Quantum key distribution (QKD) enables secure communication for vehicular networks, but its range limitation calls for multi-hop relaying. Unmanned aerial vehicles (UAVs) can serve as flexible QKD relays, yet their selfish behavior and dynamic mobility pose significant challenges. This paper proposes a secure routing framework for UAV-assisted vehicular QKD that jointly addresses incentive compatibility and path reliability. We model the interaction between the source vehicle and UAVs as a Stackelberg game, where the source sets the relay reward and UAVs decide their participation. A smoothed relay path formulation ensures differentiability and equilibrium tractability. To enable decentralized learning of relay strategies, we adopt a multi-agent deep deterministic policy gradient (MADDPG) algorithm. Furthermore, a Bayesian optimization module is integrated to adaptively search for the optimal reward value that maximizes the source vehicle's utility. Simulation results show that the proposed Stackelberg-MADDPG-Bayesian scheme outperforms existing baselines in terms of relay request success rate and secure key generation rate, and remains robust under various UAV selfishness levels and network dynamics.
Quantum key distribution (QKD) enables secure communication for vehicular networks, but its range limitation calls for multi-hop relaying. Unmanned aerial vehicles (UAVs) can serve as flexible QKD relays, yet their selfish behavior and dynamic mobility pose significant challenges. This paper proposes a secure routing framework for UAV-assisted vehicular QKD that jointly addresses incentive compatibility and path reliability. We model the interaction between the source vehicle and UAVs as a Stackelberg game, where the source sets the relay reward and UAVs decide their participation. A smoothed relay path formulation ensures differentiability and equilibrium tractability. To enable decentralized learning of relay strategies, we adopt a multi-agent deep deterministic policy gradient (MADDPG) algorithm. Furthermore, a Bayesian optimization module is integrated to adaptively search for the optimal reward value that maximizes the source vehicle's utility. Simulation results show that the proposed Stackelberg-MADDPG-Bayesian scheme outperforms existing baselines in terms of relay request success rate and secure key generation rate, and remains robust under various UAV selfishness levels and network dynamics.
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 integration of unmanned aerial vehicles (UAVs) into vehicular ad hoc networks (VANETs) has emerged as a promising solution to overcome the limited coverage of conventional roadside unit (RSU)-based infrastructures. However, UAVs operate in open environments and cannot be fully trusted, while the rapid advancement of quantum computing threatens the long-term security of classical public-key cryptographic systems. As a result, many existing UAV-based VANET authentication schemes face fundamental limitations in future deployments. Most existing schemes either lack post-quantum security or incur excessive computational and communication overhead, making them unsuitable for real-time and high-mobility vehicular environments. In addition, the common assumptions of trusted UAVs do not align with realistic threat models. To address these issues, this paper proposes a lightweight post-quantum authentication and key exchange protocol based on the module learning with errors (MLWE) problem and physically unclonable functions (PUFs). The proposed scheme treats UAVs as untrusted relay nodes and excludes them from session key generation. Its security is evaluated using informal analysis, the real-or-random (RoR) model, BAN logic, and AVISPA, while performance evaluation indicates improved efficiency compared to existing schemes.
Maritime communication systems face critical challenges in ensuring secure, reliable, and long-range connectivity due to inherent vulnerabilities in traditional encryption, coverage gaps in remote sea areas, and susceptibility to eavesdropping under complex maritime conditions. While Unmanned Aerial Vehicle (UAV)-assisted networks have expanded coverage, security remains a persistent concern. Quantum communication, leveraging quantum key distribution (QKD), offers a paradigm shift by ensuring information-theoretic security. This paper proposes a UAV-based quantum relay maritime communication system that integrates QKD, UAV relays, and satellite networks to establish a hybrid quantum-classical architecture. The innovation lies in deploying UAVs as dynamic relay nodes to enable BB84-protocol-based QKD, while satellites ensure global coverage. Key advancements include: (1) a unidirectional quantum channel design featuring UAV-mounted quantum emitters (Alice) and ship-based receivers (Bob), minimizing technical complexity while ensuring secure key distribution; (2) a multi-layered network integrating ship-UAV-ship, ship-UAV-base station, and ship-satellite communication modes, enhancing resilience against environmental interference; and (3) real-time quantum-classical synergy, where classical data channels (e.g., AES-encrypted) are dynamically secured by QKD-generated keys, achieving compatibility with existing infrastructure while eliminating key leakage risks. Simulations validate the system’s resilience under maritime conditions: optimizing parameters such as detection efficiency (eff > 90%) and dark count (dc < 10⁻⁶/pulse) achieves a stable secure key rate over 100 km, with a Quantum Bit Error Rate (QBER) below 0.5% at 200 km. The proposed architecture offers transformative applications in maritime safety, emergency response, and offshore operations, providing eavesdropping-resistant links for remote vessels and secure data exchange for critical infrastructure. By bridging the gap between quantum security and practical maritime deployment, this work establishes a scalable blueprint for next-generation communication systems in military, commercial, and environmental monitoring domains.
Unmanned aerial vehicles (UAVs) enable rapidly deployable secure communication, while quantum key distribution (QKD) ensures information-theoretic security over free-space optical channels. In UAV-assisted QKD, however, atmospheric turbulence and mobility-induced misalignment introduce stochastic channel fluctuations that cause intermittent secrecy degradation. Existing trajectory optimization methods are largely deterministic or risk-neutral and fail to address rare but severe deep-fading events. We develop a risk-sensitive stochastic control framework for robust UAV-assisted free-space QKD under atmospheric uncertainty. The UAV motion is modeled as a continuous-time stochastic dynamical system, and the quantum channel transmissivity is characterized by mobility-dependent log-normal fading, yielding a stochastic secret key rate process. The exponential performance functional simultaneously penalizes the propulsion energy and secrecy outage, resulting in a nonlinear risk-sensitive Hamilton-Jacobi-Bellman equation. Simulation results show that there is a significant reduction in secrecy outage probability and key rate variation when atmospheric turbulence exists, but there is only a relatively minor rise in the energy consumption for propulsion, achieving a robustness-efficiency tradeoff for secure aerial quantum communication.
The increasing demands for ultra-reliable low-latency communication (URLLC), high-capacity, connectivity, and mobility in next-generation wireless networks have driven the integration of unmanned aerial vehicles (UAVs) as flexible and adaptive communication nodes. UAV-assisted networks offer enhanced coverage, dynamic deployment, and cost-effective infrastructure for the sixth generation (6G) and beyond. However, optimizing UAV trajectory planning, resource allocation, and interference management is particularly challenging, especially in dynamic and large-scale environments. Classical reinforcement learning (RL) techniques, while effective in adaptive optimization, suffer from computational inefficiencies and slow convergence in high-dimensional state-action spaces. To address these constraints, quantum reinforcement learning (QRL) emerges as a novel paradigm that leverages quantum computing principles, such as superposition and entanglement, to enhance decision-making efficiency. This study provides a comprehensive overview of QRL algorithms and investigates their transformative potential in UAV-assisted wireless communication networks. We analyze UAV deployment strategies, network architectures, and topologies, highlighting their advantages as well as inherent challenges. Furthermore, we examine the limitations of classical RL algorithms and assess how QRL overcomes them while improving computational efficiency and exploration capabilities. In addition, we present a detailed review of existing QRL algorithms applicable to next-generation UAV-enhanced wireless networks, identifying key challenges that must be addressed for practical deployment. Finally, we identify open research challenges and offer future directions for integrating QRL into UAV-assisted communication, paving the way for scalable, intelligent, and high-performance wireless systems.
The proliferation of quantum key distribution (QKD) systems on unmanned aerial vehicles (UAVs) has demonstrated the technical feasibility of mobile quantum networks, yet practical deployment faces critical challenges in scalability, resource allocation, and network reliability. We present Q-MESH (Quantum Multi-tier Entanglement-based Scalable Hierarchy), a novel three-tier hierarchical architecture that leverages heterogeneous UAV swarms to establish metropolitan-scale quantum networks with unprecedented flexibility and fault tolerance. Our architecture introduces dynamic layer transitions, intelligent quantum routing protocols based on fidelity-aware graph theory, and adaptive resource allocation using reinforcement learning. Through comprehensive mathematical analysis and simulation, we demonstrate that Q-MESH achieves 3.7Ö higher effective key generation rates compared to flat network topologies while maintaining 99.2% uptime through autonomous reconfiguration. The architecture supports concurrent operation of 15-25 UAVs across three altitude tiers (0.5 km, 8 km, 18 km), providing continuous coverage over 150 km² metropolitan areas with average end-to-end key rates of 2.8 kbit/s and worst-case latency under 500 ms. Our innovation lies in the integration of adaptive optics coordination, turbulence-aware routing, and quantum memory-free relay protocols that collectively enable practical deployment of aerial quantum networks. Q-MESH represents a critical step toward ubiquitous quantumsecure communications infrastructure.