Unmanned aerial vehicles (UAVs), characterized by their agility, affordability, and flexible deployment, exhibit significant advantages in scenarios such as disaster monitoring, target tracking, and environmental data collection. However, the limited onboard energy of UAVs poses a major challenge for long-duration or large-scale missions. To address this issue, this paper proposes a dynamic trajectory planning framework for cooperative task search involving multiple UAVs. First, a UAV capability evaluation approach is developed to assess the relative performance of heterogeneous UAVs. Next, a density-aware clustering mechanism is employed to partition the search region based on spatial distance and regional density. After clustering, a sequential matching strategy is employed to assign UAVs with higher capabilities to larger or more complex task regions, ensuring efficient resource utilization. The problem is then formulated as a combinatorial optimization task, and a pointer network is designed to generate UAV trajectories. The network is trained using deep reinforcement learning to produce near-optimal paths, thereby minimizing the overall system cost. Experimental results confirm that the proposed method can substantially lower total task execution expenditure.

With the rapid development of the internet of things (IoT), IoT devices are now capable of real-time monitoring and collecting environmental and production data through integrated sensors. However, these devices often face challenges related to limited storage capabilities and transmission range. Furthermore, the widespread deployment of IoT devices has raised significant concerns regarding privacy security. To enhance data collection efficiency and ensure the security of location privacy, this study proposes a high altitude platform (HAP) data collection and trajectory design scheme that is aware of location privacy. Firstly, our scheme utilizes HAPs to quickly cover the collection area and transmit data in real time via satellites. Secondly, a differential privacy-based perturbation mechanism is applied to reduce the risk of location information leakage. Finally, the trajectory optimization problem, incorporating privacy awareness, is modeled as a Markov decision process (MDP) and solved using deep reinforcement learning (DRL) techniques to determine the movement decisions of the HAPs. Experimental results demonstrate that this scheme effectively protects location privacy while enhancing the efficiency and security of data collection.

The rapid growth of graph-structured data necessitates partitioning and distributed storage across decentralized systems, driving the emergence of federated graph learning to collaboratively train Graph Neural Networks (GNNs) without compromising privacy. However, current methods exhibit limited performance when handling hypergraphs, which inherently represent complex high-order relationships beyond pairwise connections. Partitioning hypergraph structures across federated subsystems amplifies structural complexity, hindering high-order information mining and compromising local information integrity. To bridge the gap between hypergraph learning and federated systems, we develop FedHGL, a first-of-its-kind framework for federated hypergraph learning on disjoint and privacy-constrained hypergraph partitions. Beyond collaboratively training a comprehensive hypergraph neural network across multiple clients, FedHGL introduces a pre-propagation hyperedge completion mechanism to preserve high-order structural integrity within each client. This procedure leverages the federated central server to perform cross-client hypergraph convolution without exposing internal topological information, effectively mitigating the high-order information loss induced by subgraph partitioning. Furthermore, by incorporating two kinds of local differential privacy (LDP) mechanisms, we provide formal privacy guarantees for this process, ensuring that sensitive node features remain protected against inference attacks from potentially malicious servers or clients. Experimental results on seven real-world datasets confirm the effectiveness of our approach and demonstrate its performance advantages over traditional federated graph learning methods.

Highly dynamic heterogeneous networks (HDHNs), characterized by high node mobility and heterogeneity, frequently experience complex and recurrent network faults. Conventional centralized fault diagnosis methods demand real-time collection of extensive network-wide data, while distributed approaches often exhibit limited fault detection capabilities. Additionally, machine learning-based fault diagnosis methods are challenged by the scarcity of labeled fault samples required for training. To address these limitations, this study proposes a semi-distributed network fault diagnosis architecture based on a digital twin network (DTN). The proposed architecture facilitates the extraction of a comprehensive labeled fault dataset that closely replicates real-world network conditions. Using this dataset, we perform centralized training of an enhanced anomaly detection model, FTS-LSTM, to infer fault types at the node level. To overcome the drawbacks of both centralized and distributed approaches, we further introduce a semi-distributed fault diagnosis algorithm (SDFD) that integrates fault types and severity levels identified by nodes to infer overall network faults. The proposed fault diagnosis scheme is validated on a semi-physical DTN simulation platform, demonstrating its effectiveness in realistic scenarios.

With the continuous development of airborne communication, the demand for efficient internet access on airplanes has been increasing. To enhance the communication service quality for airborne users and address the challenge of high content request latency, a three-layer communication structure with satellite and terrestrial-assisted caching is proposed. In this structure, satellites, base stations, and aircraft cooperatively cache content to serve users aboard airplanes. Considering variations in request preferences, content popularity in aircraft, base stations, and satellites, as well as constraints related to cache space and communication duration, a content placement problem is formulated to minimize the total system latency. To tackle this problem, the content placement and delivery process is modeled as a Markov decision process (MDP). Subsequently, a Deep Reinforcement Learning (DRL)-based airborne communication cache placement algorithm named ACCP is introduced to derive optimal content placement decisions. Additionally, we expedite the convergence of ACCP with a prioritized experience replay mechanism and reduce time complexity using a sumTree data structure. Simulation results demonstrate that the proposed method significantly improves cache hit rate and reduces content delivery latency compared to other schemes.