As a structured representation of real-world facts, knowledge graphs (KGs) play a vital role in IoT applications, due to their strong reasoning capabilities and interpretability. However, private user IoT KG data often needs to be centrally collected for embedding training, which poses significant privacy risks and limits the scalability of knowledge-driven downstream applications in distributed IoT environments. Federated learning (FL) has emerged as a promising solution for decentralized model training, eliminating the need for direct data collection. However, existing federated knowledge graph embedding (KGE) methods often struggle to preserve the inherent graph structure of entities and relations, leading to fragmented and incomplete representations. Additionally, they struggle to effectively capture diverse relational dependencies within personal KGs. To address these challenges, this article proposes an enhanced federated KG embedding method for personal knowledge sharing (FPKS) to enable privacy-preserving KGE training. The FPKS framework consists of a central server and multiple federated clients. To enhance entity and relationship alignment across clients, FPKS maintains separate embedding tables for entities and relationships on the server. Moreover, to capture the structural and contextual information of personal KGs, we introduce a local encoder-decoder architecture that employs a graph convolutional network (GCN) variant as an encoder and a KGE scoring function as a decoder. Furthermore, we propose a bidirectional composite operator for GCN (BiDGCN) to enhance multi-relational information aggregation. Extensive experiments on two widely used KG datasets demonstrate that FPKS significantly outperforms existing methods, improving the quality of learned embeddings while ensuring data privacy. Our approach facilitates decentralized personal knowledge sharing, marking an advancement in secure and efficient IoT knowledge-driven services.

The Mobile Edge Computing (MEC)-Empowered Internet of Vehicles (IoVs), a transformative paradigm characterized by real-time decision-making and distributed processing via data offloading to the network edge, has garnered substantial research interest. Nonetheless, emerging applications such as autonomous driving pose dual challenges of computational bottle necks and inadequate wide-area coverage in resource scheduling. To this end, we propose a Digital Twin (DT)-enhanced, satellite assisted resource allocation framework that synergistically coordinates MEC and satellite networks while leveraging DTs for network virtualization and collaborative training, thereby fully exploiting the low-latency capabilities of edge computing, the high-capacity processing of satellite computing, and the cost efficient simulation advantages of DT technology. During the training phase, we develop DR4SV, a Deep Reinforcement Learning (DRL)-based online optimization method that constructs specialized intelligent agents tailored for satellite-collaborative IoV scenarios. By incorporating depthwise over-parameterized convolutional layers, DR4SV enables multi-channel synergistic processing across heterogeneous communication channels while establishing robust policy alignment between physical agents and their DT counterparts. This architecture facilitates real-time state mapping and multi-dimensional parallel simulation within the digital space, enhancing resource allocation efficiency while dynamically balancing latency and energy consumption with accelerated convergence. Extensive experiments are conducted based on real-world datasets, encompassing hyperparameter con figurations and rigorous performance evaluations. The numerical results demonstrate that DR4SV achieves reductions in both time and energy consumption, with optimization rates reaching up to 6.8% and 19.1%, respectively. Meanwhile, the correlation between DTs and physical entities reaches up to 97%, thereby validating the effectiveness and feasibility of DT technology in satellite-terrestrial integrated vehicular networks.

Smart ports rely on the low-latency and high-reliability data processing capabilities of mobile edge computing (MEC) to sustain the stable operation of core tasks such as real-time terminal scheduling. However, extreme events like typhoons trigger a surge in heterogeneous multi-source data, leading to im-balances in edge computing resource supply and demand, which in turn degrade the responsiveness and accuracy of emergency responses. To address the challenge of dynamic task offloading in MEC-enabled smart ports under such risks, this paper proposes a Risk-Aware Proximal Policy Optimization (RAPPO) algorithm. RAPPO integrates a multi-dimensional risk-aware mechanism into the traditional PPO framework, incorporates a dynamic reward function for proactive risk perception and response, and enhances convergence stability and robustness via clipped target restriction and advantage estimation. Experiments based on typhoon data from the China Meteorological Administration and port throughput data from the Fujian Provincial Department of Transportation show that RAPPO outperforms benchmark algorithms in terms of average response latency, energy consumption, and the number of overdue tasks, confirming its effectiveness and feasibility.

Knowledge graph (KG)-based service recommendation methods address issues such as data sparsity and cold start in real-world service recommendations by integrating external knowledge as auxiliary information. Recently, large language models (LLMs) have gained significant attention due to their powerful comprehension and reasoning capabilities. LLM-based recommendation systems also demonstrate advantages in interpretability and few-shot service reasoning. However, the integration of LLMs and KGs into existing service recommendation methods presents two major challenges: (1) the difficulty of aligning service recommendation tasks with language modeling tasks, and (2) the lack of interpretable quantification of the relationship between knowledge and personalized preferences. To address these challenges, this paper proposes WPKL (Weighted Preference Optimization based on KG and LLM). WPKL leverages external knowledge to assist LLMs in modeling user preferences and employs a hybrid graph neural network (GNN) framework to enhance preference representation. Additionally, a weighted preference optimization (WPO) approach is proposed to fine-tune the LLM, enabling interpretable quantification of user preferences and personalized knowledge. Extensive experimental results demonstrate that WPKL achieves high-quality service recommendations.

Chatbots have advanced from basic conversational agents to versatile tools by integrating external services. However, traditional chatbots are constrained by predefined service boundaries, limiting their ability to handle complex tasks with unintegrated services. While most research focuses on improving service discovery and invocation through data-intensive pretraining, only 13.29% of services are well-documented, hindering practical deployment. This paper proposes a self-improving workflow for chatbots, using a “wide in, strict out” self-supervised learning approach to acquire domain knowledge efficiently and generate high-quality service documents. Compatible with existing methods, it eliminates the need for dataset collection or pre-training. Experiments demonstrate that our workflow significantly improves the pass and success rate of chatbots in utilizing unintegrated services, offering a powerful solution for real-world applications where service integration is limited.

To address the limitations of existing microservice bad smell (MBS) detection and refactoring tools, particularly the lack of fully automated architectural bad smell refactoring solutions, this paper proposes a MAPE-K-based self-adaptive framework for microservice bad smell refactoring (SABER). The framework aims to eliminate architectural smells through closed-loop self-repair, thereby reducing risks related to main-tainability, scalability, and security. SABER employs a cloud-edge collaborative architecture: edge-side components collect real-time metrics from a Kubernetes cluster, while cloud-side components detect architectural smells and dynamically generate refactoring strategies. These strategies include service merging, splitting, adding, and adjustment. By automatically executing these strategies, SABER achieves architectural bad smell elimi-nation. Experimental results show that the framework achieves 95.53 % precision and 84.71 % recall across ten benchmark systems, significantly improving refactoring efficiency compared to semi-automated and manual methods. Its deep integration with DevOps pipelines validates its effectiveness in sustaining microservice health, offering a novel paradigm for autonomous maintenance in distributed systems.

In the era of industrial internet, achieving a dynamic balance between mass production and personalized customization has become a core demand for industrial development. This necessitates that product service systems can accurately capture users' personalized requirements. Although large language models (LLMs) possess powerful dialogue and reasoning capabilities, enabling them to identify implicit requirements, they still exhibit limitations in supply-demand matching, particularly in the precise alignment between personalized requirements and product capabilities. To this end, this study innovatively proposes a personalized product customization service framework (Req2Sol) based on fine-grained supply-demand cognition. This framework formalizes the modeling of supply-demand capabilities and finegrained personalized requirements through a knowledge graph (KG), integrating them into the LLM training process. This significantly enhances the model's understanding of supplydemand relationships, enabling accurate product configuration and customization recommendations. Firstly, a multi-view modeling approach for supply-demand capabilities and personalized fine-grained requirements is proposed, constructing a requirementproduct knowledge graph. Secondly, the knowledge graph is utilized as pre-training data to achieve domain-specific finetuning of LLMs. By introducing conditional scenarios and strategies, a five-level quantitative evaluation system for Req2Sol is established, improving its performance by 7.3 % compared to the baseline model when handling unconventional or inaccurately expressed user requirements. Finally, using the air conditioning domain as a case study, the effectiveness of the framework in achieving precise supply-demand cognition and customized product recommendations is validated through the Req2Sol-QAS system developed by invoking Req2Sol services.

The integration of Mobile Edge Computing (MEC) into the Industrial Internet of Things (IIoT) has markedly improved resource accessibility and propelled digital-intelligent advancements. Nevertheless, the substantial costs of MEC infrastructure also pose critical challenges to incentive mechanism design. Specifically, there is still an absence of a standardized and widely recognized incentive framework for the dynamic and non-cooperative interactions between edge service requesters and providers. Furthermore, the highly complicated characteristics of IIoT necessitate a greater reliance on dependable and trustworthy edge resource provision than other paradigms, which implies that human-centric factors (e.g., credit) are equally crucial as profit-driven metrics (e.g., price) in incentive design. To tackle these challenges, we propose In3Edge, a Stackelberg game-based incentive mechanism that systematically considers the interplay between profit-driven and interest-oriented indicators while accommodating heterogeneous peers, subjective interest divergences, and objective resource disparities. Particularly, leveraging convex optimization theory, we provide rigorous proofs and in-depth analyses of the intrinsic properties of In3Edge, encompassing the concavity/convexity of utility functions, equilibrium solution boundaries, optimal responses under peer/interest heterogeneity, and closed-form solutions for symmetric multipeer scenarios while articulating a series of propositions and theorems to underpin future research. Finally, extensive experiments are constructed under diverse dynamically changing scenarios with distinct characteristics, confirming the strong motivational capabilities of In3Edge in MEC-empowered IIoT.

The predominant obstacles confronting contemporary cyber-physical systems (CPSs) are their extensive heterogeneity and stringent constraints, such as diverse applications and real-time service requirements. While the incorporation of mobile edge computing could alleviate some of these constraints, challenges persist in equilibrating services and loads due to the finite computational resources of edge servers. Concurrently, the implementation of associated modules or methodologies has been prompted by escalating apprehensions regarding service security, which may have a detrimental impact on the performance of CPS, particularly in terms of resource occupation and service overhead. To this end, we develop an end-edge-cloud-collaborative CPS framework in which tasks are modeled as workflow applications, followed by constructing a multi-stage service-enhanced method named SerFlow. In SerFlow, a relevance-aware security precaution mechanism is devised, which evaluates the connection hierarchy and correlation metrics across subtasks, subsequently establishing the service anti-conflict mechanism to augment security levels. Particularly, a comprehensive evaluation score for security precaution levels is provided, which enables the security precaution to engage in the following optimization operations and evaluation modules as a quantifiable metric. Leveraging the non-Euclidean geometry framework, we then develop a Pareto frontier modelling method that integrates Newton-Raphson and geodesic while a survival value evaluation strategy with correlation constraints is involved in accomplishing coarse-grained cluster selection. Subsequently, an improved value-based and model-free deep reinforcement learning algorithm is suggested to generate fine-grained service strategies in end-edge-cloud collaborative scenarios. Finally, comprehensive experiments demonstrate the effectiveness of SerFlow in achieving enhanced security precaution levels while maintaining superior service efficiency.

Summary By providing distributed and ultra‐low‐latency communication between industrial devices and resource components, the Industrial Internet of Things (IIoT) is at the forefront of a new trend. Such a distributed paradigm is viewed as a collection of autonomous computing resources utilized by multiple heterogeneous devices to achieve higher‐quality interconnection and data exchange. However, stringent requirements of exceptional service and fairness guarantees pose many formidable challenges. To this end, this study investigates the aforementioned concerns in an integrated manner and further proposes a fairness‐aware task offloading method, called FOIMAM. Specifically, the ‐norm is introduced to accommodate the Pareto plane under the non‐Euclidean geometry framework while the evaluation and elimination of low‐quality solutions are completed based on survival scores. Particularly, the fairness requirements are formulated as a multi‐constraint problem and resolved using weighted max‐min fairness. Eventually, numerical results indicate that the proposed method brings substantial improvement in both service efficiency and fairness guarantees.

The Internet of Things plays an important role in the process of industrial intelligence. It facilitates the connection between devices and the Internet to enable the gathering and analysis of industrial data. However, industrial Internet of Things (IIoT) applications are highly susceptible to latency, have strict energy consumption limitations, and impose specific demands on the resources of IIoT devices. Fortunately, edge computing can migrate tasks to the edge server or cloud platform to deal with the above shortcomings. Nevertheless, tasks generated by IIoT devices in industrial production have a higher rate of repetition, and repetitive execution of the same task diminishes the operational efficiency of the edge system. In this regard, we can cache high-value tasks through task caching to reduce redundant execution. The aforementioned idea can be described as a joint optimization problem of computation offloading and task caching with resource constraints in IIoT. To address this challenge, we establish an edge-empowered service model and propose a cache-assisted computation offloading algorithm for IIoT applications. Extensive experiments demonstrate that the proposed algorithm outperforms some critical methods in terms of energy consumption and latency.

Owing to the breakthrough in mobile wireless communication technologies, almost everyone has been immersed into social networks, while fake news and misinformation are also being pushed into people’s minds with astonishing speed and breadth. The rising disparity between limited computing resources and the exploding news size necessitates innovative solutions to handle the challenge posed by booming data volume and make it more likely to differentiate fake news. In response to the aforementioned dilemma, the social-aware computation offloading system is analyzed, where the digital twin (DT) paradigm is used to simulate tasks offloading and assess the associated costs. Next, to obtain the best offloading choice, we fully consider the social relationship constraints and further propose an online task execution method that includes two stages of cluster selection and computing offloading, named TOFDS. Specifically, it exploits the technologies from multiobjective optimization and deep reinforcement learning (DRL) and realizes the joint optimization of resource utilization, load balancing, service latency, and energy consumption. Eventually, the comparative experiments demonstrate that TOFDS performs well when dealing with fake news data and can adapt to changes in dataset size and service clusters.