Performance collapse is an intractable issue of Differentiable Architecture Search (DAS), where severe performance degradation of DAS happens when it trains on different search spaces or datasets. We theoretically analyze the issue from the information bottleneck (IB) perspective, and disclose that a solution to overcome this problem is to seek the bifurcation point of IB tradeoff between compression and prediction of the supernet. To this end, we propose a simple yet highly effective method, namely, Batch Entropy-decay Regularization (BER), to guide the learning of DAS, which restricts compression in DAS by imposing a penalty on the architecture parameters. Comprehensive theoretical analyses demonstrate that BER is able to completely resolve DAS's performance collapse issue. Compared with a number of state-of-the-art DAS variants, BER shows its overwhelmingly better performance on 7 search spaces (i.e., NAS-Bench-201, DARTS, S1-S4, MobileNet-like) and 5 popular datasets (i.e., CIFAR-10, CIFAR-100, ImageNet1k, PASCAL VOC 2007, and MS COCO 2017).

ABSTRACT Representation learning has become a cornerstone of artificial intelligence, designed to automatically extract low‐dimensional, meaningful features from high‐dimensional, sparse raw data. By drastically reducing the reliance on manual feature engineering, representation learning enhances model performance across a wide range of tasks. The field has evolved significantly over the past decades, transitioning from early linear methods, such as Principal Component Analysis (PCA), to modern deep learning paradigms powered by neural networks, generative adversarial networks (GANs), and pre‐trained models. Although the rapid development of representation learning has significantly promoted the progress of natural language processing (NLP), computer vision, and recommender systems, the general practitioners still have a poor understanding of its historical background, core principles, and wide range of applications. To some extent, this limits the full development of its potential. To this end, this survey aims to provide a comprehensive and easily understandable overview for a wider audience. This survey conducts a systematic literature review to tease out the evolution of representation learning and analyse its core drivers. At the same time, this survey deeply explains the basic principles of representation learning, and introduces its practical application cases in various fields. This survey also points out the main limitations of current models and prospects the future research directions.

IIoT sensors collect large volumes of vibration time-series data at high sampling rates. These data are nonstationary and multi-scale and are essential for condition monitoring across different devices and operating conditions. Deep compression sensing reduces data volume while preserving critical information, enabling efficient and low-cost data processing in IIoT systems. However, existing methods for industrial time-series data processing struggle to preserve features effectively under stringent bandwidth and storage constraints. Moreover, most deep compression sensing models overlook computational limitations and robustness demands in noisy industrial settings. Therefore, we propose an Attention-Based Deep Compressed Sensing with Multi-Loss Optimization for IIoT Vibration Data (DCS-AMTD). The model achieves efficient compression and high-quality reconstruction while improving both resource efficiency and noise robustness. Specifically, we design a dual-path convolutional module that incorporates dilated convolutions to capture multi-scale local and global features. We design a one-dimensional convolutional block attention module (CBAM1D) for industrial vibration signals to dynamically reweight multi-scale features and enhance discriminative representations. Furthermore, we design a joint time-frequency loss with multi-domain constraints to improve reconstruction quality under strict bandwidth and computational constraints. Experiments on the Case Western Reserve University (CWRU) and Paderborn (PB) datasets demonstrate that our method achieves superior reconstruction performance across various compression ratios, outperforming existing approaches.

To Date, the application of digital twin (DT) in the industrial internet of things (IIoT) has been continuously promoted and deepened, and has become the focus of the industry. IIoT serves as the foundational infrastructure that enables pervasive connectivity, real-time data acquisition, and intelligent control within industrial environments. DTs provide enterprises with an empathetic, virtual environment that enables them to manage and operate their production facilities in a more efficient and intelligent manner. However, there is not a special summary and analysis of the combinability and combination mode of the two. Therefore, this paper firstly sorted out the professional definitions, characteristics and frameworks of IIoT and DT, and deeply analyzed the semantic context of data flow. Secondly, this paper discusses the combinability and combination mode of IIoT and DT, and summarizes the enabling technologies and tools at each layers. Finally, the applications status of DT empowered IIoT in different fields was summarized, and the challenges of the combined application of the two were analyzed.

The proliferation of Unmanned Aerial Vehicles (UAVs) in 6G-enabled edge computing presents a dual challenge of ensuring secure data transmission and performing accurate object detection on resource-constrained platforms. This paper proposes a Synergistic Encryption and Detection framework (SED-UAV) that integrates a lightweight chaotic image encryption algorithm, LB-ICA, and an enhanced small object detector, YOLO-LFP. The LB-ICA algorithm leverages an improved chaotic map and a plaintext-aware key mechanism using SHA-256. It achieves a key space of over 2100 and an average encryption speed of 20.3 ms per block, providing robust security against differential and chosen-plaintext attacks with high efficiency suitable for UAVs. For detection, the YOLO-LFP model enhances the YOLOv8 baseline with a hybrid attention module, an adaptive feature fusion strategy, and a dedicated small object head. Experimental results on the VisDrone2019 benchmark show YOLO-LFP achieves a state-of-the-art performance of 44.7% [email protected], significantly outperforming existing models. This work provides a comprehensive solution for deploying secure, real-time computer vision applications on UAV platforms.

Semantic segmentation of high-resolution remote sensing imagery plays a critical role in applications such as disaster assessment. However, deploying large models on Autonomous Aerial Vehicles (AAVs) remains challenging due to inherent conflicts among accuracy, model size, and computational efficiency. To address these challenges, we propose FMC-ULite, a novel lightweight architecture designed to achieve a better balance between accuracy and efficiency for real-time processing. Our model incorporates four key innovations, including a Fast Fourier Transform (FFT)-based fusion module for enhanced edge feature extraction and noise suppression in the frequency domain, a simplified MobileNetV3-Large encoder that substantially reduces parameter count, a cross-layer feature fusion (CLFF) module to effectively integrate multi-scale semantic and detail information, and an attention-gated decoder with multi-scale dilated convolutions to prioritize critical disaster regions. Furthermore, an adaptive combined loss function is introduced to alleviate class imbalance. Experiments conducted on the RescueNet dataset show that our model achieves competitive accuracy compared to advanced lightweight methods under a comparable parameter budget, demonstrating its strong suitability for real-time disaster assessment using AAVs.

MicroRNAs (miRNAs) are small non-coding RNAs orchestrating regulatory networks through sequence-specific target recognition. Understanding miRNA-disease correlations is crucial as high-throughput sequencing data growth outpaces experimental validation, necessitating computational approaches for association discovery. Existing frameworks model miRNA-disease interactions as uniform binary relationships, overlooking semantic diversity in different association mechanisms. We propose BKAMDA (MiRNA-Disease Associations prediction Based on Knowledge-Awareness), a novel knowledge-aware model for predicting miRNA-disease associations. Unlike existing methods learning only from miRNA-disease networks, BKAMDA leverages knowledge graphs to delineate distinct association types. By simulating informational propagation within knowledge graphs across diverse miRNA-disease relationships, the model investigates latent connections across various relationship types. Comparative analysis with competitive baselines using real-world experimentally validated datasets demonstrates excellent performance across multiple metrics. Three disease case studies further confirm model accuracy and effectiveness for precision medicine applications. Our knowledge-aware approach significantly advances miRNA-disease association prediction by capturing semantic diversity in biological interactions.

Nowadays, the development of Collaborative Edge Computing (CEC) has greatly accelerated deep neural network (DNN) inference by enabling edge service providers (ESPs) in the JointCloud federation to deploy computational resources and well-trained DNN models at the edge of the network for collaborative inference with mobile devices (MDs), thereby promoting the rapid advancement of intelligent applications. This raises the need for an effective DNN inference offloading mechanism between MDs and ESPs. However, existing schemes often lack market efficiency and fail to ensure desirable economic properties. To this end, we propose a truthful double auction mechanism for delay-aware DNN inference offloading (TDAD), which integrates a dynamic programming approach with an adaptive resource allocation and pricing strategy to maximize social welfare while ensuring truthfulness, budget balance, and individual rationality. Specifically, TDAD first employs a binary search-based delay-aware partitioning and offloading method to determine the minimum feasible resource profile for each MD, and then applies a dynamic programming-based double auction to match MDs' inference demands with ESPs' combinatorial resources, and compute the corresponding payments and rewards. Theoretical analysis proves that TDAD satisfies the desired economic properties, while experiments in realistic CEC environments validate its effectiveness and efficiency.

The structural balance problem in signed social networks targets at detecting the unbalanced edges and minimizing the cost to make a network balanced. Current studies mainly focus on the cost difference between changing positive and negative edges, ignoring the influence from nodes’ neighbors on the cost to change unbalanced edges. In this article, we propose a novel structural balance model by considering the influences of the edge weights and relationships among nodes’ neighbors on cost of structural balance in networks. To optimize the proposed model, this article combines label propagation algorithm with reinforcement learning. First, a label propagation algorithm for signed social networks is designed by separately considering the labels of positive neighbors and negative neighbors, since the existing label propagation algorithms are mostly used for unsigned social networks. Then, reinforcement learning performs the initialization operation based on the result of the proposed label propagation algorithm. Consequently, the proposed algorithm can not only automatically select the number of clusters for different networks, but also run from a good initial policy and improve the performance. Extensive experiments on five networks demonstrate the performance of our method in terms of convergence, stability and efficiency.

This study addresses the bipartite output consensus problem of heterogeneous uncertain multiagent systems (MASs) under hybrid event-triggered control mechanism. Initially, a fully distributed adaptive bipartite compensator is composed, which consists of time-varying coupling weights and hybrid event-triggered mechanism to estimate the state of the leader. The hybrid event-triggered mechanism includes the event-triggered mechanism for the leader and the edge-event triggered mechanism for all edges to reduce the information transmission among agents. Then, a novel distributed output feedback controller is put forward for uncertain system dynamics. With the aid of the proposed controller, the bipartite output consensus problem of heterogeneous uncertain MASs can be resolved. Furthermore, the above results can be extended to the case of switching topology. Lastly, the validity of the theoretical findings is confirmed through four simulation examples.

Electroencephalography (EEG) emotion recognition plays a key role in improving human-machine interactions. Advanced algorithms have been proposed for this task. However, two challenges remain, i.e., unclear decision boundary in the embedded space and noise in physiological signals from various devices. To this end, we develop a novel framework, namely, UACL-Net, for EEG emotion recognition. It is based on uncertainty-aware contrastive learning (UACL) and frequency-aware self-attention (FASA). Specifically, UACL uses a multivariate Gaussian distribution to construct the latent space for different emotions. It is able to highlight interclass differences, thereby improving the robustness of model decisions. In addition, FASA generates learnable weights by applying self-attention (SA) to the real and imaginary components in the frequency domain. This helps adaptively reduce noise and capture global dependencies in temporal sequences. Our model is trained and tested on four benchmark datasets, achieving up to 94.88%, 98.71%, 96.91%, and 99.29% accuracy on SEED, DEAP, DREAMER, and FACED, respectively. Experimental results demonstrate that it is effective and has advantages over peer state-of-the-art (SOTA) methods.

Supported by space-air-ground integrated networks (SAGIN), Internet of Remote Things (IoRT) is regarded as a cornerstone for realizing global connectivity in 6 G networks. The integration of high-altitude platforms (HAPs) and unmanned aerial vehicles (UAVs), offering both wide coverage and agile data access, becomes a promising paradigm for IoRT data collection. However, sustaining reliable and efficient transmission is challenged by the mobility and constrained onboard energy of HAPs and UAVs, as well as atmospheric fading effects. To address these issues, we propose a green and efficient HAP-UAV collaborative design for IoRT data collection, which jointly considers both transmission performance and energy consumption. Firstly, we introduce a novel metric, Overall Energy Efficiency (OEE), to quantify the balance between cooperative transmission performance and the total energy cost under dynamic trajectory planning. Secondly, we formulate a joint optimization problem that simultaneously optimizes UAV/HAP trajectories, UAV power control, HAP selection, and bandwidth allocation. Thirdly, to address the formulated non-convex fractional problem, we develop an energy efficiency maximization strategy based on the successive convex approximation technique. Extensive simulation results demonstrate that the proposed strategy achieves significant gains in OEE, achieving superior trade-offs between energy consumption and transmission performance in HAP-UAV-assisted IoRT networks.

As a ubiquitous portable device, mobile phones play an important role in large-scale data collection and remote health detection. Parkinson's disease (PD), a typical movement disorder, can be detected by capturing digital biomarkers using mobile phone sensors. Nevertheless, it is difficult to obtain reliable label information in large-scale remote data collection, especially for time-series digital biomarkers. Based on this, we develop a novel multi-dimensional self-supervised contrastive learning framework for remote detection of early PD by mobile phone time-series digital biomarkers. Specifically, depending on two different augmentation views, the proposed framework considers temporal contrasting, spatial contrasting, contextual contrasting, and inter-modal contrasting to enable the model to learn more discriminative features. For temporal and spatial contrasting, certain time steps (channels) of one view are used to predict the next time steps (channels) of the other view, thereby constructing a cross-view prediction task. Meanwhile, contextual contrasting is introduced into the contrast framework to consider temporal prediction and spatial prediction context representation, respectively, further increasing similarity between positive pairs and decreasing it between negative pairs. In addition, inter-modal contrasting is used to force the model to capture the potential relationship between different modal data. Experimental results show that fine-tuning with only 10% of the labeled data can outperform supervised learning and generally surpass state-of the-art algorithms.

We consider the energy-constrained task allocation problem in large-scale Aerial Edge Computing (AEC) systems, which encompasses a series of tightly coupled decision-making processes, includingwhichtasks need to be processed by unmanned aerial vehicles (UAVs),howto allocate these tasks and balance energy across UAVs for delay-sensitive requirements. However, little attention has been devoted to exploring the above coupled decision-making problem in AEC with various resource and energy constraints, which is further complicated by energy dynamics (UAV battery states), task-specific consumption, and allocation-feedback balance. In this paper, we formulate a multi-dimensional joint optimization problem, simultaneously optimizing task allocation and energy rewarding to maximize long-term system rewards while balancing service quality and energy efficiency. To this end, we propose a green aerial edge computing framework where partial UAVs are equipped with energy harvesting modules to collect ambient energy. To circumvent the intractable computational complexity arising from the coupled energy states of massive UAVs, we design a distributed solution method based on the mean field game, which decouples the dense multi-agent interactions into a game between an individual UAV and the aggregate population state, thereby transforming the complex global optimization problem into a set of equivalent scalable subproblems. We develop an optimal energy valuation scheme to guide UAV behavior. Numerical results show that our mechanism can effectively ensure sustainable system operation while maintaining high quality of service for metaverse users, outperforming existing methods in both system sustainability and service responsiveness.

As mobile applications become increasingly computation-intensive, mobile devices (MDs) face growing limitations due to their constrained computational capabilities and battery life. Collaborative Edge Computing (CEC) has emerged as a promising solution to address these challenges by enabling multiple edge service providers (ESPs) to offer computation offloading services to MDs. As such, a CEC resource trading market is essential for efficient interactions between MDs and ESPs. However, jointly determining the offloading ratios, allocating combinatorial computation and communication resources, and designing appropriate pricing strategies in a dynamic market remains a significant challenge. To this end, we propose a truthful online double auction based resource allocation mechanism for partial computation offloading (TRAPO) that explicitly accounts for the stochastic nature of both MDs and ESPs. TRAPO first leverages spatial diversity to construct a set of bids for each MD by mapping their task requirements into resource demands through considering MDs' preferences and partial offloading. Next, we match resource-demanding MDs with resource-supplying ESPs based on adaptive valid price thresholds to maximize social welfare, and calculate the payments of MDs and the rewards of ESPs. Theoretical analyses demonstrate that TRAPO satisfies truthfulness, budget balance, individual rationality, and computational tractability. Simulation experiments further verify the effectiveness and efficiency of TRAPO.

Network Function Virtualization (NFV) facilitates on-demand and flexible service provisioning to meet the escalating demands of Internet of Things (IoT) applications, enabled by Service Function Chain (SFC) technique. The widespread deployment of 5G has connected a massive number of devices and users to IoT networks, accelerating the expansion of IoT scales. IoT users’ service requirements exhibit heightened diversity and dynamism. Consequently, the SFC placement problem in Next Generation Multi-domain IoT (NGMIoT) networks has garnered significant attention. How to efficiently place SFCs under uncertain resource demands to adapt to evolving service request dynamics poses substantial challenges. Therefore, this paper investigates the Robust SFC Placement (RSFCP) problem in NGMIoT networks under resource demand uncertainty. Specifically, we formulate the RSFCP problem as an integer linear programming model to minimize overall SFC placement cost while ensuring service quality. We further prove the RSFCP problem is NP-hard and propose a greedy strategy based heuristic SFC placement algorithm to solve it. Finally, extensive simulation experiments are conducted to evaluate performance, demonstrating that the proposed algorithm outperforms benchmark mechanisms in terms of service acceptance rate and placement cost.

Large language models (LLMs) are revolutionizing various fields due to their powerful generation capabilities. However, their immense computational complexity poses significant challenges in resource consumption, inference latency, and data privacy for traditional cloud-centric deployments. Edge artificial intelligence (Edge-AI) offers promising LLMs deployment solutions by leveraging distributed resources at the network edge. However, existing approaches struggle to adapt to dynamic workloads and efficiently utilize heterogeneous resources in Mobile Edge Computing (MEC) environments. This paper proposes aDynamicBatching andAdaptivePartitioning (DyBAP) scheme for LLMs deployment, which utilizes ubiquitous geo-distributed resources via end-edge-cloud collaboration. Firstly, we formulate a collaboration deployment optimization problem to minimize inference latency and resource usage under heterogeneous resource and user requirements for latency and accuracy constraints, which is NP-hard. Secondly, to solve this, we develop a dynamic batch fusion optimization algorithm that optimizes the batch size of inference by utilizing the parallel processing power of computing units to balance the latency and resource usage. A block-aware partition optimization algorithm based on multi-agent reinforcement learning (MARL) is proposed for efficient transformer block allocation, integrating mobility awareness for optimal partitioning across dynamic network environments. Simulation results demonstrate the superiority of DyBAP over other benchmarks, reducing inference latency by 17.94% and saving 11.12% in memory resource consumption compared to the end-edge-cloud collaboration approaches.

Rumor blocking approaches in social networks aim to identify a small set of counter-rumor seed nodes and compete with rumor cascades to quickly stop the propagation of rumors. However, current rumor blocking methods assume complete knowledge of rumor node positions, which is often unattainable in real-world scenarios. In this paper, we introduce the concept of Uncertainty Rumor Blocking, where we address the uncertainty surrounding rumor node locations by considering a set of suspicious nodes, each associated with a probability indicating the likelihood of rumor propagation. As traditional node selection algorithms become inadequate under uncertain conditions, we propose a Graph Neural Network-based Inverse Reinforcement Learning (G-IRL) approach to effectively select counter-rumor seed nodes. Through comprehensive experimentation on three datasets, we demonstrate the consistent superiority of our G-IRL over state-of-the-art baseline methods for node selection in the context of uncertainty rumor containment.

Identifying the source of an epidemic involves inferring its origin based on observations of the infected population, where timely isolation of the source is critical to mitigating outbreak spread. Existing approaches predominantly assume static network topologies, yet real-world networks exhibit dynamic changes in nodes and edges over time, significantly challenging conventional source detection methods designed for static networks. To address this limitation, we propose a dynamic network source detection framework based on aMulti-observationSourceFusionSpatio-TemporalGraphConvolutionalNetwork (MSF-STGCN). Our method first partitions epidemic data into multi-frame static graphs, distinguishing between observed and unobserved node states relative to the target detection time. Next, we leverage graph neural networks to extract spatio-temporal features from multi-snapshot sequence data. Finally, we introduce a dynamic message-passing algorithm to process unobserved network components and integrate the results with multi-snapshot spatio-temporal features for accurate source identification. Extensive experiments on synthetic networks and real-world datasets demonstrate the superior performance of our approach compared to state-of-the-art baselines.

Software-Defined Networks (SDN) bring unprecedented agility and programmability to traditional networks by decoupling the control plane and data plane. However, this separation enables adversaries to manipulate data plane forwarding behaviors or modify packet payloads, thereby violating the network security policies set by the control plane and leading to information leakage, network congestion, or even network collapse. In this article, we propose an Enhanced Lightweight Path Validation Scheme (EL-PVS) for the SDN environment. Firstly, we propose a packet forwarding path validation scheme that verifies the paths traversed by packets, alongside a theoretical analysis of this validation process. Then, we extend the scheme with a network flow-level path validation to improve the validation efficiency, and present a storage optimization method to reduce the storage overhead in the validation process. To support large-scale deployment, we design a path partition scheme and present a Greedy-based KeySwitch Node Selection Algorithm (GKSS) to pinpoint optimal switches for path partition, significantly reducing overall data plane storage usage and the total number of paths requiring validation. In addition, we extend our path validation scheme to detect packet payload modification, where a multi-phase packet modification detection approach is designed, and then the detection results are integrated with path validation information to minimize switch-to-controller bandwidth usage. Finally, we present an anomaly switch identification technique to identify abnormal switches when the controller encounters validation failure. The evaluation results verify that EL-PVS enables flow-level path validation and packet modification detection with small validation header, minimizing processing delay and switch storage overhead.

The rise of edge intelligence is driving distributed machine learning toward a new paradigm of edge-collaborative computing. To overcome the severe communication bottleneck in this paradigm, In-Network Aggregation is a critical enabling technology. However, its effectiveness is fundamentally undermined by the profound resource heterogeneity of edge networks. Specifically, edge devices, adapting to hardware constraints, operate at varying numerical precisions, leading to significant data inflation as gradients are aggregated. Compounding this, unevenly distributed network resources and traditional, precision-oblivious routing strategies often misallocate critical, high-precision gradients to low-quality paths. This mismatch creates severe network congestion, crippling the efficiency of distributed training. To address this, we propose the Precision-Aware Hierarchical In-Network Aggregation (PAHInA) framework, the first, to our knowledge, to perform routing optimization for in-network aggregation that explicitly considers precision heterogeneity. The core of PAHInA is an intelligent control-plane scheduler that co-optimizes for gradient priority and path cost, dynamically planning the most cost-effective aggregation strategy for each flow. This fine-grained scheduling guarantees that high-priority gradients are routed through premium, low-latency paths, minimizing global communication overhead. On the data plane, we leverage the eXpress Data Path (XDP) for high-performance packet processing to reduce aggregation-induced overhead. Extensive simulations show that, compared to state-of-the-art baselines, PAHInA significantly mitigates network congestion, reducing end-to-end communication time by up to 33% and boosting overall training throughput by approximately 30%.

In cloud-edge-end (CEE) collaboration, the resource optimization based on deep reinforcement learning have achieved significant performance improvements in time-slot systems. However, some studies only focus on computing delay and energy consumption in each time slot, ignoring the impact of task backlog queues on system performance. In addition, the delay-oriented optimization tends to offload a large number of tasks to servers, failing to fully utilize the computing resource of mobile devices. To address these issues, we propose the multi-dimensional preference-driven resource optimization framework (MPROF). This study includes several key points: 1) constructing a three-layer heterogeneous architecture that applying the collaboration among edges for CEE; 2) proposing the task backlog estimation mechanism, which mitigates the impact of previous unfinished tasks on the current time slot; 3) proposing the group relative direct-preference policy optimization (GRDPO) that incorporates the preference information for efficient task offloading, and combines it with mathematical programming for the system resource optimization. The simulation experiments are conducted across multiple typical scenarios. The results show that, the proposed framework outperforms existing mainstream methods in task offloading, system delay, task backlog, and energy consumption control, demonstrating certain practical application prospects.

In cloud-edge collaborative networking, intelligent devices often need to offload tasks that they cannot handle to edge or cloud servers for processing. So the key problem is how to deploy various types of services and offload tasks to the appropriate servers. Deep Reinforcement Learning (DRL) algorithms have been widely used to address these issues. However, existing DRL solutions typically use centralized training methods, which can not address the challenge of obtaining global network states in practical scenarios due to the extremely large network scale and privacy concerns. In this paper, we designed a Knowledge-Defined Cloud-Edge Collaborative Networking (KDCECN) architecture for managing network information and proposed a Partially Observed Lightweight exchange Hierarchical DRL algorithm (PO-LeHDRL). This algorithm fully considers factors such as the tolerable delay of tasks, the size of tasks, and different service deployment strategies, which solves the joint service placement and task offloading problems in the cloud-edge collaborative network in a distributed training and decision-making manner. The innovation of this scheme lies in achieving data privacy protection. In this scheme, edge nodes do not need to share their respective network measurement information and apply Laplace noise to the reward value using the differential privacy mechanism, effectively reducing additional communication overhead and protecting the privacy of network data on edge devices. The experimental results show that compared with the baselines, our scheme can improve the system's task completion rate and reduce the task completion delay, and it shows scalability across different network environments.

Generative AI (GenAI) has become a research hotspot for the task of content creation and production, which suffers from the issue of high latency due to cloud transmission. One effective solution is to integrate serverless computing with mobile edge computing (MEC) to build a communication-efficient GenAI system, where serverless functions are executed via containers on edge servers. However, the nonnegligible latency of container deployment and cold starts degrades the quality of GenAI service. This issue becomes even more serious in dynamic MEC with mobile and uncertain users. In this paper, we study the provisioning of latency-sensitive query services in GenAI-enabled serverless MEC through dynamic utility maximization. While GenAI of users deployed in cloud refers to as primary GenAI, we deploy their GenAI replicas based on serverless functions in edge servers to maximize user service satisfaction (i.e., utility function). We first formulate a joint decision problem, i.e.,GenAIReplicaAllocation andPlacement (GRAP) problem, under various resource constraints. For this problem, we propose an approximation solver with a provable approximation ratio. Then, we consider an dynamic GRAP problem with uncertain values of users and stochastic request arrivals, and devise a performance-guaranteed online algorithm for a special case of the problem by assuming only a small subset of edge servers suffers significant utility degradation. Finally, we conduct theoretical analysis and experimentation to validate the effectiveness of the proposed mechanisms. Experimental results demonstrate that the proposed mechanisms consistently outperform baseline methods in both service latency and user satisfaction.

Traditional computation offloading and resource allocation strategies encounter several issues that lead to poor service experience and resource wastage. The resource allocation scheme lacks the flexibility to adapt to the time-varying offloading demands of User Equipment (UEs). Furthermore, there is an imbalance between UEs seeking better service and Service Providers (SPs) aiming to minimize cost expenditures. In this paper, we propose a knowledge-defined networking-based Multi-Layer Computation Offloading and Resource Allocation strategy optimization (ML-CORA) architecture. Based on the ML-CORA, we design a Multi-Layer Local-Global Collaborative computation offloading and resource allocation strategy optimization (ML2GC) algorithm. The basic level of the ML2GC algorithm expresses and optimizes computation offloading demands from the perspective of UE (local), while the meta level optimizes the resource allocation strategy on demand from the perspective of the SP (global), achieving a collaborative multi-objective optimization for a win-win system between UEs and SPs. The two-layer structure of the ML2GC algorithm outputs continuous and discrete actions respectively, which improves the flexibility and efficiency of the algorithm while effectively balancing the interests of all parties and promoting efficient resource utilization. Simulation results based on the real-world dataset of Shanghai Telecom indicate that the ML2GC algorithm significantly improves both social welfare and resource utilization compared to baseline algorithms.

Infectious diseases have historically had profound effects on global health, economies, and social structures. Effective tracing of infectious diseases is essential not only for immediate public health responses but also for shaping future prevention strategies. Traditional tracing methods often emphasize homogeneous networks, overlooking the diverse transmission characteristics of heterogeneous populations. This research addresses two critical challenges: the heterogeneity of transmission across various media and modes, and the significant yet underexplored influence of community structures on epidemic spread and tracing.We propose a Heterogeneous Hypergraph Attention Network (HHAN) modelthat accounts for multiple transmission pathways and patterns within heterogeneous networks. HHAN integrates a heterogeneous graph neural network module to handle the complexity of communication among different populations, and an Agent-Based Modeling Module that combines agent-based ideas to model individual behaviors. This approach effectively captures complex interactions within community structures and addresses individual variability. Experimental results on three real-world datasets demonstrate that the HHAN model significantly outperforms other state-of-the-art methods in tackling the complex challenge of tracing infectious diseases in heterogeneous populations.

Mixed Precision Quantization (MPQ) has become an essential technique for optimizing neural network by determining the optimal bitwidth per layer. Existing MPQ methods, however, face a major hurdle: they require a computationally expensive search for quantization strategies on large-scale datasets. To resolve this issue, we introduce a novel approach that first searches for quantization strategies on small datasets and then generalizes them to large-scale datasets. This approach simplifies the process, eliminating the need for large-scale quantization fine-tuning and only necessitating model weight adjustment. Our method is characterized by three key techniques: sharpness-aware minimization for enhanced quantized model generalization, implicit gradient direction alignment to handle gradient conflicts among different optimization objectives, and an adaptive perturbation radius to accelerate optimization. It offers advantages such as no intricate computation of feature maps and high search efficiency. Both theoretical analysis and experimental results validate our approach. Using the CIFAR10 dataset (just 0.5\% the size of ImageNet training data) for MPQ policy search, we achieved equivalent accuracy on ImageNet with a significantly lower computational cost, while improving efficiency by up to 150\% over the baselines.

The advancement of Low Earth Orbit (LEO) satellite technology has enabled rapid progress in on-orbit machine learning. However, limited on-board computational resources hinder large-scale model training on individual satellites. Furthermore, the highly dynamic network topology and resource heterogeneity of LEO satellite constellations make collaborative training prone to single-point failures and privacy risks. To address these issues, this paper proposes ConSFL, a lightweight Contrastive learning-driven Split Federated Learning framework. ConSFL enables local feature extraction from unlabeled remote sensing data under resource-constrained conditions, while preserving both model completeness and data privacy. By performing federated learning across heterogeneous submodels, the training of the global model can focus on learning features within specialized semantic dimensions, thereby enhancing overall performance. Additionally, we introduce a spatial attention pooling (SAP) method into ConSFL to aggregate intermediate features with larger feature map sizes from submodel outputs. Simulation results show that ConSFL achieves higher Top-1 accuracy across submodels compared to the best baseline, while SAP enhances ConSFL's ability to capture feature-space information and improves submodel performance under earlyexit mechanisms.

Blockchain is increasingly integrated in Multiaccess Edge Computing (MEC) to coordinate secure and lowlatency resource provisioning and service orchestration among resource-constrained embodied AI devices. However, conventional blockchains perform costly transaction verification during propagation, which can be exploited by spam transaction attacks and overload resource-limited edge devices. To mitigate the substantial overhead of verification, we propose a two-stage BLS multi-signature backed transaction propagation mechanism for blockchain-enabled MEC: a small-scope random-walk phase with deep verification and signing, followed by a large-scope propagation phase with probabilistic verification. In the first stage, nodes conduct deep verification and sign valid transactions using the BLS multi-signature, then forward the signed transaction to a small and randomly sampled subset of neighbors to rapidly accumulate valid signatures. In the second stage, transactions whose aggregated signature count exceeds a threshold will be broadcast throughout the entire blockchain network and undergo deep verification with a specific probability, relieving edge nodes' verification burden. Moreover, verifiers record signers associated with failed deep verifications. Signers whose failures exceed a system threshold are quarantined to restrain the spread of spam transactions. Experimental results demonstrate that the proposed mechanism reduces energy consumption by at least 60% and 18.6% compared with the original and benchmark mechanisms respectively, while maintaining nearly identical transmission performance and ensuring that the proportion of invalid transactions propagated to honest nodes does not exceed 14%.

Since its inception, blockchain technology has been characterized by its core attributes of immutability, traceability, and decentralization, which are fundamental to ensuring data security. In the contemporary digital landscape, medical data has emerged as a critical asset, and the integration of blockchain into healthcare has facilitated a range of innovative solutions for secure and efficient data sharing. Beyond its role in data security, blockchain’s smart contracts have attracted significant research interest due to their potential to automate processes and enhance efficiency in medical research and healthcare operations. In this context, this survey provides a systematic and in-depth exploration of blockchain applications in healthcare, with a focus on: (1) analyzing the technical foundations of blockchain and its suitability for healthcare applications; (2) synthesizing the eight key domains where blockchain has demonstrated impact in the healthcare sector; and (3) critically examining the challenges that hinder blockchain adoption in healthcare while identifying future research directions. By presenting a comprehensive review of blockchains transformative potential in healthcare, this survey offers valuable insights for researchers and practitioners engaged in this evolving interdisciplinary field.

Connected and autonomous vehicles (CAVs) are an important paradigm of intelligent transportation systems. Cooperative perception (CP) and vehicular edge computing (VEC) enhance CAVs’ perception capacity of the region of interest (RoI) while alleviating the pressure of intensive computation on onboard resources. However, existing CP and computation schemes are based on inefficient broadcast communications and still face challenges, such as highly dynamic communication link channel conditions caused by vehicle mobility, and limited computing resources in VEC environments. Considering the delay sensitivity of CAVs’ perception tasks and the need for enhanced perception, we propose a unicast-based cooperative perception and computation scheme to achieve more efficient resource utilization and perception task execution in VEC scenarios. Our goal is to maximize CP gain and minimize task execution delay by optimizing the decision of each ego CAVs. To solve the sequential decision-making problem of multiobjective optimization, we propose a solution based on improved multiagent proximal policy optimization deep reinforcement learning, where CAVs agents make adaptive decisions distributed based on partial observations. Simulation results show that compared with the baseline algorithm, our proposed scheme effectively reduces the execution delay of ego CAVs perception tasks and ensures a high perception gain.

With the recent advancement in network technologies, the vehicle based medical networks extend medical services to mobile vehicles, thereby offering flexible and efficient healthcare services for vehicle users in need. The integration of vehicle based medical network and edge computing enables computation intensive medical service tasks to be offloaded on edge servers, to provide fast service response for vehicle users. An efficient task offloading and resource allocation strategy is critical for Vehicle based Medical Edge Computing System (VMECS) to satisfy real-time and reliability requirements while ensuring service performance. To this end, in this paper, we investigate the problem of task computation allocation in VMECS networks. By introducing deep reinforcement learning, we first present a novel VMECS architecture to automatically achieve the optimal task offloading and resource allocation through the multi-agent collaboration, thereby improving service performance. Then, we formulate the problem of task offloading and resource allocation in VMECS networks as an optimization model with the aim of maximizing task success rate by jointly considering communication interferences, resource allocation and delay requirements. To solve it, we further devise a Distributed distributional deterministic policy gradients based Task offloading and Resource allocation (DTR) algorithm. Final simulation results demonstrate that compared with benchmark algorithms, DTR algorithm can obtain higher task success rate, smaller service time, and less task processing time.

In post-disaster emergency medical rescue operations, rapidly establishing an adaptive and flexible edge computing (EC) network, balancing data offloading with energy consumption, and ensuring the stable operation of the network have become urgent priorities. To address these challenges, we proposed an unmanned aerial vehicle (UAV)-assisted microservice mobile edge computing (MEC) architecture. The architecture can be rapidly deployed to provide temporary network coverage and EC services in disaster-stricken areas. A transformer-based resource management (TBRM) approach is utilized to optimize data offloading efficiency and reduce energy consumption, thereby maximizing the service time of the architecture. To enhance the security and reliability of the architecture, four microservices are designed to manage the full UAV lifecycle, and UAV identity authentication is implemented through dual digital signature certificates. Large-scale simulation experiments have demonstrated the effectiveness of the architecture in complex rescue scenarios, providing strong technical support for postdisaster medical rescue efforts.

Blockchain enhances trust and collaboration among entities through its inherent features of transparency, immutability, and traceability, leading to its extensive integration into Multi-access Edge Computing (MEC). However, existing transaction propagation mechanisms require MEC devices to consume significant computing resources for complex transaction verification, increasing their vulnerability to malicious attacks. Adversaries can exploit this by flooding the blockchain network with spam transactions, aiming to deplete device energy and disrupt system performance. To cope with these issues, this paper proposes a reputation-based energy-efficient transaction propagation mechanism that alleviates spam transaction attacks while reducing computing resources and energy consumption. Firstly, we design a subjective logic-based reputation scheme that assesses node trust by integrating local and recommended opinions and incorporates opinion acceptance to counteract false evidence. Then, we optimize the transaction verification method by adjusting transaction discard and verification probabilities based on the proposed reputation scheme to curb the propagation of spam transactions and reduce verification consumption. Finally, we enhance the transaction transmission strategy by prioritizing nodes with higher reputations, enhancing both resilience to spam transactions and transmission reliability. A series of simulations demonstrate the effectiveness of the proposed mechanism.

One-shot neural architecture search (NAS) trains an over-parameterized network (termed as supernet) that assembles all the architectures as its subnets by using weight sharing for computational budget reduction. However, there is an issue of multi-model forgetting during supernet training that some weights of the previously well-trained architecture will be overwritten by that of the newly sampled architecture which has overlapped structures with the old one. To overcome the issue, we propose an orthogonal gradient learning (OGL) guided supernet training paradigm, where the novelty lies in the fact that the weights of the overlapped structures of current architecture are updated in the orthogonal direction to the gradient space of these overlapped structures of all previously trained architectures. Moreover, a new approach of calculating the projection is designed to effectively find the base vectors of the gradient space to acquire the orthogonal direction. We have theoretically and experimentally proved the effectiveness of the proposed paradigm in overcoming the multi-model forgetting. Besides, we apply the proposed paradigm to two one-shot NAS baselines, and experimental results demonstrate that our approach is able to mitigate the multi-model forgetting and enhance the predictive ability of the supernet with remarkable efficiency on popular test datasets.

In large-scale dynamic network environments, optimizing the computation offloading and resource allocation strategy is key to improving resource utilization and meeting the diverse demands of User Equipment (UE). However, traditional strategies for providing personalized computing services face several challenges: dynamic changes in the environment and UE demands, along with the inefficiency and high costs of real-time data collection; the unpredictability of resource status leads to an inability to ensure long-term UE satisfaction. To address these challenges, we propose a Knowledge-Defined Networking (KDN)-based Adaptive Edge Resource Allocation Optimization (KARO) architecture, facilitating real-time data collection and analysis of environmental conditions. Additionally, we implement an environmental resource change perception module in the KARO to assess current and future resource utilization trends. Based on the real-time state and resource urgency, we develop a deep reinforcement learning-based Adaptive Long-term Computation Offloading and Resource Allocation (AL-CORA) strategy optimization algorithm. This algorithm adapts to the environmental resource urgency, autonomously balancing UE satisfaction and task execution cost. Experimental results indicate that AL-CORA effectively improves long-term UE satisfaction and task execution success rates, under the limited computation resource constraints.

Unmanned Aerial Vehicles (UAVs) play a crucial role in next-generation mobile communication systems, serving as aerial base stations to provide services when ground base stations fail to meet coverage requirements. However, trajectory planning and power allocation for collaborative UAVs as Aerial Base Stations (UAV-ABSs) face several challenges, including energy limitations, flight time constraints, high optimization complexity due to dynamic environment interactions, and insufficient decision-making information. To address these challenges, this paper proposes a multi-agent reinforcement learning algorithm, namely Communication Actor Centralized Attention Critic Algorithm (CATEN), to jointly optimize the flight trajectory and power allocation strategies of UAV-ABSs. The proposed algorithm aims to maximize the number of users meeting Quality of Service (QoS) requirements while minimizing UAV-ABSs energy consumption. To achieve this, firstly, an information sharing mechanism is designed to improve the collaboration efficiency among UAV-ABSs. It leverages distributed storage, intelligent scheduling of UAV-ABSs interaction experiences, and gating units to enhance information screening and fusion. Secondly, a multihead attention critic network is proposed to capture correlations among UAV-ABSs from different subspaces. This allows the network to prioritize value information, reduce redundancy, and strengthen UAV-ABSs collaboration and decision-making capabilities. Simulation results demonstrate that CATEN achieves better performance in terms of the number of served users and energy consumption compared to existing algorithms, exhibiting good robustness and adaptability in dynamic environments.

With the advent of network function virtualization (NFV), many users resort to network service provisioning through virtual network function instances (VNFIs) run on the standard physical server in clouds. Following this trend, NFV markets are emerging, which allow a user to procure VNFIs from cloud service providers (CSPs). In such procurement process, it is a significant challenge to ensure differential privacy and truthfulness while explicitly considering dynamic resource provisioning, location sensitiveness and budget of each VNFI. As such, we design a differentially private and truthful reverse auction with dynamic resource provisioning (PTRA-DRP) to resolve the VNFI procurement (VNFIP) problem. To allow dynamic resource provisioning, PTRA-DRP enables CSPs to submit a set of bids and accept as many as possible, and decides the provisioning VNFIs based on the auction outcomes. To be specific, we first devise a greedy heuristic approach to select the set of the winning bids in a differentially privacy-preserving manner. Next, we design a pricing strategy to compute the charges of CSPs, aiming to guarantee truthfulness. Strict theoretical analysis proves that PTRA-DRP can ensure differential privacy, truthfulness, individual rationality, computational efficiency and approximate social cost minimization. Extensive simulations also demonstrate the effectiveness and efficiency of PTRA-DRP.

With the continuous development of Internet technology, social networks have provided convenient conditions for information dissemination. The rapid dissemination of information has provided us with great convenience, but some criminals extensively spread rumors based on such convenience, adversely affecting social stability. In this context, two key challenges arise: the survivability of rumors, which refers to their persistence and long-term impact, and the dynamic viewpoint changes of individuals, which influence how rumors spread and diminish over time. This article proposes a dynamic-susceptible-exposed-infected-recovered (DSEIR) rumor propagation model based on human social behavior to solve the spread of rumors problem. This model considers the characteristics of rumors spread in social networks with the Markov chain and makes the simulation more authentic. To suppress rumor propagation, we introduce the concept of rumor survivability and propose a dynamic truth movement blocking strategy, which adapts to people’s evolving viewpoints to curb the influence of rumors effectively. Finally, we analyze the proposed model and blocking strategy in four real networks. The experimental results show that the proposed propagation model can authentically simulate the propagation of rumors in social networks and the proposed blocking strategy efficiently suppresses the rumor propagation. We have released our code here:https://github.com/gf9264/DSEIR.

Sentiment propagation plays a crucial role in the continuous emergence of social public opinion and network group events. By analyzing the maximum Influence of sentiment propagation, we can gain a better understanding of how network group events arise and evolve. Influence maximization (IM) is a critical fundamental issue in the field of informatics, whose purpose is to identify the collection of individuals and maximize the specific information's influence in real-world social networks, and the sentiments expressed by nodes with the greatest influence can significantly impact the emotions of the entire group. The IM issue has been established to be an NP-hard (nondeterministic polynomial) challenge. Although some methods based on the greedy framework can achieve ideal results, they bring unacceptable computational overhead, while the performance of other methods is unsatisfactory. In this article, we explicate the IM problem and design a local influence evaluation function as the objective function of the IM to estimate the influence spread in the cascade diffusion models. We redefine particle parameters, update rules for IM problems, and introduce learning automata to realize multiple search modes. Then, we propose a multisearch particle Swarm optimization algorithm (MSPSO) to optimize the objective function. This algorithm incorporates a heuristic-based initialization strategy and a local search scheme to expedite MSPSO convergence. Experimental results on five real-world social network datasets consistently demonstrate MSPSO's superior efficiency and performance compared with baseline algorithms.

Vehicular Edge Computing (VEC) is an emerging computing paradigm that serves as a specific application of Mobile Edge Computing (MEC) in intelligent transportation systems. As a core technology of VEC, task offloading improves computing efficiency and service quality by offloading computing tasks from vehicular devices to edge nodes. However, the high mobility of vehicles, heterogeneity of resources, and real-time requirements present significant challenges for task offloading. To address the issue of reducing overall system latency and increasing the offloading success rate in multi-task offloading, we propose a task offloading scheme combining Deep Reinforcement Learning (DRL) and Kuhn-Munkres (KM) Matching algorithm, named the PDQKM offloading scheme. Firstly, to mitigate the delay caused by frequent Roadside Unit (RSU) handovers, we propose a method to detect whether a vehicle is within the coverage area of the RSU. This method filters out unreasonable offloading decisions, avoiding the overhead associated with frequent RSU handovers. Secondly, the combination of DRL and the KM matching algorithm leverages the strengths of both approaches. DRL provides initial offloading strategies in highly dynamic and high-dimensional decision environment. Although DRL may get stuck in local optima, it can quickly adapt to environmental changes. The KM matching algorithm, a classic solution for perfect task-resource matching, performs global optimization on the initial strategies provided by DRL. This integration overcomes the limitations that a single algorithm might have. Finally, to effectively coordinate and manage the heterogeneous resources of RSU, we utilize an improved KM matching algorithm to update computational resources in real time, enhancing matching efficiency. Experimental results demonstrate that PDQKM outperforms comparable offloading schemes in terms of overall system latency and offloading success rate optimization.

Rumor suppression is targeted at diminishing the impact of false and negative information within social networks by decreasing the prevalence of belief in such rumors among individuals, utilizing diverse strategies. Previous studies have broadly delineated rumor suppression strategies into two primary categories: targeting key nodes or edges for obstruction, and enlisting high-influence nodes to disseminate truth-related accurate information. Traditionally, employing a singular strategy involves utilizing a static algorithm throughout the rumor suppression endeavor. This method, however, encounters difficulties in adapting to fluctuating external conditions, rendering it less efficacious in the management of rumor proliferation. In response to these challenges, we introduce the concept of Adaptive Rumor Suppression (ARS), which aims to dynamically counter rumors by taking into account the nuances of propagation dynamics and the surrounding environmental context. We propose a multi-label state transition linear threshold model to more closely mirror the complex process of information diffusion across social networks. Furthermore, we advocate for a multi-round hybrid strategy that amalgamates blocking and clarification tactics to address the ARS problem within the confines of limited resource allocations. To navigate the complexities of ARS, we introduce the Hybrid Strategy of Each Round (HS-R) algorithm, which synergizes multiple strategies to effectively counter the spread of rumors. In extension, we present the Multi-Round Multi-Label (MRML) algorithm, designed to augment the efficiency of the HS-R algorithm. Experimental evaluations conducted on authentic social network datasets illustrate that our methodologies significantly outshine baseline algorithms, offering a more effective and adaptable solution to curb rumor propagation across varied environments.

With the widespread application of 5G and artificial intelligence (AI) technology, the Internet of Things (IoT) has been expanding and integrated into various aspects of our daily lives. However, this also poses challenges such as the ubiquitous demand for communication and computing resources, and data privacy issues. Considering its flexible deployment, high security, and ease of scalability, blockchain-enabled edge computing IoT network (BECIN) has become a promising solution to provide secure and fast communication and computing services. However, existing research on computation offloading in edge computing largely overlooks the stochastic arrival of computational tasks and the potential variability in the number, locations, and resource provisions of edge computing service providers. Therefore, we propose a dynamic, self-adjusting BECIN framework aimed at providing long-term stable, efficient, and secure edge computing data offloading services for ground users in a specific region. This framework supports the dynamic entry and exit of edge computing service providers. Additionally, we introduce a novel dynamic Dueling DDQN approach to update the offloading and resource management policies based on changes in resource provisioning. Experimental results demonstrate the feasibility and superior performance of our framework on system cost and system latency.

Mobile edge computation (MEC) is envisioned as a prospective approach for processing the computation-intensive and delay-sensitive tasks of smart mobile devices (SMDs) through offloading them to base stations (BSs) nearby. In fact, efficient task offloading mechanisms are crucial to accomplish an MEC system. The key challenge is to make on-spot decisions upon the arrival of each task and at the same time achieve truthfulness of each SMD. The challenge further escalates, when the unique characteristics of an MEC system, such as locality constraint, delay constraint, etc., are explicitly considered. To solve the challenge, we present a truthful online combinatorial auction-based mechanism (TOCA) for task offloading in an MEC system. Specifically, we first devise the candidate offloading scheme determination algorithm, aiming to determine the candidate offloading schemes of an SMD upon the arrival of its task. Next, we devise the winning offloading scheme selection and pricing algorithm based on the online primal-dual optimization framework, to decide the winning scheme among the SMD's candidate offloading schemes and calculate its payment. By solid theoretical analysis, we verify that TOCA achieves truthfulness, individual rationality and computational efficiency and a smaller competitive ratio. Trace-driven simulation studies validate the effectiveness and efficacy of TOCA.

As an emerging architecture, edge computing enables resource limited terminal devices to offload their computation tasks to edge servers in the vicinity, to efficiently reduce delay and energy consumption. However, the continuous expansion of network scale and rapid growth of network traffic in recent years have brought huge challenges to task offloading and resource allocation. To tackle the challenges, by integrating Knowledge Defined Networking (KDN) and edge computing technologies, we design a novel Knowledge defined Edge Computing (KEC) architecture, to achieve intelligent resource allocation and task offloading in dynamic large-scale edge computing networks. We formulate the task offloading and resource allocation optimization problem, to minimize delay and energy consumption, by considering resource requirements and controller deployment. To solve it, we present an intelligent Resource Allocation based Task Offloading (TORA) mechanism, where a Multi-Agent SD3 based resource allocation (MASD3) algorithm is devised to perform efficient resource allocation. To adapt to the rapid expansion of network scale, we design a resource Allocation based Controller Deployment and task offloading Decision (DACD) algorithm, to perform the optimal controller deployment and task offloading. Extensive simulation experiments demonstrate the effectiveness and efficiency of our proposed solution, and TORA mechanism outperforms comparison mechanisms on delay and energy consumption.

Software-Defined Data Center Networks (SDDCNs) utilizes Software Defined Networking (SDN) as a network architecture to achieve highly flexible, programmable, and automated management of Data Center Networks (DCNs). The high energy consumption of DCNs remains a persistent and significant challenge. Thus, the energy saving is crucial and imperative for DCNs. Current energy-efficient solutions primarily rely on flow consolidation and dynamic device sleeping techniques to reduce energy consumption. However, these approaches often yield long Flow Completion Time (FCT), potentially resulting in violations of service-level agreements, particularly for delay-sensitive applications. In this paper, we formulate the problem of minimizing the power consumption in SDDCNs as key objective while ensuring timely FCT for delay-sensitive applications. To solve this problem, we first introduce the Active Network Generation (ANG) approach, which generates a minimal active subnet with the least number of active devices while meeting the current traffic demand. Subsequently, we propose two algorithms based on the type of applications: the Delay-Tolerant Flow Route (DTFR) algorithm for delay-tolerant applications and the Delay-Sensitive Flow Route (DSFR) algorithm for delay-sensitive applications. Simulation results demonstrate that our propose solution achieves an energy-saving rate of up to 67.77% and significantly reduces FCT compared to benchmark solutions.

Personalized Federated Learning (pFL) can improve the accuracy of local models and provide enhanced edge intelligence without exposing the raw data in Mobile Edge Computing (MEC). However, in the MEC environment with constrained communication resources, transmitting the entire model between the server and the clients in traditional pFL methods imposes substantial communication overhead, which can lead to inaccurate personalization and degraded performance of mobile clients. In response, we propose a Communication-Efficient pFL architecture to enhance the performance of personalized models while minimizing communication overhead in MEC. First, a Knowledge-Aware Parameter Coaching method (KAPC) is presented to produce a more accurate personalized model by utilizing the layer-wise parameters of other clients with adaptive aggregation weights. Then, convergence analysis of the proposed KAPC is developed in both the convex and non-convex settings. Second, a Bidirectional Layer Selection algorithm (BLS) based on self-relationship and generalization error is proposed to select the most informative layers for transmission, which reduces communication costs. Extensive experiments are conducted, and the results demonstrate that the proposed KAPC achieves superior accuracy compared to the state-of-the-art baselines, while the proposed BLS substantially improves resource utilization without sacrificing performance.

Competitive opinion maximization (COM) aims to determine some individuals (i.e., seed nodes) from social networks, propagating the desired opinions toward a target entity to their neighbors through social relationships when facing with its competitors (components) and maximize the opinion spread after the specific time. Current studies on COM are still in its infancy, while the only work merely considers the scenario that the strategy of competitors is known but ignores the unknown scenario. In addition, previous studies on COM cannot easily address the situation where some users might dynamically change their opinions. To address the COM issue, we investigate the multistage COM and propose a brand-new Q-learning-based opinion maximization framework (QOMF). Our QOMF consists of two components: dynamic opinion propagation and seeding process. We formulate the COM problem by maximizing relative effective opinions. To produce a dynamic opinion series more realistically, we design an opinion propagation model by joining the activation process and a dynamic opinion process. Moreover, we also verify that the opinion propagation model can reach convergence within finite iterations. To acquire the seed nodes, we design a multistage Q-learning seeding scheme by considering known and unknown competitor strategies, respectively. Experimental results on three real datasets demonstrate that the proposed method outperforms the benchmarks on reaching relatively effective opinions.

More and more application providers (APs) start to deploy their geo-distributed services in multiple cloud environments, such as JointCloud, federated clouds and InterCloud. Thus, massive cross-cloud traffic is generated from the services of APs, who need to pay Internet service providers (ISPs) for using their bandwidth. As such, an effective cross-cloud link bandwidth allocation and pricing mechanism is needed between APs and ISPs. Existing fixed-price scheme lacks market efficiency. Thus, we propose a truthful padding-based auction mechanism (TPAM) for cross-cloud bandwidth, which introduces the padding method and well-designed pricing strategy to ensure desirable properties. This mechanism is flexible enough to allow each AP to win the whole request, or win the specified proportional request, or lose and get nothing. Specifically, we first devise a linear-program-based method to calculate the padding vector for each candidate AP. Next, we design a padding-based method to determine the winning APs and match them with ISPs who offer the cheapest bandwidth. Finally, we design a critical-value-based pricing strategy and a marginal-cost-based pricing strategy for APs and ISPs to achieve truthfulness and budget balance. Theoretical analyses prove that TPAM achieves truthfulness, budget balance, individual rationality, asymptotic efficiency and computational tractability. Trace-driven simulation results also validate the effectiveness and efficiency of TPAM.

With the widespread adoption of the Internet of Things (IoT) and various smart medical devices, the volume of medical data has dramatically increased, making the processing of medical Internet of Things (IoMT) data increasingly challenging. Due to the integration of edge computing and cloud computing, IoMT can allocate increased computing and storage resources in proximity to the terminal, addressing the low-latency requirements of computationally intensive tasks. While existing initiatives have shifted services to edge servers, they have not taken into account the joint impact of task priorities and mobile computing services on Mobile Edge Computing (MEC) networks. Fortunately, the rapidly advancing field of Artificial Intelligence (AI) has proven effective in some resource allocation applications in recent years. In this article, we propose a mobile edge computing-based intelligent healthcare multitasking processing system aimed at addressing the issue of service prioritization in medical scenarios. Considering energy consumption and latency, we present a multi-objective task-aware service offloading algorithm under the framework of end-edge-cloud collaborative IoMT systems, employing deep deterministic policy gradients (DDPG). Adaptability to the diversity of different services is achieved through dynamic adjustments based on various business types and system requirements. Finally, the effectiveness of DDPG for IoMT is validated using real-world data.

Edge intelligence (EI) entails deploying artificial intelligence algorithms at the network’s edge. Utilizing edge computing infrastructure enables local data processing and decision-making, resulting in decreased latency, bandwidth consumption, and improved privacy security. With increasing the number of Internet of Things (IoT) devices and the development of 6G communication technologies, there is a growing demand for fast, efficient, and low-latency data processing, which has led to the rise of EI. This survey comprehensively reviews and analyzes the current state of research on EI from the perspective of technological development, with a particular focus on the following key aspects: (1) We review the basic concepts of EI and its distinctions from traditional cloud computing and edge computing; (2) We explore the technological framework of EI in detail, including key technologies such as edge computing and federated learning, and analyze how these technologies integrate with modern communication technologies like IoT devices and 6G networks; (3) We discuss the challenges faced when implementing EI technologies, such as data privacy and security issues, device resource constraints, and propose corresponding solutions or research directions. The survey also outlines the main research directions and technical challenges driving the future development of EI, providing valuable insights and guidance for researchers and practitioners in the field.

Telemedicine Monitoring (TM) integrates mobile communication technology and Internet of Things (IoT) technology for health monitoring and data management. Amidst the escalating demand for telemedicine, traditional cloud computing struggles to guarantee real-time performance and data privacy. To address these challenges, we systematically survey the application of fog and edge computing technologies in TM systems. We focus on the following key aspects: (1) We delve into the theoretical foundations of fog and edge computing, underscoring their salient advantages including low latency, location awareness, high mobility, and more. (2) We elaborate on the architecture of a TM system hinged on fog and edge computing. (3) We outline key challenges facing fog/edge computing-based TM systems, including bandwidth limitations, low latency, data security, privacy, heterogeneity, and reliability. (4) We discuss the need for future advancements in the realms of security defense capability, system adaptability, and convergence of scheduling algorithms to refine the construction of the TM system and stimulate the development of telemedicine.

Edge-Cloud-End Collaborative (ECEC) computing emerges as a promising paradigm to support computationally intensive applications within Digital Twin (DT) networks, providing flexibility and real-time performance for heterogeneous task scheduling and resource allocation. However, effectively balancing key Service Level Agreement (SLA) parameters, such as response delay, availability, and throughput in dynamic network environments remains challenging, especially for complex, large-scale applications. Existing solutions typically lack SLA-oriented proactive resource reservation schemes. To address these limitations, we propose an SLA-driven hierarchical bidirectional closed-loop DT Network Architecture (SLA-DTNA), comprising four layers: real network, local DT, edge DT, and cloud DT. The proposed architecture systematically decomposes SLA requirements into measurable system parameters, aiming to minimize overall system cost. Specifically, a differentiated task management mechanism is designed at the local DT layer to ensure Quality of Service (QoS) for critical tasks. At the edge DT layer, we propose a heuristic-based lightweight scheduling algorithm leveraging DT capabilities for efficient task resource mapping and reduced scheduling complexity. At the cloud DT layer, we apply the Deep Deterministic Policy Gradient (DDPG) algorithm for adaptive resource reservation, dynamically adapting schemes based on task preferences and historical behavior patterns. Simulation and experimental results validate that the proposed SLA-DTNA enables fine-grained and intelligent resource allocation, enhances overall network performance, and effectively satisfies dynamic SLA requirements.

By the limitations of storage capacity and computing power, Internet of Things (IoT) devices may prefer to outsource their valuable and sensitive data to cloud storage providers (CSPs) for further analysis, so it is critical to design protocols that can verify the integrity of these data remotely, while preserving the privacy of their owners. This article proposes a novel remote data integrity checking method for IoT, namely, FDUP-remote data integrity checking (RDIC), which achieves fully decentralized, efficient, and unconditionally privacy-preserving checking simultaneously, that is, the proof-checking is performed efficiently on the blockchain by the smart contracts integrated with native C/C++ codes, while the blockchain or any other entity cannot learn any information about the data content, even they have unbounded computing power. Furthermore, it is optimized for low-power IoT devices by greatly reducing the exponentiations of generating homomorphic verifiable tags to be nearly independent of the block size of the outsourced data. To defend against untrusted IoT and CSP, we present strict proofs and analyses in the aspect of correctness, soundness, and unconditionally privacy-preserving. The evaluation of theoretical performance and the prototype system deployed on a blockchain platform indicate that FDUP-RDIC is suitable for real-world IoT applications.

Performance predictors are used to reduce architecture evaluation costs in neural architecture search, which however suffers from a large amount of budget consumption in annotating substantial architectures trained from scratch. Hence, how to leverage existing annotated architectures to train a generalized predictor to find the optimal architecture on unseen target search spaces becomes a new research topic. To solve this issue, we propose a Single-Domain Generalized Predictor (SDGP), which aims to make the predictor only trained on a single source search space but perform well on target search spaces. In meta-learning, we firstly adopt feature extractor in learning the domain-invariant features of the architectures. Then, a neural predictor is trained to map the architectures to the accuracy of the candidate architectures over the target domain simulated on the source search space. Moreover, a novel multi-head attention driven regularizer is designed to regulate the predictor to further improve the generalization ability of the predictor for the feature extractor. A series of experimental results have shown that the proposed predictor outperforms the state-of-the-art predictors in generalization and achieves significant performance gains in finding the optimal architectures with test error 2.40% on CIFAR-10 and 23.20% on ImageNet1k within 0.01 GPU days.

Social networks currently serve as one of the primary sources from which people obtain news, with the spread of rumors emerging as a major concern. The goal of rumor suppression is to minimize the number of individuals affected by rumors through various methods, such as blocking and disseminating the truth. Although this problem has evolved into a popular research topic, existing solutions often overlook the temporal impact of rumor-refuting information and the influence of user opinions on rumor spreading. In the study, we first investigate the two-stage rumor minimization problem. The problem primarily considers two situations about only the propagation of rumors and the simultaneous propagation of rumor and rumor-refuting information, aiming to minimize the impact of rumors. We propose the two-stage user opinion rumor propagation model (TSUORP), which fully incorporates the timing of official releases of rumor-refuting information and their influence on the generation of rumors propagation. Based on this, we propose an approach using the knowledge graph convolutional network (KGCN) algorithm to rapidly and effectively select rumor-refuting information seed nodes based on user opinions. To assess the validity of our proposed approach, we perform experiments on three authentic datasets, showcasing its notable advantages.

Traditional routing algorithms cannot dynamically change network environments due to the limited information for routing decisions. Meanwhile, they are prone to performance bottlenecks in the face of increasingly complex business requirements. Some approaches, such as deep reinforcement learning (DRL) have been proposed to address the routing problems. However, they hardly utilize the information about the network environment fully. The Knowledge Defined Networking (KDN) architecture inspires us to develop new learning mechanisms adapted to the dynamic characteristics of the network topology. In this paper, we propose an effective scheme to solve the routing optimization problem by adding a graph neural network (GNN) structure to DRL, called Message Passing Deep Reinforcement Learning (MPDRL). MPDRL uses the characteristics of GNN to interact with the network topology environment and extracts exploitable knowledge through the message passing process of information between links in the topology. The goal is to achieve the load balance of network traffic and improve network performance. We have conducted experiments on three Internet Service Provider (ISP) network topologies. The evaluation results show that MPDRL obtains better network performance than the baseline algorithms.

Recently, computation offloading methods have greatly improved the Quality of Experience (QoE) in Multi-access Edge Computing (MEC) by offloading tasks to the edge servers. Since well-coordinated actions of Terminal Devices (TDs) are critical to improving the performance of the entire individual system, many practical MEC-based applications, i.e., firefighting robots and unmanned aerial vehicles, require great teamwork among TDs. However, real-world scenarios are usually bound by resource conditions. For instance, network connectivity may weaken or experience interruptions during emergency situations. In cases where the communication medium is utilized by multiple TDs, achieving effective coordination poses a significant challenge. In this paper, we propose a computation offloading scheme based on Scheduled Multi-agent Deep Reinforcement Learning (SMDRL) to make the most efficient decision in a resource-constrained scenario. First, we design a virtual energy queue based on the MEC system and maximize the QoE (related to service delay and energy consumption) in a real-time manner. Subsequently, we propose a scheduled multi-agent deep reinforcement learning algorithm to support each TD in learning how to encode messages, select actions, and schedule itself based on the received messages. Furthermore, a TopK mechanism is introduced. This mechanism chooses the most crucial TDs to broadcast their messages, and then the computation offloading problem in a communication-constrained MEC environment can be solved in a low-communication manner. Also, we prove that even under limited communication conditions, our proposed methods can still lead to the close-to-optimal performance. The final performance analysis shows that the developed scheme has significant advantages over other representative schemes.

Intelligent Transportation Systems (ITS) aim to enhance traffic management by improving connectivity and data sharing among vehicles and road infrastructure. In a Mixed Connected and Connectionless Vehicles (MCCV) scenario consisting of connected vehicles equipped with On-Board Units (OBUs) and non-connected vehicles lacking OBUs, communication disparities create challenges in critical lane-changing overtaking decisions. These discrepancies hinder the adaptation of fully connected scenarios to dynamic interactions among these different types of vehicles. Considering the diversity in decision-making ways and capabilities of non-connected vehicles in MCCV scenarios, ensuring the coordinated execution of safe and efficient lane-changing overtaking maneuvers by multiple connected vehicles is crucial for enhancing traffic efficiency. Therefore, we propose a collaborative strategy to facilitate safer and more efficient lane-changing overtaking maneuvers for connected vehicles in the MCCV scenario. First, we design a multi-criteria priority detection, and a dynamic event-triggered mechanism based on confidence intervals to foster efficient collaboration among connected vehicles, optimizing decision-making and reducing conflicts. Second, to accommodate diverse driving styles of autonomous and human-driven vehicles, we introduce an Improved Dynamic Precise Fuzzy C-Means (IDP-FCM) algorithm to dynamically identify and adapt to different driving styles, thereby improving safety. Finally, tackling the challenge of multiple connected vehicles performing lane-changing overtaking involving hybrid action space, our proposed Multi-agent Contrastive Parameterized Dueling Deep Q-Network (MCPDDQN) algorithm incorporates contrastive learning to improve strategy stability in complex driving scenarios. Experimental results demonstrate the effectiveness of our strategy in improving road safety and traffic efficiency of the MCCV scenario.

With the widespread popularity of the Internet of Things and various intelligent medical devices, the amount of medical data is rising sharply, and thus medical data processing has become increasingly challenging. Mobile edge computing technology allows computing power to be allocated at the edge closer to users, which enables efficient data offloading for healthcare systems. However, existing studies on medical data offloading seldom guarantee effective data privacy and security. Moreover, the research equipping data offloading architectures with Blockchain neglect the delay and energy consumption costs incurred in using Blockchain technology for medical data offloading. Therefore, in this paper, we propose a data offloading scheme for healthcare based on Blockchain technology, which achieves optimal medical resource allocation and simultaneously minimizes the cost of offloading tasks. Specifically, we design a smart contract to ensure secure data offloading. And, we formulate the cost problem as a Markov Decision Process, solved by a policy search-based deep reinforcement learning (Asynchronous Advantage Actor-Critic) scheme, where we jointly consider offloading decisions, allocation of computing resources and radio transmission bandwidth, and Blockchain data security audits. The security of our smart-contract-based mechanism is theoretically and empirically proved, while extensive experimental results also show that our solution can obtain superior performance gains with lower cost than other baselines.

The current global economy is undergoing a transformative phase, emphasizing collaboration among multiple competing entities rather than monopolization. Economic globalization is accelerating the adoption of globalized cloud services, and in line with this trend, cloud 2.0 introduces the concept of “cloud cooperation”. JointCloud, as a novel computing model for Cloud 2.0, advocates for the establishment of an evolving cloud ecosystem. However, a critical challenge arises due to the lack of direct incentives for a cloud to join the JointCloud ecosystem, leading to uncertainty regarding the rationale for the existence of the JointCloud ecosystem. To address this ambiguity, we draw inspiration from supply chain competition and formulate the market dynamics of resources within the JointCloud ecosystem. Our focus is particularly on the analysis of data resource trade within the JointCloud market. To comprehensively analyze the JointCloud market, we propose a market game that examines the competition among clouds within the ecosystem. We theoretically prove that a Nash Equilibrium always exists under the JointCloud market. Subsequently, we conduct an in-depth analysis of the profits of cloud resource manufacturers and cloud resource retailers as the number of clouds varies within the JointCloud ecosystem. Based on our analysis, we further explore the incentives for a cloud to participate in the JointCloud ecosystem. We then evaluate the performance of the proposed market game through extensive experiments, illustrating how process variables and profits change with the market size. The experiments demonstrate that the trends of various variables are aligned with our analysis obtained from the market game. Compared with the Cournot model, our proposed model captures the market power of both manufacturers and retailers, resulting in a model that closely mirrors real market dynamics. Our findings provide valuable insights into the cloud market within Cloud 2.0, offering guidance for stakeholders navigating the evolving landscape of cloud cooperation and competition.

Network Function Virtualization (NFV) provides a flexible way to provision new services by decoupling network functions from hardware and implementing them as Virtual Network Functions (VNFs). However, the rapid development of technologies greatly promotes the explosion of diverse services, which directly results in the exponential increase of heterogeneous traffic. In addition, such a tremendous amount of heterogeneous traffic will generate bursts in a more dynamic and unexpected manner, so it becomes extremely hard to satisfy the customer demands. Aiming at addressing these challenges, this work proposes a positive and elastic VNF deployment mechanism for service provisioning, which introduces three novelties:1) a Gated Recurrent Unit (GRU) based traffic prediction model is established to predict the unexpected and dynamically changing traffic behaviors in advance with the accuracy over 98%; 2) a closed-loop system is formed, in which the prediction model can learn and evolve continuously to respond to more complex scenarios; 3) different states of VNF are introduced and dynamically switched to deal with the current demands with reduced cost by avoiding frequent VNF initialization and destroy.The experimental results indicate that the proposed mechanism outperforms the state-of-the-art methods, which include achieving over 98% prediction accuracy, improving the service acceptance rate by more than 18%, and reducing the overall cost by more than 20%.

With the proliferation of devices connected to the Internet of Things (IoT), the complexity of network management has increased. To intelligently manage large-scale networks, we propose a Knowledge-Defined Edge Computing Networks (KDECN) architecture. Edge Nodes (ENs) deployed in the KDECN architecture are responsible for collecting and preprocessing the relevant information uploaded by User Devices (UDs), and provide computation resources for UDs. Futhermore, since multiple UDs share system computation resources, one computing decision will affect the subsequent decision-making of other UDs. Thus, accurately predicting the demands for UD task requests is a key challenge to maximize long-term execution utility. To this end, we deploy the LSTM-based Task Request Demand Prediction (TRDP) method on the management plane of KDECN architecture to predict the task request quantity of UDs in each future time slot. In order to maximize long-term execution utility of the system, we propose a Deep Reinforcement Learning (DRL)-based Long-term Computation Offloading and computation Resource Allocation (L-CORA) algorithm. Specifically, the proposed L-CORA algorithm makes computing decisions based on the prediction of the offloading task quantity and the personalized demands of UDs to ensure the long-term quality of computing service. Extensive experiments with Shanghai real-world datasets to prove that the KDECN-based L-CORA algorithm effectively improves the average utility of the system.

Information diffusion problems, such as the spread of epidemics or rumors, are widespread in society. The inverse problems of graph diffusion, which involve locating the sources and identifying the paths of diffusion based on currently observed diffusion graphs, are crucial to controlling the spread of information. The problem of localizing the source of diffusion is highly ill-posed, presenting a major obstacle in accurately assessing the uncertainty involved. Besides, while comprehending how information diffuses through a graph is crucial, there is a scarcity of research on reconstructing the paths of information propagation. To tackle these challenges, we propose a probabilistic model called DDMSL (Discrete Diffusion Model for Source Localization). Our approach is based on the natural diffusion process of information propagation over complex networks, which can be formulated using a message-passing function. First, we model the forward diffusion of information using Markov chains. Then, we design a reversible residual network to construct a denoising-diffusion model in discrete space for both source localization and reconstruction of information diffusion paths. We provide rigorous theoretical guarantees for DDMSL and demonstrate its effectiveness through extensive experiments on five real-world datasets.

MicroRNAs (miRNAs) are a family of non-coding RNA molecules with vital roles in regulating gene expression. Although researchers have recognized the importance of miRNAs in the development of human diseases, it is very resource-consuming to use experimental methods for identifying which dysregulated miRNA is associated with a specific disease. To reduce the cost of human effort, a growing body of studies has leveraged computational methods for predicting the potential miRNA-disease associations. However, the extant computational methods usually ignore the crucial mediating role of genes and suffer from the data sparsity problem. To address this limitation, we introduce the multi-task learning technique and develop a new model called MTLMDA (Multi-Task Learning model for predicting potential MicroRNA-Disease Associations). Different from existing models that only learn from the miRNA-disease network, our MTLMDA model exploits both miRNA-disease and gene-disease networks for improving the identification of miRNA-disease associations. To evaluate model performance, we compare our model with competitive baselines on a real-world dataset of experimentally supported miRNA-disease associations. Empirical results show that our model performs best using various performance metrics. We also examine the effectiveness of model components via ablation study and further showcase the predictive power of our model for six types of common cancers. The data and source code are available from https://github.com/qwslle/MTLMDA.

Multiaccess edge computing (MEC) provides task offloading services to facilitate the integration of idle resources with the network and bring cloud services closer to the end user. By selecting suitable servers and properly managing resources, task offloading can reduce task completion latency while maintaining the Quality of Service (QoS). Prior research, however, has primarily focused on tasks with strict time constraints, ignoring the possibility that tasks with soft constraints may exceed the bound limits and failing to analyze this complex task constraint issue. Furthermore, considering additional constraint features makes convergent optimization algorithms challenging when dealing with such complex and high-dimensional situations. In this article, we propose a new computational offloading decision framework by minimizing the long-term payment of computational tasks with mixed bound constraints. In addition, redundant experiences are gotten rid of before the training of the algorithm. The most advantageous transitions in the experience pool are used for training in order to improve the learning efficiency and convergence speed of the algorithm as well as increase the accuracy of offloading decisions. The findings of our experiments indicate that the method we have presented is capable of achieving fast convergence rates while also reducing sample redundancy.

The rapid growth of mobile device (e.g., smart phone and bracelet) has spawned a lot of new applications, during which the requirements of applications are increasing, while the capacities of some mobile devices are still limited. Such contradiction drives the emergency of computation migration among mobile edge devices, which is a lack of research currently. In this article, we focus on addressing the computation migration oriented resource allocation problem among mobile edge devices. Specifically, we first construct a framework for Mobile Social Cloud(MSC), in which the mobile devices with rich resources are abstracted as resource suppliers and those resource-lacking devices are abstracted as resource demanders. Then, a mathematical model is formulated and an evolutionary algorithm is proposed to effectively solve this model based on decomposition, dominance, and genetic operations. Moreover, the parallel computing is introduced to further improve the efficiency of the proposed algorithm. The experimental results indicate that the proposed algorithm outperforms the other state-of-the-art methods and it improves the calculation efficiency by about 178 percent (2 cores) and 262 percent (3 cores) by introducing parallel computing.

The rumors in the healthcare system have the attributes of fast spread and severe social influence. Even worse, it may cause the collapse of medical services and the death of many patients. To prevent its serious impact on society, the target of rumor suppression for the healthcare system is to restrain the spread of rumors (negative opinions) and maximize the spread of antirumors (positive opinions). Therefore, in this article, for the first time, we propose comprehensive learning-based particle swarm optimization with opinion maximization (OM) to address the rumors suppression problem in the healthcare system. We define the rumor suppression problem in the healthcare system based on OM and devise two opinion propagation models. Then, we propose a directed acyclic graph-based objective function to evaluate the opinion propagation and solve this problem using comprehensive learning particle swarm optimization. Experimental results show that our proposed scheme achieves better results for positive opinion propagation in the scenario of rumor suppression in the healthcare system than the baseline algorithms.

Misinformation and rumors can spread rapidly and widely through online social networks, seriously endangering social stability. Therefore, rumor blocking on social networks has become a hot research topic. In the existing research, when users receive two opposing opinions, they tend to believe the one arrives first. In this article, we argue that users will dialectically trust the information based on their own opinions rather than the rule of first-come-first-listen. We propose a confidence-based opinion adoption (CBOA) model, which considers the opinion and confidence according to the traditional linear threshold (LT) model. Based on this model, we propose the directed graph convolutional network (DGCN) method to select the$k$most influential positive cascade nodes to suppress the propagation of rumors. Finally, we verify our method on four real network datasets. The experimental results show that our method can sufficiently suppress the propagation of rumors and obtains smaller number of rumor nodes than the baseline algorithms.

This article focuses on the bipartite output tracking control for heterogeneous linear multiagent systems under the asynchronous edge-based event-triggered transmission mechanism. First, the distributed bipartite edge-based event-triggered compensator is established to estimate the state of the exosystem. The estimated state of the compensator is the same as the state of the exosystem in modulus and opposite in sign because of the existence of antagonistic communications. To be independent of the topology information, the adaptive compensator with an edge-based event-triggered mechanism is then established. And the observer is proposed to recover the unmeasurable system states. Then, the distributed control scheme based on the compensator and the observer is designed to address the bipartite output tracking problem. Moreover, the results in the signed fixed graph are extended to signed switching graphs. The Zeno behavior of each edge is ruled out. Finally, two numerical examples, one application example and one comparison example, are given to demonstrate the feasibility of the main theoretical findings.

Opinion Maximization (OM) aims at determining a small set of influential individuals, spreading the expected opinions of an object (e.g., product or individual) to their neighbors through the social relationships and eventually producing the largest opinion spread. In previous studies, once the corresponding nodes are activated, their opinions usually keep unchanged, which fails to capture the real scenarios where the opinion of each node on the object can dynamically change over time. In this view, we propose a Dynamic Opinion Maximization Framework (DOMF) to settle the OM problem, which consists of two parts: dynamic opinion formation and adaptive seeding process. Specifically, we formulate the OM problem by maximizing rational opinions, and prove that: 1) the OM problem within a constant ratio is NP-hard, and 2) the objective function does not satisfy the monotonicity and submodularity properties anymore. To model the dynamic opinion issue, we propose adaptive cooperation model based on Q-learning theory, which is proved to be capable of eventually reaching convergence. Moreover, to dynamically generate the initial seed nodes, we design the Multi-stage Heuristic Algorithm (MHA). Experimental results demonstrate that each component of our model is effective, and the proposed approach improves the rational opinion spread.

This study investigates the adaptive bipartite event-triggered time-varying output formation tracking for heterogeneous linear multi-agent systems (MASs) under signed directed communication topology. Both cooperative communication and antagonistic communication among agents are considered. The fully distributed bipartite compensator based on the novel composite event-triggered transmission mechanism is first put forward to estimate the state of the leader. Compared with the existing methods, our compensator can save communication resources using event-triggered transmission mechanism; is independent of the global information of the network graph; and is applicable for the signed directed graph. With the developed compensator, the distributed control protocol is designed to achieve the time-varying output formation tracking. Moreover, the case that the networked systems subject to external disturbances is also considered. To estimate the state of leader with disturbance, the fully distributed bipartite compensator based on an innovative composite event-triggered mechanism is presented. And the novel distributed control protocol is proposed to address the output formation tracking issue for linear MASs with heterogeneous dynamics and external disturbances. It is shown that the Zeno-behavior can be excluded in both transmission mechanisms. Finally, the effectiveness of the developed control methods is illustrated through three simulation examples.

Nonnegative matrix factorization (NMF) has been widely used to learn low-dimensional representations of data. However, NMF pays the same attention to all attributes of a data point, which inevitably leads to inaccurate representations. For example, in a human-face dataset, if an image contains a hat on a head, the hat should be removed or the importance of its corresponding attributes should be decreased during matrix factorization. This article proposes a new type of NMF called entropy weighted NMF (EWNMF), which uses an optimizable weight for each attribute of each data point to emphasize their importance. This process is achieved by adding an entropy regularizer to the cost function and then using the Lagrange multiplier method to solve the problem. Experimental results with several datasets demonstrate the feasibility and effectiveness of the proposed method. The code developed in this study is available at https://github.com/Poisson-EM/Entropy-weighted-NMF.

This article investigates how to enhance the Multi-access Edge Computing (MEC) systems performance with the aid of device-to-device (D2D) communication computation offloading. By adequately exploiting a novel computation offloading mechanism based on D2D collaboration, users can efficiently share computational resources with each other. However, it is challenging to distinguish valuable information that truly promotes a collaborative decision, as worthless information can hinder collaboration among users. In addition, the transmission of large volumes of information requires high bandwidth and incurs significant latency and computational complexity, resulting in unacceptable costs. In this article, we propose an efficient D2D-assisted MEC computation offloading framework based on Attention Communication Deep Reinforcement Learning (ACDRL), which simulates the interactions between related entities, including device-to-device collaboration in the horizontal and device-to-edge offloading in the vertical. Second, we developed a distributed cooperative reinforcement learning algorithm that includes an attention mechanism that skews computational resources towards active users to avoid unnecessary resource wastage in large-scale MEC systems. Finally, to improve the effectiveness and rationality of cooperation among users, we introduce a communication channel to integrate information from all users in a communication group, thus facilitating cooperative decision-making. The proposed framework is benchmarked, and the experimental results show that the proposed framework can effectively reduce latency and provide valuable insights for practical design compared to other baseline approaches.

Aerial Radio Access Networks (ARANs) are used to connect aerial nodes (such as satellites, aircraft, floating balloons) and ground infrastructures, which enables a global network coverage and provides a wide range of high-quality network services. At present, extensive researches are to integrate it with Mobile Edge Computing (MEC), to achieve more efficient data computing, data storage, and cache. In this paper, we primarily focus on exploring the edge computing architecture integrated with ARANs. Since the existing MEC architecture is not deeply integrated with ARANs, we propose the scenario of a complete four-tier MEC architecture that allows MECs and ARANs to collaborate effectively. Besides, for environmental protection and cost reduction, we propose a Q-learning algorithm based on the improved ϵ – greedy model to complete the MEC server selection and resource allocation. Finally, the simulation results are compared with other benchmark methods, and the effectiveness of the proposed method is proved. The energy consumption of the proposed method is significantly reduced.

Software defined networking (SDN) have the advan-tages of centralized control, global visibility, and programmabil-ity, but these features also bring new security issues, such as Topological Poisoning Attack (TPA), where attackers can attack topology discovery services by stealing host locations or forging link information. Considering the three levels of identity, data package and path, this paper designs a chain authentication defense scheme. The scheme includes authentication mechanism, transaction information storage mechanism, source IP authenti-cation mechanism and smart contract notification mechanism. The received packets are authenticated by digital signature algorithm, and the trusted identity and location information are stored securely. At the same time, an improved block storage structure is designed to avoid data redundancy, and malicious information is processed by smart contract notification and stream rule installation. The experimental results show that the defense scheme designed in this paper can effectively defend against TPA attacks. Compared with the benchmark mechanism, the deployment of this scheme has less impact on controller performance and less impact on the delay of topology discovery in SDN.

Computation offloading decisions play a crucial role in implementing mobile-edge computing (MEC) technology in the Internet of Things (IoT) services. Mobile network operators (MNOs) can employ computation offloading techniques to reduce task completion delay and improve the Quality of Service (QoS) for users by optimizing the system’s processing delay and energy consumption. However, different IoT applications (e.g., entertainment and autonomous driving) generate different delay tolerances and benefits for computational tasks from the MNO perspective. Therefore, simply minimizing the delay of all tasks does not satisfy the QoS of each user. The system architecture design should consider the significance of users and the heterogeneity of tasks. Unfortunately, rare work has been done to discuss this practical issue. In this article, from the perspective of MNO, we investigate the computation offloading optimization problem of multiuser delay-sensitive tasks. First, we propose a new optimization model, which designs different optimization objectives for the cost and revenue of tasks. Then, we transform the problem into a Markov decision processes problem, which leads to designing a multiagent iterative optimization framework. For the strategic optimization of each agent, we further propose a cooperative multiagent deep reinforcement learning (CMDRL) algorithm to optimize two different objectives at the same time. Two agents are integrated into the CMDRL framework to enable agents to collaborate and converge to the global optimum in a distributed manner. At the same time, the priority experience replay method is introduced to improve the utilization rate of effective samples and the learning efficiency of the algorithm. The experimental results show that our proposed method can effectively achieve a significantly higher profit than the alternative state-of-the-art method and exhibit a more favorable computational performance than benchmark deep reinforcement learning methods.

Competitive opinion maximization (COM) in signed social networks targets at selecting a subset of influential individuals (i.e., seed nodes), spreading the desired opinions of the product to their neighbors against its opponents, and eventually achieving the maximum opinion propagation. Current studies mainly focus on competitive influence maximization and opinion maximization. However, COM in signed social networks has not been studied in depth. In this article, we study the COM in signed social networks and propose a novel reinforcement-learning-based opinion maximization framework (RLOM) to solve the COM problem. The proposed RLOM is composed of two phases: the activated dynamic opinion model and the reinforcement-learning-based seeding process. We theoretically prove the COM problem to be NP-hard. To model the opinion propagation process, we propose the activated dynamic opinion model based on a stateless Q-learning approach. Moreover, we propose the reinforcement-learning-based seeding scheme, which is leveraged in an unknown opponent strategy. Experiment results verify the effectiveness of our method in terms of effective opinions on three signed datasets.

This article considers the distributed bipartite adaptive event-triggered fault-tolerant consensus tracking issue for linear multiagent systems in the presence of actuator faults based on the output feedback control protocol. Both time-varying additive and multiplicative actuator faults are taken into account in the meantime. And the upper/lower bounds of actuator faults are not required to be known. First, the state observer is designed to settle the occurrence of unmeasurable system states. Two kinds of event-triggered mechanisms are then developed to schedule the interagent communication and controller updates. Next, with the developed event-triggered mechanisms, a novel observer-based bipartite adaptive control strategy is proposed such that the fault-tolerant control problem can be addressed. Compared with some related works on this topic, our control scheme can achieve the intermittent communication and intermittent controller updates, and the more general actuator faults and network topology are considered. It is proved that the exclusion of Zeno behavior can be realized. Finally, three illustrative examples are given to demonstrate the feasibility of the main theoretical findings.

This study investigates the bipartite fixed-time time-varying output formation-containment tracking issue for heterogeneous linear multiagent systems with multiple leaders. Both cooperative communication and antagonistic communication between neighbor agents are taken into account. First, the bipartite fixed-time compensator is put forward to estimate the convex hull of leaders' states. Different from the existing techniques, the proposed compensator has the following three highlights: 1) it is continuous without involving the sign function, and thus, the chattering phenomenon can be avoided; 2) its estimation can be achieved within a fixed time; and 3) the communication between neighbors can not only be cooperative but also be antagonistic. Note that the proposed compensator is dependent on the global information of network topology. To deal with this issue, the fully distributed adaptive bipartite fixed-time compensator is further proposed. It can estimate not only the convex hull of leaders' states but also the leaders' system matrices. Based on the proposed compensators, the distributed controllers are then developed such that the bipartite time-varying output formation-containment tracking can be achieved within a fixed time. Finally, two examples are given to illustrate the feasibility of the main theoretical findings.

The energy consumed by data centers has been growing rapidly in recent years. Among all the major contributors to the power consumption of entire data centers, data center network (DCN) can account for up to 20% of the total power consumption. In this article, we first devise a power-efficient software-defined DCN (PESD-DCN) framework, which can achieve desirable power efficiency, avoid potential link congestion, and reduce frequent device state transition. Then, we formulate the optimization problem of maximizing the radio full-utilized devices to all devices. To solve it, we propose correlation-aware flow routing (CFR) algorithm, which leverages correlation-aware flow consolidation (CFC) technique to improve energy efficiency, avoid the potential link congestion, and reduce frequent device state transition. Moreover, to further improve the DCN energy efficiency, we propose flow rerouting, link rate adaptation, and device sleeping (FLD) algorithm. Finally, simulation results demonstrate that PESD-DCN can achieve a good performance. More specifically, in comparison to the other baseline algorithms, PESD-DCN can achieve up to 79.19% energy efficiency, 67.1% decrease in switch state transition (SST), and 55.4% decrease in link state transition (LST).