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Swarm of UAVs for Network Management in 6G: A Technical Review

Muhammad Asghar Khan, Neeraj Kumar, Syed Agha Hassnain Mohsan, Wali Ullah Khan, Moustafa M. Nasralla, Mohammed H. Alsharif, Justyna ywioek, Insaf Ullah

arXiv:2210.03234v1cs.NIeess.SP

TL;DR

UAV integration into 6G promises more capable and ubiquitous networks but faces security, privacy, resource, and deployment challenges. This paper reviews these issues and synthesizes blockchain, AI/ML, and other 6G-oriented solutions, identifying findings, open problems, and future research directions.

  • Problem

    6G UAV networks face security and privacy concerns, limited onboard energy and processing, and new integration challenges that constrain deployment.

  • Method

    The paper conducts a literature review using database searches and screening, covering UAV integration into 6G and connecting blockchain and AI/ML with UAV networks.

  • Results

    The review outlines key barriers, potential solutions, research challenges, and future prospects for secure and efficient UAV networks in 6G.

  • Takeaways & Limitations

    Developing 6G UAV networks requires balancing communication technologies, security schemes, intelligence, and energy-harvesting methods.

  • Takeaways & Limitations

    The reviewed literature includes substantial prior work on UAV communication over 5G/B5G, while open research topics remain.

Abstract

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Fifth-generation (5G) cellular networks have led to the implementation of beyond 5G (B5G) networks, which are capable of incorporating autonomous services to swarm of unmanned aerial vehicles (UAVs). They provide capacity expansion strategies to address massive connectivity issues and guarantee ultra-high throughput and low latency, especially in extreme or emergency situations where network density, bandwidth, and traffic patterns fluctuate. On the one hand, 6G technology integrates AI/ML, IoT, and blockchain to establish ultra-reliable, intelligent, secure, and ubiquitous UAV networks. 6G networks, on the other hand, rely on new enabling technologies such as air interface and transmission technologies, as well as a unique network design, posing new challenges for the swarm of UAVs. Keeping these challenges in mind, this article focuses on the security and privacy, intelligence, and energy-efficiency issues faced by swarms of UAVs operating in 6G mobile networks. In this state-of-the-art review, we integrated blockchain and AI/ML with UAV networks utilizing the 6G ecosystem. The key findings are then presented, and potential research challenges are identified. We conclude the review by shedding light on future research in this emerging field of research.

I. INTRODUCTION

6G is envisioned to connect UAV swarms through integrated communication, computation, sensing, and control while supporting massive connectivity, high throughput, low latency, AI, energy efficiency, and adaptive security. The review situates this vision against prior UAV communication surveys and highlights enabling technologies and remaining integration challenges.

  • 6G is expected to support millions of interconnected devices with diverse QoS requirements, ubiquitous coverage, embedded AI, efficient energy use, and adaptive security.
  • UAVs can operate as an aerial network layer connecting ground and satellite stations within integrated space-air-ground 6G networks.
  • 6G targets speeds exceeding 1 Tbps and latency below 1 ms, while using Sub-6 GHz, mmWave, THz, and optical wireless communications for high-rate links.
  • AI/ML, blockchain, edge computing, THz communication, VLC, and related technologies are identified as enablers for improving UAV-network QoS, QoE, security, and fault management.
  • Existing literature and their limitations: Earlier reviews largely focused on UAV communication over 5G/B5G, covering physical and network layers, cooperation, computation, caching, applications, and related challenges.
  • Existing literature and their limitations: Prior work also examined blockchain-assisted security, mobile edge computing, intelligent reflecting surfaces, short-packet transmission, energy harvesting, and joint communication and radar sensing.

B. Research Methodology

The review used a bibliographic screening procedure to identify and filter literature on UAV integration into 6G networks. Searches across major databases began with 202 publications, followed by exclusion based on detailed examination of titles, abstracts, and keywords.

  • The study applied a bibliographic procedure to collect, analyze, and present literature on UAV integration into 6G networks.
  • The search covered IEEE Xplore, Web of Science, ScienceDirect, and MDPI using UAV, security, privacy, and 6G-related keywords.
  • 202 publications were initially identified before irrelevant or unqualified items were removed through title, abstract, and keyword screening.

C. Contributions with Organization of the Article

The review addresses the integration of UAV swarms into 6G across architecture, security, blockchain, AI/ML, energy efficiency, challenges, and open research topics.

  • The review identifies a gap in prior work, which largely focuses on 5G/B5G and does not cover all UAV-network aspects over 6G.
  • Integration of UAVs into 6G networks: It presents a 6G UAV-network architecture and discusses a possible six-F trend relevant to UAV networks.
  • Security Landscape: The security review covers confidentiality, integrity, availability, authentication, trust, non-repudiation, authorization, lightweight cryptography, and adaptive security tools.
  • Blockchain Technology: The article examines blockchain characteristics—immutability, decentralization, and transparency—as potential responses to UAV-network security challenges.
  • AI/ML Techniques: AI/ML and reinforcement learning are investigated for supporting UAV swarms, including optimal collision-avoiding path planning during real-time navigation.
  • Energy Efficiency: The review treats finite onboard battery life and resource-intensive applications as central energy-efficiency problems for UAV adoption.
  • Challenges and Open Research Topics: It identifies challenges spanning safety, energy, storage, computation, routing, compatibility, spectrum exploitation, standardization, and regulation, and surveys open research topics.

II. INTEGRATION OF UAVS INTO 6G NETWORKS

The section describes 6G service classes and enabling technologies for UAV networks, while highlighting capabilities such as broad coverage, intelligence, satellite integration, and precise positioning alongside mobility and link challenges.

  • 6G service classes include ultra-high data density, ultra-high-speed low-latency communications, and ubiquitous mobile ultra-broadband for diverse UAV applications.
  • Six-F trend: The proposed six-F trend comprises full spectral, full coverage, full dimension, full convergence, full photonics, and full intelligence.
  • Full convergence: Full convergence combines communication, control, sensing, computing, and imaging in a multifunctional 6G system.
  • Full photonics and Full intelligence: Full photonic processing is described as a means for improving UAV-network energy efficiency, while full intelligence provides distributed computing and intelligence across layers.
  • Satellite integration: Satellite integration is associated with centimeter-level positioning, global coverage, heterogeneous QoS, 1 TBPS peak throughput per device, and 1,000 km/h autonomous mobility.
  • Challenges: UAVs’ three-dimensional mobility, speeds of about 30–460 km/h, antenna tilt, and ground-oriented base stations can produce link fluctuation and coverage loss.
  • Positioning and navigation: Autonomous missions require collision avoidance, while GNSS alone is inadequate for some BLOS navigation activities; 6G can add positioning through beamforming and triangulation.

A. Summary

The review concludes that 6G integration can expand UAV capabilities while leaving security and privacy as central concerns, including cyberattacks and GPS spoofing.

  • 6G integration could provide UAV swarms with rapid computing, greater mobility, increased scalability, and increased availability.
  • Satellite integration is described as enabling centimeter-level positioning, global coverage, heterogeneous QoS, 1 TBPS per device, and 1,000 km/h autonomous mobility.
  • The security discussion concerns threats and requirements for deploying UAV networks in 6G applications.
  • Small UAV design restrictions leave networks exposed to cyber and physical attacks, including request flooding, eavesdropping, and fabricated data exchange.
  • GPS spoofing manipulates navigation signals so an attacker can redirect a UAV toward a predetermined location for interception.

B. Security and Privacy Requirements

6G-enabled UAV networks require security and privacy protections spanning confidentiality, integrity, availability, authentication, trust, non-repudiation, and authorization. Their mobility, diversity, scalability, physical exposure, and AI-enabled functions create additional security requirements.

  • Core requirements: UAV networks must protect confidentiality, integrity, availability, authentication, trust, non-repudiation, and authorization.These requirements address data exposure, tampering, service access, identity verification, reliance on entities, repudiation, and resource privileges.
  • Threat environment: Security measures must account for high scalability, device diversity, high mobility, AI-related attacks, and UAV vulnerability to physical attacks.
  • Core requirements: Confidentiality protects mission data and control exchanges, while integrity protects flight-critical information from modification, fabrication, substitution, and injection.
  • Access control: Authentication verifies participating UAVs and prevents impersonation, while authorization restricts data and resources to permitted users.
  • Research directions: Lightweight cryptography and adaptive security protocols remain necessary research directions for efficient, secure UAV communication and real-time threat mitigation.Deep-learning methods such as GANs and GNNs are described as tools for robust threat detection and proactive security analysis.

C. Summary

Blockchain is reviewed as a decentralized security mechanism for 6G UAV networks, offering immutable, transparent, and distributed data management. Its deployment remains constrained by UAV resource limits and rapidly changing network topology.

  • Blockchain security: Blockchain can strengthen UAV-6G cybersecurity through immutability, decentralization, and transparency.
  • Blockchain security: Immutable blockchain records can support secure data storage and exchange across UAV networks.
  • Blockchain security: Decentralization avoids reliance on a central authority and can provide a robust database platform with low data-retrieval latency.
  • Blockchain security: Transparent replicated records enable network-wide transaction validation and may improve cooperation and data integrity among UAV nodes.
  • UAV network integration: Blockchain can support secure spectrum sharing, trust establishment, and malicious-node identification through distributed transactions and periodic UAV evaluations.
  • Deployment challenges: Resource-constrained UAVs, computationally intensive cryptography or consensus, topology changes, and blockchain scalability limit practical deployment.Lightweight cryptography and efficient routing are identified as possible directions, but further scalability research is required.

A. Summary

The review surveys AI/ML integration for enabling intelligent 6G UAV networks, including communication, deployment, navigation, and mission operations. It identifies distributed learning and computational efficiency as important research directions.

  • A. Summary: AI, deep learning, and machine learning have improved UAV efficiency, resilience, and robustness across communication and network-management tasks.
  • A. Summary: Reviewed applications include processing-path selection, transmission prediction, millimeter-wave relaying, visible-light deployment, congestion-aware aerial base stations, and disaster-response networking.
  • A. Summary: Existing studies largely use centralized reinforcement learning, while distributed reinforcement learning is presented as promising for collaborative multi-agent UAV decisions.
  • A. Summary: AI/ML integration faces challenges in method selection, computational intensity, latency, large-scale deployment across network levels, position verification, routing, and mission-success estimation.
  • A. Summary: Reinforcement learning is studied for real-time path planning, navigation, and collision avoidance in UAV swarms.

VI. ENERGY EFFICIENCY

Energy efficiency is a central constraint in UAV-supported networks because communication energy affects network throughput and UAV endurance. The review contrasts centralized and distributed topology control while emphasizing propulsion energy and QoS-delay constraints.

  • Topology control: Centralized topology control uses a common transmit power PTx for links within range and relies on a central or global controller.
  • Topology control: Distributed topology control gives each UAV variable transmission-power regulation to reduce energy consumption while maintaining robust network connectivity.
  • Topology control: Space partitioning can reduce end-to-end energy consumption while maintaining suitable node degree and hop count.
  • Energy modeling: Prior work sometimes omits propulsion energy, although propulsion consumption is typically higher than communication energy and directly affects UAV operation time.
  • QoS constraints: Energy-efficient UAV networking must also provide users’ required QoS delay, although deterministic wireless delay is difficult to achieve.
  • VI. ENERGY EFFICIENCY: Energy limitation is a bottleneck in UAV communication scenarios, and energy losses through communication can reduce total network throughput.

A. Summary

UAV networks in 6G face major deployment barriers from safety risks, constrained onboard resources, and limited flight duration. These constraints make resource-intensive applications and long-running operations difficult.

  • Energy Efficiency: Around thirty minutes of flight time restricts commercial UAV applications requiring long-running operations.The limitation is primarily associated with small UAVs’ extremely limited onboard batteries.
  • Deployment Challenges: UAV communication and networking over 6G remains at an early stage and requires analysis for secure integration and physical-resource allocation.The review identifies flexible deployment, security, and effective resource allocation as challenges for successful integration.
  • Safety: Safety risks include crashes caused by technical failures, insufficient service, mid-flight collisions, operator error, and extreme weather.Turbulence, lightning, battery limitations, and inadequate lifting capabilities are also identified as collision concerns.
  • Resource Constraints: Limited battery, storage, and computation constrain small UAVs and make timely execution of resource-intensive applications difficult.Small UAV batteries cannot practicably be replaced during flight, and collected data may exceed one UAV’s onboard processing and storage capacity.

C. Routing

Routing and communication design in 6G UAV networks must accommodate mobility, three-dimensional movement, changing topology, extreme throughput demands, and emerging spectrum regulations. The review identifies blockchain-enabled softwarization and decentralized control as open research directions.

  • C. Routing: High mobility, 3D movement, and frequent topology changes make routing one of the most challenging UAV-networking issues.Routing must support reliable, stable, and efficient information exchange for sensitive applications.
  • C. Routing: Supporting 1 Tbps throughput, AI, extended reality, and integrated sensing on standalone UAVs is difficult because of resource constraints.An onboard wireless module capable of mmWave transmission and reception is also hard to develop.
  • C. Routing: Tbps communications using THz and visible-light bands still face challenges in achieving effective and optimal spectrum utilization.These technologies are presented as promising options for extending 6G spectrum capacity.
  • C. Routing: Global coordination of governments and locations is required to establish standardized THz spectrum allocation and satellite-communication rules.Satellite orbit and spectrum resources also require consultation among governments.
  • C. Routing: The review identifies these barriers as open research topics for successful UAV-network deployment in 6G.The open topics are summarized in the review’s Table III.
  • Blockchain-enabled UAV Softwarization: Blockchain can protect UAV data privacy and integrity, while blockchain-enabled softwarization could support dynamic, adaptive, on-the-fly 6G services.Real-time deployment for highly mobile UAVs and avoidance of centralized-controller single-point failure remain challenges.

B. High-speed Backhaul FSO Connectivity

6G UAV networks require high-speed, scalable backhaul and adaptive mobility and access management. The review discusses hybrid FSO/RF links, intelligent handovers, deep reinforcement learning, and RIS-assisted coverage as relevant approaches.

  • B. High-speed Backhaul FSO Connectivity: A super-high-speed, cost-effective, easy-to-deploy, scalable backhaul is needed to connect UAV networks with the 6G core network.FSO is proposed as a promising option for addressing the backhaul bottleneck.
  • B. High-speed Backhaul FSO Connectivity: Hybrid FSO/RF links can address weather-related link degradation by switching between FSO and mm-wave communications.FSO may be used in rain, while mm-waves may be preferred in fog; mm-waves are attenuated by water molecules.
  • Security Management: Real-time intrusion detection and UAV forensics must balance broader information gathering against communication and computation costs.The review calls for lightweight IDSs and notes that standardized forensic models covering commercial UAVs remain lacking.
  • Mobility and Access Management: Dynamic multiple-access protocols must adapt orthogonal or non-orthogonal, random or scheduled access to application demands and network state.UAV altitude adds a new dimension that changes connectivity and requires novel handover protocols.
  • Mobility and Access Management: Fast-moving UAVs and satellites complicate mobility management because changing locations alter inter-UAV and inter-satellite connections and network topology.Handover control mechanisms must account for these high-mobility scenarios.
  • Mobility and Access Management: Deep reinforcement learning can dynamically adjust handovers and discover collision-avoiding paths for real-time planning and navigation.The passage presents these as potential uses of deep reinforcement learning in UAV mobility management.
  • RIS-Assisted Communications: RISs can intelligently reflect incoming signals to improve coverage and capacity while supporting massive connectivity in 6G networks.RISs are also described as low-cost, green, sustainable, and energy-efficient at the system level.

G. Energy Harvesting Technologies

Energy harvesting is presented as a response to UAV battery and payload limitations, while 6G integration also involves cloud, non-terrestrial, quantum, and multiple-access technologies. The review concludes that secure, efficient UAV networks require balancing communication, security, intelligence, and energy methods.

  • G. Energy Harvesting Technologies: Energy harvesting technologies, including improved solar cells, are proposed to address insufficient battery capacity and limited UAV flight time.Solar-cell advances make the added weight of onboard energy sources more practical to consider.
  • G. Energy Harvesting Technologies: SDN and NFV can reduce UAV-network management complexity and the need to deploy particular network devices.SDN can also connect different virtual network functions, while 6G can connect UAV networks to the Internet through cloud and web technologies.
  • G. Energy Harvesting Technologies: Non-terrestrial infrastructures are relevant for connecting UAV networks in oceanic, mountainous, and wild locations where ground networks are impractical or costly.Satellites have historically provided communication in these environments.
  • G. Energy Harvesting Technologies: Quantum backscatter communications can improve channel error exponents and enable secure communication through quantum cryptography.The passage also limits QBC’s suitability for large-scale deployment of diverse low-power UAV networks.
  • IX. CONCLUSION: The review outlines key barriers, potential solutions, findings, challenges, and future research directions for UAV communication and networking over 6G.The paper specifically focuses on security and privacy, AI/ML, and energy-efficiency solutions.
  • IX. CONCLUSION: The review identifies security, privacy, onboard energy, and processing limitations as barriers to UAV use across applications.It calls for a balance among communication technologies, security schemes, intelligence, and energy harvesting methods.
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