Point Cloud Neural Networks (PCNNs) have emerged as a vital tool for latency-sensitive 3D perception applications, such as autonomous driving and AR/VR. However, their inherent computational redundancy—arising from excessive global sampling/search operations and repeated feature updates/aggregations caused by shared neighbors—severely constrains execution efficiency. More critically, conventional redundancy elimination methods usually introduce operations that are highly GPU-unfriendly, resulting in high memory overhead, increased branch divergence, irregular memory access, and serial dependencies, which together pose a significant challenge to PCNN acceleration.

The effectiveness of federated learning (FL) systems relies on extensive client participation. However, the existing incentive mechanisms often overlook the intrinsic coupling between participation and privacy strategies. This oversight leads to mismatches between incentive distributions and clients’ privacy costs and contribution quality, limiting the effectiveness of incentives for enhancing contribution quality and participation scale. To address this, DIM-NEG, a dynamic incentive model based on smart contracts and nested evolutionary games, is proposed. First, we construct a nested evolutionary game framework linking external participation with internal privacy strategies. By utilizing the internal equilibrium payoff as a feedback parameter for the external game, we achieve unified modeling of these coupled decision processes. Second, on the basis of this structure, a functionally decoupled dual-incentive lever mechanism is proposed. The server employs the strategy incentive to guide the internal strategy portfolio and the participation incentive to regulate the participation scale, enabling separate optimization of the strategy composition and overall participation level of the system via hierarchical control. Finally, utilizing blockchain-based smart contracts, we design an automated mechanism that encodes rules on-chain to resolve trust issues associated with centralized servers. A theoretical analysis and simulation results demonstrate that the DIM-NEG model achieves superior global accuracy, training efficiency, and communication cost-effectiveness, while exhibiting strong robustness in non-IID environments. The model adequately motivates users to participate in high-quality data sharing tasks and maintains system stability, thereby maximizing the overall effectiveness of the federated learning system.

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.

Edge Learning environments, characterized by limited wireless resources, encounter significant bottlenecks in network performance, particularly in Federated Learning (FL) tasks. Current resource allocation strategies are primarily classified into “AI for Networking” and “Networking for AI”. However, both approaches fail to adequately address the interaction between network states and AI task requirements, thereby limiting their overall effectiveness. To address this, we propose a novel bidirectional dynamic collaborative optimization mechanism that enables real-time interaction between AI task performance and network states. This mechanism adjusts both AI task resource requirements and network configurations based on performance feedback, breaking away from traditional unidirectional optimization approaches. We introduce the AI-network unified algorithm, which incorporates data-driven dynamic sensing and enhances system adaptability and robustness, achieving self-optimization in edge learning. Theoretical analysis and simulation results demonstrate the significant advantages of our approach in simultaneously improving network resource utilization and AI task performance, providing an effective solution for the future wireless network.

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.

Causal inference and root cause analysis play a crucial role in network performance evaluation and optimization by identifying critical parameters and explaining how the configuration parameters affect network key performance indicators (KPIs). Traditional performance evaluation methods can evaluate KPIs based on configuration parameters, but they are unable to explain how configuration parameters affect KPIs. Moreover, static causal discovery and inference methods are not directly applicable to dynamic networks. To address these challenges, we propose a self-iterative performance optimization method based on causal inference and interpretable network evaluation (CiiNet). CiiNet constructs causal graphs through change-point detection and hierarchical incremental causal discovery. Then, CiiNet introduces causal inference for critical parameter analysis (CPA). Using intervention analysis and regression-based parameter learning, CiiNet infers and evaluates the impact of critical parameters on KPIs. Based on the interpretable evaluation, CiiNet can further self-iteratively optimize the critical parameters for optimal network performance and dynamically obtain the optimal configurations. Our extensive experiments show that CiiNet outperforms other baseline methods regarding causal discovery, network performance evaluation, and CPA.

We propose using the Poisson point process (PPP) for accurate modeling in Space-air-ground integrated networks (SAGIN). SAGIN is experiencing significant growth, aiming for ultra-fast speeds, low latency, and integration of AI, IoT, and other emerging technologies. To achieve better performance than existing hybrid bands in SAGIN, we utilize three distinct frequency bands—Radio frequency (RF), Terahertz (THz), and Free-space optical communication (FSO)—to compensate for their shortcomings effectively. Then, we derive not only the coverage probability but also the rate coverage probability formula of the network and utilize Newton's method to deduce the most optimal channel allocation scheme. Moreover, we use comparison experiments between different frequency bands, altitudes, and other conditions to affirm its reliability and effectiveness in advancing SAGIN coverage and rate coverage probability. The results show that our proposed scheme performs up to 3 times better than a single-spectrum scheme and 2 times better than the existing mixed method.

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.

Machine learning as a service (MLaaS) has gained significant popularity and market traction in recent years, driven by advancements in Artificial Intelligence particularly Generative AI (GAI). However, MLaaS faces severe challenges from sampling-based model stealing attacks (MSAs), where attackers strategically query the targeted ML models provided by MLaaS providers to minimize the query burden while closely replicating the model’s functionality. Such MSAs pose severe consequences, including intellectual property (IP) theft and potential leakage of private training data. Unfortunately, existing defenses either sacrifice model utility or fail to generalize across diverse MSAs.In this paper, we propose DIARY, an innovative detection method specifically tailored to sampling-based MSAs by exploiting their inherent sophistication. Our key insight is that ‘clever’ malicious queries tend to extract more information from the targeted (victim) model than typical benign queries, as these attacks iteratively refine their queries by examining and analyzing prior queries and the corresponding responses. Hence we design DIARY to extract timing dependence within a query sequence and incorporate contrastive learning for properly characterizing such dependency that holds for different sampling-based MSAs. Comprehensive evaluations using five different sampling-based MSAs and two state-of-the-art defense baselines across four popular datasets consistently validate DIARY’s superior performance.

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.

Anomaly detection in multivariate time series data is critical across a variety of real-life applications. The predominant anomaly detection techniques currently rely on reconstruction-based methods. However, these methods often overfit the abnormal pattern and fail to diagnose the anomaly. Although some studies have attempted to prevent the incorrect fitting of anomalous data by enabling models to learn the trend of data variations, they fail to account for the dynamic nature of data distribution. This oversight can lead to the erroneous reconstruction of anomalies that do not exist. To address these challenges, we propose RTdetector, a Transformer-based time series anomaly detection model leveraging reconstruction trends. RTdetector employs a novel global attention mechanism based on reconstruction trends to learn distinguishable attention from the original sequence, thereby preserving the global trend information intrinsic to the time series. Additionally, it incorporates a self-conditioning transformer, based on reconstruction trend enhancement to achieve superior predictive performance. Extensive experiments on four datasets demonstrate that RTdetector achieves state-of-the-art results in multivariate time series data anomaly detection. Our code is available at https://github.com/CSUFUNLAB/RTdetector.

The Contrastive Language-Image Pretraining (CLIP) model has significantly advanced vision-language modeling by aligning image-text pairs from large-scale web data through self-supervised contrastive learning. Yet, its reliance on uncurated Internet-sourced data exposes it to data poisoning and backdoor risks. While existing studies primarily investigate image-based attacks, the text modality, which is equally central to CLIP's training, remains underexplored. In this work, we introduce ToxicTextCLIP, a framework for generating high-quality adversarial texts that target CLIP during the pre-training phase. The framework addresses two key challenges: semantic misalignment caused by background inconsistency with the target class, and the scarcity of background-consistent texts. To this end, ToxicTextCLIP iteratively applies: 1) a background-aware selector that prioritizes texts with background content aligned to the target class, and 2) a background-driven augmenter that generates semantically coherent and diverse poisoned samples. Extensive experiments on classification and retrieval tasks show that ToxicTextCLIP achieves up to 95.83\% poisoning success and 98.68% backdoor Hit@1, while bypassing RoCLIP, CleanCLIP and SafeCLIP defenses. The source code can be accessed via https://github.com/xinyaocse/ToxicTextCLIP/.

Existing anti-jamming strategies for unmanned aerial vehicle (UAV) networks largely assume homogeneity among UAVs, neglecting the differences in hardware configurations, task requirements, and environmental adaptability. In the face of such heterogeneity, these strategies often fail to effectively counter intelligent jamming and co-channel interference. To address this issue, this paper proposes an intelligent anti-jamming framework designed specifically for the heterogeneous UAV network, allowing each UAV to autonomously adjust its transmission channel and power based on its hardware capabilities and task requirements in a distributed environment. This aims to optimize communication efficiency and reduce energy consumption. We formulate the anti-jamming problem as an adversarial game and confirm the existence of a unique equilibrium point within this model. Moreover, we introduce the novel Personalized Federated Soft Actor-Critic (PFSAC) algorithm, which combines the global model with local models to customize personalized anti-jamming strategies for each UAV, significantly enhancing network performance in complex jamming environments. Simulation results indicate that compared to other methods, our proposed algorithm significantly enhances the anti-jamming capability of heterogeneous UAV networks and performs better than them.

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 advancements in six-generation (6G) communication technology, there is a growing need for comprehensive and interpretable network performance evaluation for network optimization. Traditional evaluation methods often overlook uncertainties and are limited to a single time scale or performance dimension, while the recent machine learning-based method lacks interpretability. To address this issue, we propose a multidimensional performance evaluator for interpretable and traceable network performance evaluation (MPITE). MPITE, constructed with a three-layer evaluation model incorporating physical, logical, and causal topology structures, reflects the causal relationship of communication system configurations, the changing network states, and performance metrics. We introduce a multidimensional performance index that considers value, time, and certainty dimensions to evaluate network performance comprehensively. We propose interpretable Bayesian theory-based network inference algorithms to derive network certainty for interpretable network performance evaluation. Then, we intelligently derive optimal network configuration parameters through reverse inferencing for network tracing. Experimental results demonstrate the advantage, interpretability, and traceability of MPITE.

The Space-Air-Ground integrated network (SAGIN) has gained significant attention due to the explosive growth in mobile data traffic. In this network, Unmanned Aerial Vehicles (UAVs) play a critical role as air relay nodes, bridging ground and space networks. However, challenges arise from the dynamic position angles between UAVs and satellites, as well as fixed UAV altitudes, limiting air-to-space transmission capacity. Moreover, the finite UAV battery capacity carries the risk of energy interruptions during SAGIN transmissions. To address these issues, we propose an integrated model that considers UAV channel fading, energy consumption, and harvesting. This model allows us to comprehensively analyze SAGIN transmission performance. Within this framework, we calculate the UAV energy outage probability and signal-to-noise ratio (SNR) outage probability for SAGIN uplink transmission. Based on our network performance analysis, we derive an expression for the optimal UAV altitude, ensuring uninterrupted energy supply and preventing SNR outage. To assess the fairness of SAGIN transmission performance, we compare the capabilities of Ground-to-Air-to-Space and Ground-to-Space transmissions. Additionally, we provide closed-form expressions for the transmission time gap in both scenarios. Our numerical results validate the accuracy of these derived expressions and evaluate how key parameters impact the optimal UAV altitude in the SAGIN uplink.

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.

With the explosive growth of mobile devices, wireless networks play a crucial role in people’s daily lives. This article addresses the issue of network congestion control in wireless environments and proposes an improved algorithm based on the quick UDP Internet connection (QUIC) protocol, namely the CubicBytes-N algorithm. By introducing a bandwidth estimation mechanism, this algorithm successfully distinguishes congestion-induced packet loss from random packet loss, addressing the blind window reduction issue in the traditional CubicBytes algorithms. Afterward, the congestion level is set based on the estimation results, and the slow start threshold is further adjusted to make the algorithm more robust. Through a series of experiments conducted in the network simulator-3 (NS3) simulation environment, we validated the performance of the CubicBytes-N algorithm in wireless networks with random packet loss rates ranging from 0% to 5%. The experimental results demonstrate that the CubicBytes-N algorithm not only excels in improving network bandwidth utilization but also avoids preempting the bandwidth required by the traditional CubicBytes algorithm in high packet loss rate environments, ensuring fairness. Overall, the CubicBytes-N algorithm provides an effective congestion control solution for the application of the QUIC protocol in wireless networks.

In the domain of medical diagnostics, precise identification of various skin and oral diseases is vital for effective patient care. In particular, Mpox is a potentially dangerous viral disease with zoonotic origins, capable of human-to-human transmission, underscoring the urgency of precise diagnostic methods for timely intervention. This paper introduces a novel approach named the Choquet Fuzzy Integral-based Ensemble (CFI-Net) for accurate classification of skin diseases, with a specific emphasis on detecting Mpox, foot ulcers, and various mouth and oral diseases. Our methodology begins with Transfer Learning, enhancing the classification capabilities of base classifiers (DenseNet169, MobileNetV1 and DenseNet201) by incorporating additional layers. Subsequently, we aggregate the prediction scores from each base classifier using the Choquet fuzzy integral (CFI) to derive the final predicted labels, thus ensuring dynamic and robust predictions. Fuzzy measures, a crucial component of this fuzzy integral-based ensemble method, are typically determined through manual experimentation in previous approaches. However, in our study, we have tackled the challenge of manual tuning by employing meta-heuristic optimization algorithm to precisely configure the fuzzy measures for optimal performance. A rigorous evaluation is conducted on four publicly available datasets, encompassing two Mpox datasets, a foot ulcer dataset, and a mouth and oral disease dataset. The experiments reveal the remarkable effectiveness of CFI-Net in significantly improving disease classification accuracy. Additionally, we employ Grad-CAM analysis to provide insights into the decision-making processes of our models. Our findings underscore the exceptional performance of CFI-Net, achieving accuracy rates of 98.06% and 94.81% for Mpox detection, 99.06% for foot ulcer detection, and an impressive 99.61% for mouth and oral disease classification. This research not only contributes to the advancement of disease diagnosis but also demonstrates the effectiveness of ensemble learning techniques coupled with fuzzy integral-based fusion in enhancing diagnostic accuracy.

The Space-Air-Ground Integrated Network (SAGIN) plays a pivotal role as a comprehensive foundational network communication infrastructure, presenting opportunities for highly efficient global data transmission. Nonetheless, given SAGIN's unique characteristics as a dynamically heterogeneous network, conventional network optimization methodologies encounter challenges in satisfying the stringent requirements for network latency and stability inherent to data transmission within this network environment. Therefore, this paper proposes the use of differentiated federated reinforcement learning (DFRL) to solve the traffic offloading problem in SAGIN, i.e., using multiple agents to generate differentiated traffic offloading policies. Considering the differentiated characteristics of each region of SAGIN, DFRL models the traffic offloading policy optimization process as the process of solving the Decentralized Partially Observable Markov Decision Process (DEC-POMDP) problem. The paper proposes a novel Differentiated Federated Soft Actor-Critic (DFSAC) algorithm to solve the problem. The DFSAC algorithm takes the network packet delay as the joint reward value and introduces the global trend model as the joint target action-value function of each agent to guide the update of each agent's policy. The simulation results demonstrate that the traffic offloading policy based on the DFSAC algorithm achieves better performance in terms of network throughput, packet loss rate, and packet delay compared to the traditional federated reinforcement learning approach and other baseline approaches.

Space-air-ground integrated networks (SAGIN) incorporating multi-band terahertz (THz) and radio frequency (RF) communication have gained increasing attention in the 6G era. However, the heterogeneity, self-organization, and time-variability of SAGIN pose challenges in accurately modeling, quantitatively analyzing, and optimizing these networks. Additionally, the dynamic topology and randomness of the nodes, including low-earth orbit satellites and high-altitude platforms, make the conventional THz/RF channel allocation method of terrestrial networks unsuitable for SAGIN. To address these challenges, we construct an accurate model of SAGIN based on the binomial point process (BPP) model in stochastic geometry. Subsequently, we analyze the network performance, specifically the joint coverage and transmission rate, through the proposed model. We then propose a simulated annealing algorithm-based optimization algorithm to achieve the optimal THz and RF channel allocation, effectively improving the joint coverage and transmission rate performance. Our simulation results demonstrate the effectiveness of the optimization algorithm and provide insights into the deployment rules of SAGIN.

Massive videos are released every day particularly through video-focused social media apps such as TikTok. This trend has fostered the quick emergence of video retrieval systems, which provide cloud-based services to retrieve similar videos using machine learning techniques. Adversarial example (AE) attacks have been shown to be effective on such systems by perturbing an unaltered video subtly to induce false retrieval results. Such AE attacks can be easily detected because the adversarial perturbations are all over pixels and frames. In this paper, we propose DUO, a stealthy targeted black-box AE attack which uses DUal search Over frame-pixel to generate sparse perturbations and improve stealthiness. DUO is motivated by two observations: only “key frames” in a video decide model predictions, and different pixels and frames contribute far differently to AEs. We implement DUO into a sequential attack pipeline consisting of two components (i.e., SparseTransfer and SparseQuery) built upon such intuitions. In particular, DUO uses SparseTransfer to generate initial perturbations and then SparseQuery to further rectify them. Extensive evaluations on two popular datasets confirm the higher efficacy and stealthiness of DUO over existing AE attacks on video retrieval systems. In particular, we show that DUO achieves higher precision while significantly reducing adversarial perturbations by more than ×100 than the state-of-the-art AE attack.

Sentiment analysis is a crucial task that aims to understand people's emotional states and predict emotional categories based on multimodal information. It consists of several subtasks, such as emotion recognition in conversation (ERC), aspect-based sentiment analysis (ABSA), and multimodal sentiment analysis (MSA). However, unifying all subtasks in sentiment analysis presents numerous challenges, including modality alignment, unified input/output forms, and dataset bias. To address these challenges, we propose a Task-Specific Prompt method to jointly model subtasks and introduce a multimodal generative framework called UniSA. Additionally, we organize the benchmark datasets of main subtasks into a new Sentiment Analysis Evaluation benchmark, SAEval. We design novel pre-training tasks and training methods to enable the model to learn generic sentiment knowledge among subtasks to improve the model's multimodal sentiment perception ability. Our experimental results show that UniSA performs comparably to the state-of-the-art on all subtasks and generalizes well to various subtasks in sentiment analysis.

With the explosive growth of mobile devices and communication facilities, electromagnetic interference (EMI) has become a common phenomenon affecting the communication band. Based on the shielding capability of electromagnetic bands in EMI, frequency selective surfaces (FSSs) are used to shield or suppress specific electromagnetic bands. Additionally, EMI can be negative control and may change the EMI band. Thus, a single FSS cannot effectively shield EMI due to its limited shielding capacity. To address this issue, we first construct a novel interference shielding model to guard the target area. The related shielding problem is modeled as the UAV-driven FSS (UFSS) configuration problem. Second, we propose an intelligent configuration method based on a stochastic game to solve the configuration optimization problem effectively. In the proposed method, we model the interaction between UFSSs and interferers as a stochastic game, where we provide each UFSS with two different options for updating its shielding configuration strategy. According to the shielding configuration strategy generated by the proposed stochastic game, we propose a square loop resource allocation model based on resource constraints to promote each UFSS to update its square loop. Finally, the numerical results and analysis show that our proposed method is more effective and feasible than other band shielding configuration schemes.

In the face of intensive computing tasks with massive data, cloud computing is difficult to provide high-quality services. Edge computing extends cloud services to the edge of the network by introducing edge devices between terminal devices and the cloud. For limited edge server resources, it is especially important to optimize offload strategies by accurately predicting the load on the terminal device. This paper proposes a C-LSTM prediction model based on deep neural network to predict the CPU utilization of terminal equipment in the future, and then proposes a distributed greedy algorithm for offloading decision. The simulation results show that the accuracy of C-LSTM prediction model is higher than other baseline models, reduces energy consumption and delay, and provides high-quality computing services.

Single image super-resolution (SISR) is a challenging ill-posed problem due to multiple high-resolution (HR) images can degenerate into the same low-resolution (LR) image. However, existing deep learning-based super-resolution (SR) methods always have blurred edge structures in the restored images. In addition, they mainly build more profound and more complex convolutional neural networks (CNN), which leads to substantial computational overhead. To address these issues, we propose the feature fusion super-resolution network (FFSRN) that uses the gradient map of the image to guide the restoration. In FFSRN, we propose the split and shuffle concat block (SSCB), which can extract rich features while controlling the model size and computational effort. We also introduce gradient branching to provide additional structural priors for the reconstruction process to restore high-resolution gradient mapping. Experimental results show that this method has a better peak signal-to-noise ratio, computational overhead and visual quality than the existing super-resolution algorithms. Code is available at https://github.com/Qyzs/FFSRN.

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.

Due to the birth of various new Internet of Things devices, the rapid increase of users, and the limited coverage of infrastructure, computing resources will inevitably become insufficient. Therefore, we consider an unmanned aerial vehicle (UAV)–assisted mobile edge computing system with multiple users, an edge server, a remote cloud server, and an UAV. A UAV, as a relay node, can provide users with extensive communications and certain computing capabilities. Our proposed scheme aims to optimize the unloading decision of the tasks among all users and the allocation of computing and communication resources to minimize overall energy consumption and costs of computing and maximum delay. To solve the joint optimization problem, we propose an efficient USS algorithm, which includes a UAV position optimization algorithm, semi-qualitative relaxation method, and self-adaptive adjustment method. Our numerical results show that the proposed algorithm can significantly reduce the unloading cost of multi-user tasks compared with four other unloading decisions, such as traditional cloud computing, which uses only the edge server.

With the expansion of the scale of wireless sensor networks, people have higher requirements for the real-time nature of receiving information and the power consumption of network equipment. LoRa technology for low-power wide-area networks has been produced, but the energy consumption of network terminal nodes is still facing huge problems. Challenge. In this thesis, the REDS strategy based on LoRa technology is proposed. First, the residual energy is used to detect the preamble in the beacon, so that CAD can judge the effective preamble from the noise to avoid false wake up. Then use the remaining energy of the nodes in the remote Sink area of the network to activate the node and perform CAD channel detection. The node selects the communication access method according to the channel quality. When the network traffic load is low, the node communicates according to the CSMA-CA competition method. When the traffic load in the network is high, the nodes communicate and access according to the dynamic duty cycle method. It is proved that the network delay can be reduced by 29% and the energy efficiency can be improved by 19%. Thereby avoiding data collisions, improving channel utilization, and weighing the trade-off between network delay and network energy consumption.

Fog computing is a distributed computing model that can utilise the storage, analysis and processing capabilities of fog nodes near edge devices. Although fog computing can support task processing for various Internet of Things (IoT) systems, Fog‐IoT architecture faces several new challenges with the rapid development of IoT systems, especially delay‐sensitive IoT systems, such as stochastic and dynamic data arrival, optimal utility and deadline of tasks. To address these challenges, workload scheduling toward worst‐case delay and optimal utility for single‐hop Fog‐IoT architecture are studied and the workload dynamic scheduling algorithm (WDSA) is proposed. The proposed WDSA algorithm can maximise the average throughput utility while guarantees the worst‐case delay of task processing. In addition, it is online and needs no prior information about future. The algorithm performance is analysed from the perspective of optimality and worst‐case delay, demonstrating that the proposed WDSA algorithm can get an approximate optima and worst‐case delay guarantees. Finally, simulation results demonstrate that the efficiency and efficacy of this kind of the algorithm can meet the requirement.

An Aggregate Signature based Trust Routing (ASTR) scheme is proposed to guarantee safe data collection in WSNs. In ASTR scheme, firstly, the aggregate signature approach is used to aggregate data and keep data integrity. What is more important, a light aggregate signature based detour routing scheme is proposed to send abstract information which includes the data sending time and ID of data, nodes’ ID to the sink over different paths which can verify whether the data reaches the sink safely. In addition, the trust of a path is evaluated according to the success rate of the path. The trust of paths susceptible to frequent attack will be lowered and the path with high trust will be selected for data routing to avoid data gathering through low trust path and thereby increase the success rate of data gathering. Our comprehensive performance analysis has shown that, the ASTR scheme is able to effectively ensure an increase in success rate of data transmission by 23.23%, reduce the data amount loaded by the node by 53.59%, reduce the redundant data by 41.70%.

To tackle the issue in deep crowd sensing, a Time and Location Correlation Incentive (TLCI) scheme is proposed for deep data gathering in crowdsourcing networks. In TLCI scheme, a metric named “Quality of Information Satisfaction Degree” (QoISD) is to quantify how much collected sensing data can satisfy the application’s QoI requirements mainly in terms of data quantity and data coverage. Two incentive algorithms are proposed to satisfy QoISD with different view. The first algorithm is to ensure that the application gets the specified sensing data to maximize the QoISD. Thus, in the first incentive algorithm, the reward for data sensing is to maximize the QoISD. The second algorithm is to minimize the cost of the system while meeting the sensing data requirement and maximizing the QoISD. Thus, in the second incentive algorithm, the reward for data sensing is to maximize the QoISD per unit of reward. Finally, we compare our proposed scheme with existing schemes via extensive simulations. Extensive simulation results well justify the effectiveness of our scheme. The QoISD can be optimized by 81.92%, and the total cost can be reduced by 31.38%.

The vehicular communication networks, which can employ mobile, intelligent sensing devices with participatory sensing to gather data, could be an efficient and economical way to build various applications based on big data. However, high quality data gathering for vehicular communication networks which is urgently needed faces a lot of challenges. So, in this paper, a fine‐grained data collection framework is proposed to cope with these new challenges. Different from classical data gathering which concentrates on how to collect enough data to satisfy the requirements of applications, a Quality Utilization Aware Data Gathering (QUADG) scheme is proposed for vehicular communication networks to collect the most appropriate data and to best satisfy the multidimensional requirements (mainly including data gathering quantity, quality, and cost) of application. In QUADG scheme, the data sensing is fine‐grained in which the data gathering time and data gathering area are divided into very fine granularity. A metric named “Quality Utilization” (QU) is to quantify the ratio of quality of the collected sensing data to the cost of the system. Three data collection algorithms are proposed. The first algorithm is to ensure that the application which has obtained the specified quantity of sensing data can minimize the cost and maximize data quality by maximizing QU. The second algorithm is to ensure that the application which has obtained two requests of application (the quantity and quality of data collection, or the quantity and cost of data collection) could maximize the QU. The third algorithm is to ensure that the application which aims to satisfy the requirements of quantity, quality, and cost of collected data simultaneously could maximize the QU. Finally, we compare our proposed scheme with the existing schemes via extensive simulations which well justify the effectiveness of our scheme.

An Adaptive Transmission Range Based Topology Control (ATRTC) scheme is proposed to reduce delay and improve reliability for data collection in delay and loss sensitive wireless sensor network. The core idea of the ATRTC scheme is to extend the transmission range to speed up data collection and improve the reliability of data collection. The main innovations of our work are as follows: (1) an adaptive transmission range adjustment method is proposed to improve data collection reliability and reduce data collection delay. The expansion of the transmission range will allow the data packet to be received by more receivers, thus improving the reliability of data transmission. On the other hand, by extending the transmission range, data packets can be transmitted to the sink with fewer hops. Thereby the delay of data collection is reduced and the reliability of data transmission is improved. Extending the transmission range will consume more energy. Fortunately, we found the imbalanced energy consumption of the network. There is a large amount of energy remains when the network died. ATRTC scheme proposed in this paper can make full use of the residual energy to extend the transmission range of nodes. Because of the expansion of transmission range, nodes in the network form multiple paths for data collection to the sink node. Therefore, the volume of data received and sent by the near‐sink nodes is reduced, the energy consumption of the near‐sink nodes is reduced, and the network lifetime is increased as well. (2) According to the analysis in this paper, compared with the CTPR scheme, the ATRTC scheme reduces the maximum energy consumption by 9%, increases the network lifetime by 10%, increases the data collection reliability by 7.3%, and reduces the network data collection time by 23%.

In real network environment, nodes may acquire the communication destination during data transmission and find a suitable neighbor node to perform effective data classification transmission. This is similar to finding certain transmission targets during data transmission with mobile devices. However, the node cache space in networks is limited, and waiting for the destination node can also cause end‐to‐end delay. To improve the transmission environment, this study established Data Transmission Probability and Cache Management method. According to selection of high meeting probability node, cache space is reconstructed by node. It is good for nodes to improve delivery ratio and reduce delay. Through experiments and the comparison of opportunistic network algorithms, this method improves the cache utilization rate of nodes, reduces data transmission delay, and improves the overall network efficiency.

Energy efficient dense wireless sensor network design makes extensive use of spatial redundancy and appropriate MAC mechanism. CSMA is integrated with TDMA while filtering out the spatial correlation with a so-called HS-Sift MAC protocol. The entire sensing region is divided into three sub-areas for different channel access methods. The nodes near to the border sleep most of the time, nodes close to the event claim high channel utilisation and those lie in between compete under different priorities. A software simulation verifies effectiveness of the proposed scheme for energy consumption and access delays.