The unmanned aerial vehicle (UAV) network has gained significant attentions in recent years due to its various applications. However, the traffic security becomes the key threatening public safety issue in an emergency rescue system due to the increasing vulnerability of UAVs to cyber attacks in environments with high heterogeneities. Hence, in this paper, we propose a novel anomaly traffic detection architecture for UAV networks based on the software-defined networking (SDN) framework and blockchain technology. Specifically, SDN separates the control and data plane to enhance the network manageability and security. Meanwhile, the blockchain provides decentralized identity authentication and data security records. Beisdes, a complete security architecture requires an effective mechanism to detect the time-series based abnormal traffic. Thus, an integrated algorithm combining convolutional neural networks (CNNs) and Transformer (CNN+Transformer) for anomaly traffic detection is developed, which is called CTranATD. Finally, the simulation results show that the proposed CTranATD algorithm is effective and outperforms the individual CNN, Transformer, and LSTM algorithms for detecting anomaly traffic.

The increasing deployment of unmanned surface vehicles (USVs) require computational support and coverage in applications such as maritime search and rescue. Unmanned aerial vehicles (UAVs) can offer low-cost, flexible aerial services, and ground stations (GSs) can provide powerful supports, which can cooperate to help the USVs in complex scenarios. However, the collaboration between UAVs and GSs for USVs faces challenges of task uncertainties, USVs trajectory uncertainties, heterogeneities, and limited computational resources. To address these issues, we propose a cooperative UAV and GS based robust multi-access edge computing framework to assist USVs in completing computational tasks. Specifically, we formulate the optimization problem of joint task offloading and UAV trajectory to minimize the total execution time, which is in the form of mixed integer nonlinear programming and NP-hard to tackle. Therefore, we propose the algorithm of generative artificial intelligence-enhanced heterogeneous agent proximal policy optimization (GAI-HAPPO). The proposed algorithm integrates GAI models to enhance the actor network ability to model complex environments and extract high-level features, thereby allowing the algorithm to predict uncertainties and adapt to dynamic conditions. Additionally, GAI stabilizes the critic network, addressing the instability of multi-agent reinforcement learning approaches. Finally, extensive simulations demonstrate that the proposed algorithm outperforms the existing benchmark methods, thus highlighting the potentials in tackling intricate, cross-domain issues in the considered scenarios.

The unmanned aerial vehicles (UAVs) are efficient tools for diverse tasks such as electronic reconnaissance, agricultural operations and disaster relief. In the complex three-dimensional (3D) environments, the path planning with obstacle avoidance for UAVs is a significant issue for security assurance. In this paper, we construct a comprehensive 3D scenario with obstacles and no-fly zones for dynamic UAV trajectory. Moreover, a novel artificial potential field algorithm coupled with simulated annealing (APF-SA) is proposed to tackle the robust path planning problem. APF-SA modifies the attractive and repulsive potential functions and leverages simulated annealing to escape local minimum and converge to globally optimal solutions. Simulation results demonstrate that the effectiveness of APF-SA, enabling efficient autonomous path planning for UAVs with obstacle avoidance.

As the demands for immediate and effective responses increase in both civilian and military domains, the unmanned aerial vehicle (UAV) swarms emerge as effective solutions, in which multiple cooperative UAVs can work together to achieve specific goals. However, how to manage such complex systems to ensure real-time adaptability lack sufficient researches. Hence, in this paper, we propose the cooperative cognitive dynamic system (CCDS), to optimize the management for UAV swarms. CCDS leverages a hierarchical and cooperative control structure that enables real-time data processing and decision. Accordingly, CCDS optimizes the UAV swarm management via dynamic reconfigurability and adaptive intelligent optimization. In addition, CCDS can be integrated with the biomimetic mechanism to efficiently allocate tasks for UAV swarms. Further, the distributed coordination of CCDS ensures reliable and resilient control, thus enhancing the adaptability and robustness. Finally, the potential challenges and future directions are analyzed, to provide insights into managing UAV swarms in dynamic heterogeneous networking.

The unmanned aerial vehicle (UAV) network is popular these years due to its various applications. In the UAV network, routing is significantly affected by the distributed network topology, leading to the issue that UAVs are vulnerable to deliberate damage. Hence, this paper focuses on the routing plan and recovery for UAV networks with attacks. In detail, a deliberate attack model based on the importance of nodes is designed to represent enemy attacks. Then, a node importance ranking mechanism is presented, considering the degree of nodes and link importance. However, it is intractable to handle the routing problem by traditional methods for UAV networks, since link connections change with the UAV availability. Hence, an intelligent algorithm based on reinforcement learning is proposed to recover the routing path when UAVs are attacked. Simulations are conducted and numerical results verify the proposed mechanism performs better than other referred methods.

With the continuous increment of maritime applications, the development of marine networks for data offloading becomes necessary. However, the limited maritime network resources are very difficult to satisfy real-time demands. Besides, how to effectively handle multiple compute-intensive tasks becomes another intractable issue. Hence, in this paper, we focus on the decision of maritime task offloading by the cooperation of unmanned aerial vehicles (UAVs) and vessels. Specifically, we first propose a cooperative offloading framework, including the demands from marine Internet of Things (MIoTs) devices and resource providers from UAVs and vessels. Due to the limited energy and computation ability of UAVs, it is necessary to help better apply the vessels to computation offloading. Then, we formulate the studied problem into a Markov decision process, aiming to minimize the total execution time and energy cost. Then, we leverage Lyapunov optimization to convert the long-term constraints of the total execution time and energy cost into their short-term constraints, further yielding a set of per-time-slot optimization problems. Furthermore, we propose a Q-learning based approach to solve the short-term problem efficiently. Finally, simulation results are conducted to verify the correctness and effectiveness of the proposed algorithm.