This paper introduces a distributed edge intelligence framework for secure and efficient data sharing in 6G-Internet-of-Vehicles (6G-IoV) environments. The approach integrates a hierarchical blockchain architecture with an innovative asynchronous federated learning algorithm to address the challenges of privacy preservation, communication efficiency, and scalability in 6G-IoV scenarios. The proposed framework employs a multi-layer structure comprising vehicles, roadside units, and cloud servers, each playing a distinct role in the distributed learning process. We present a comprehensive latency model that accounts for computation and communication aspects, considering the unique characteristics of vehicular networks. The asynchronous federated learning algorithm incorporates genetic algorithm-based resource optimization to adaptively allocate communication resources, significantly enhancing efficiency in heterogeneous 6G-IoV environments. Furthermore, we introduce the hierarchical edge intelligence consensus mechanism, a lightweight consensus protocol tailored for edge intelligence scenarios, which accelerates blockchain consensus while maintaining security. Simulations using MNIST and SVHN datasets demonstrate that the proposed framework outperforms state-of-the-art methods regarding model accuracy, communication efficiency, and privacy preservation.

Particularly as environmental issues and data privacy become more important, the fast growth of supply chain systems calls for ever more complex demand forecasting methods. Often, conventional techniques find it difficult to compromise these conflicting needs, which results in poor performance in either data security, environmental effect, or prediction accuracy. Artificial intelligence Internet of Things-enabled federated demand forecasting for green supply chains with a privacy-preserving carbon reference effect mechanism (AFED-Green/PCREM) is presented in this study. This new framework combining artificial intelligence and the Internet of Things (AIoT) features with federated learning provides sustainable supply chain demand forecasting. Using a distributed network of AIoT sensors, our method allows merchants to train demand prediction models cooperatively while preserving data privacy and, including environmental consciousness. The system uses a carbon reference effect mechanism that tracks how past emission levels affect consumer behavior and demand trends. Simulation findings show better performance than state-of-the-art baselines, with a 27% increase in prediction accuracy, a 42% drop in carbon emissions, and information leakage below 0.05%.

The rapid growth of social media platforms has led to an unprecedented increase in user-generated content and social interactions, posing significant challenges for recommendation systems. This article addresses the challenges of recommendation in large-scale dynamic social environments, where user interactions and preferences evolve rapidly across vast networks. In large-scale dynamic social networks, knowledge discovery requires methods to efficiently process vast amounts of data while capturing the evolving nature of user interactions and preferences. Multigraph neural networks offer a promising approach for this task, as they can model complex relationships and temporal dynamics in these environments. This article proposes a novel social-aware multigraph neural network for the session-based recommendation (SAMGNN-SR) model that leverages dynamic social information and multigraph neural networks to enhance recommendation accuracy and knowledge discovery in complex social computing environments. The model constructs a global social-aware interaction graph from all user session sequences and employs an adaptive subgraph sampling strategy to extract relevant collaborative signals efficiently. A dynamic interest extraction module utilizing dual-direction information propagation captures users' evolving preferences, while a social information fusion network based on graph attention mechanisms models the dynamic nature of social influences. Experiments on three real-world datasets (Douban, Delicious, and Yelp) demonstrate the superiority of SAMGNN-SR over nine state-of-the-art baselines, with improvements of up to 6.79% in NDCG@20 and 6.19% in Hit@20. Ablation studies validate the effectiveness of each model component in capturing complex social dynamics and session-based user behaviors.

Accurate driver emotion prediction is critical for enhancing safety and efficiency in 6G-enabled Intelligent Transportation Systems (ITS). In real-world ITS, drivers may face complex situations, and as a results they may express complex emotion. However, traditional driver emotion analysis struggles to capture the complexity of such blended emotions. To address this challenge, we apply emotion distributions to represent complex driver emotions. Besides, we apply the Label Enhancement (LE) to transform logical emotion labels into more informative emotion distributions, which reflect the varying relevance of multiple emotions. We propose in this paper a novel distributed LE method, called Distributed Label Enhancement with Label Manifold (DLEM). It leverages the manifold structure of logical emotions to enhance them into emotion distributions. DLEM is well-suited for edge and large-scale Internet-of-Vehicles (IoV) systems. Additionally, we design the Distributed Emotion Distribution Learning (DEDL) to learn and predict driver emotions from such enhanced emotion distributions. Finally, we conduct experiments on three large-scale emotion datasets. The experimental results show that DLEM significantly outperforms several state-of-the-art approaches and achieves the best performance.

With the rapid development of Internet of Vehicles technology, communication and data exchange between vehicles have become an important part of modern traffic management. A content placement and edge collaborative caching solution based on deep reinforcement learning is proposed in this paper, aiming to address the data processing and storage challenges faced by Internet of Vehicles systems. Utilizing the collaborative caching between smart vehicles and roadside units employs deep reinforcement learning methods to find and design a collaborative caching solution for the Internet of Vehicles edge. It uses content segmentation technology to divide and cache content fragments in advance to reduce the central server load and network pressure, thereby adapting to the randomness of vehicle mobility and communication duration. The experimental results show that the proposed scheme can effectively reduce the load on the central server, reduce network latency, and improve cache hit rate, providing a flexible and efficient solution for real-time communication and data exchange in the Internet of Vehicles system.

The rapid evolution of transportation cyber-physical systems (T-CPS) has led to unprecedented advancements in mobility and efficiency. However, ensuring the safety and reliability of these complex systems remains a critical challenge, particularly in verifying and refining their temporal behaviors. This paper presents a novel compositional approach for temporal behavior analysis and synthesis in safety-critical T-CPS, the safety-critical temporal analysis, and refinement for the T-CPS (STAR-TCPS) framework, which combines iterative compositional verification with L*-based learning techniques to analyze and refine timing behaviors efficiently. The method leverages the clock constraint specification language for high-level timing specifications and introduces a systematic refinement process to transform abstract requirements into implementable task models. The framework incorporates innovative constraint handling mechanisms tailored for T-CPS, addressing unique challenges such as vehicle-to-infrastructure communication delays and adaptive traffic management timing. Experimental evaluations, including a case study on an intelligent vehicle system, demonstrate STAR-TCPS’s superiority over existing methods. Results show significant improvements in verification time (up to 63% reduction), memory usage (44% decrease), and task generation efficiency (18-25% fewer tasks) compared to state-of-the-art approaches. The STAR-TCPS framework enhances T-CPS’s safety and reliability by enabling more efficient verification and refinement of critical timing properties, paving the way for safer and more robust transportation systems.

Conventional security architectures and models are considered single-network architecture solutions, which assume that devices authenticated within the network are implicitly trusted. However, such an approach is unsuitable for next-generation networks (NGNs). Zero-trust security was introduced to overcome these challenges using context-aware, dynamic, and intelligent authentication schemes. This paper proposes a novel zero-trust blockchain-enabled framework for secure next-generation healthcare communication network (HCN). The proposed framework integrates zero-trust and blockchain to provide a decentralized, secure, and intelligent solution for healthcare communication in NGNs. The system model comprises three components: HCN user identity modeling, blockchain and risk assessment-based access control, and dynamic trust gateway. The user identity modeling component utilizes attribute-based user behavior trajectory features, while the access control component leverages smart contracts-based risk assessment. The dynamic trust gateway component employs a consensus mechanism to achieve dynamic gateway switching and enhance network resilience. Simulation results demonstrate that the proposed framework achieves 31% lower calculation delays, 3% higher trust values, and 3% better attack detection accuracy compared to best baseline methods. It also exhibits a 2% improvement in access control granularity and maintains 95% network throughput under various failure scenarios.

Considering the extremely low signal-to-noise ratio (SNR) environment in future satellite internet of Things (IoT) transmission in which the traditional Hamming coded LoRa (Long Range) system in the ground IoT communication is of difficulty to guarantee high reliability, optimal coding scheme selection for the LoRa-based satellite IoT is studied for the first time. In specific, the corresponding system model is described in detail, followed by derivations of two noncoherent detection methods of coded LoRa signals. Subsequently, the channel capacity of the coded LoRa system is provided and used to develop the corresponding relationship between the signal-to-noise ratio (SNR) and the code rate. Based on the obtained relationship and the Monte-Carlo method, it is found that the coded LoRa system with different spreading factors (SFs) can achieve the lowest SNR when the code rate is close to 1/2. Finally, the performance of several coded LoRa systems (including Hamming code, BCH code, RS code, Turbo code, LDPC code and LDGM code with code rates of about 1/2) is compared by means of the Monte-Carlo method. Simulation results show that Turbo or LDPC coded LoRa systems exhibit more than 4.5 dB performance advantages over the Hamming coded LoRa system.

As an advanced biomedical knowledge modeling technology, biomedical ontology models the biomedical domain. However, since the lack of uniform standards for constructing biomedical ontologies, the biomedical ontologies obtained by different construction methods for the same thing may be different, which is known as the biomedical ontology heterogeneity problem. To solve this problem, we need to execute the biomedical ontology matching process, where it is important to integrate different similarity measures to improve the quality of alignment. Evolutionary Algorithm (EA) is an effective algorithm to address the biomedical ontology meta-matching problem. However, the classical EA-based biomedical ontology meta-matching technique needs to traverse reference alignment to evaluate the individuals, which makes the algorithm have high running time. To overcome this drawback, we propose an Interpolation Model (IM) based Hybrid EA (IM-HEA), which combines EA with a problem-specific IM to evaluate the individual and execute the local search process. In particular, we use lattice design to divide the feasible domain into several uniform sub-regions, and on this basis, approximately evaluate the fitness of newly generated individual. In addition, to avoid the algorithm falling into the local optimum, we further introduce an IM-based local search process into EA’s evolving process. In the experiment, we test IM-HEA on OAEI’s Benchmark and Anatomy and compared them with classical EA in terms of alignment’s quality and running time. The experimental results show that IM-HEA greatly enhances the efficiency of EA with little sacrifice on the alignment’s quality.

An ontology provides a formal description on the domain concepts and their relationships. Due to the subjectivity of ontology engineers, one concept might be expressed in various ways, yielding the so-called ontology heterogeneity problem, and ontology matching is a ground method to address this problem. Ontology matching technique uses the similarity measure to determine the correspondences between two heterogeneous ontology entities. In order to improve the quality of ontology alignment, it is necessary to combine different kinds of similarity measures, and how to optimize the aggregating weights is called the ontology meta-matching problem. Tin this work, a heuristic evaluating metric on the ontology alignment is presented to evaluate the ontology alignment's quality, and a mathematical model on ontology meta-matching problem is constructed. Then, an Adaptive Evolutionary Algorithm (AEA) is proposed to effectively solve this problem. In particular, when the elite solution remains unchanged, AEA adaptively activates three independent exploring strategies, which, respectively use the adaptive selection, crossover and mutation operators based on the population diversity metric. In the experiment, we compare AEA among EA based matching technique and the state-of-the-art ontology matching technique, and the experimental results show its effectiveness.

Ontology matching is able to identify the entity correspondences between two heterogeneous ontologies, which is an effective method to solve the data heterogeneous problem on the Semantic Web. Traditional fully-automatic ontology matching techniques suffer from the limitation of similarity measure, whose alignment’s quality cannot be ensured. To overcome this drawback, in this work, an Interactive Compact Genetic Algorithm (ICGA)-based ontology matching technique is proposed, which utilizes both the compact encoding mechanism and expert interacting mechanism to improve the algorithm’s performance and the alignment’s quality. In addition, an optimization model is established to formally define the ontology entity matching problem, and an efficient interacting strategy is proposed, which is able to reduce the expert’s workload and maximize his working value. The experiment uses Ontology Alignment Evaluation Initiative (OAEI)’s benchmark to test our proposal’s performance. The experimental results show that our approach is able to make use of the expert knowledge to improve the alignment’s quality, and it also outperforms OAEI’s participants.

With the widespread adoption of sensors, many research efforts in recent years have focused on the wireless sensor networks. Since a wireless sensor network consists of a large number of sensors, its trusted communication become a hot research topic. Sensor ontology matching technology is able to solve the sensor information heterogeneity problem, which ensures the communication quality among different wireless sensor networks. In the matching process, different classes of similarity measures have different contributions in matching sensor ontologies. How to determine the optimal weights to aggregate multiple similarity measures to obtain high quality ontology alignment becomes a challenge in sensor ontology matching domain. To face this challenge, this work proposes a neural network-based sensor ontology matching technique. In particular, a single layer perceptron is used to aggregate multiple similarity measures and the neural model is trained with different training examples to obtain higher ontology matching accuracy. The experimental results show that the proposed approach is able to determine higher quality alignment results compared to other matchers under different domain knowledge such as bibliographic and real sensor ontologies.

Aiming at enhancing the communication and information security between the next generation of Industrial Internet of Things (Nx-IIoT) sensor networks, it is critical to aggregate heterogeneous sensor data in the sensor ontologies by establishing semantic connections in diverse sensor ontologies. Sensor ontology matching technology is devoted to determining heterogeneous sensor concept pairs in two distinct sensor ontologies, which is an effective method of addressing the heterogeneity problem. The existing matching techniques neglect the relationships among different entity mapping, which makes them unable to make sure of the alignment’s high quality. To get rid of this shortcoming, in this work, a sensor ontology extraction method technology using Fuzzy Debate Mechanism (FDM) is proposed to aggregate the heterogeneous sensor data, which determines the final sensor concept correspondences by carrying out a debating process among different matchers. More than ever, a fuzzy similarity metric is presented to effectively measure two entities’ similarity values by membership function. It first uses the fuzzy membership function to model two entities’ similarity in vector space and then calculate their semantic distance with the cosine function. The testing cases from Bibliographic data which is furnished by the Ontology Alignment Evaluation Initiative (OAEI) and six sensor ontology matching tasks are used to evaluate the performance of our scheme in the experiment. The robustness and effectiveness of the proposed method are proved by comparing it with the advanced ontology matching techniques.

In order to enhance the communication between sensor networks in the Internet of things (IoT), it is indispensable to establish the semantic connections between sensor ontologies in this field. For this purpose, this paper proposes an up‐and‐coming sensor ontology integrating technique, which uses debate mechanism (DM) to extract the sensor ontology alignment from various alignments determined by different matchers. In particular, we use the correctness factor of each matcher to determine a correspondence’s global factor, and utilize the support strength and disprove strength in the debating process to calculate its local factor. Through comprehensively considering these two factors, the judgment factor of an entity mapping can be obtained, which is further applied in extracting the final sensor ontology alignment. This work makes use of the bibliographic track provided by the Ontology Alignment Evaluation Initiative (OAEI) and five real sensor ontologies in the experiment to assess the performance of our method. The comparing results with the most advanced ontology matching techniques show the robustness and effectiveness of our approach.

Due to the problem of data heterogeneity in the semantic sensor networks, the communications among different sensor network applications are seriously hampered. Although sensor ontology is regarded as the state‐of‐the‐art knowledge model for exchanging sensor information, there also exists the heterogeneity problem between different sensor ontologies. Ontology matching is an effective method to deal with the sensor ontology heterogeneity problem, whose kernel technique is the similarity measure. How to integrate different similarity measures to determine the alignment of high quality for the users with different preferences is a challenging problem. To face this challenge, in our work, a Multiobjective Evolutionary Algorithm (MOEA) is used in determining different nondominated solutions. In particular, the evaluating metric on sensor ontology alignment’s quality is proposed, which takes into consideration user’s preferences and do not need to use the Reference Alignment (RA) beforehand; an optimization model is constructed to define the sensor ontology matching problem formally, and a selection operator is presented, which can make MOEA uniformly improve the solution’s objectives. In the experiment, the benchmark from the Ontology Alignment Evaluation Initiative (OAEI) and the real ontologies of the sensor domain is used to test the performance of our approach, and the experimental results show the validity of our approach.

Data collection is an essential operation in wireless sensor networks (WSNs). Topology control and duty-cycle are two popular schemes in WSNs to improve the utilization of various network resource. The problem of low-cost topology control in duty-cycle wireless sensor networks is investigated in this paper. Due to each sensor's awake/sleep schedule, the topological graphs in duty-cycle WSNs are changed over time. A space-time graph model is presented to describe the dynamics of a series of topological graphs. The new topology control problem in a spacetime graph is defined, and then two heuristic algorithms are proposed to find the low-cost topological structures, in which the connectivity from each sensor to the sink is maintained. Simulations validate the effectiveness of the proposed algorithms.

Routing design is an effective mechanism to improve the energy‐efficiency in packet transmissions for wireless multihop networks. Meanwhile, the total energy consumption for packet transmissions over a path comprising of several unreliable links depends on the power assignment on each link. In this study, the authors address the energy‐efficient routing, which integrates with the optimal power assignment, in end‐to‐end (E2E) retransmission systems. The process of packet transmission across a path in E2E systems is modelled as a random walk. The expected energy consumed for the packet transmission is then calculated. As the expected energy consumption closely relies on the transmit power assigned on the path, it is necessary to integrate the power assignment into the energy‐efficient routing. The optimal power assignment on the candidate path is analytically derived for minimising the expected energy consumption. Specially, the energy‐efficient routing integrated with power sssignment (EERPA) algorithm is developed to find the optimal path among all candidate paths with all possible power assignments. Simulations demonstrate the efficiency of the authors' EERPA algorithm.

Quasi‐cyclic (QC) LDPC codes play an important role in 5G communications and have been chosen as the standard codes for 5Genhanced mobile broadband(eMBB) data channel. In this paper, we study the construction of QC LDPC codes based on an arbitrary given expansion factor (or lifting degree). First, we analyze the cycle structure of QC LDPC codes and give the necessary and sufficient condition for the existence of short cycles. Based on the fundamental theorem of arithmetic in number theory, we divide the integer factorization into three cases and present three classes of QC LDPC codes accordingly. Furthermore, a general construction method of QC LDPC codes with girth of at least 6 is proposed. Numerical results show that the constructed QC LDPC codes perform well over the AWGN channel when decoded with the iterative algorithms.