# Blockchain-Based Security Architecture for Unmanned Aerial Vehicles in
B5G/6G Services and Beyond: A Comprehensive Approach

Senthil Kumar Jagatheesaperumal  Mohamed Rahouti  Kaiqi Xiong  Abdellah Chehri
Nasir Ghani  Jan Bieniek

###### Abstract

Unmanned Aerial Vehicles (UAVs), previously favored by enthusiasts, have
evolved into indispensable tools for effectively managing disasters and
responding to emergencies. For example, one of their most critical
applications is to provide seamless wireless communication services in remote
rural areas. Thus, it is substantial to identify and consider the different
security challenges in the research and development associated with advanced
UAV-based B5G/6G architectures. Following this requirement, the present study
thoroughly examines the security considerations about UAVs in relation to the
architectural framework of the 5G/6G system, the technologies that facilitate
its operation, and the concerns surrounding privacy. It exhibits security
integration at all the protocol stack layers and analyzes the existing
mechanisms to secure UAV-based B5G/6G communications and its energy and power
optimization factors. Last, this article also summarizes modern technological
trends for establishing security and protecting UAV-based systems, along with
the open challenges and strategies for future research work.

###### keywords:

Unmanned Aerial Vehicle , B5G/6G communication , security , energy ,
Artificial Intelligence , IoT.

††journal: Computers & Security

long-short 3d3DThree-Dimensional 3gpp3GPPThird Generation Partnership Project
4g4GFourth Generation 5g5GFifth Generation 5g-cpe5G-CPE5G Customer Premise
Equipment 6g6GSixth Generation aiAIArtificial Intelligence afarAFARForward
Area Based Routing-algorithm afwAFWAutonomous Flight Wireless aoAOAlternating
Optimization bcotBCoTBlockchain of Things cdfCDFsCumulative Distribution
Functions crCRCognitive Radio cmpCMPCoordinated Multiple Points csiCSIChannel
State Information dcDCDifference-of-Concave dltDLTDistributed Ledger
Technology dtnDTNDelay Tolerant Networks dqnDQNDeep Q-Networks dsmDSMDynamic
Spectrum Management gisGISGeographic Information Systems g2uG2UGround-to-UAV
gtGTsGround Terminals gpsGPSGlobal Positioning System hpppHPPPHomogeneous
Poisson Point Process iacoIACOImproved Ant Colony Optimization ipIPIntercept
Probability ioeIoEInternet of Everything ioitIoITInternet of Intelligent
Things iotIoTInternet of Things kkkKKKKarush-Kuhn-Tucker losLOSLine of Sight
lteLTELong Term Evolution m2mM2MMachine to Machine mbbMBBMobile Broadband
mecMECMobile Edge Computing misomeMISOMEMultiple-Input-Single-Output Multiple-
Eavesdropper mimoMIMOMulti-Input Multi-Output mlMLMachine Learning nfzNFZsNo-
Fly Zones ndnNDNNever Die Networks nomaNOMANon-Orthogonal Multiple Access
ofdmOFDMOrthogonal Frequency-Division Multiplexing powPoWProof-of-Work
qosQoSQuality of Service ranRANRadio Access Network rfRFRadio Frequency
rmicnRMICNRouter-movable Information-centric Networking scpSCPSecure
Connection Probability seemSEEMSecrecy Energy Efficiency Maximization
smfSMFSession Management Function sdnSDNSoftware-Defined Networking
slamSLAMSimultaneous Localization and Mapping snrSNRSignal-to-Noise Ratio
scaSCASuccessive Convex Approximation swiptSWIPTSimultaneous Wireless
Information and Power Transfer thzTHzTerahertz tdmaTDMATime Division Multiple
Access uavUAVUnmanned Aerial Vehicle vrVRVirtual Reality u2gU2GUAV-to-Ground
upfUPFUser Plane Function wifiWiFiWireless Fidelity wsnWSNwireless sensor
network wcsrWCSRWorst-Case Secrecy Rate wimaxWiMAXWorldwide interoperability
for Microwave Access Network

[label1]organization=Department of Electronics and Communication Engineering,
Mepco Schlenk Engineering College,city=Sivakasi, state=Tamil Nadu,
country=India

[label2]organization=Department of Computer and Information Sciences, Fordham
University, city=New York, postcode=10023, state=NY, country=USA

[label3]organization=Cyber Florida, University of South Florida,city=Tampa,
postcode=33620, state=FL, country=USA

[label4]organization=Royal Military College of Canada,city=Kingston,
postcode=K7K 7B4, state=Ontario, country=Canada

[label5]organization=Electrical Engineering Department, University of South
Florida,city=Tampa, postcode=33620, state=FL, country=USA

##  1 Introduction

Unmanned aerial vehicles (UAVs) or drones have attracted rising attention
towards wireless communications applications owing to their numerous
advantages, such as easy mobility control and establishment of required
communication links at low cost. They can also be used as an aerial base
station to enhance wireless capacity, coverage, and energy efficiency. UAVs
can be like small aircraft balloons or drones; they can be remotely controlled
or pre-programmed. UAVs have many applications in military, surveillance,
search and rescue localization, and telecommunications. Technical challenges
exist in providing an analytical framework and efficient algorithms that can
be used to analyze and optimize the design of drone-based communication
systems.

Under Project SkyBender, Google experimented with drones to deliver high-speed
5G Internet services Zhou et al. (2018b). The millimeter-wave technology has
the potential to transmit gigabytes of data every second with speeds up to 40
times faster than 4G internet. However, the millimeter-wave signals are
absorbed by atmospheric gases, while rain and humidity can also reduce signal
strength. Millimeter waves also travel via line of sight, which means objects,
including trees and buildings, can blog them.

A blockchain system enhances the security and dependability of the underlying
infrastructure without relying on human trust Rahouti et al. (2018).
Blockchain networks are built in such a way that it is assumed any individual
node could attack them at any time. Consensus protocols like proof-of-work
(PoW) ensure that even if the attack happens, the network completes its
functions as intended, regardless of human cheating or intervention. The
blockchain allows one to store data and secure it using various cryptographic
properties such as digital signatures and hashing as soon as data enters a
block in a blockchain network. If intruders try to tamper with the blockchain
database, consensus would recognize and shut down the attempt. The consensus
prevents data tampering and network attacks.

Blockchains are made up of nodes, which can be within one institution, or
multiple institutions all over the world on the computer of any citizen that
wants to participate in any decision to be made. They form a democratic
system, so if anyone node is compromised and malicious actions are taken, the
other nodes would automatically recognize the problem and simply not execute
the inappropriate activity. The blockchain has incredible security features,
so everyone uses it to store essential data correctly. One of the striking
features of blockchains is their scalability features, in which transactions
per second measure blockchains with nodes all over the world run by
independent citizens.

Using UAVs as a service (e.g., Drone-as-a-Service) is a revolutionary
development for the proposition, similar to the Infrastructure-as-a-Service
model, UAV-as-a-Service would be directly structured to perform UAV tasks and
responsibilities Nguyen et al. (2023). With the inclusion of blockchain,
businesses can benefit from increased continuity by providing that service.
Applicable technologies such as 5G and 6G allow for intercommunication between
multiple parties, enabling users of UAV technology to benefit from cross-
functional use cases that serve as a service platform. With UAVs on the
horizon, there is potential for market capital in opportunistic delivery to
civilians without needing a driver, thereby serving society by completing
tasks and assignments.

Furthermore, adopting blockchain technology into UAV environments enables a
”zero-trust policy,” ensuring that all parties can see transparency in the
service and data provided. With both UAVs and the Internet of Things (IoT)
architectures still in their adolescence, there is no plausible way to justify
keeping private data away from its viewers. The public blockchain is known to
be a public structure, which further emphasizes the need for a zero-trust
policy. Allowing for zero trust would build trust in the overall ecosystem
nurtured by all parties. Specifically, zero-trust security frameworks can
authenticate, authorize, and validate all users, thereby sourcing the
responsibility for all actions and outcomes Yang et al. (2023). Congruent
methods to this approach would also be valid examples of potential security
solutions for the overall security concept in UAV infrastructure.

###  1.1 Contributions of this work

In this article, a detailed investigation and analysis of the usage of
blockchain security solutions have been carried out for UAV communication. The
motivation behind the compilation of the recent research work towards UAVs in
5G/6G environments and applications is to encourage researchers to develop
better connectivity systems even in remote areas. The review provides
awareness about the state-of-the-art modern communication services and
associated solutions for UAV communications. It intends to offer state-of-the-
art blockchain-enabled security frameworks with the support of modern enabling
technologies, relevant approaches, and future perspectives. The major
contributions of this article are summarized as follows.

  * 1.

To provide a detailed taxonomy of the evolution of 5G/6G services and their
applications to UAVs.

  * 2.

To provide a deep discussion and insight into using UAVs for communication
services and domains.

  * 3.

To provide a deep discussion and insight on the use of security concerns of
UAV communication services.

  * 4.

To provide a deep discussion and insight into modern technological trends
supporting energy and power-optimized solutions.

  * 5.

To provide an exhaustive summary of the state-of-the-art algorithms for
security enhancement in UAV communication.

  * 6.

To provide the blockchain-enabled security solution for UAV communication.

  * 7.

To provide a few case studies on enhancing security aspects in blockchain-
based solutions to understand the subject of discussions better.

  * 8.

To provide the state-of-the-art research challenges and the comparative
discussion on existing articles.

###  1.2 Organization of the article

This review article is organized as follows. Section 2 summarizes the related
survey towards the chosen domain. Section 3 discusses the evolution of B5G/6G
Communication services from 5G services in the market. It summarizes the
application of UAVs in different domains and their unique characteristics
towards communication services. Section 4 discusses the security concerns of
UAV-based architectures and the appropriate measures to ensure privacy and
security in communication. Section 5 summarizes the energy and power
optimization requirement for security enforcement in UAV-based Architectures.
In Section 6, Blockchain-based security architecture for UAV in B5G/6G is
proposed with detailed architecture and summary. Specific open issues,
challenges, and applications in the security aspects of UAV architecture are
discussed in Section 7 along with the key findings and summary of the article
towards establishing the best and secure means of UAV-based architectures. It
also encapsulates the recent trends and future scope of research left as open-
end challenges for researchers. Finally, Section 8 concludes this work.

##  2 Summary of Related Surveys from Literature

This section deals with the compilation of the contributions of various
researchers toward the growth and usage of UAVs for various application
domains. This section also summarizes the 5G services and architectures from
the literature. Furthermore, blockchain-related surveys towards security
enforcement for different applications are discussed from the surveys’
outcomes, and the potential research gaps are pointed out for elaboration in
the rest of this survey article. The few most promising survey articles
related to the scope of this article are compiled and presented with their
applications next.

Fotouhi et al. Fotouhi et al. (2019a) conducted a systematic literature review
on UAV cellular communications to better integrate UAVs in cellular networks.
The authors also reviewed types of consumer UAVs, cyber-physical security,
ongoing prototyping, challenges, and opportunities for assisting cellular
communications. A recent survey of the Physical layer security techniques on
passive wireless eavesdropping and its applications is available in Hamamreh
et al. (2019). In this review, the authors also discuss the expandable
framework for future research directions and the support of IoT and 5G
technologies for future wireless systems.

Fight against active and passive eavesdropping in UAVs and potential research
challenges described in this section has been proposed in Sun et al. (2019b),
where Sun et al. also suggest using trajectory design and cooperative UAVs
Physical Layer Security in UAVs can be ensured with improved spectral
efficiency. The survey by Wang et al. Wang et al. (2019a) covers the secure
wireless communications involved in the physical-Layer of UAVs that focus on
security aspects considering various roles of a UAV with optimization at the
degree of freedom level.

The survey by Wang et al. Wang et al. (2019b) covers the Physical Layer
Security aspects of 5G wireless communication for IoT devices along with their
application scenarios, security threats, and communication technologies in 5G
towards IoT, and management of physical-layer threats. The survey conducted in
Wu et al. (2019) includes the mechanisms for tackling the new issues that
arise in the physical-layer security of wireless communication networks with
validated numerical results and future direction for research.

Case studies on UAVs in aerial mode and flying base station modes with the
demonstration of physical layer security mechanisms providing superior
performance gains are summarized in Li et al. (2019b). In the studies,
securing communications is ensured over 5G networks, and also emerging future
research directions are highlighted.

Table 1: Existing surveys on most relevant secure communication architectures
for UAVs. Related
works |  Year |  Topic |  Key contributions |  Research gaps
---|---|---|---|---
Chen et al. (2020) |  2020 |  Swarm communication |  Surveys the UAV swarm routing protocols and communication architectures. |  No direct focus on security.
Hentati and Fourati (2020) |  2020 |  Communication networks |  Surveys the UAV communication protocols, architectures, and their applications. |  No discussion on secure deployments.
Gupta et al. (2021a) |  2021 |  Drone networking |  Surveys secure UAV communication with the fusion of AI and Blockchain. |  No in-depth coverage on security among a wide range of applications.
Ullah et al. (2020) |  2020 |  Cognition aspects |  Studies the cognition capabilities of the UAV-based communication. |  No wide focus on security aspects.
Shakoor et al. (2019) |  2019 |  Public safety |  Summarizes the impact of UAVs in public safety communications. |  Addresses safety only from the perspective of energy efficiency.
Aggarwal et al. (2020) |  2020 |  6G networks |  Surveys on 6G use cases on the taxonomy of the UAV communication. |  No stringent efforts on addressing security challenges.
McCoy and Rawat (2019) |  2019 |  SDN |  Surveys on SDN-based solutions for addressing vulnerabilities in the UAV network. |  No direct focus on the range of security measures.
Nawaz et al. (2021) |  2021 |  Communication networks |  Surveys on the issues in UAV communication networks. |  Security aspects are least considered.
Noor et al. (2020) |  2020 |  Ad-hoc networks |  Reviews on the key enabling communications technologies of flying ad-hoc networks. |  No considerable focus on security.
Qadir et al. (2021) |  2021 |  Path planning |  Reviews on the UAV communication network employed for disaster management in smart cities. |  No key focus on security from the UAV perspective.
Al-Turjman et al. (2020) |  2020 |  SDN IoT |  Surveys on the applications of the UAV base station enabled with SDN support. |  Only on-demand security concerns are highlighted.
Zhi et al. (2020) |  2020 |  Security and privacy |  Surveys on the UAV safety aspects considering communication, sensors, and multi-UAVs. |  No potential impact on the security of multi-UAVs.
Vashisht et al. (2020) |  2020 |  MAC protocols |  Reviews on the support of MAC protocols for UAV-based communication systems. |  Security aspects are not highlighted for the prescribed ecosystem.
Chriki et al. (2019) |  2019 |  Ad-Hoc network |  Surveys on the existing communications architectures for the flying ad-hoc networks. |  Very sparse focus on the security challenges.
Wheeb et al. (2022) |  2022 |  Routing Protocols and Mobility |  Reviews on the mobility models and routing protocol for flying ad hoc networks. |  Lacks discussion on the security aspects of the protocols.
Rugo et al. (2022) |  2022 |  UAV network security |  Reviews on the security aspect of UAV networks considering a diversified range of parameters. |  Security aspects not focused on Blockchain.
Duong et al. (2022) |  2022 |  Caching in 6G networks |  Surveys on the analyses of cache models for the UAV communication through 6G services. |  No considerable focus on security aspects.
Siddiqi et al. (2022) |  2022 |  Security analysis |  Analyses security and privacy concerns of UAVs subjected to the prescribed regulation. |  Least focus on communication security.
Our Paper |  2023 |  Blockchain for securing UAV communication |  A comprehensive survey on Blockchain and 5G/6G services integration for UAVs; The roles of Blockchain in various key 5G/6G-enabled UAV systems; The use of Blockchain in dependable 5G applications, including edge devices and their potential to meet 6G requirements. |  -

Figure 1: Taxonomy of Commercial and General use cases of UAVs.

##  3 A Detailed Taxonomy on the Evolution of B5G/6G Communication Services

This section provides a comprehensive overview of the state-of-the-art
existing works related to B5G/6G-based communication services and their
evolution. Communication with UAVs can be established through WiFi networks or
private wireless networks. In the case of connected UAVs, they use cellphone
network services such as 5G/B5G/6G to communicate with the controller.
Generally, connected UAVs have six modules in these systems.

  * 1.

Communication System: The UAVs need to communicate with the network and
receive the control command from the network. They also are engaged in
uploading pictures and videos to the network. Such UAVs for establishing a
connection with the network must have a sim card with 5G/B5G/6G support.
Particularly for establishing B5G/6G services, the UAV must be deployed with
B5G/6G customer premise equipment (CPE), which acts as a gateway for
integrating UAVs with the environment and the controller.

  * 2.

Flight control system: For controlling the position of the UAVs navigating in
the air, we need to have a flight control system. They must possess higher
reliability and higher stability with lightweight. They must be intelligent
and efficient enough to control the positions of UAVs. They usually receive
the command from the ground controller through the network. Based on the
commands, the UAVs avoid obstacles and maneuver through the desired path.

  * 3.

Navigation System: The navigation system helps plan the paths for the UAVs. It
should possess low latencies, large bandwidth, and provision over-the-horizon
remote control. It should also ensure to provide autonomous navigation through
appropriate path-planning algorithms. High-precision positioning and cluster
flight are other vital features of the navigation system that could assist
UAVs in finding the best paths and routes.

  * 4.

Flight Management System: This subsystem includes authentication and security
encryptions, so we cannot allow the UAVs to be controlled by unauthorized
persons.

  * 5.

Task Load System: Most of the UAVs that would be employed for navigation can
carry some lightweight objects such as cameras and food items. The objects
should be lightweight and miniature airborne equipment. The task load systems
with a diversified range of airborne equipment will be in much demand for
commercial applications. Further, the real-time network transmission should
also be sustained with the load in the UAVs.

  * 6.

Airborne Computers: Sensing and other intelligent observations from the UAV
environment during its navigation could be programmed in an airborne computer.
The data accumulated from the environment could be used for the second level
of application development with the support of the programmed airborne
computer.

###  3.1 Role of UAVs in General and Communication Services

The modules and protocols in these networks are of the most significant
importance to enable a reliable communication network for UAVs. Several
methods have been introduced in the past by both research and industry
communities. However, the vast majority of these research efforts only
consider a few critical communication factors, including resource management,
antenna design, and communication network architecture. Thus, this section
will review and discuss the communication modules related to UAVs and their
associated networking paradigms. Further, a thorough comparison will be
elaborated on the mechanisms and algorithms used in 5G-enabled UAV
communications.

####  3.1.1 5G Architecture

The Radio Access Network (RAN) belonging to 5G network services, popularly
known as new radio, has been standardized to allow tight internetworking with
the existing LTE services. 5G network architectures enabling tight
internetworking among LTE and new radio services are known as non-standard
architectures. These allow a smooth and relatively simple evolution toward a
complete end-to-end 5G system. They enable the reuse of the existing LTE-based
base stations and 4G core networks by a simple software upgrade among the new
radio base stations. The combination of a standalone new radio base station
and a 5G core network is known as a 5G system.

Each use case category of 5G includes many individual applications, as shown
in Fig. 5. The existing applications included voice service, video streaming,
Internet access, social media, and instant message applications. Many emerging
applications include automatic vehicles, remote home automation, smart cities,
and smart wearables. Many of these applications require components from
multiple use case categories. For instance, virtual reality (VR) requires low
latency to provide high mobile broadband (MBB) and responsiveness throughputs
for rapid content transfer. 3GPP ensures that 5G services have sufficient
flexibility and capability to address the diverse requirements of this broad
range of applications. Also, 3GPP has specified reference-based and service-
based architecture for the 5G systems.

Figure 2: The 5G technology usage scenarios.

The reference-based architecture is based on network elements that use point-
to-point interfaces to interconnect those network elements. Signaling
procedures are specified for each point-to-point interface. It leads to
repetition with the specifications if the same signaling procedure is used
across multiple interfaces. The service-based architecture replaces the
network elements set with the network functions. Each network function can
provide services to other functions by acting as a service provider. Here, the
point-to-point interfaces are replaced by a common bus that interconnects all
the network functions. One of the crucial characteristics of a 5G system is
the separation of the user plane and control plane functions. The session
management function (SMF) is used for IP address allocation, and the user
plane function (UPF) is meant for packet forwarding in the 5G system
architecture. This supports the independent scaling of these two functions
because of the separation of the user and control planes. This feature allows
the operators to add more user plane capability without more control plane
capability. Furthermore, the user plane functions could be distributed, and
the control plane functions could be centralized.

Figure 3: The architecture of 5G technology (network slicing).

####  3.1.2 Networking Technologies in UAV Communication Systems

Notable research works have been conducted in the past with a focus on the
various aspects of technologies-related communication networks of drones,
enabling UAV technological enhancement with robust and resilient networks.
Among these works, Rahman et al. Rahman (2014) notably considered worldwide
interoperability for microwave access network (WiMAX) as a proper technology
for examining different wireless-based communication protocols that are based
on the SHERPA network standard criteria, including ZigBee, WiFi, and XBee.
Furthermore, the authors in Lee et al. (2016) deployed the Forward Area Based
Routing-algorithm (AFAR) along with Geographic Information Systems (GIS) to
design an adaptive solution for drone communication improvement.

Moreover, the authors in Sharma et al. (2020) proposed Router-movable
Information-centric Networking (RMICN), a communication system to enable
disjoint-pair communication among drones. Their solution leverages the relay
nodes and the movement of flying routers’ physical control to ensure
efficiency and communication reliability. Further studies such as Akka and
Khaber (2018) addressed the path planning strategies problem in drones and
proposed Improved Ant Colony Optimization (IACO), a path planning solution for
a set of mobile robots.

As resource allocation is another vital aspect of communication technologies
of UAVs, the authors in Yoshikawa et al. (2017) aimed to explore the optimal
frequency band to provide communication efficiency and optimization while
avoiding interference in the case of simultaneous communications. This work
further enhances the usage of the primary band used by the individual drones
in accordance with the total number of drones. Other works such as Fabra et
al. (2017) also addressed the interference problem in drone communication. The
authors in Fabra et al. (2017), in particular, examined the high interference
problem at the radio control and presented a comprehensive evaluation of the
existing interference optimization techniques. Their results validated the
incompatibility of WiFi with the radio control unit in the 2.4 GHz wireless
band. This is mainly due to the high number of remote control devices that use
this communication band.

Building upon this, the authors in Shrit et al. (2017) developed a lightweight
framework to facilitate drone synchronization using ad-hoc communication when
forming a swarm network. Here, to operate a swarm network, the drones
autonomously follow the lead of an individual leader drone, which a human must
pilot.

Last, self-recovery network technologies have drawn remarkable attention to
the UAV domain. For instance, Uchida et al. Uchida et al. (2014) proposed a
self-recovery-based resilient communication solution comprising Delay Tolerant
Networks (DTN), Never Die Networks (NDN), and Autonomous Flight Wireless (AFW)
devices to provide reliable and resilient communication with wireless stations
in isolated geographical regions.

####  3.1.3 UAV-Assisted WSN and UAV-Assisted Vehicular Communication Systems

The deployment of a Wireless sensor network (WSN) for UAV environments is
challenging because of the distributed localization of sensors in a large
geographical area (i.e., dense sensors). It has been demonstrated that the
static-based integration of WSN is significantly less efficient, and it incurs
progressive phases of damage or disaster Erdelj et al. (2017). To alleviate
and manage potential disasters, recommendations are provided for the UAV and
WSN based on three key classifications: pre-disaster preparedness, disaster
assessment, and disaster response/response. A notable research work by Wu et
al. Wu et al. (2017) suggested incorporating mobile data in WSN by a UAV,
where a regulated greedy-based routing mechanism was leveraged to address the
problem of route selection and communication association. In Sharma et al.
(2020), the authors explored the possible means of communication technologies
for UAVs through various ranges of network architectures, resource management
platforms, and antennas. Further, the interplay between the IoT networks for
advanced cellular communication for UAVs is focused on a diversified range of
UAV use cases, from navigation to surveillance.

###  3.2 B5G/6G-enabled UAV Communications

Recent communication technologies have started exploiting the potential of
using B5G/6G technology to enable efficient and secure communication among
UAVs. This section highlights the key features of 6G technology, which can
allow UAVs to operate at higher speeds, cover larger distances, and perform
more complex tasks. It also discusses the challenges and opportunities
associated with B5G/6G-enabled UAV communication, including spectrum
allocation, network architecture, and security considerations.

####  3.2.1 Towards B5G/6G with Connected Sky

Figure 4: B5G/6G technology-supported UAV communications architecture.

B5G/6G technology promises to bring many advanced features and provide faster
data transfer rates, low latency, and improved network coverage. Future
connected societies will be powered by this technology, enabling virtual and
augmented reality, autonomous vehicles, and IoT. Secure communication is an
essential aspect of B5G/6G technology De Alwis et al. (2021). It is expected
that B5G/6G technology will provide a secure and reliable communication
infrastructure for users who have increased concerns about privacy and
security in the era of digital communication. It is also designed to address
the challenges posed by current wireless communication technologies, including
the high risk of cyber-attacks and data breaches Zhou et al. (2021). This is
achieved through advanced security protocols and encryption technologies that
protect against unauthorized access and ensure data privacy in transit. The
protocols are designed to detect and prevent malicious activities such as man-
in-the-middle attacks, denial-of-service attacks, and other hacking attempts.

Table 2: Summary on state-of-art usage of modern communication services for UAVs and their limitations. References |  Year |  Single/Multi UAVs |  Communication Network |  Communication Strategy |  Limitations
---|---|---|---|---|---
Yu et al. Yu et al. (2021) |  2021 |  Multi UAVs |  5G, B5G |  Multi-hop relay, multi-access edge computing |  Limited coverage area, high latency
Shnaiwer et al. Shnaiwer et al. (2022) |  2022 |  Multi UAVs |  5G, 6G |  Multi-hop relay, beamforming, edge computing |  Limited coverage area, high latency
Pan et al. Pan et al. (2021) |  2021 |  Single UAV |  5G |  Single-hop relay |  Limited coverage area, high latency
Zhao et al. Zhao et al. (2019) |  2019 |  Single UAV |  5G |  Direct communication, beamforming |  Limited coverage area, high latency
He et al. He et al. (2021) |  2021 |  Single UAV |  5G |  Direct communication, multi-hop relay |  Limited coverage area, high latency
Amponis et al. Amponis et al. (2022) |  2022 |  Multi UAVs |  B5G |  Multi-hop relay, edge computing |  Limited coverage area, high latency
Qi et al. Qi et al. (2019) |  2019 |  Single UAV |  5G |  Direct communication, beamforming |  Limited battery life
Alsamhi et al. Alsamhi et al. (2022) |  2022 |  Multi UAVs |  6G |  Multi-hop relay, edge computing |  Still in development, limited availability

B5G/6G technology is also anticipated to provide secure and reliable
communication in crucial infrastructure systems such as transportation
systems, power grids, and healthcare systems that demand low latency and high
reliability for the seamless transmission of critical data in real time Javed
et al. (2022). Its security features make it well suited for use in these
systems, ensuring the protection and transmission of confidential information
without any risk of compromise. Moreover, B5G/6G technology will play a
crucial role in ensuring security in large networks, in association with the
growing demand for IoT and the increasing number of connected devices.
Further, it is also expected to establish a robust and secure infrastructure
capable of handling the vast amounts of data generated by these devices,
including end-to-end encryption, secure device authentication, and robust
network security protocols Khan et al. (2022a).

As shown in Fig. 4, the role of UAVs in B5G/6G scenarios is of substantial
importance, as flying devices will typically be anticipated to densely
populate aerial space, enabling a medium network layer between space networks
and ground networks Mozaffari et al. (2021). The ever-growing and large-scale
deployment of UAV technology in a broad range of applications is anticipated
to be a primary component of beyond 6G wireless networks in the next couple of
decades. The efficacious support of such an enormous employment of UAV
technology will require ensuring secure, reliable, resilient, and cost-
efficient wireless connectivity.

####  3.2.2 Existing Solutions Leveraging B5G/6G, Blockchain, and UAVs

The new generation of mobile networks is a residence for edge communication
technologies that provide unprecedented performance and capabilities Kumari et
al. (2021). Aggarwal et al. Aggarwal et al. (2020) considered the evolution of
the 6G network and its applications in various areas and the potential of
blockchain characteristics in UAV communication. Moreover, the authors
proposed solutions and future issues according to the integrations of
blockchain-based UAVs and 6G networks. Raja et al. Raja et al. (2022)
discussed a nexus of 6G and blockchain for authentication (NBA) system to
overcome the loss of authentication data due to node capture and lack of
sensor location verification. The NBA system uses permissioned blockchain-
based UAVs and intelligent sensor networks to detect code tampering.

Kalla et al. Kalla et al. (2022) considered four directions of the 6G
Internet, namely, Trust-based Secure Networks (TBSN), Harmonized Mobile
Networks (HMN), Hyper Intelligent Networks (HIN), and Resource Efficient
Networks (REN), through blockchain-based 6G Internet. Kalla et al. Kalla et
al. (2022) also discussed that these four categories could characterize future
technologies that utilize 6G networks. Additionally, AI/ML-based hyper-
intelligent networks focus on artificial intelligence by 6g design. To
implement 6G grids, the research identifies significant scientific and
technical difficulties and possible rulings for the 6G blockchain.

Pokhrel Pokhrel (2020) presented drone-assisted federated learning with
blockchain at the 6G edge for the emergency response scenario. Through
simulation and modeling, inPokhrel (2020), the author has shown that
transmission parameters like power and the number of miners significantly
impact the overall energy consumption of a blockchain.

UAVs are expected to play a critical role in the 6G network, as they are
expected to occupy airspace densely and act as a network layer between ground
and space networks. Khan et al. Khan et al. (2022a) have discussed security
and privacy, AI/ML, and energy efficiency solutions paired with 6G UAV
networks. They provided an overview of how a 6G wireless line can integrate
blockchain and AI/ML with UAV networks. Further, the review results were
presented together with the probable difficulties. Kumar et al. Kumar et al.
(2022) discussed the security aspect of secure UAV communication and designed
a novel secure authentication and key agreement framework. The proposed
framework preserves the four-factor authentication of an entity, namely unique
identity, pseudo-identity, certificate, and timestamp. Additionally, the
framework supports establishing session keys with mutual authentication to
secure communication between entities.

Decentralization, transparency, and immutability are the critical missions of
blockchain technology that can improve the system’s security. Gupta et al.
Gupta et al. (2021b) demonstrated a secure interconnection of UAVs along with
blockchain support via the 6G mobile network. García-Magariño et al. García-
Magariño et al. (2019) presented a blockchain-based security mechanism for
detecting compromised UAVs in UAV networks. This mechanism also uses a trust
policy that relies on information received from most UAVs. Using a secure
signature system based on asymmetric encryption and blockchain records, it is
impossible to sign false control on behalf of other drones. A compromised
drone can enter fake information with its real ID, but the trust policy solves
this problem.

Shah et al. Shah et al. (2022) discussed industrial applications of 6G and
blockchain technologies, followed by open issues and challenges. Further, Rana
et al. Rana et al. (2019) considered blockchain for security purposes to avoid
problems related to data leakage or loss during transfer between UAVs. The
data transfer process occurs within a blockchain framework that allows all
user information to be stored and records to be shared for security
management.

###  3.3 Deployment Concerns

####  3.3.1 Data amount and speed

The development of UAVs directly aligns with collecting the data generated
with these devices Nex et al. (2022). Maturing the environment of UAVs will
further pursue the need to develop real-time machine learning (RTML)
stability. As machine learning (ML) and deep learning (DL) continue to develop
and become more secure, the more capable the outcome desired will be.
Nonetheless, there will always be a constant threat of data poisoning, which
the infrastructure may not be attainable to defend against. UAV technology is
expected to become more dependent on ML algorithms; the justification for
secure ML programming and practices is a mandatory indication of how serious
the impact a threat actor can possess in the event of a data poisoning attack
against UAV systems.

####  3.3.2 Network availability

Wireless network Availability is constructed to relate directly to the
decision-making and analysis of AI-supported UAV systems. Verizon issued a
statement regarding the use case of 5G and B5G/6G networking communication to
become a more viable procurement mechanism that will accompany the advancement
of self-driving vehicles and UAVs. With technology as promising as 6G towards
UAV environments, the architecture and machine infrastructure are not
conditionally ready to be incorporated in mass deployment. The ever-growing
deployment of 6G in UAV systems is still far away, as communities must
continue undergoing intensive testing of proactive security practices. With
these challenges, a larger-functioning backbone for high-frequency data is
needed to analyze and adopt 6G into the real-time decision-making capabilities
for UAV systems.

####  3.3.3 Underdeveloped infrastructure

The containment and transference of data must be viewed through a more secure
approach Arabi et al. (2018). Notably, the categorical solution of data
transference must be sequential to the overall processing that a UAV can
produce. The computation of data at the rate of production UAV may not satisfy
the desired security. From a convenience standpoint, users and consumers would
not be able to tell the difference until the unfortunate event that an
accident or tragedy occurs. There is no telling what version of a disaster is
imminent when a security model is issued with proper research, simulations,
testing, and procurement.

##  4 Security Concerns of UAV-based Architectures

With the rise in the usage of UAVs, various UAV-based architectures have been
developed and tailored for various applications. However, the security of
these architectures needs to be taken into account to guarantee the safety and
confidentiality of transmitted data. This section investigates the security
issues with UAV-based architectures, such as potential threats and
vulnerabilities. Additionally, we will analyze the strategies and methods
utilized to counteract these concerns and explore the challenges associated
with securing UAV-based architectures. A better understanding of these
security concerns could lead to developing more reliable and secure UAV-based
architectures, helping to prevent potential security breaches.

Wang et al. Wang et al. (2017) developed an iterative algorithm as a solution
for Physical Layer Security enhancement using UAV enabled mobile relaying
technique. This technique uses the difference-of-concave (DC) program to prove
Karush-Kuhn-Tucker (KKT) conditions for guaranteeing convergence, thereby
enhancing the secrecy rate. In He et al. (2017), He et al. proposed a
communication security mechanism for low-cost implementation for safeguarding
against the WiFi and GPS spoofing attack on the targeted UAV. Wang et al. Wang
et al. (2018) reported their experimental simulation results using an
iterative algorithm based on Dinkel-bach’s method and sequential convex
programming (SCP) for secrecy energy efficiency maximization (SEEM) as a
solution to UAV’s trajectory planning problem.

Zhu et al. Zhu et al. (2018) used the Matérn hardcore point process in the 3-D
antenna gain to improve the secrecy performance of UAV-enabled millimeter-wave
networks. Here, the transmit jamming strategy is used to enhance the secrecy
performance by considering the practical constraints in deploying UAV
communications. To combat the spoofing attack in the UAV control signals Huang
and Wang (2018) uses a physical layer approach to determine the source of the
control signal packets. In the developed approach, a generalized log-
likelihood ratio test framework is constructed to handle the signal spoofing
attack of the control signals effectively. According to Li and Zhang (2018),
there are solutions to maximize the secrecy rate of UAV communication using
UAV-aided mobile jamming. Based on their experimentation, a block coordinate
descent-based iterative algorithm is used to solve the non-convex optimization
problem to achieve better secrecy rate gain.

A dual UAV-enabled secure communication system is introduced in Cai et al.
(2018). Moreover, the joint optimization algorithm is developed for managing
the jamming caused by multiple UAVs using orthogonal time-division multiple
access. From the results, better performance is observed through the usage of
joint optimization algorithms. Hierarchical way of intrusion Detection and
Response method Sedjelmaci et al. (2018) provides a high detection rate while
monitoring the UAV behavior with lower false positives, lower communication
overhead, and increased network density observed through extensive
simulations. In contrast, the work in Lee et al. (2018) creates a UAV-enabled
secure communication system for sending private messages to the users at
ground stations. This approach using block successive upper bound minimization
techniques efficiently solves the non-convex problem, providing better user
scheduling and an improved minimum secrecy rate.

Liu et al. Liu et al. (2018) explained achieving secrecy outage metrics for
secure low-altitude UAV swarm communications. This scheme uses eavesdropping
probability, backhaul reliability, and Monte Carlo simulations for estimating
the secrecy outage probability considering the UAV cooperation, Nakagami-m
fading parameters, and eavesdropping probability. Three-dimensional spatial
deployment Zhou et al. (2018a) has been introduced to enhance the Physical
Layer Security using UAV-friendly jamming based on the outage probability of
the intended receiver. In this process, a new security measure is developed
based on the eavesdropper’s intercept probability (IP), reducing the
complexity of an exhaustive search.

To enhance secure transmission in UAV-aided multiple-input-single-output
multiple-eavesdropper (MISOME) systems, secure cooperative transmission is
presented along with their functionalities Chen and Zhang (2019). In this
scheme, an optimal UAV placement strategy is proposed to enhance the secrecy
rate and secure energy efficiency performance in the presence of cooperative
jamming along with noise beamforming. Even with partial information about
eavesdropping, secure UAV communications can be established using cooperative
jamming for effective monitoring Li et al. (2019e). The results are observed
from the physical-layer security perspective using an iterative optimization
method called block coordinate descent, in which the system’s worst-case
secrecy rate (WCSR) is improved.

Ma et al. Ma et al. (2019) utilized a cooperative jamming scheme for securing
the millimeter-wave communication using UAV enabled relay mechanism. They use
multiple UAV-enabled relays and jammers to degrade the signal quality of
eavesdropping channels to improve the physical layer security with better
secrecy outage probability. Sun et al.Sun et al. (2019b) also used an optimal
UAV position for maximizing the secrecy rate with the existence of homogeneous
Poisson point process (HPPP) eavesdroppers being independent. Consequently,
the millimeter-wave transmissions are secured with the support of the derived
SWIPT policy for the UAV-based relay systems.

It is likely that for a Secure Amplify and Forward System, physical-layer
security and transmission reliability Tatar Mamaghani and Hong (2019) will be
necessary for providing desired secrecy metrics for low-altitude UAVs. Li et
al. Li et al. (2019a) designed a novel UAV-enabled mobile jamming scheme using
Cooperative Jamming for ground wiretap channel, which provides results with a
significant increase in the secrecy rate of the wiretap system.

Further, Zhong et al. Zhong et al. (2019) overlooked the work done on Secure
UAV Communication with a new cooperative jamming approach for defending
against eavesdropping. In this technique, two scenarios are considered for
experimentation, and using successive convex approximation and alternating
optimization techniques, secure communication is ensured. To maintain security
in UAV networks, Li et al. Li et al. (2019f) developed an iterative algorithm
to solve the non-convex optimization problem. The joint optimization was
performed on UAV flight trajectory, and the results provided an improved
minimum secrecy rate compared to conventional schemes.

Communication between UAV-to-UAV systems must be secured to avoid intrusion
from other random UAVs. In Ye et al. (2019), the authors investigated the
signal-to-noise ratio (SNR) over the source UAV and the intended UAV receiver
through the proposed analytical models with simulations using the Monte-Carlo
technique. Based on the idea of UAV Secure Transmission with caching and joint
optimization of time scheduling the trajectory of UAV Cheng et al. (2019), an
iterative algorithm is put forward through successive convex optimization that
provides a benchmark scheme for maximizing the average secrecy rate.

Based on the analysis of hybrid outage probability for safeguarding UAV
Communications, Liu et al. (2019) proposes a secure transmission scheme. This
scheme is employed on the wiretap channel, where the source avoids the
eavesdropper and communicates only with the legitimate UAV, providing optimal
power allocation despite the full-duplex and active eavesdropper in the
network. Secrecy in the presence of UAV Jammer and random UAV Eavesdroppers is
imparted using secure connection probability (SCP) and piecewise function
described in Tang et al. (2019). This technique approximates the line-of-sight
probability for the communication links and uses sigmoid-based fitting to
provide better approximation and secure connection.

The efficiency of the multi-hop transmission can be exploited with an optimal
number of hops for secure UAV communication Wang et al. (2020). In this case,
multi-hop network parameters are tuned carefully to optimize the throughput
and establish secrecy against UAV surveillance and covert communication. In
the work done by Zhou et al. (2020a), UAV-enabled mobile edge computing using
an iterative algorithm of low complexity is designed to optimize the
offloading ratio, transmitting power with better secrecy capacity.

##  5 Security Support and Enforcement Aspects

The security and privacy services give users the right to control the data and
resources accessed by the UAVs in the network. In other words, such security
enforcement enables the owners of the data and resources to streamline and
disclose their information. This is generally realized by letting the users
estimate the energy demands, technological requirements, and security
policies. In this section, we investigate the requirements of provisioning
robust security support, its importance for the UAVs engaged in communication
services, energy demands, power optimization, modern technological support,
and state-of-the-art security enforcement algorithms.

###  5.1 Energy and Power Optimization Towards Security Enforcement

Security challenges in the physical layer are tackled using UAV mobility
through its trajectory design as presented in Cui et al. (2018), where a power
design methodology for secure UAV Communications is presented. This strategy
uses a successive convex optimization method, S-procedure, and the block
coordinate descent method for efficiency improvement in the average worst-case
secrecy rate. Successive convex approximation (SCA) and alternating
optimization (AO) provide a suboptimal solution while trying to optimize the
transmit power and trajectory to secure against the UAV over the no-fly zones
(NFZs) Gao et al. (2019), and the simulation results provide a better
computational efficiency resource allocation model and a multi-tenant
configuration environment.

Effective power allocation in UAV-enabled relaying systems was implemented by
Sun et al. (2019a) with slack variables and difference-of-concave (DC)
programming. Better secrecy rate maximization and optimized source/relay
transmission power are obtained with accurate location information.

By adopting the advantage of coordinated multiple points (CoMP) for
strengthening the Physical Layer Security in UAV Swarm Wang et al. (2019d),
which is an aerial network that considers the power allocation strategy using
large-scale channel state information (CSI). Based on the observed results,
the effectiveness of the proposed scheme is validated in a trajectory-oriented
manner. Joint control of trajectory and power is presented in Zhang et al.
(2019), where the UAV communications are secured using the physical layer
security technique. In this joint control mechanism, the average secrecy rates
of transmissions are maximized, and the trajectory of the UAV is optimized.

Energy-efficient jamming strategy from Li et al. (2019d), wherein connectivity
of legitimate UAV is established to track the intruding UAVs using tracking
algorithms implemented using a new simulation framework. This surveillance
outcome ensures better accuracy in tracking suspicious UAVs and an effective
packet eavesdropping rate.

Secure communications are enabled in the physical layer of the UAV network by
formulating an optimization technique to improve the average secrecy rate as
presented and discussed in Zhou et al. (2019a). The results are obtained by
decomposing the optimization task and solving using successive convex
approximation techniques and an alternating iterative algorithm. For UAV
mobile-edge computing systems, an energy-efficient computation structure with
offloading technique is proposed Bai et al. (2019). The deployment was based
on surveillance and traffic management, from which three offloading solutions
obtained optimal solutions under different scenarios.

Secure data transmissions in millimeter-wave Simultaneous Wireless Information
and Power Transfer (SWIPT) UAV were done based on relay networks Sun et al.
(2019c), and also adopting a combination of design parameters of the multi-hop
network in the physical layer to increase the reliability in transmission and
reduce the leakage of information with optimal hop counts. Recently, Zhou et
al. Zhou et al. (2020b) proposed an optimization technique for securing the
UAV-enabled cognitive radio (CR) networks. The developed iterative algorithm
is based on S-Procedure, and Bernstein-type inequalities are targeted for
optimizing the transmission power and trajectory of the UAV.

###  5.2 Modern Technology Support for Security Enhancement

As the future UAV network services are anticipated to become ad-hoc and more
decentralized, they are primarily vulnerable to cyber-physical attacks and
DoS. Recently, AI techniques and other modern supporting technologies have
been used as practical solutions to prevent and avoid attacks. In this
section, we review the applications of modern supporting technologies in
enhancing security challenges.

####  5.2.1 Role of IoT towards security enhancement

Imparting security features for industrial Internet of Things (IIoT) devices
used UAV-enabled friendly jamming scheme was developed by Wang et al. Wang et
al. (2019c) using an anti-eavesdropping scheme on multiple UAVs for analyzing
the local as well as overall eavesdropping probability.

One of the solutions for safeguarding the UAV IoT Communications at their core
physical layer is based on the implementation presented in Lei et al. (2020)
where the cumulative distribution functions (CDFs) of eavesdropping UAV and
main UAV are derived. From the analysis using the signal-to-noise ratio (SNR),
the average secrecy rate and the secrecy outage probability are derived to
ensure secure UAV IoT Communications.

  * 1.

Machine learning: For establishing cellular connectivity to UAVs, wireless and
security challenges need to be mitigated to guarantee secured operation
Challita et al. (2019), particularly for UAV-based intelligent transportation
systems, delivery systems, and multimedia streaming applications. The authors
used artificial neural networks and machine learning solutions to guarantee
secure real-time communication in UAVs through cellular networks.

  * 2.

Deep learning: Deep neural networks are used for employing strategies on anti-
intelligent UAV jamming in Gao et al. (2020). In this work, the authors
developed a deep Q-networks (DQN) capable of operating in a two-dimension
space and combining the network with the Markov decision process, which was
partially observable. From the results, optimal communication trajectory is
obtained with reduced time complexity in UAV jamming applications.

  * 3.

Federated learning: The open nature of 5G technology poses threats against the
safe data sharing. Specifically, leakage may produce severe losses for users.
Federated learning can provide a promising solution to address some of these
challenges. Federated learning allows data models to be shared instead of raw
data, thereby reducing the risk of privacy leakage. However, the traditional
federated learning frameworks have certain limitations related to the
centralized aggregation server, poisoning attacks, and communication barriers.
It’s worth noting that research and development in the field of federated
learning are actively addressing these limitations to improve the privacy,
security, and scalability of the framework. As the technology evolves, we can
expect advancements that further enhance the safety and usability of federated
learning in the context of 5G networks. Notably, Feng et al. Feng et al.
(2021) propose a blockchain-empowered decentralized horizontal federated
learning framework. This framework enables authentication of cross-domain UAVs
by using multi-signature smart contracts. Instead of a centralized server,
smart contracts are also used by this framework to compute the global model
updates.

###  5.3 Existing Algorithms Related to UAV Security

Security is a critical aspect of any digital or communication-enabled system.
In UAV-supported communication systems, security is an even more severe
challenge. This is mainly due to the system’s remote wireless communication
and unmanned nature. For instance, unlike terrestrial base stations, if a
flying cellular base station is compromised or under an adversarial attack,
its serving user equipment may potentially lose cellular connectivity, and the
UAV itself is likely to crash Fotouhi et al. (2019b).

Another security issue is the potential interference in UAV systems, where the
line of sight (LOS) links can cause significant interference to cellular user
equipment served by the terrestrial base station if an adversary compromises
the UAV. Ensuring UAV environment security, especially in cases where drones
are deployed for cellular communications, is highly important. This section
reviews existing algorithms and solutions to improve and enforce security
aspects in UAV systems.

####  5.3.1 Routing and trajectory

Routing is a grand challenge in UAV-aided communication networks due to the
dynamic and rapid changes in topology, frequent link disconnections, and high
mobility of drones. Thus, designing and implementing reliable routing
protocols/solutions that fulfill the minimum overhead and delay constraints
face various limitations and challenges, including detecting malicious
vehicles.

Designing efficient routing mechanisms for VANETs, which comprise sets of
vehicles connected via wireless mediums, is a highly challenging task due to
the characteristics and applications of the networks Fatemidokht et al.
(2021). Ad hoc mode-oriented routing protocols exist between UAVs and vehicles
and between UAVs. The work by Fatemi et al. Fatemidokht et al. (2021) presents
a type of routing protocol that leverages two different protocols, supporting
communication between UAVs and vehicles, as well as between UAVs themselves.
The routing approach works by appraising the density of vehicles in a
particular road segment based on ”Hello” messages exchanged between vehicles.
A trust-enabled algorithm is further used collaboratively with UAVs to
identify illegitimate vehicles changing pseudonyms.

Despite their advantages in terms of easy deployment and high mobility, UAVs
pose a challenge to the security of information associated with air-to-ground
line-of-sight communication channels. The broadcast nature of these channels
is a security concern that needs to be addressed. Cui et al. Cui et al. (2018)
address this physical layer-level challenge by exploring the mobility of UAVs
through trajectory design. In their work, the UAV-ground communication
platform is considered with multiple eavesdroppers located on the ground, and
the location information of these eavesdroppers is imperfect. An optimization
problem is established to maximize the secrecy rate of the platform by
enhancing the trajectory planning and power transmission of the UAV within a
given flight window.

Moreover, Zhang et al. Zhang et al. (2019) also address physical-layer
security in UAV communication systems by examining both uplink and downlink
communications with a ground node, namely ground-to-UAV (G2U) and UAV-to-
ground (U2G) communications, which are susceptible to eavesdropping. Instead
of relying on ground nodes at fixed or quasi-static locations for security,
Zhang et al. Zhang et al. (2019) use the high mobility of UAVs to proactively
establish secure and insecure channels for legitimate and illegitimate links
(e.g., eavesdroppers) through trajectory design. Iterative optimization
algorithms like successive convex optimization and block coordinate descent
can be used to improve the secrecy rates of the G2U and U2G transmissions.

Efficient trajectory and resource allocation are crucial for providing a
trustworthy and energy-efficient downlink communication platform for UAVs. In
a notable research work by Cai et al. Cai et al. (2020), a joint optimization
algorithm is proposed to improve resource allocation, trajectory planning, and
jamming policy, ultimately maximizing energy efficiency. To enhance UAV
networks and navigation, Krishna et al. Krishnan and Manimala (2020) proposed
an optimization algorithm that dynamically adjusts the trajectory to avoid
obstacles and secure the UAV navigation task. In another study, Li et al. Li
et al. (2019g) proposed an algorithm that maximizes the minimum secrecy rate
while ensuring fairness among ground terminals (GTs) served by UAVs.

####  5.3.2 Resource allocation

The expectations for future wireless communication technologies, including
massive connectivity, low latency, and ultra-high data rates, have
dramatically increased. This has led to significant challenges in wireless
communication technologies and their associated facilities. Fortunately, UAV-
assisted communication platforms offer a promising solution Fotouhi et al.
(2019b); Zeng et al. (2019) that can alleviate the limitations of conventional
wireless communications at the physical layer. By leveraging the high mobility
and flexibility of UAVs, the performance of the communication platform can be
improved by moving the UAVs closer to end users. However, despite this
potential, technical challenges still need to be addressed to realize the
promised performance gains fully.

Rigorous power limitations primarily hinder effective UAV communications. Due
to UAVs’ size and weight limitations, their onboard batteries’ energy storage
capacity is typically small. Furthermore, the power consumption of flight and
communication systems heavily depends on the UAV’s velocity and trajectory.
Therefore, enhancing the energy efficiency of UAVs has garnered tremendous
interest and research efforts in the literature. Notably, Cai et al. Cai et
al. (2020) propose a joint trajectory and resource allocation approach to
improve UAV communication systems’ energy consumption and security. The
proposed approach optimizes the trajectory of the information UAV, resource
allocation, and jammer UAV’s jamming policy to maximize the overall system
energy efficiency.

Li et al. Li et al. (2020) propose a resource allocation scheme for secure
multi-UAV communication systems that relies on multi-purpose base stations
dispatched to ensure secure communications with authorized ground users in the
presence of eavesdroppers. The authors leverage orthogonal frequency division
multiple access (OFDMA) to enable active base stations to communicate with
legitimate ground users through assigned subcarriers. By jointly optimizing
the UAV’s trajectory and communication subcarrier allocation policy, the
solution ensures fairness in secure communication and maximizes the minimum
secrecy per user.

Hong et al. Hong et al. (2019) proposed a method for resource allocation in a
secure UAV-assisted simultaneous wireless information and power transfer
(SWIPT) system in the presence of multiple eavesdroppers. They aim to maximize
the per-user secrecy rate by jointly optimizing the trajectory and
transmission power of the UAV while solving the non-convex problem. Xu et al.
Xu et al. (2020) developed a method to maximize the minimum secure computing
capacity for both non-orthogonal multiple access (NOMA) and time division
multiple access (TDMA) in UAV-assisted mobile edge computing (MEC) systems.
They achieve this by jointly optimizing the UAV trajectory, communication
resources, and computation resources. These works highlight the importance of
joint optimization of UAV trajectory, transmission power, and resource
allocation for achieving efficient and secure communication in UAV networks.
Finally, another research interest in this area is resource allocation for
multi-UAV collaborative tasks, which can be approached as a combinatorial
optimization problem and solved using an improved clustering methodology. The
work by Fu et al. Fu et al. (2019) addresses this optimization problem while
allowing multiple UAVs to work collaboratively and complete tasks with lower
energy consumption.

####  5.3.3 Authentication and encryption

UAVs are now used in various fields, such as rescue and search missions,
military operations, and monitoring tasks. However, security remains a
significant concern, as UAVs handle sensitive data related to individuals’
privacy that adversaries should not compromise or disclose. Researchers have
addressed specific security vulnerabilities in UAV systems, including Yoon et
al.’s study Yoon et al. (2017), which proposed a solution to prevent hijacking
attempts against commercial UAVs’ network channels. The authors presented an
algorithm to maintain control of UAVs in hijacking situations and establish a
security-enhanced communication channel with an authentication layer.

To ensure the reliable, resilient, rapid, and secure delivery of services to
end-users through Drones as a Service (DaaS), rigorous guidelines are required
for designing a 5G-UAV network. One possible solution is to leverage
blockchain consensus and its decentralization feature, where UAVs can act as
mobile access points, service suppliers, or routing nodes. Aloqaily et al.
Aloqaily et al. (2021) propose a 5G network-supported environment based on
UAVs and blockchain to fulfill fluctuating user demands with access supply.
The proposed solution facilitates decentralized service delivery (DaaS) and
the capability of routing to/from end-users in a trustworthy way. A remarkable
feature of this work is that it ensures authenticated information
communication, which achieves a high delivery success rate.

Furthermore, despite the widespread use of IoD in various applications,
ranging from civilian to military, privacy and security remain critical
challenges that must be adequately addressed during communication, whether
with the moving access points in the sky or the control station server. One
solution to this problem in IoD is presented by Bera et al. Bera et al.
(2020), a blockchain-enabled secure data management scheme that provides
resistance against various threats in IoD communication entities.

The data link performance is another critical metric in UAVs that could be
potentially affected by the communication channel, which could ultimately
impact the overall reliability of data transmission. Furthermore, wireless
communication channels can also be a surface for various remote attacks Atoev
et al. (2019). Therefore, it is essential to ensure that the wireless
communication channels between the ground control station (GCS) and vehicles
are resilient while maintaining data link performance efficiency by employing
lightweight security authentication solutions. For instance, the lightweight
security authentication mechanism for UAV environments Teng et al. (2019) and
secure UAV systems in the presence of eavesdropping UAV nodes Zhou et al.
(2019b) are viable solutions to ensure secure communication channels.

Artificial intelligence (AI) can be integrated with blockchain technology to
enhance the underlying networking infrastructure of UAVs, including 5G
communications. Notably, Gupta et al. Gupta et al. (2021a) present an AI-based
architecture design to secure 5G communications in the drone environment.
Specifically, the presented design leverages InterPlanetary File System (IPFS)
as a platform for information storage, enhancing overall communication privacy
and security, underlying network performance, and reducing transaction storage
overhead while facilitating on-the-fly decision-making. Wazid et al. Wazid et
al. (2020) propose another solution incorporating AI techniques and private
blockchain infrastructure to secure communications in IoT-based drone-aided
healthcare services. AI was also leveraged by Zhang et al. Zhang et al. (2020)
to address the trajectory design challenge for multi-UAV cooperation scenarios
through a multi-agent reinforcement learning approach.

Table 3: A taxonomy of existing solutions for UAV-enabled secure communication architectures. Reference |  Solution |  Key contributions |  Implications
---|---|---|---
Faraji-Biregani and Fotohi (2021) |  Smart agents |  Establishes secure communication between UAVs with the support of smart agents |  Implements a three-step negotiation process to identify reliable neighbors, and discard the traffic generated by the malicious UAVs
Li et al. (2018b) |  Q-learning-based algorithm |  Increase the system secrecy capacity, and decrease the attack rate |  Smartly handles the attacks even under imperfect estimation of the channel with the support of Nash equilibrium
Khan et al. (2022b) |  Secure communication protocol |  Leverage vulnerabilities in Mavlink packets by encrypting through additional security layers |  The compromised security requirements were overcome through Mavlink as one of the lightweight open-source protocols
Khan et al. (2020) |  Secure communication protocol for UAVs |  Overview of UAVs, their communication protocols, and security threats |  The proposed secure communication protocol can enhance the security of UAVs and encourage their wider adoption
Jain et al. (2021) |  AI-based UAV-borne secure communication framework for Industry 5.0 |  The proposed model involves a novel image steganography technique with optimal pixel selection and encryption processes for classification of securely received UAV images |  The presented model shows good performance indicating the potential for enhancing the security and reliability of UAV communication in Industry 5.0 and other safety-critical applications
Fotohi et al. (2020) |  SID-UAV method to secure UAV-to-UAV communications and resist malicious UAVs |  Self-matching system that detects the safest path between UAVs |  The proposed method outperforms other existing methods indicating its effectiveness in enhancing the security of UAV networks
Huang et al. (2020) |  3D trajectory optimization model for a solar-powered UAV |  Secure communication against eavesdroppers while avoiding no-fly zones and considering energy consumption and solar power harvesting |  Rapidly-exploring Random Tree (RRT) method is developed to construct the UAV trajectory, and computer simulations show that the proposed method outperforms a baseline method
Rashid et al. (2019) |  Security model based on Identity Based authentication scheme for UAV-integrated HetNets |  A scheme that it is resistant to vulnerabilities of intruders such as replay and impersonation |  The proposed scheme can help to mitigate security issues in UAV-integrated HetNets, particularly in military scenarios, and ensure secure communication among military users using the network

##  6 Blockchain-based Security Architecture for UAV in B5G/6G

This section summarizes the typical blockchain-based security architectures
meant for UAVs in conjugation with the B5G/6G services. The presented
solutions are based on existing studies and reflect UAV systems’ fundamental
ideas and mechanisms. However, it is unfortunate that only some of the
horizons of the related proposed architectures in the literature could be
covered due to the unique intention of each framework in terms of the design,
navigating the environment, and wireless communication strategies.

Figure 5: Trustworthy blockchain services.

As of today, due to the heterogeneous network infrastructure and integrated
ubiquitous devices in B5G/6G services, the security demands of B5G/6G networks
are to be integrated with blockchain services. Given this, Nguyen et al.,
Nguyen et al. (2019) surveyed the potential of utilizing blockchain to protect
the 5G services from external attacks. Anticipating the B5G/6G demands and
securing its services, Praveen et al. Praveen et al. (2020) identified the
specific areas where blockchain could enhance the privacy and security of 5G
services. Mehta et al. Mehta et al. (2020) summarize the taxonomy of security
challenges in 5G-enabled UAVs and provide security solutions using blockchain
for industrial applications. Further, the authors have also presented a model
for Multi-Operator network slicing and implemented the same, securing the 5G
services using blockchain frameworks.

The survey in Ullah et al. (2020) presented the regulations and
standardization of UAVs for potential applications and the energy harvesting,
collision avoidance, channel modeling, and security issues that can ensure new
business openings. Further, to enhance the security within UAV networks,
Alladi et al. Alladi et al. (2020) reported using blockchain. In this study,
the authors addressed the key enabling technologies and various applications
of blockchain for deployment in UAV networks. In particular, the challenges
existing in UAV-based surveillance, network security, inventory management,
and decentralized storage were considered for analysis with blockchain
integration and suggested a few research directions.

###  6.1 Spectrum Management

As the development of blockchain-based technology is getting popular, they are
also used by UAV operators for secure spectrum trading Qiu et al. (2020).
Here, an incentive-based spectrum blockchain framework is proposed for
improving the efficiency of the spectrum trading environment for UAV-assisted
cellular networks. Distribution of underutilized spectrum to human-to-human
users and machine-to-machine (M2M) are performed using 5G heterogeneous
networks considering the privacy ensured using blockchain frameworkZhou et al.
(2020c). Moreover, this work addresses the efficiency of the spectrum
allocation, along with the secure sharing of spectrum and incentive mechanism
with a relevant case study.

Qiu et al. Qiu et al. (2019) were among the first to pioneer blockchain-
enabled spectrum trading for UAVs. The researchers offered the solution from
the mobile network operator perspective, which could be used for security
assessment in spectrum trading in UAV-enabled cellular networks. The authors
observed that blockchain technology significantly improves the security
aspects of spectrum sharing.

Figure 6: Blockchain-enabled edge computing for UAVs in the junction of user
and infrastructure layers.

###  6.2 Secured Path Planning

Specific UAVs demand marked paths in their environment, few others require
cleared flight paths, and some need to be effectively managed in a 3D space.
Considering all such constraints, Dasu et al. Dasu et al. (2018) implemented
geofencing and established air-traffic control based on different modes by
herding the UAVs with the support of blockchains and 5G service.

###  6.3 Privacy Preservation and Intrusion Detection

From the intrusion detection perspective, the blockchain-based delivery system
through UAV services, named DeliveryCoin Ferrag and Maglaras (2019) employs
short signatures and hash functions. The authors achieved a consensus inside
the delivery platform through the UAV-driven forwarding schemes by applying
this privacy-preserving scheme. The proposed intrusion detection systems in
each 5G cell could handle network attacks and improve the effectiveness of the
UAV communication services.

In Chamola et al. (2020), Chamola et al. reviewed the impact of 5G services
and blockchain technology for assessing the health implication due to the
outbreak of COVID-19. They have highlighted its effects on the global economy
in association with other supporting technologies such as IoT, UAVs, and AI
towards mitigating the COVID-19 outbreak. Inspired by the work mentioned
above, Islam and Shin Islam and Shin (2020) developed a blockchain-based
secure healthcare scheme with the support of UAVs and IoT. Here, the UAVs
communicate with body sensor hives via tokens and share the keys, enabling
low-power and secure communication through 5G services.

###  6.4 Cooperative Navigation Strategies

Cooperated blockchain-5G networks are summarized with the open-end research
challenges persisting in this domain. A similar work reported by Li et al. Li
et al. (2018a) surveyed the usage of 5G services based on UAV platforms with
the joint consideration of operation in various layers and communication
strategies. The game theoretical approach of blockchain-based spectrum
sharing, introduced by Choi et al. Choi and Lee (2019), uses a tit-for-tat
strategy to establish user cooperation in the 5G-Enabled IoT network. The
strategy’s performance was assessed in a dense network by encouraging the
users to share the information regularly. Through this technique, it was
evident that the spectrum-sharing features are optimized compared to the
traditional approaches.

###  6.5 Secured IoT Services for UAVs

Operating UAVs in aerial mode and base station mode demands a confidential
exchange of messages. So ensuring the best security mechanisms in the physical
layer enhances the secured and controlled mobility with better performance Li
et al. (2019c). Each UAV in the network of UAVs acts as a Blockchain node that
holds onboard functionality for observing and interpreting block transactions
and can exchange information with other UAV nodes in the network Kuzmin and
Znak (2018). Blockchain technology is also widely used to address the
challenges in IoT services, mainly driven through 5G services. Dai et al. Dai
et al. (2019) introduced the term blockchain of things (BCoT) and presented
its architecture. Further, the authors elaborated on the implications of this
technology along with 5G services targeted for industrial applications.

Nguyen et al.Nguyen et al. (2020) surveyed cooperated blockchain-5G networks
by compiling the works in this domain related to IoT, transportation,
healthcare, and UAVs. Similarly, 5G-enabled IoT in industrial applications
with blockchain technology as its backbone is surveyed in Mistry et al.
(2020), where Mistry et al. shed more light on the privacy preservation of
autonomous vehicles towards secured automation. Subsequently, Ge et al. Ge et
al. (2020) propose a novel IoT communication architecture based on blockchain
technology. Their solution implements a distributed scheme with blockchain’s
privacy and security benefits while mitigating computation and storage
requirements.

###  6.6 Secure Data Management

A blockchain-empowered security feature is enabled in a UAV swarm for data
acquisition tasks from IoT devices and further routed to the closest node in
the UAV swarm Islam and Shin (2019). Here, the sender is validated using the
digital signature algorithm, and the UAV swarm implements the $\pi$-hash bloom
filter with two-phase validation. For the blockchain-based, IoT, and
5G-enabled UAV environment, a secure data delivery and collection scheme was
proposed by Bera et al. (2020), aiming to secure data management among the UAV
communication. As the blockchain-based secure framework needs to manage
several potential attacks to impart better security and functionalities, the
computation and communication overheads need considerable attention. The
proposed architecture is based on decentralized nature that performs the
security analysis with considerably moderate communication efforts and less
computational overheads than conventional schemes. Secure data acquisition
scheme from IoT devices enabled using blockchain concept was used in a UAV
swarm Islam and Shin (2019). In this scheme, the IoT devices forward the
encrypted data to the UAV swarm, providing secure connectivity and better
energy consumption.

###  6.7 Edge Computing

Edge computing, along with 5G and UAV, may provide an opportunity for new
business values for the telecom sector. According to Sharma et al. (2019),
ultra-reliable communication is employed in mobile edge computing by utilizing
efficient cache-enabled UAVs as on-demand nodes. Here, the deployed neural-
blockchain-based provides a flat architecture with reliable means of
communication. The work in Miatton (2020) proposed using distributed ledger
technology with 5G for unlocking the new values of utilizing UAVs for
connected supply chains. However, research needs to be carried out to ensure
the validity of the blockchain involved in edge computing services on data
storage, retrieval, and processing. Guan et al. Guan et al. (2020) proposed
their blockchain-based distributed security solution for the UAV-enabled
mobile edge computing model that reduces fraud occurrences and reasonably
rewards authenticated participants. The framework comprises a smart contract,
and the sub-blockchain enhances the system’s stability. Further, based on the
conceptualized framework, the results show that the proposed scheme consumes
minimum resources and time for computation.

###  6.8 Trust Protocol for Internet of Drones

Mershad et al. Mershad (2022) propose a framework that assumes the existence
of multiple UAV networks that can connect to each other via ground control
stations (GCSs). A central or distributed authority controls each group of
GCSs. The blockchain network consists of UAVs and GCSs cooperating to generate
and store blockchain blocks. The proposed system assumes that certain types of
data will be stored within blockchain blocks saved at drones and GCSs, while
other categories of data will be saved within blocks that only need to be
stored at the GCSs. Two main types of transactions exist in the blockchain:
the first type will contain complete data related to the transaction. In
contrast, the second type will include transaction metadata and a reference to
the actual data stored on an external storage system.

##  7 Open Issues, Challenges and Future Trends in the Security of UAV
Architecture

The section provides a summary of key findings and lessons learned from
previous research in the field of UAV security. It also outlines some of the
open issues and challenges that still need to be addressed, such as developing
secure communication protocols and implementing robust authentication
mechanisms. Furthermore, it may discuss future trends and potential solutions
for addressing these challenges, such as using machine learning algorithms for
anomaly detection and developing advanced encryption techniques.

###  7.1 Summary of Key Findings and Lessons Learned

The ongoing research in enhancing security in UAV-based systems involves
applying iterative algorithms, joint optimization techniques, and cooperative
jamming. These methods improve secrecy rate, reduce communication overhead,
and increase network density while providing desired secrecy metrics for low-
altitude UAVs. In addition, the development of dual UAV-enabled secure
communication systems and hierarchical intrusion detection and response
methods has also demonstrated improved performance.

Due to UAVs’ unmanned and remote wireless nature, ensuring security in UAV-
based communication systems is critical. Compromised UAVs can result in loss
of connectivity and interference. In this article, we discuss different
essential solutions for solving this problem. The solutions presented include
routing with trust-enabled algorithms, mobility, and optimization, physical
layer security via trusted and distrusted channels, secrecy rates, and
resource allocation for energy efficiency and secure navigation. However,
challenges still exist, such as fairness and limitations of the physical
layer.

Blockchain and 5G services have shown great potential in addressing these
challenges, including secure spectrum trading, path planning, privacy
preservation, and intrusion detection systems for UAVs. They are also utilized
for cooperation in 5G-enabled IoT networks and secure data management in UAV
swarms, among other applications. By using UAVs as blockchain nodes and
employing a game-theoretical approach for efficient spectrum sharing,
blockchain can serve as a backbone for secure and private data management
while mitigating computation and storage requirements in IoT communication
architecture.

###  7.2 Open Issues and Challenges

Despite the existence of many articles in the literature, including this one,
covering and categorizing a broad range of solutions to the different
identified security issues and threats, it is anticipated that we will see
more specifically designed UAV solutions. The expected security designs will
pay more attention to the low node density (e.g., flying ad-hoc networks),
restricted power consumption, highly dynamic topology changes, highly dynamic
mobility of nodes, and high dependence on radio propagation.

Although we categorized many security solutions in this article specifically
for UAV network architectures, many of these solutions need to be more
suitable and flexible for UAVs with high mobility, i.e., high-speed movement.
Furthermore, UAVs’ standardized communication channels for authorization,
authentication, and access control must be improved in flying ad-hoc networks.

####  7.2.1 Security and Privacy

Apart from enhancing security, it is crucial to prioritize protecting
sensitive information, safeguarding against potential malicious attacks, and
establishing robust measures to prevent unauthorized access to the blockchain
network. Ensuring privacy is a critical aspect that necessitates carefully
considering and implementing encryption techniques and access controls.
Moreover, the design of a secure UAV architecture must account for the dynamic
nature of UAVs, which are susceptible to physical tampering and signal
interference. Therefore, incorporating mechanisms for tamper-proofing the UAVs
and implementing secure communication protocols are essential elements to
mitigate potential vulnerabilities. Additionally, maintaining the integrity
and immutability of data stored within the blockchain is paramount,
necessitating the establishment of stringent auditing and verification
mechanisms. By addressing these challenges, a secure UAV architecture
utilizing blockchain technology can foster trust, reliability, and enhanced
security in UAV communication, leading to expanded applications and
advancements in next-generation wireless networks.

Furthermore, UAVs generate and exchange sensitive data, including flight
telemetry, sensor data, and mission-related information. Ensuring the security
and privacy of this data is paramount. Integrating blockchain technology can
provide tamper-resistant and transparent data storage, but challenges remain
in designing robust security mechanisms, authentication protocols, and
encryption techniques tailored for UAVs in the context of blockchain-5G
integration.

####  7.2.2 Regulatory and Legal Considerations

The utilization of UAVs in wireless networks operates within the framework of
regulations and legal standards. Integrating blockchain technology into this
context introduces a new set of considerations, including additional
compliance requirements and challenges pertaining to data ownership,
liability, and accountability. Adhering to regulatory frameworks is vital to
ensure UAVs’ responsible and lawful operation in wireless networks.
Blockchain-based systems introduce unique data ownership challenges, as
multiple entities may contribute to and access the blockchain network. As a
result, clear protocols and agreements must be established to define data
ownership and the corresponding rights and responsibilities.

Moreover, liability concerns may arise when using UAVs equipped with
blockchain technology, particularly in scenarios involving data breaches or
accidents. Determining the appropriate parties responsible for such incidents
and allocating liability can be complex. Therefore, a comprehensive legal
framework that considers the implications of blockchain integration within UAV
operations is necessary. Accountability is another key aspect that needs to be
addressed. Establishing mechanisms to trace and attribute actions performed
within the blockchain network is crucial for maintaining transparency and
ensuring accountability among the involved parties. By navigating the
complexities of regulations, data ownership, liability, and accountability,
integrating blockchain technology into UAVs in wireless networks can promote
responsible and compliant use, fostering trust among stakeholders and
facilitating the advancement of this technology in various industries.

Last, UAVs are subject to various regulations and legal requirements,
particularly in terms of flight operations, privacy, and data handling.
Integrating blockchain and 5G introduces additional considerations for
compliance and governance. Addressing these legal and regulatory challenges,
ensuring compliance with existing frameworks, and developing new guidelines
specific to blockchain-5G integrated UAV systems is essential.

####  7.2.3 Standardization

Standardization plays a crucial role in developing secure UAV architectures
incorporating blockchain technology. Establishing standardized protocols and
frameworks is imperative to ensure seamless interoperability, compatibility,
and widespread adoption across diverse platforms and vendors. By implementing
standardized practices, the UAV industry can overcome barriers such as
fragmentation and proprietary solutions, facilitating a more cohesive
ecosystem. Standardization efforts should focus on defining standard
communication protocols, data formats, and security measures that enable UAVs
and blockchain systems to interact efficiently and securely. Moreover,
standardized frameworks promote collaboration and knowledge-sharing among
stakeholders, enabling the development of innovative solutions and fostering a
healthy competitive environment. These standards also facilitate regulatory
compliance, as regulatory bodies can refer to established protocols and
frameworks when assessing the security and reliability of UAV architectures.
Furthermore, standardization promotes scalability and future-proofing by
ensuring that UAV architectures incorporating blockchain technology evolve and
integrate seamlessly with emerging technologies and advancements.

####  7.2.4 Trust and Governance

Building trust and implementing effective governance models are vital to
ensuring the secure operation of UAVs within a blockchain network.
Establishing trust among the participating nodes is essential, fostering a
reliable and collaborative environment. This can be achieved by developing
consensus mechanisms that enable nodes to collectively agree on the validity
of transactions and ensure the integrity of the blockchain data. By addressing
issues related to node reputation, such as evaluating the reliability and
performance of individual nodes, the overall trustworthiness of the UAV
architecture can be enhanced. Trust management mechanisms, including
reputation systems and identity verification, play a crucial role in
establishing trust among network participants. Robust and transparent
governance models are also necessary to govern the interactions and decision-
making processes within the blockchain network. Defining clear rules and
procedures for participation, consensus, and dispute resolution ensures a fair
and accountable environment for all stakeholders. Moreover, governance
frameworks need to consider the evolving nature of UAV technology and adapt
accordingly to accommodate emerging challenges and opportunities. By
emphasizing trust and governance, a secure UAV architecture utilizing
blockchain technology can inspire confidence, foster collaboration, and enable
UAVs’ safe and reliable operation in a wide range of applications and
industries.

####  7.2.5 Scalability and Interoperability

Blockchain networks, such as the popular public blockchain Ethereum, face
scalability issues when it comes to handling a large number of transactions.
UAVs generate vast amounts of data that need to be securely stored and
processed in real-time. Developing scalable blockchain solutions capable of
handling the high transaction volume and data throughput from UAVs is an
ongoing challenge.

Moreover, UAVs are often part of larger systems, such as IoT networks or smart
city infrastructures. Ensuring interoperability between different blockchain
platforms, 5G/6G networks, and other technologies involved in UAV operations
is crucial for seamless integration. Developing standards and protocols that
allow for interoperability and data exchange between different systems will be
a significant challenge.

####  7.2.6 Latency and Real-Time Communication

UAVs require low-latency and reliable communication for tasks like remote
control, navigation, and data transmission. While 5G networks offer high
bandwidth and reduced latency, ensuring seamless and uninterrupted
communication between UAVs and blockchain networks is still a challenge.
Optimizing protocols and network architectures to support real-time
communication and synchronization with blockchain systems is crucial.

####  7.2.7 Energy Efficiency

UAVs operate on limited battery power, and energy efficiency is a critical
factor for their endurance and overall performance. Both blockchain and 5G
technologies consume significant energy resources. Finding ways to optimize
the energy consumption of blockchain systems and 5G networks in the context of
UAV applications can enhance their practicality and extend flight times.

Overall, the integration of blockchain technology and 5G/6G networks into UAVs
presents remarkable opportunities, but it also requires addressing various
technical, security, regulatory, and interoperability challenges. Continued
research, collaboration between different stakeholders, and iterative
development are essential to overcome these obstacles and unlock the full
potential of this integration.

###  7.3 Recent Trends and Future Directions

In this subsection, we highlight the future research directions in accordance
with the current open challenges and issues pertaining to UAV networks and how
blockchain technology can help alleviate some of these open challenges.
Although the BC-aided B5G/6G UAV architecture enhances the different UAV
services by empowering privacy, security, trust, and improving resource
management, integrating such an architecture faces several challenges,
detailed next.

####  7.3.1 QoS and scalability

The existing literature contains many research works demonstrating blockchain
technology’s throughput, scalability, and delay/latency. This is mainly due to
the increasing volume of data communicated through the network, which is
continuously growing. Additionally, the consensus mechanisms in blockchain can
affect the overall Quality of Service (QoS) of the system in terms of
throughput, scalability, and delay, as the rate of data generation is usually
faster than the mining process. Scalability has always been a major concern in
blockchain technology, and researchers must continue to seek improvements to
enhance the QoS of blockchain-assisted systems, such as UAVs.

####  7.3.2 Federated learning and UAV

5G technology provides an excellent communication infrastructure to support a
wide range of intelligent services and applications in UAVs. For example,
drones flying near end-users can act as relay nodes to transmit data and
strengthen edge servers. Moreover, through federated learning (FL) techniques,
UAVs can assist in processing and managing collected data and sharing learned
models with cloud/fog nodes for further aggregation and comprehensive
comparison to aid decision-making. However, using this approach often incurs
additional computation overhead and energy consumption, which requires
optimization of UAV resources (such as the battery, CPU, node election, and
formation) based on advanced task scheduling and allocation.

####  7.3.3 Diversity of data

In UAV systems, devices (i.e., drones) typically exchange a broad range of
message types, which must be handled differently. Some of these messages are
used to instruct drones to complete their tasks, such as IoT data created by
the UAV, data exchanged among UAVs, instruction data received from the pilot,
and other data generated from other UAV system entities and relayed by flying
drones. Therefore, future blockchain-assisted UAV systems should consider the
diversity of UAV data by adopting more flexible transactional systems while
preserving the security and privacy of the various data types.

####  7.3.4 Considerations for privacy & security in the design phase

When designing B5G/6G UAV architectures with blockchain assistance or
developing blockchain-enabled applications for deployment in UAV networks such
as the IoD, privacy, and security should be considered essential components.
Incorporating privacy and security approaches is highly recommended as best
practice to avoid or reduce threats. Furthermore, forensic mechanisms can be
adopted to design blockchain-assisted UAV systems. Forensic-by-design, for
instance, is a promising mechanism that can be incorporated since it has been
proven effective in other cyber-physical environments and networks. This
forensic mechanism is particularly suitable for tracing attack sources and
reconstructing threat events.

####  7.3.5 Real-time isolation

Although localization is critical when direct UAV location measurement is
unavailable, localization errors can persist. Therefore, future blockchain-
assisted 5G UAV systems need an approach that efficiently and quickly isolates
the compromised device exposed to security and privacy breaches. This approach
will serve as a fault isolation solution, providing a protection layer against
a holistic failure of the UAV network. Furthermore, since the line of sight
connection can be lost at any moment, future UAV systems are expected to
provide a strategy that facilitates the drone’s return to the initial base
station upon the occurrence of this situation.

####  7.3.6 Collision and obstacle avoidance

Despite the significant advances in UAV technology to address drone collisions
and obstacle avoidance, the potential for crashing drones remains one of the
most significant risks associated with this technology for both properties and
people, and drones may even collide with flying planes. As a result, it is
crucial for the existing IoD to carefully consider obstacle detection and
deploy real-time, reliable solutions to handle the presence of obstacles. If
blockchain technology is deployed for UAV 5G infrastructures, it is essential
to have a comprehensive understanding of the range of IoD communication
services.

####  7.3.7 Computer vision

At present, on-board surrounding analytical systems are being widely used,
leading to a significant increase in energy consumption and weight for the
devices. If blockchain technology is deployed in UAV systems, advanced and
sophisticated computer vision tools must be integrated to stream the
surrounding views, such as images, and feed them back to the object
recognition entity for feature processing and analysis. Moreover, leveraging
computer vision tools with blockchain technology can potentially increase the
UAV systems’ computational complexity and resource requirements, making it
critical to optimize the system for energy consumption and storage capacity.
Therefore, researchers must develop efficient algorithms and techniques to
improve the computational efficiency of computer vision tools in blockchain-
assisted UAV systems. Additionally, optimizing UAV devices’ power management
and storage capacity will be crucial to support the increased computational
demand while ensuring the system’s longevity.

####  7.3.8 AI-enabled blockchain attack mitigation

The IoD network is now facing several new and sophisticated attacks, which
require implementing artificial intelligence (AI) based mitigating mechanisms.
AI techniques such as artificial neural networks, deep learning, and machine
learning algorithms can be used to optimize the security and privacy of the
IoD network. However, implementing these techniques presents significant
challenges, and selecting the appropriate AI algorithm for specific security
and privacy requirements still requires further research and exploration. In
addition, it is crucial to monitor the IoD network for emerging threats
continuously and to adapt the AI-based mitigating mechanisms to address new
attack vectors. This requires a proactive approach to security and privacy
that can quickly respond to changing circumstances and protect the network
from evolving threats.

####  7.3.9 Trustworthy data aggregation

As drones collect and incorporate a large amount of data in the network,
securing and encapsulating this data within a single unit is essential before
sending it. Future blockchain-assisted UAV 5G architectures can deploy
flexible and secure aggregation mechanisms for drone data. Aggregating this
data before sending it can reduce communication and energy costs, which are
critical for UAV network resources. Cryptographic techniques can be leveraged
to aggregate multiple ciphertexts into a single unit, simultaneously enforcing
access control and confidentiality of the aggregated data. Such techniques can
be used to create a secure and efficient mechanism for drone data aggregation.

####  7.3.10 Intrusion detection

Integrating blockchain technology into a UAV 5G system may introduce
additional security threats to its network, which are specific to the
blockchain. Therefore, intelligent and robust intrusion detection and
prevention systems (IDPSs) are required for such a network. UAV researchers
are recommended to take advantage of the recent advances in intrusion
detection solutions to design and develop more robust and more intelligent
IDPSs to mitigate existing threats in UAV systems and the threats that may
arise from integrating blockchain technology. It is crucial to ensure the
security and privacy of UAV systems, as they are becoming increasingly
prevalent in various fields and industries. Moreover, developing effective
security measures for UAV 5G blockchain systems is essential not only to
protect against potential cyber attacks but also to establish trust and
confidence among users of these systems.

####  7.3.11 Standardization and regulatory policies

Ensuring standardization and regulatory compliance remains a significant
concern for both UAVs and blockchain technologies. To regulate and standardize
the usage and integration of UAV devices, particularly drones, in urban areas,
future policies and laws must be designed and enforced for privacy, safety,
and security purposes. Additionally, regulatory rules are necessary to control
service providers’ and entities’ access to specific data in the decentralized
ledger. Efficient Implementation of these policies and regulations requires
collaboration among industry stakeholders, policymakers, and regulatory
bodies. Close cooperation can help ensure that UAVs and blockchain
technologies are used and integrated in a manner that promotes public safety
and respects privacy while also advancing the development of these emerging
technologies.

####  7.3.12 Off-chain storage

As mentioned earlier, UAV technology facilitates the exchange of various data
types or messages. Some of this data may be significant in size, making it
difficult to store in the ledger, especially if it requires frequent deletion
or modification. To address this challenge and improve the overall performance
of the UAV system, off-chain storage may be a viable solution. Off-chain
storage also provides the added benefit of reducing the blockchain’s
computational workload and network traffic, which can enhance the system’s
speed and efficiency. Additionally, off-chain storage can help minimize the
costs associated with storing large amounts of data on the blockchain, making
it a more cost-effective solution in certain scenarios.

####  7.3.13 Location and divisibility

Ensuring mutual agreement or consensus on allocating services and location
among service providers is another significant challenge for UAV technology.
Failure to address this challenge could impact the operability of UAV
technology and hinder its large-scale deployment. Furthermore, it could delay
the integration of future broadband cellular networks, including 6G, requiring
seamless coordination and collaboration among various stakeholders.

To overcome this challenge, it is necessary to establish clear guidelines and
regulations for allocating services and locations among service providers.
These guidelines should take into account the various requirements and
constraints of different stakeholders, including UAV operators, service
providers, and regulators. By fostering greater collaboration and cooperation
among these stakeholders, it is possible to enhance the operability and
deployment of UAV technology while ensuring that it remains compatible with
future broadband cellular networks.

##  8 Conclusion

This review article offers a comprehensive overview of UAV-based B5G/6G
communication technologies and architectures that support seamless services.
While UAVs were once considered a hobbyist’s tool, they now play a critical
role in disaster management and emergency response and can even provide
wireless communication services. As the demand for establishing seamless
B5G/6G-based communication services to remote areas increases, drones and UAV
technology are becoming more popular. However, it is crucial to address
security concerns in the research and development of advanced UAV-based 5G
architecture. To address this need, this article conducts a comprehensive
review of the security aspects of UAVs in the context of B5G/6G system
architecture, including enabling technologies, privacy issues, and security
integration at all protocol stack layers. Additionally, this survey analyzes
existing mechanisms to secure UAV-based B5G/6G communications and energy and
power optimization factors. The article also summarizes the use of modern
technological trends for establishing security and protecting UAV-based
systems, along with identifying open challenges and strategies for future
research in this area.

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