspects like link budgeting and channel fading. Another survey [14] examines the temporal and
spatial properties of nonstationary channels, highlighting under-researched areas such as airframe shadowing and offering statistical models to aid in
UAV communication. To better UAV communication in shadowed areas, a new, simpler channel model is proposed in [35], tested against empirical data
to verify its efficacy.
2.4. Interference Management
Interference is a critical factor in wireless communications, significantly impacting UAV network performance. Recent studies focus on interference
management, viewing UAVs as extensions of cellular networks or integrating them with power control strategies. In [36], a protocol that optimizes path
planning to reduce latency and interference in ground networks is analyzed, using a game-theoretic interference mitigation strategy and deep
reinforcement learning. A similar approach is taken in [37], where the focus is on maximizing UAV energy efficiency while reducing latency and
interference, employing game theory.
Research in [38] explores UAVs supporting integrated access and backhaul (IAB) of cellular networks to diminish interference, framing the task as an
optimization problem to maximize overall network performance, achieving superior results compared to existing methods. Power control is also pivotal
in managing interference. A machine learning model employing K-means and affinity propagation is introduced in [39] to control UAV power and
placement effectively. Zhang, S. et al. [40] split the issue into determining optimal transmit power and trajectory planning. Zhang, J. et al. [41] not only
consider power control but also integrate UAV clustering into their game-theoretical approach to maximize network rates.
3. Autonomous Features in UAV Networks
The introduction of autonomous capabilities into UAV networks aims to optimize specific network problems while accommodating their dynamic nature.
This includes designing features like resource management, multiple access protocols, and energy-efficient power control, as categorized in Figure 3.
These innovations are central to enhancing UAV network performance and adapting to evolving demands.
Figure 3. Autonomous features classification for UAV networks.
3.1. Resource Management and Network Planning
Effective management and strategic planning of network resources are essential for the performance of any network, especially those with limited human
oversight. This is particularly significant in the context of UAVs, where numerous researchers are concentrating on this aspect. They are recognizing and
tackling various challenges. Table 2 summarizes the surveys related to UAV network resource management and planning.

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Table 2. Summary of related survey: UAV network resource management and planning.
Scope
Autonomous
Features
Computational
Intelligence
Channel Modeling
Interference
Management
Security
and Safety
Reference
Channel modeling
√
Shadowing channel
√
-
[35]
Interference
management
Path planning
√
Rician distribution
√
-
[36]
Interference-aware
path planning
Path planning
√
Free-space path loss
with 6 GHz
√
-
[36]
Interference
management
Spatial
configuration
√
Customized
√
-
[38]
Interference
management
Transmission
power, trajectory
planning
√
Large-scale path loss
√
-
[40]
Interference
management
Transmission
power
√
Free-space path loss
√
-
[41]
Resource
management
Energy
consumption,
transmission
power
√
Various
√
-
[42]
Resource
management
User association
√
Free-space path loss
√
-
[46]
Delay-aware
throughput
maximization
Trajectory
planning
√
Free-space path loss
-
-
[47]
UAV placement
Energy
efficiency and
optimization
√
Path loss
outdoor/indoor
penetration
-
-
[48]
Collision free
navigation
Trajectory
planning
√
-
√
-
[49]
Swarm-based UAV
Path planning
√
-
-
-
[50]
Physical layer
security
security and
cooperation
√
Free-space path loss
√
√
[51]
Secure UAV
communication
Cooperative
scheduling
√
Free-space path loss
-
√
[52]
Physical layer
security
Cooperative
trajectory and
optimization
√
Free-space path loss
√
√
[53]
Physical layer
security
Cooperative
resource
allocation
√
Free-space path loss
-
√
[54]
Quality of
Experience (QoE)
Cooperative
resource
allocation
√
LOS and Non-LOS
-
-
[55]
In a study cited as [42], researchers are exploring how to efficiently manage resources in UAV networks using game theory. They discuss five different
game theory models—coalition, potential, graphical, mean-field, and Stackelberg—each chosen for its relevance to specific goals, utility functions, and
strategic applications in UAV operations.

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Another paper, [43], investigates real-time UAV path planning in dynamic environments. It utilizes a discrete algorithm and a probabilistic graph to
design collision-free routes. A similar approach is taken in [44], where information from both static and dynamic environments is integrated to refine
UAV path planning. This study introduces an adaptive technique aimed at optimizing the path planning process.
Additionally, a noteworthy concept from [45] considers UAVs as new participants in 5G cellular networks. This inclusion presents several challenges for
service providers who need to accommodate these new users efficiently.
3.2 Multiple Access and Routing Protocols
Access and routing protocols pose significant challenges in UAV networks. Initially, UAVs predominantly used space division multiple access techniques
due to their co-location with other networks. However, researchers are increasingly investigating more efficient alternatives, such as orthogonal, non-
orthogonal, and rate-splitting techniques. A summary of these can be found in related literature surveys.
Orthogonal Multiple Access (OMA) techniques minimize interference by ensuring that communications are mutually non-interfering. Various studies
have explored time division (TDMA), frequency division (FDMA), code division (CDMA), orthogonal frequency division (OFDMA), and space division
(SDMA) access methods. The downside to OMA is its limited spectral efficiency due to the finite number of non-interfering channels available.
Non-Orthogonal Multiple Access (NOMA) techniques address these limitations by optimizing base station functions such as power settings, flight
paths, and positioning. NOMA can support more users on overlapping but correlated channels, but its performance might suffer if the number of antennas
exceeds the number of users.
Rate-Splitting Multiple Access (RSMA) techniques aim to bridge the benefits of both OMA and NOMA. These techniques involve coordinating rates
among multiple UAVs to maximize network performance, and have been the subject of extensive research.
3.3 Power Control and Energy Efficiency
For drones and UAVs, managing power and optimizing energy usage are crucial due to their aerial nature. Researchers have proposed various solutions
to enhance power control and energy efficiency. These solutions include the use of multiple power sources such as batteries, hydrogen fuel cells, solar
panels, and hybrid systems. Additionally, studies have looked into maximizing energy efficiency, which is vital for all UAV operations.
UAV Placement and Path Planning: Optimal UAV placement and efficient path planning are critical for energy management. Techniques explored
include sample-based planning, comprehensive space searches, and biologically inspired algorithms. These methods aim to refine the trajectory and
positioning of UAVs to conserve energy and enhance overall operational efficiency.
Table 3. Summary of related survey: UAV multiple access and routing protocols.
Scope
Autonomous
Features
Computational
Intelligence
Channel Modeling
Interference
Management
Security
and Safety
Reference
Channel access
Cyclic
multiple
channel
access
√
Free-space path loss
with LOS
-
-
[61]
MAC protocol
Energy
consumption,
Packet-error-
rate (PER)
√
Free-space path loss
with LOS
-
-
[62,63]
Performance
evaluation of
MAC
PER
√
Rician fading
-
-
[64]
Trajectory
optimization
Trajectory
planning
√
Free-space path loss
with LOS, correlated
Rician fading, Rayleigh
fading, Rician K-factor
√
-
[67]
mm wave UAV
cellular
network
Beam
forming
√
Quasi-static, Rayleigh
fading
√
-
[68]

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Channel access
with time-
modulated
array (TDM)
Beam
forming,
performace
√
Free-space path loss
with LOS
√
-
[70]
Trajectory
optimization
Trajectory
planning,
power control
√
Additive white Gaussian
noise (AWGN)
√
-
[74]
MAC protocol
Power
optimization
√
Rician fading
√
-
[75]
MAC protocol
Throughput
optimization
√
Free-space path loss
-
-
[76]
MAC protocol
Power
optimization
√
Free-space path loss
with LOS
√
-
[77]
MAC protocol
Trajectory
planning,
resource
management
√
LOS and Non-LOS
(NLOS)
√
-
[78]
MAC protocol
Power
optimization
√
LOS, NLOS
√
-
[79]
4. Security, Safety, and Privacy Management
As technology advances and becomes more widespread, its security can be put at risk. Managing security, safety, and privacy is increasingly critical, and
we need to do so with great care and efficiency. This topic is divided into two main categories, as illustrated in Figure 4.
Figure 4. Security, safety, and privacy classification of UAV networks.
4.1 Physical Layer Security and Safety
We can broadly define physical layer security as methods used to protect communications over a channel from different types of attacks, such as
eavesdropping or jamming. Figure 5 shows two common scenarios for UAV communication that illustrate this concept.
Figure 5. UAV communication scenarios: (a) UAV communicating to a ground station, (b) ground station communicating to UAV.

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Figure 5a depicts a scenario where a UAV is attempting to communicate with an Earth station, which makes it relatively easy for unauthorized users to
intercept the communications. Conversely, when the Earth station initiates communication with the UAV, intercepting these communications becomes
more challenging. Scenario (b), shown in Figure 5b, inherently offers better security than Scenario (a).
To address these security vulnerabilities, one approach is to detect and track any potential eavesdroppers. This has been the focus of several studies,
which suggest solutions like installing cameras or radars to monitor suspicious activities. Another strategy involves deploying anti-jamming techniques
that introduce interference signals to thwart eavesdroppers, as discussed in various research papers.
Both scenarios in Figure 5 represent passive eavesdropping attacks, where the eavesdropper silently intercepts communications. However, active
eavesdropping, which involves direct interference with communication channels, poses a greater threat. To counter this, strategies such as optimizing
UAV flight paths and resource distribution have been proposed. These adjustments aim to weaken signals to unauthorized receivers while ensuring robust
communication to intended recipients. Moreover, limiting the onboard resources available to UAVs can help minimize vulnerabilities and secure
communications around key ground nodes. A summary of these methodologies and their effectiveness in enhancing UAV security, safety, and privacy
can be found in Table 4.
Table 4. Summary of related survey—UAV security, safety, and privacy management.
Scope
Autonomous Features
Computational
Intelligence
Channel
Modeling
Interference
Management
Security
and Safety
Reference
Resource
management
Energy consumption,
trajectory planning
√
Free-space path
loss with LOS
-
-
[65]
Computation
optimization with
energy management
Computation
performance, energy
consumption
√
Block-fading,
LOS
√
-
[66]
Electric UAV,
Fuzzy state machine
Energy management
√
-
-
-
[87]
Compressed
hydrogen, fuel cells
Energy management
√
-
-
-
[89]
Hydrogen, fuel cells
Energy management
√
-
-
-
[90,93]
Solar power
Energy management
√
-
-
-
[91,92]
hybrid fuel
Energy management
√
-
-
-
[94]
UAV backhaul
network
Energy efficiency,
placement optimization
√
Free-space
optical link
(FSO), LOS
√
-
[95]
Secure UAV
communication
Jamming power
√
Free-space
pathloss with
LOS
-
√
[103]
Co-channel
interference
management
Transmission power
√
Free-space path
loss with line of
sight (LOS)
√
-
[39]
Another area of research that some investigators are exploring is the defense of networks against unauthorized UAVs or drones. This issue typically arises
when UAVs not belonging to a given network inadvertently intercept communications or when they are intentionally used for eavesdropping purposes.
In these situations, these UAVs are external to the network and should not have the ability to capture its communications. To combat this problem,
numerous studies have suggested the use of reinforcement learning techniques [56] and various detection strategies to pinpoint these unwanted UAVs
within the network [57].
4.2. Privacy
Privacy remains a crucial concern for both our society and our community. While UAVs and drones offer several benefits due to technological
advancements, they also pose a significant threat to individual privacy. Several research papers [104, 105] have been undertaken to examine public
perceptions concerning drones and the impact of these devices on personal privacy.
A particular study [106] aims to rehabilitate the reputation of UAVs and drones concerning privacy invasion. The authors of this study argue that drones
do not employ any novel technology; instead, they utilize a combination of pre-existing technologies that have already received privacy approvals. The

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paper recommends perceiving UAVs and drones as standard aircraft which, due to their unmanned nature, require cameras for operation. It also identifies
UAVs and drones as major nodes for data collection, although the resolution of the images they capture is reportedly not high enough to compromise
personal privacy significantly.
5. Challenges and Open Research Areas
Unmanned aerial vehicles (UAVs) are a burgeoning field of research. Despite various studies noted in networking literature, the performance issues and
systemic challenges within this area remain unresolved. The following are highlighted as significant challenges and potential research opportunities:
5.1. Network Coverage
Research has been ongoing to enhance UAV network coverage, yet noticeable coverage gaps are observed especially when UAVs operate at lower speeds
[107]. This presents an open area for research, potentially involving modifications to existing mobility models to boost coverage.
Additionally, emerging technologies like 5G coupled with UAVs can provide network coverage in areas immediately following disasters such as
earthquakes and tsunamis. This is another vital research area where UAV-mounted cellular bases could be strategically employed.
5.2. MAC Protocol Design
Selecting an appropriate MAC protocol is critical for the efficiency of UAV-integrated networks. Various adaptations of traditional MAC protocols have
been proposed to better suit UAV requirements. Nevertheless, for optimal efficiency, MAC protocols need to be specifically designed for UAV networks,
possibly incorporating cognitive radio technologies.
5.3. AI Algorithm Design
Given that UAV operations often do not involve direct human intervention, the implementation of AI is indispensable. Deep learning applications could
be specifically tailored to UAV-centric tasks such as emergency responses, event coverage, and servicing rural communities to achieve the best system
performance.
5.4. Privacy and Security
As previously mentioned in Section 4 Scenario (b), the use of cameras on UAVs raises significant privacy concerns. This gap in research could be filled
by integrating alternative technologies like LiDAR along with other sensors. A recent publication [108] addresses this concern, suggesting a focus on
privacy enhancements could provide additional benefits. Furthermore, the integration of blockchain technology could bolster security measures for
communications between drones and from users to drones.
The implementation of a blockchain framework could serve as a security layer for UAV communications, addressing both drone-to-drone and user-to-
drone data exchanges. This is a crucial area for future research focused on securing UAV systems.
6. Conclusions
This paper presents a detailed survey on AI-based autonomous UAV networks, highlighting essential aspects such as autonomous features, network
management, channel access, routing protocols, and privacy and security management. The findings suggest that AI-driven UAV networks are a feasible
technological approach for cost-effective deployment in future networks. Success in this domain will require concerted efforts between industry,
academia, and government bodies, including telecommunications and regulatory agencies.
Looking ahead, we identify and discuss promising research directions including enhancements in network coverage, access protocols, AI algorithms, and
the security and privacy frameworks of UAV networks. New research initiatives are essential to develop effective UAV network architectures and address
the numerous design challenges inherent in AI-powered autonomous UAV communication systems. Future works might also explore the integration of
AI algorithms for a simpler, sub-optimal solution.
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