Unmanned Aerial Vehicles (UAVs), as key enablers of 6G-enabled Internet of Things (IoT) ecosystems, facilitate dynamic aerial coverage, seamless edge intelligence, and adaptive routing. However, despite these advantages, the reliability and trustworthiness of UAV swarms in 6G remain critical concerns due to rising quantum threats and limitations of traditional machine learning approaches. The paper presents QuaRTA-6G, a Quantum-Resilient, Trust-aware, and Accountable framework that provides a unified solution for 6G UAV swarms. QuaRTA-6G achieves security, trust, and efficiency by seamlessly combining post-quantum cryptography for secure communication, a decentralized ledger for identity and trust management, and Variational Quantum Federated Learning (VQFL) for efficient swarm intelligence. For quantum-resistant authentication and immutable UAV identity verification, QuaRTA-6G leverages CRYSTALS-Kyber with blockchain. Through simulations, QuaRTA-6G achieves secure authentication handshakes in under 2 ms and scales robustly to swarms of more than 100 UAVs, keeping high integrity even under 20% packet loss. Experiments demonstrate that, even under strong data poisoning attacks, the framework achieves 92% accuracy and 85% mission success rate, while comprehensive resource analysis confirms its feasibility across both standard and resource-constrained UAV platforms. Furthermore, an ablation study demonstrates that each module of QuaRTA-6G is essential for ensuring a responsible and trustworthy 6G UAV framework.
Unmanned Aerial Vehicles (UAVs), as key enablers of 6G-enabled Internet of Things (IoT) ecosystems, facilitate dynamic aerial coverage, seamless edge intelligence, and adaptive routing. However, despite these advantages, the reliability and trustworthiness of UAV swarms in 6G remain critical concerns due to rising quantum threats and limitations of traditional machine learning approaches. The paper presents QuaRTA-6G, a Quantum-Resilient, Trust-aware, and Accountable framework that provides a unified solution for 6G UAV swarms. QuaRTA-6G achieves security, trust, and efficiency by seamlessly combining post-quantum cryptography for secure communication, a decentralized ledger for identity and trust management, and Variational Quantum Federated Learning (VQFL) for efficient swarm intelligence. For quantum-resistant authentication and immutable UAV identity verification, QuaRTA-6G leverages CRYSTALS-Kyber with blockchain. Through simulations, QuaRTA-6G achieves secure authentication handshakes in under 2 ms and scales robustly to swarms of more than 100 UAVs, keeping high integrity even under 20% packet loss. Experiments demonstrate that, even under strong data poisoning attacks, the framework achieves 92% accuracy and 85% mission success rate, while comprehensive resource analysis confirms its feasibility across both standard and resource-constrained UAV platforms. Furthermore, an ablation study demonstrates that each module of QuaRTA-6G is essential for ensuring a responsible and trustworthy 6G UAV framework.
The emergence of sixth-generation (6G) wireless networks introduces unprecedented requirements for ultra-secure, low-latency communication across heterogeneous space–air–ground integrated network (SAGIN). Existing drone communication frameworks including LoRaWAN, Long Term Evolution, and Ad Hoc mesh architectures exhibit critical vulnerabilities to eavesdropping, jamming, and quantum-computational attacks due to their reliance on classical cryptographic primitives. To address these challenges, this work presents the Quantum-Secured Adaptive Routing Algorithm (QSARA), a novel framework designed for 6G-enabled unmanned aerial vehicle (UAV) networks that integrates Quantum Key Distribution (QKD), Reconfigurable Intelligent Surfaces (RIS), and Joint Communication and Sensing (JCAS) to enhance information-theoretic security and real-time performance. The proposed framework employs a quantum-augmented dynamic graph model to represent UAV swarm networking and uses Proximal Policy Optimisation (PPO)-based deep reinforcement learning to optimise routing under adversarial and uncertain conditions. A multi-objective cost function jointly captures classical quality of service metrics, such as latency, bandwidth, and energy consumption alongside with quantum-layer security indicators, including quantum bit error rate, key pool entropy, and key availability. High-fidelity simulations with 500 mobile drones under diverse adversarial threats demonstrate that the proposed framework achieves a key establishment success rate of 96.2%, end-to-end latency of 23.7 milliseconds, energy consumption of 7.8 watt-hours, and a packet delivery ratio of 94.1%, outperforming state-of-the-art classical and quantum-aware baselines. These results position the QSARA as a scalable and quantum-resilient routing solution for mission-critical UAV networking in next-generation 6G smart mobility ecosystems.
The emergence of 6G networks precipitates unprecedented challenges in securing multi-UAV spectrum sharing due to increased connectivity demands and exposure to sophisticated cyber threats. This research proposes a quantum-enabled blockchain protocol tailored for secure, decentralized spectrum management among multiple unmanned aerial vehicles (UAVs) in 6G environments. Leveraging quantum key distribution (QKD) integrated within a blockchain framework, the protocol ensures information-theoretic security while maintaining transparency and immutability of spectrum allocation records. The protocol incorporates consensus mechanisms optimized for low-latency UAV networks, enabling rapid transaction validation and conflict resolution. Simulation results indicate a substantial reduction in communication overhead and vulnerability to malicious spectrum hijacking, achieving over 95% detection accuracy of unauthorized access attempts. This hybrid cryptographic architecture addresses the dual challenges of scalability and security, paving the way for resilient spectrum sharing in UAV swarms under 6G paradigms.
The rapid evolution of autonomous systems—such as Unmanned Ground Units (UGUs), Unmanned Aerial Vehicles (UAVs), and drone networks—has generated unprecedented demands for secure, intelligent, and efficient data processing. This study presents a comprehensive review of recent advancements integrating Quantum Machine Learning (QML) and blockchain technologies to address these challenges. Following a systematic review approach, relevant studies published between 2020 and 2024 were analyzed to evaluate their contributions to enhancing decision-making, communication reliability, and data protection in autonomous networks. The findings reveal that QML enables adaptive learning and optimization in dynamic environments, while blockchain ensures data immutability, decentralized trust, and robust security. Together, these technologies offer a promising foundation for next-generation autonomous systems characterized by transparency, resilience, and computational efficiency. The paper concludes by outlining open research issues, including scalability, interoperability, and quantum resource constraints, and highlights future research opportunities for developing unified QML–blockchain frameworks in autonomous networks.
This study presents "Quantum Lattice," an innovative theoretical framework designed to ensure the security of Unmanned Aerial Vehicle (UAV) swarm communications in the era after quantum computing. Given the risk posed by quantum computing to conventional cryptography techniques, it is imperative to develop security solutions that are immune to quantum attacks specifically designed for UAV systems. Utilizing sophisticated lattice-based encryption, we employ the Short Integer Solution (SIS) problem and a novel Aerial Lattice Traversal (ALT) algorithm to provide strong security while preserving real-time performance essential for UAV operations. Simulations performed on a testbed which incorporates quantum technology show substantial enhancements compared to conventional approaches. These benefits include a 42% decrease in latency, a 28% increase in throughput, a 63% reduction in packet loss, and a 37% decrease in energy consumption, especially in dense swarm setups with up to 1000 nodes. The mathematical principles that underlie our approach, including δ--ary lattices and their cryptographic applications, are thoroughly discussed. In addition, the study investigates the incorporation of our framework with developing technologies like neuromorphic computing and AI-powered adaptive security mechanisms. In the quantum computing era, our results provide a foundation for secure, scalable, and robust UAV swarm operations, effectively tackling significant issues in both civilian and military contexts.
Unmanned Aerial Vehicles (UAVs) have gone from single platforms that were remotely piloted to fully networked, autonomous cyber-physical systems that operate in the civilian, industrial and defense arenas. Their increasing reliance on wireless communication for command and control, cooperative sensing, distributed decision-making and swarm coordination has raised the level of communication security to the point where it has become a mission-critical requirement. Contemporary UAV security architectures are much based on classical public-key–based cryptography (RSA and elliptic are the most well-known) whose security assumptions become fundamentally vulnerable against the presence of large-scale quantum computing. This chapter gives a comprehensive and future-oriented discussion about post-quantum cryptography (PQC)-based security models for UAVs communication and information exchange networks. Quantum resistance should be applied not only at the algorithmic level but across the whole UAV communications trust stack, which is the main argument. Part I set the conceptual and technical ground by affecting a short range of the improvement of UAV communication systems, in articulating a system quantum-era threat model and giving a motivation for mission-adaptive, cross-layer security architectures. The discussion makes the long-term confidentiality, survivability of the system and continuing operation more robust, and the security of UAV with PQC enabled is no longer speculative, but is considered a strategic requirement.
Unmanned Aerial Vehicles (UAVs), commonly known as drones, are increasingly being employed across a broad spectrum of applications, ranging from military operations to commercial purposes. However, as UAVs become more integrated into everyday life, security and privacy concerns are similarly escalating due to vulnerabilities arising from operating on open wireless channels and having limited onboard computational resources. Moreover, with the emergence of quantum computers, conventional cryptographic methods that ensure the security and privacy of UAV communications are at severe risk. These risks encompass the possibility of unauthorized access, breaches of data, and cyber-physical attacks that jeopardize the integrity, confidentiality, and availability of UAV operations. Quantum computers are expected to break the conventional cryptography methods, such as symmetric and asymmetric schemes, with the support of Grover’s and Shor’s algorithms, respectively. Consequently, traditional cryptographic algorithms must give way to quantum-resistant algorithms, referred to as Post-Quantum Cryptography (PQC) algorithms. Although researchers actively develop, test, and standardize new PQC algorithms, the threat persists despite the progress made through these consistent efforts. This review article first examines the security and privacy landscape, including threats and requirements of UAVs. This article also discusses PQC and various PQC families and the status of the NIST’s implementation and standardization process. Lastly, we explore challenges and future directions in implementing PQC for UAVs.