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.

As global demand for efficient network services increases, the limitations of traditional terrestrial wireless networks are becoming more apparent. Space-air-ground integrated networks (SAGIN) have emerged as a promising solution to advance next-generation network infrastructure. However, SAGIN faces significant security challenges—including the lack of a robust security architecture, trust and data reliability issues in multi-hop transmissions, and the need to enhance network performance without compromising security—rendering traditional boundary-based defenses inadequate. Therefore, we propose SECURELINK, a decentralized zero-trust architecture tailored for SAGIN, which replaces traditional perimeter defenses with a ”never trust, always verify” approach. This approach strengthens network security and flexibility through continuous verification and adherence to the principle of least privilege. SECURELINK integrates blockchain technology to establish a multi-layered security verification and data processing scheme, addressing the dynamic and decentralized features of SAGIN. Additionally, we introduce DFRIO, a zero-trust traffic offloading method based on decentralized federated reinforcement learning and blockchain, designed to enhance network performance within maintaining security. Simulation results demonstrate that our solution significantly enhances SAGIN’s defense capability without compromising network stability, outperforming the two baseline schemes by 18.3% and 42.6%, respectively.

Network device and system health management is the foundation of modern network operations and maintenance. Traditional health management methods, relying on expert identification or simple rule-based algorithms, struggle to cope with the heterogeneous networks (HNs) environment. Moreover, current state-of-the-art distributed fault diagnosis methods, which utilize specific machine learning techniques, lack multi-scale adaptivity for heterogeneous device information, resulting in unsatisfactory diagnostic accuracy for HNs. In this paper, we develop an LLM-assisted end-to-end intelligent network health management framework. The framework first proposes a multi-scale data scaling method based on unsupervised learning to address the multi-scale data problem in HNs. Secondly, we combine the semantic rule tree with the attention mechanism to propose a Multi-Scale Semanticized Anomaly Detection Model (MSADM) that generates network semantic information while detecting anomalies. Finally, we embed a chain-of-thought-based large-scale language model downstream to adaptively analyze the fault diagnosis results and create an analysis report containing detailed fault information and optimization strategies. We compare our scheme with other fault diagnosis models and demonstrate that it performs well on several metrics of network fault diagnosis.

The integration of Time-Sensitive Networking (TSN) with 5G technology provides Industrial IoT (IIoT) systems with essential low latency, high flexibility, and reliability. However, a key challenge in combining 5G and TSN is the deterministic scheduling of cross-domain flows, which requires precise time synchronisation and the ability to handle unpredictable changes in wireless channels. To address this challenge, we propose an online asynchronous scheduling mechanism. This mechanism is implemented at the 5G-TSN gateway, dynamically allocating TSN network time slot resources to enhance the network's deterministic scheduling capability in the presence of time asynchrony and network fluctuations. Extensive simulations on the OMNeT++ platform demonstrate that our online asynchronous algorithm effectively utilises network resources, reduces delays caused by wireless fluctuations and time asynchrony, and improves network throughput.

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.

The rapid proliferation of industrial equipment and its widespread deployment across diverse sectors have introduced substantial challenges for fault diagnosis. Current equipment operates under a range of disparate fault coding standards, marked by pronounced heterogeneity and fragmentation—particularly in Identification and classification of equipment and faults. The lack of a standardized representation has been shown to impede cross-domain data integration and to constrain the adaptability and generalizability of existing diagnostic models in complex, multi-source environments. To address these limitations, this study proposes FUSE74 — a novel Fault Unification and Semantic Encoding scheme that standardizes fault information using a structured 74-bit representation. This scheme defines a generalized and extensible coding structure, supported by a rule-based mapping mechanism that links fault codes to semantic representations. Such a design enables the standardized expression of fault-related information across heterogeneous systems. Building upon this foundation, the paper further introduces a diagnostic framework driven by a large language model (LLM), which utilizes the LLM’s semantic reasoning capabilities to perform automated fault analysis, health assessment, and maintenance recommendation. Experimental evaluations demonstrate the proposed framework’s effectiveness in achieving robust cross-equipment adaptability and high diagnostic accuracy, thereby providing a practical solution for intelligent fault management in complex industrial contexts.

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.

Vector quantization (VQ) is a key technique in high-resolution and high-fidelity image synthesis, which aims to learn a codebook to encode an image with a sequence of discrete codes and then generate an image in an auto-regression manner. Although existing methods have shown superior performance, most methods prefer to learn a single-modal codebook (\emph{e.g.}, image), resulting in suboptimal performance when the codebook is applied to multi-modal downstream tasks (\emph{e.g.}, text-to-image, image captioning) due to the existence of modal gaps. In this paper, we propose a novel language-guided codebook learning framework, called LG-VQ, which aims to learn a codebook that can be aligned with the text to improve the performance of multi-modal downstream tasks. Specifically, we first introduce pre-trained text semantics as prior knowledge, then design two novel alignment modules (\emph{i.e.}, Semantic Alignment Module, and Relationship Alignment Module) to transfer such prior knowledge into codes for achieving codebook text alignment. In particular, our LG-VQ method is model-agnostic, which can be easily integrated into existing VQ models. Experimental results show that our method achieves superior performance on reconstruction and various multi-modal downstream tasks.

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.

In the future era of intelligent networks, communication technology and network architecture need to be further developed to provide users with high-quality services. The Space-Air-Ground Integrated Networks (SAGIN) is seen as a potential architecture to provide ubiquitous communication and drive the era of the intelligent global network. The space and air segments in SAGIN can assist in offloading traffic from the ground segment. However, in a highly dynamic and heterogeneous network like SAGIN, offloading decisions are easily affected by the incorporated/malicious nodes. How to ensure security and improve network performance becomes a critical problem. In this paper, we address the above problem by jointly using blockchain and federated reinforcement learning (FRL). Firstly, we propose a blockchain-based secure federated learning framework that combines topology information chain and model chain to assist traffic offloading. Then, we propose a node security evaluation and an enhanced practical byzantine fault tolerance (EPBFT) algorithm to secure the traffic offloading process. Furthermore, we describe the traffic offloading problem as a Markov decision problem (MDP) and employ the Blockchain-based Federated Asynchronous Advantage Actor-Critic (BFA3C) algorithm to solve this problem. Finally, the simulation results show that the BFA3C-based algorithm used in SAGIN with/without malicious nodes achieves superior performance in terms of latency and security.

The thousands of streaming data overwhelmingly provide for Internet users on Twitter every day, especially for those Twitter users with many friends. However, the useful tweets that users are really interested in personally could be covered by massive other uninformative and uninteresting information. Therefore, how to bring immediately the interesting tweets for users is always a challenging issue. In this paper, we consider the user friendships in detail and build an effective and practical model to calculate the friendships among users. Certainly, we also take user interests to tweets into account. We then propose a personalized approach for tweets ranking, which focus on the user friendships and the personal interests to tweets. The experimental results demonstrate that our proposed method greatly outperforms several baselines and the user friendships have really important effect on tweets ranking.

Although the spiral of silence theory has been studied thoroughly in the traditional dissemination field, to our best knowledge, no one has clearly verified the applicability of the spiral of silence theory in social networks based on the real information propagation datasets. In this paper, we focus on the disparity between majority and minority opinions, we verify the applicability of the spiral of silence theory in social networks by taking into account 4 factors, including the propagation width, the propagation depth, the message sentiment and the modularity through a large amount of data-driven experiments based on the real-world information propagation datasets which collected on Sina Weibo. We also investigate the applicability of tweets with different categories, our data-driven experimental results show that the spiral of silence theory is still applicable in social networks but different tweets with different categories have different applicability of the spiral of silence theory.