With the continuous advancement of 6G technology, SAGINs provide seamless coverage and efficient connectivity for future communications by integrating terrestrial, aerial, and satellite networks. Unmanned aerial vehicles (UAVs), owing to their high maneuverability and flexibility, have emerged as a critical component of the aerial layer in SAGINs. In this paper, we systematically review the key technologies, applications, and challenges of UAV-assisted SAGINs. First, the hierarchical architecture of SAGINs and their dynamic heterogeneous characteristics are elaborated on, and this is followed by an in-depth discussion of UAV communication. Subsequently, the core technologies of UAV-assisted SAGINs are comprehensively analyzed across five dimensions—routing protocols, security control, path planning, resource management, and UAV deployment—highlighting the progress and limitations of existing research. In terms of applications, UAV-assisted SAGINs demonstrate significant potential in disaster recovery, remote network coverage, smart cities, and agricultural monitoring. However, their practical deployment still faces challenges such as dynamic topology management, cross-layer protocol adaptation, energy-efficiency optimization, and security threats. Finally, we summarize the applications and challenges of UAV-assisted SAGINs and provide prospects for future research directions.

The Industrial Internet of Things (IIoT) enables communication among automation systems, machinery, and sensors in an industrial setting. To optimize critical industrial operations, a substantial volume of data concerning diverse in-factory activities and automation services is generated by IoT devices and sensors. This data are subsequently transferred to distant processing systems for analysis and decision-making. Nevertheless, a substantial latency in data transmission or any abnormality in the generated data may result in delayed or erroneous decisions, consequently impacting the efficacy of essential industrial systems. To address these challenges, we established an intelligent network architecture utilizing software-defined networking that achieves tactile latencies efficiently while handling industrial data traffic in an energy-efficient manner. To address the initial challenge, the suggested architecture utilizes the self-organized maps approach to distinguish between industrial traffic requiring tactile latencies and nontactile traffic. We utilize a binary tree-based flow table mapping method to enhance flow table matching and decrease lookup times. To address the second challenge, we employ the Support Vector Machine technique to identify anomalies in real-time industrial data traffic. The Hadoop system and Mininet emulator are utilized to evaluate the proposed architecture using the UNSW dataset. The results demonstrate the effectiveness of the suggested solution in providing energy-efficient tactile assurances and identifying anomalies in traffic.

Assessing risk in traffic scenarios is crucial for advancing autonomous vehicle safety. As the number of vehicles and their interactions increase, there is a heightened need for prompt and precise risk evaluation. Traditional collision warning systems, which rely on conflict points, are limited to specific traffic scenarios and are inadequate for the dynamic demands of complex and diverse traffic environments. Additionally, current field-based risk assessment methods evaluate collision risks based only on instantaneous vehicle motion states, lacking in both timeliness and accuracy. To address these challenges, this paper proposed a novel risk assessment method. It involves constructing a vehicle-road cooperative environment using a distributed digital twins system and employs a multi-agent multi-modal trajectory prediction algorithm to forecast future vehicle motion states. By leveraging predictive multi-dimensional kinematic features, the proposed method dynamically generates future driving risk fields, effectively assesses collision risks in complex traffic scenarios by modeling multi-source collision risks within future traffic scenes. Extensive simulation results and analyses reveal that DPDRF significantly improves the average Pre-Collision Warning Time (PCWT) by 2.53 times compared to DDRPF and by 1.81 times compared to DSF. Furthermore, DPDRF reduces the average Pre-Collision Warning Error (PCWE) by 79% relative to PRF.

RDMA over Converged Ethernet v2 (RoCEv2) is one of the most popular high-speed datacenter networking solutions. Watermark is the general term for various trigger and release thresholds of RoCEv2 flow control protocols, and its reasonable configuration is an important factor affecting RoCEv2 performance. In this paper, we propose ByteTuning, a centralized watermark tuning system for RoCEv2. First, three real cases of network performance degradation caused by non-optimal or improper watermark configuration are reported, and the network performance results of different watermark configurations in three typical scenarios are traversed, indicating the necessity of watermark tuning. Then, based on the RDMA Fluid model, the influence of watermark on the RoCEv2 performance is modeled and evaluated. Next, the design of the ByteTuning is introduced, which includes three mechanisms. They are (1) using simulated annealing algorithm to make the real-time watermark converge to the near-optimal configuration, (2) using network telemetry to optimize the feedback overhead, (3) compressing the search space to improve the tuning efficiency. Finally, We validate the performance of ByteTuning in multiple real datacenter networking environments, and the results show that ByteTuning outperforms existing solutions.

Medical report generation has made significant progress in recent years. However, generated reports still suffer from issues such as poor readability, incomplete and inaccurate descriptions of lesions, and challenges in capturing fine-grained abnormalities. The primary obstacles include low image resolution, poor contrast, and substantial cross-modal discrepancies between visual and textual features. To address these challenges, we propose an Anomaly-Driven Cross-Modal Contrastive Network (ADCNet), which aims to enhance the quality and accuracy of medical report generation through effective cross-modal feature fusion and alignment. First, we design an anomaly-aware cross-modal feature fusion (ACFF) module that introduces an anomaly embedding vector to guide the extraction and generation of anomaly-related features from visual representations. This process enhances the capability of visual features to capture lesion-related abnormalities and improves the performance of feature fusion. Second, we propose a fine-grained regional feature alignment (FRFA) module, which dynamically filters visual and textual features to suppress irrelevant information and background noise. This module computes cross-modal relevance to align fine-grained regional features, ensuring improved semantic consistency between images and generated reports. The experimental results from the IU X-Ray and MIMIC-CXR datasets demonstrate that the proposed ADCNet method significantly outperforms existing approaches. Specifically, ADCNet achieves notable improvements in natural language generation metrics, as well as the accuracy, completeness, and fluency of medical report generation.

Nonterrestrial networks (NTNs) enabled Internet of Things (IoT) extends connectivity to remote and underserved areas, enhances network reliability and coverage, and supports diverse IoT applications in challenging environments, such as rural, maritime, and disaster-stricken regions. As an emerging and fast-evolving IoT scheme, NTN-enabled IoT requires extensive evaluation to ensure effective deployment in real-world scenarios, such as connectivity, performance, and security evaluation. Since conducting testing in remote and diverse environments is logistically challenging and costly, we propose a generative artificial intelligence (GAI)-based synthetic traffic generation framework that facilitates comprehensive traffic analysis and performance evaluation. The proposed framework employs a GAI model to learn the traffic pattern and generate synthetic traffic from historical data. Our approach includes an embedding-based model for representing network flow attributes and a conditional generative adversarial network (CGAN) for generating traffic flows. Considering both source-destination information and statistical features achieves more comprehensive characterization of traffic flows. Finally, the simulation results demonstrate that the proposed approach can generate high quality traffic that conforms to real data distribution and shows obvious difference between multiple applications.

The efficient and secure management of resources within flying ad-hoc networks (FANETs) poses formidable challenges. FANETs constitute a pivotal element of the space-air-ground-integrated network (SAGIN), employing network virtualization (NV) technology in tandem with service function chain (SFC) to facilitate end-to-end network services, akin to terrestrial networks. Nonetheless, the transient, dynamic nature of FANETs coupled with their susceptibility to network attacks engenders considerable complexity in the placement of SFCs within these networks. To address the rationality and security of resource allocation for SFC placement, this article proposes a reinforcement learning algorithm that sets strict security-level restrictions on the placement process and fully extracts the key features in FANETs. Additionally, a multilayer policy network is devised to dynamically perceive alterations in the FANET environment and compute an optimal SFC placement strategy. The proposed algorithm exhibits real-time adaptability to the dynamic environment, quantifies influential factors during placement, and achieves dynamic SFC placement. To assess the efficacy of the algorithm, three evaluation metrics-namely, SFC placement success rate, long-term average revenue, and long-term revenue cost ratio-are formulated and extensively evaluated through a plethora of experiments. Comparative analysis against alternative algorithms demonstrates enhancements of 20.6%, 15.3%, and 12.1% in the aforementioned metrics, respectively. The experimental findings substantiate both the convergence and efficiency of the proposed algorithm.

In this letter, a comprehensive and detailed introduction to the current security risks faced by networked printers is given, the security monitoring platform and attack detection method are explained, and the actual monitoring results are analyzed. In particular, the most comprehensive feature knowledge database for networked printer is organized and published.

The current audio single-mode self-supervised classification mainly adopts a strategy based on audio spectrum reconstruction. Overall, its self-supervised approach is relatively single and cannot fully mine key semantic information in the time and frequency domains. In this regard, this article proposes a self-supervised method combined with knowledge distillation to further improve the performance of audio classification tasks. Firstly, considering the particularity of the two-dimensional audio spectrum, both self-supervised strategy construction is carried out in a single dimension in the time and frequency domains, and self-supervised construction is carried out in the joint dimension of time and frequency. Effectively learn audio spectrum details and key discriminative information through information reconstruction, comparative learning, and other methods. Secondly, in terms of feature self-supervision, two learning strategies for teacher-student models are constructed, which are internal to the model and based on knowledge distillation. Fitting the teacher’s model feature expression ability, further enhances the generalization of audio classification. Comparative experiments were conducted using the AudioSet dataset, ESC50 dataset, and VGGSound dataset. The results showed that the algorithm proposed in this paper has a 0.5% to 1.3% improvement in recognition accuracy compared to the optimal method based on audio single mode.

With the widespread adoption of edge computing and the rollout of 5G technology, the edge network is experiencing rapid growth. Edge computing enables the execution of certain computational tasks on edge devices, fostering more efficient resource utilization. However, the reliability of the edge network is constrained by its network connections. Network instability can significantly compromise service quality. An effective service function chain (SFC) migration algorithm is essential to optimize resource utilization, enhance service quality. This paper begins by analyzing the current research landscape of edge networks and SFC migration algorithms. Subsequently, the challenges associated with edge network and SFC migration are formally articulated, leading to the proposal of a SFC migration algorithm based on deep reinforcement learning (DRL) with a focus on reliability assurance (RA-SFCM). The algorithm leverages multi-agent deep reinforcement learning to dynamically perceive changes in the edge network environment. It introduces an advantage function to evaluate the performance of each agent relative to the average level and incorporates a central attention mechanism with multiple attention heads to better capture the interdependencies and relationships among different agents. Additionally, this paper innovatively defines and quantifies the reliability of the migration process. By introducing a reliability penalty mechanism based on the migration target nodes and link capacity, it enhances the reliability of the migration schemes. The experimental results conclusively demonstrate the remarkable advantages of the RA-SFCM algorithm in terms of real-time performance, resource utilization efficiency, and reliability. Compared to algorithms such as Sa-VNFM, ROVM, and DLTSAC, RA-SFCM exhibits superior performance. For RA-SFCM, the optimized deployment migration strategy enhances real-time performance, precise resource management improves utilization efficiency, and advanced fault tolerance mechanisms strengthen reliability.

In the Internet, ASs are interconnected using BGP. However, due to a lack of security considerations in the design of BGP, a series of security issues arise during the propagation of routing information, such as prefix hijacking, route leakage, and AS path tampering. Therefore, this paper conducts research on the detection of route leakage. By analyzing BGP routing information, we abstract the routing propagation relationship between ASs into a network topology graph, and extract graph features from the graph abstracted from routing data at certain time intervals. Based on the structural robustness features and centrality measurement features of the graph, we determine whether a route leakage has occurred during the current time period. To this end, we use machine learning methods and propose a weighted voting model. This model trains multiple single models and assigns weights to them, and through the weighted analysis of the results of multiple models, it can determine whether a route leakage has occurred. In addition, to determine the corresponding weights, we use genetic algorithms for identifying route leaks. The experimental results show that the method used in this paper has a high accuracy rate, and compared with a single model, it performs better on multiple datasets.

Unmanned aerial vehicles (UAVs) have increasingly become integral to logistics and distribution due to their flexibility and mobility. However, the existing studies often overlook the dynamic nature of customer demands and wind conditions, limiting the practical applicability of their proposed strategies. To tackle this challenge, we firstly construct a time-slicing-based UAV path planning model that incorporates dynamic customer demands and wind impacts. Based on this model, a two-stage logistics UAV path planning framework is developed according to the analysis of the customer pool updates and dynamic attitudes. Secondly, a dynamic demand and wind-aware logistics UAV path planning problem is formulated to minimize the weighted average of the energy consumption and the customer satisfaction penalty cost, which comprehensively takes the energy consumption constraints, load weight constraints, and hybrid time window constraints into consideration. To solve this problem, an improved particle swarm optimization (PSO)-based multiple logistics UAV path planning algorithm is developed, which has good performance with fast convergence and better solutions. Finally, extensive simulation results verify that the proposed algorithm can not only adhere to the UAV’s maximum load and battery power constraints but also significantly enhance the loading efficiency and battery utilization rate. Particularly, compared to the genetic algorithm (GA), simulated annealing (SA), and traditional PSO strategies, our proposed algorithm achieves satisfactory solutions within a reasonable time frame and reduces the distribution costs by up to 9.82%.

Federated learning (FL) in Industrial IoT (IIoT) facilitates collaborative model training across distributed edge devices, ensuring data privacy and localized insights without centralized data aggregation. However, the networked parameter sharing mechanism in FL renders it vulnerable to exploitation by man-in-the-middle (MITM) attackers, potentially disrupting the model training process. To mitigate this threat, this article presents a novel blockchain-reinforced FL architecture aimed at enabling cooperative intrusion detection. Initially, FL is leveraged to aggregate all learned information from edge servers, thereby disseminating extracted attack characteristics to all participants through gradient sharing. Subsequently, a blockchain-based parameter verification scheme is introduced to safeguard against tampered local parameters affecting the global model. Clients record model parameters in smart contracts deployed on a private chain, and parameter servers verify parameter confidentiality before aggregation, ensuring only valid parameters are considered. Finally, extensive experiments are conducted using an edge IIoT cybersecurity data set comprising 61 features spanning ten protocol layers and five attacks targeting IIoT connectivity protocols. Simulation results demonstrate that the proposed scheme significantly enhances intrusion detection accuracy, achieving a threefold improvement when two-thirds of federated nodes are subjected to MITM attacks.

The space-air-ground integrated network (SAGIN) comprises a multitude of interconnected and integrated heterogeneous networks. Its network is large in scale, complex in structure, and highly dynamic. Virtual network embedding (VNE) is designed to efficiently allocate resources within the physical host to diverse virtual network requests (VNRs) with different constraints while improving the acceptance ratio of VNRs. However, in a heterogeneous SAGIN environment, improving the utilization of network resources while ensuring the performance of the VNE algorithm is a very challenging topic. To address the aforementioned issues, we first introduce a services diversion strategy (SDS) to select embedded nodes based on different service types and network state, thereby alleviating the uneven use of resources in different network domains. Subsequently, we propose a VNE algorithm (GAIL-VNE) based on generative adversarial imitation learning (GAIL). We construct a generator network based on the actor-critic architecture, which can generate the probability of physical nodes being embedded based on the observed network state. Secondly, we construct a discriminator network to distinguish between generator samples and expert samples, which aids in updating the generator network. After offline training, the generator and discriminator reach a Nash equilibrium through game confrontation. During the embedding process of VNRs, the output of the generator provides an effective basis for generating VNE solutions. Finally, we verify the effectiveness of this method through experiments involving offline training and online embedding.

Satellite communication technology solves the problem that the traditional wired network infrastructure is difficult to achieve global communication coverage. However, factors such as satellite orbits introduce frequent changes to the network topology, and challenges like satellite failures and communication link interruptions are prevalent. In the face of these issues, service function chain (SFC) migration becomes a crucial method for swiftly adjusting SFCs during faults, maintaining service continuity and availability. This article proposes a latency-sensitive SFC migration algorithm tailored to satellite networks. The algorithm first models the satellite network as a multi-domain virtual network, capturing the constraints faced during SFC migration. Subsequently, a deep reinforcement learning algorithm integrated attention mechanism is designed to more accurately capture and understand the complex network environment and dynamic satellite network topology and derive optimal SFC migration strategies for superior performance. Finally, through experimentation and evaluation of the deep reinforcement learning-driven latency-sensitive service function chain intelligent migration algorithm (LS-SFCM) in satellite communication, this study validates the effectiveness and superior performance of the algorithm in latency-sensitive scenarios. It provides a new avenue for enhancing the service quality and efficiency of satellite communication networks.

A data center (DC) is supposed to efficiently distribute the bandwidth of the network to provide high-quality traffic transmission. However, the load imbalance issue can easily occur due to the complex topology and traffic features. Equal-Cost Multi-Path(ECMP) distributes traffic on different paths but doesn't consider network congestion. Although HULA solved some of ECMP's problems, it can easily congest the best path. RPS randomly distributes packets across multiple paths, potentially causing packet reordering in certain scenarios. This paper presents DHLB, a distributed hop-by-hop load balancing architecture based on in-band network telemetry. With active In-band network telemetry, DHLB gathers essential load information and systematically records it in a load information table. DHLB distributes traffic proportionally on different paths based on their load degree. We build a fat tree topology on mininet to verify the performance of our design. Experimental results indicate that DHLB outperforms other schemes regarding average Flow Completion Time (FCT). It also performs better on additional overhead than another probe-based scheme.

With the evolution of Space-based backbone networks, the demand for enhanced efficiency and stability in network resource allocation has become increasingly critical, presenting a substantial challenge to conventional allocation methods. In response, we introduce an innovative resource allocation algorithm for space-based backbone networks. This algorithm represents a synergistic fusion of Deep Reinforcement Learning (DRL) and Local Search (LS) methodologies. It is specifically designed to reduce the extensive training duration associated with traditional policy networks, a crucial aspect in assuring optimal service quality. Our algorithm is structured within a two-stage framework that seamlessly integrates DRL and LS. A distinctive feature of our approach is the incorporation of link reliability into the algorithmic design. This element is meticulously tailored to address the dynamic and heterogeneous nature of space-based networks, ensuring effective resource management. The effectiveness of our approach is substantiated through extensive simulation results. These results demonstrate that the integration of DRL with LS not only enhances training efficiency but also exhibits significant improvements in resource allocation outcomes. Our work represents a noteworthy contribution to the development of practical optimization strategies in space-based networks, merging DRL with traditional methodologies for improved performance.

Federated learning is a privacy-preserving distributed machine learning method, which facilitates clients to cooperate in training a shared model while safeguarding original data. The different distribution and quantity of training data between clients can pose significant challenges, such as data heterogeneity and class imbalance, which can greatly influence the performance of the shared model. Although many methods have been proposed to eliminate the deleterious influence of non-IID data, existing solutions usually do not perform well on tail-classes owing to the absence of attention for the long-tail distribution. We present a long-tail federated learning framework FedGCS, which can solve the global and local class imbalance problem via generic to compensate for specific. Specifically, clients separate features from the training data based on the class activation map and selectively fuse the separated class-specific features and class-generic features to restore the distribution of tail-classes. We also design a loss function-TailDistillation Loss to lessen the bias of the classifier towards head-classes. To appraise the effectiveness of FedGCS, we adapted multiple benchmark datasets to the long-tail federated learning setting. Experiments indicate that the FedGCS is an useful method, and is superior to previous approaches.

The Internet of Things (IoT) has become a core driver leading technological advancements and social transformations. Furthermore, data generation plays multiple roles in IoT, such as driving decision-making, achieving intelligence, promoting innovation, improving user experience, and ensuring security, making it a critical factor in promoting the development and application of IoT. Due to the vast scale of the network and the complexity of device interconnection, effective resource allocation has become crucial. Leveraging the flexibility of Network Virtualization technology in decoupling network functions and resources, this work proposes a Multi-Domain Virtual Network Embedding algorithm based on Deep Reinforcement Learning to provide energy-efficient resource allocation decision-making for IoT data generation. Specifically, we deploy a four-layer structured agent to calculate candidate IoT nodes and links that meet data generation requirements. Moreover, the agent is guided by the reward mechanism and gradient back-propagation algorithm for optimization. Finally, the effectiveness of the proposed method is validated through simulation experiments. Compared with other methods, our method improves the long-term revenue, long-term resource utilization, and allocation success rate by 15.78%, 15.56%, and 6.78%, respectively.