With the rapid development of Generative Artificial Intelligence (GAI), large-scale AI-Generated Content (AIGC) has been widely produced, raising critical challenges in trustworthy copyright management. Blockchain-based copyright registration or trading have become a research hotspot, but existing solutions focus on isolated stages and fail to support the full lifecycle of AIGC content, while copyright management performance, infringement detection capability, and copyright query efficiency remain challenging. To address these challenges, we designed a dual-layer blockchain framework for full-lifecycle AIGC copy-right management, which supports coordinated copyright registration, verification, trading, and traceability. The proposed framework adopts a dual-layer architecture with a main chain and multiple sub-chains, and integrates sharding with a Directed Acyclic Graph (DAG) parallel ledger to improve system scalability. Specifically, a Perceptual Hash (pHash)-based similarity detection method is introduced for copyright registration to identify plagiarism and unauthorized duplication; a hybrid indexed sharded query mechanism is designed for efficient and verifiable copyright verification; and cryptographic techniques together with zero-knowledge proofs are incorporated to enable secure and non-repudiable copyright trading. Experimental results show that the designed framework delivers about 1.1× higher throughput and achieves roughly a 29× reduction in transaction latency compared with single-chain blockchains, while the proposed query mechanism reduces query latency by up to 56× across different shard scales. These results validate the capability of the proposed framework to support secure, efficient, and scalable AIGC copyright management.

Artificial Intelligence Generated Content (AIGC) plays a key role in shaping the emerging metaverse ecosystem through its ability to efficiently and automatically generate large scale, personalized content. While high-quality AIGC generation under a cloud-edge-end three-layer architecture has attracted significant research attention, existing approaches often overlook trust challenges throughout the AIGC service lifecycle namely, in model provision, Service Provider (SP) selection, and product transaction. To address these issues, we introduce blockchain technology and propose a cloud-edge collaborative, blockchain oriented AIGC service architecture (CEAIGC). This architecture ensures secure and trustworthy interactions among AIGC model providers, SPs, and users. Specifically, we design embedded watermark coding rules for AIGC models and use blockchain to verify consistency between cloud and edge models, providing a reliable foundation for SPs. To further support trustwor thy SP selection, we formulate a multi-objective optimization problem that considers user utility, SP reputation, and energy consumption. We then propose a diffusion-model-enhanced Deep Reinforcement Learning (DRL) algorithm (DMA3C) to optimize SP selection and adaptively match metaverse user needs, enabling reliable, low-latency AIGC inference at the edge. To overcome blockchain performance bottlenecks, we employ a smart contract engine to establish state channels between transaction users. This enables efficient, secure, and atomic off-chain transfers of AIGC product ownership and service fees. Extensive experiments demonstrate that CEAIGC improves system throughput by 2.38×, and the proposed DMA3C algorithm achieves performance gains of 11.4% to 28.6% compared to other DRL-based approaches.

In recent years, the rapid development of Internet of Things (IoT) technology has led to a significant increase in the amount of data stored in the cloud. However, traditional IoT systems rely primarily on cloud data centers for information storage and user access control services . This practice creates the risk of privacy breaches on IoT data sharing platforms, including issues such as data tampering and data breaches. To address these concerns, blockchain technology, with its inherent properties such as tamper-proof and decentralization, has emerged as a promising solution that enables trusted sharing of IoT data. Still, there are challenges to implementing encrypted data search in this context. This paper proposes a novel searchable attribute cryptographic access control mechanism that facilitates trusted cloud data sharing. Users can use keywords To efficiently search for specific data and decrypt content keys when their properties are consistent with access policies. In this way, cloud service providers will not be able to access any data privacy-related information, ensuring the security and trustworthiness of data sharing, as well as the protection of user data privacy. Our simulation results show that our approach outperforms existing studies in terms of time overhead. Compared to traditional access control schemes ,our approach reduces data encryption time by 33%, decryption time by 5%, and search time by 75%.

Anomaly detection in blockchain transactions faces several challenges, the most prominent being the imbalance between positive and negative samples. Most transaction data are normal, with only a small fraction of anomalous data. Additionally, blockchain transaction datasets tend to be small and often incomplete, which complicates the process of anomaly detection. When using simple AI models, selecting the appropriate model and tuning parameters becomes difficult, resulting in poor performance. To address these issues, this paper proposes GANAnomaly, an anomaly detection model based on Generative Adversarial Networks (GANs) and Autoencoders. The model consists of three components: a data generation model, an encoding model, and a detection model. Firstly, the Wasserstein GAN (WGAN) is employed as the data generation model. The generated data is then used to train an encoding model that performs feature extraction and dimensionality reduction. Finally, the trained encoder serves as the feature extractor for the detection model. This approach leverages GANs to mitigate the challenges of low data volume and data imbalance, while the encoder extracts relevant features and reduces dimensionality. Experimental results demonstrate that the proposed anomaly detection model outperforms traditional methods by more accurately identifying anomalous blockchain transactions, reducing the false positive rate, and improving both accuracy and efficiency.

Cryptocurrencies, underpinned by blockchain technology, face persistent threats such as money laundering and extortion due to their decentralized and anonymous nature. Detecting fraudulent transactions is crucial for ensuring the security of block-chain systems. However, the existing detection methods face the following challenges: lack of labeled data, severe class imbalance in labeled data, complex network structure, numerous parameters, and long training time. To address these challenges, we propose a novel semisupervised learning framework that combines the graph augmented with pseudolabels generation (GAPLG) model and postprocessing technique. Our framework employs graph learning networks to elucidate relationships between transactions and users. By utilizing pseudolabels for unlabeled transaction data and embedding them onto diverse graph nodes, we achieve precise labels, enhancing prediction accuracy. Additionally, we employ specific post-processing technique, such as correction and smoothing (C&S) technology, to rectify residuals and refine labels, ensuring our framework rivals the best parameter and baseline models. Our method boasts high scalability and flexibility, aiding in optimizing various evaluation indicators. Experimental verification through multiple real transaction datasets under varying data segmentations, demonstrated its effectiveness when compared with other representative frameworks. The analysis validates the effectiveness and benefits of our method.

Modern internet applications exhibit characteristics of distribution and diversity. Cross-domain authentication becomes necessary when applications or services are located in different domains. The security and efficiency of information interaction is closely related to the security and efficiency of cross-domain authentication. Existing cross-domain authentication models mostly rely on trusted third parties, which pose heavy key management and private key escrow problems. A secure and efficient cross-domain authentication scheme based on blockchain is proposed in this text. The scheme uses hash function and digital signature to ensure the reliability of foreign user identity. The proposal introduces blockchain and some ideas of the Open Shortest Path First (OSPF) dynamic routing protocol.

The 6G wireless network aims to forge a new spectrum, high technical standards of high time and phase synchronization accuracy, and 100% geographic coverage to connect trillions of devices flexibly and efficiently in the future. However, as connectivity increases and applications become novel, it is a challenge to ensure the privacy and security of networks and applications. Blockchain is seen as a promising technology that can improve efficiency, reduce costs, mitigate security, and privacy threats, and establish a trusted data-sharing environment. This article presents a trusted framework based on blockchain technology from the perspective of how to build a trusted software-defined content delivery network. As the peer node of the blockchain, the software-defined network (SDN) controller establishes trust between different regions and a wide range of participants, realizing peer autonomy and flexible business orchestration. The two main purposes of the architecture are to enhance the security of network communications and establish trust relationships between entities in different domains. It includes trusted communication based on routing sandbox, service choreography based on blockchain, proxy server selection strategy based on model predictive control (MPC), and optimization consensus based on practical Byzantine fault tolerance. Some simulation experiments verify the effectiveness of the theoretical method.

To deal with the explosion connections and data volume for emergency communication or hot spot capacity enhancement with massive Internet of Things (IoT) devices, deploying aerial base stations (AeBSs) on unmanned aerial vehicles (UAVs) to generate heterogeneous space–air–ground networks is considered to be a quite effective method. However, the flying AeBSs and back-hauling to existing heterogeneous networks (HetNets) lead to network energy consumption a key point. To ensure the energy-efficient operation of space–air–ground networks for smart IoT applications, we put forward the cluster-based HetNets energy-efficient resource allocation mechanism (CHERA). The scheme first divides the entire network into multiple independent BS clusters with the K-means++ algorithm for distributed energy efficiency (EE) optimization. Then, we propose a greedy BS sleeping strategy and a Lagrangian-dual-based optimal power allocation algorithm for the maximization of EE in each BS cluster. The EE optimization of space–air–ground IoT networks is implemented under the self-organizing network framework to make sure of the efficient and reliable operation of the network. Simulation results indicate that energy consumption is effectively decreased with the mechanism. It boosts the EE of space–air–ground networks by 23.8% compared with a baseline algorithm in which BSs are all in active mode with no power optimization. The result is expected to be useful for achieving future green space–air–ground networks IoT applications.

The Industrial Internet of Things (IIoT) is one of the important applications under the 5G massive machine type of communication (mMTC) scenario. To ensure the high reliability of IIoT services, it is necessary to apply an efficient resource allocation method under the dynamic and complex environment. In view of the absence of energy-efficient resource management architecture for the entire network, this article proposes an intelligent-driven green resource allocation mechanism for the IIoT under 5G heterogeneous networks. First, an intelligent end-to-end self-organizing resource allocation framework for IIoT service is given. Next, an energy-efficient resource allocation model within the framework is proposed. It is then solved by an intelligent mechanism with the asynchronous advantage actor critic driven deep reinforcement learning algorithm. Through the comparison analysis of different methods and rewards under IIoT scenarios with proper parameters setting, the proposed method can achieve better performance than other traditional deep learning (DL) methods and maintain service quality above accepted levels as well.

TextRank algorithm tends to extract words with frequent occurrence as keywords, while TF-IDF only considers the word frequency relation in the text library to extract keywords. In order to combine the advantages of the two algorithms, this paper proposes a Weighted TF-IDF of the Same Category Historical News Library and TextRank (TFSL-TR). This method first uses the classification model based on LSTM to classify the news, and calculates the TF-IDF value as the first weight of the word by using the news library of the target news category. Then use the TextRank algorithm to calculate the second weight of words. Finally, sum the two weights by weight and take the TopK words as keywords in order of size. The experiment was carried out in the Chinese news library, and the results showed that, compared with the traditional method, TFSL-TR could effectively improve the accuracy of keyword extraction.

In the face of rapidly emerging and explosion IP services, 5G IP+optical communication network architecture will become an important mode of communication for smart grid communication network. Under the control of SDN, management and maintenance of IP+optical networks can be realized effectively. In order to improve the collaborative ability and resource utilization of 5G IP+optical networks, this paper combines the characteristics of IP services. Firstly, risk equilibrium index is designed according to the bearing characteristics of IP network and optical network. Then, combined with network delay, bandwidth, website level difference and similarity of primary and alternate routes, a reasonable primary and alternate routes allocation model is designed. Finally, a co-allocation algorithm of service routing in 5G IP+optical networks based on deep reinforcement learning is proposed. The simulation results and comparative analysis show that the method not only fully utilize the resources of IP+optical networks, but also guarantee the average service delay and reduce the network risk. Otherwise, this method effectively improves the convergence speed, which provides demonstration and theoretical guidance for the construction of the future power communication network.

Network Function Virtualization (NFV) promises a significant advantage for IoT operators to steer substantial customizable service through a sequence of virtual network function (VNF). Service Function Chain (SFC) mapping is a key problem in IoT network resource allocation. There are two challenges in virtual resource allocation include: (1) how to map SFC requests to appropriate devices in the right sequence; (2) how to assure QoS requirements of SFC requests. Therefore, to meet the sharp increase of IoT traffic amounts and the diversification of IoT service requirements, a multi-objective service function chain mapping mechanism is proposed with two sub-mechanisms. First, a SFC mapping algorithm is designed to embed VNFs onto the substrate layer based on cost and load balancing. Then a reliability-aware SFC backup algorithm combining SFC backup and VNF backup is presented to economically and efficiently improve service reliability. The simulation results show that the algorithm can significantly improve the acceptance ratio of SFC requests, reduce cost, ensure network balance, and achieve long-term sustainable operation of the network.

The satellite is a primary means for providing emergency communication backhaul in disaster areas, where large bandwidth is demanded to support communication services in a wide affected area. Millimeter-wave (mmWave) communication with sufficient spectral resources promises significant enhancement to satellite-terrestrial link capacity. However, the alignment delay and mutual interference caused by directional communications with narrow beams severely limit the capacity of mmWave communication. To this end, we optimize the beamwidth to reduce the impact of beam alignment overhead on capacity. Then, considering the multi-user interference between beams, we propose a transmission scheduling scheme based on beam sharing, namely users with strong mutual interference when served simultaneously by independent beams, share the same beam. A heuristic algorithm is proposed to derive the groups of users sharing beams, and their beamwidth. Simulation results show that the proposed scheme achieves considerable capacity enhancement compared to the one-to-one beam occupation scheme (OB) and fixed beam scheme (FB), thus improving the spectrum efficiency of mmWave satellite-terrestrial communication.

The all-terminal reliability is the probability that all nodes in the whole network remain connected, and it is instructive to analyze the operational risk of the whole network. The exact calculation of the all-terminal reliability is an NP-hard problem, and it's not suitable for analyzing the reliability of the large-scale network. This paper presents a method of calculating the all-terminal reliability of the large-scale smart grid communication system (SGCS) based on the complex network theory. The network is divided into many communities before calculating the all-terminal reliability, and the all-terminal reliability of communities is calculated instead of the whole network. The use of complex network community structure of the SGCS can effectively reduce the complexity of the topology, and simplify the complexity of the all-terminal reliability's algorithm. Compared with the traditional approximate all-terminal reliability calculation methods, the algorithm has greatly reduced the time complexity and improved the accuracy of result.

Recently, a new opportunity for on-grid energy saving is enabled by the green network infrastructure sharing. This paper mainly investigates the collaboration between multiple operators to improve the energy utilization in this scenario. Then, an energy-saving management mechanism is proposed to reduce energy consumption and optimize energy utilization. We decompose the problem into two sub problems for base station sleeping and green energy allocation. And the BS sleeping algorithm and the green energy centralized allocation algorithm are respectively proposed to solve them. Comparing with other mechanisms, simulation results show that the proposed energy-saving management mechanism can effectively reduce 65% on-grid energy consumption while guaranteeing the quality of service (QoS) to the user equipment device (UE).

Current research in base station(BS) energy consumption area is mostly devoted to the study of the static energy consumption or dynamic factor. But it lacks the research on the energy storage efficiency of the BS. In this paper, we propose a power modeling of BSs based on energy storage monitoring. Firstly, the system architecture and networking scheme of energy storage monitoring are presented, and the mathematical model of BS energy consumption is analyzed. Then, based on the current network's data, a large number of real data are collected through the energy consumption analysis system. We analyzed the effect of charge and discharge time on energy storage efficiency of power grid. Based on the monitoring data, we fit the model of energy consumption and get the model of overall power consumption. Finally, based on the use control template, the optimization scheme of reducing the power consumption of authority network is proposed, which has substantial economic and green value.

Fault location plays a crucial role in Electric Power Data Network (EPDNet). It usually realized by analyzing the connection between failures and symptoms. However, many symptoms may not synchronize with faulty nodes and potential failures with unknown types remain undetected in passive network management. In this paper, we propose a Facing-the-Impact-Factor (FIF) algorithm using active approach based on probes. We use the complex network theory to calculate the impact factor of each node according to all services in EPDNet. And probes are applied to detect failures on initiative, thus a real time network state can be showed in the form of matrix, then apply the impact factor to Bayesian network to determine the faulty nodes. Simulation results demonstrate the high accuracy and low false positive rate of FIF algorithm.

Power models are crucial to assess the power consumption of base stations (BSs) without quantitive description. Currently available models seldom consider the dynamic factors such as indoor and outdoor temperature. As power model will affect the energy-saving gains of different green resolutions, in this paper we provide such power models for mobile communication BSs relying on practical data collected from several BSs with focus on dynamic factors, e.g., traffic load, indoor and outdoor temperature. The quantitative power models for communication equipment and air conditioning are defined and validated combined with the mathematical method of linear regression. With application of the models we develop an energy saving method which can save at least 10% of the power consumption per year through the simulation and analysis. Still, the method does not affect the normal operation of communication BSs, which takes on strong economy and green significance.

In this paper, we propose a planning mechanism based on telecom user behavior to choose locations of telecom operator's service halls. Telecom service hall network consists of service requirements nodes (RNs) and telecom service hall sites (TSs). Telecom service hall location selection problem mainly focuses on choosing locations of TSs from RNs. With analysis of base station data, we formulate a method based on telecom user distribution model to group users and to find RNs. Then, we propose a theoretical model to obtain telecom operator's greatest economic income with constraints of service satisfaction perceived by telecom users. Finally, a mechanism combined with improved genetic algorithm is put forward to solve it. Our results, supported by extensive experiments using MATLAB, confirm the feasibility and flexibility of our proposed planning mechanism.

As one solution to the gradual ossification of the existing networks, network virtualization enables multiple service providers (SPs) to coexist on a shared infrastructure, and it is considered as an integral part of next generation architecture. During the run time, SPs have exclusive rights for the allocated bandwidth resources, which may result in poor performance of bandwidth utilization. In this paper, we introduce a novel bandwidth reuse (BR) mechanism to allow SPs lease their idle bandwidth resources to other SPs as a virtual infrastructure provider (InP). In essence, the BR mechanism is a truthful auction with optimal expected revenue generation, which incentivizes SPs to open up their idle bandwidth significantly. Simulation results demonstrate that the BR mechanism could efficiently generate revenue and improve bandwidth utilization.

After proposed the framework of the VPN service management, the shortage of the current management information modeling methods in the network/service management is analyzed and the advantage of the ODP/UML based modeling method is given. The applying open distributed processing/unified modeling language (ODP/UML) for the management information modeling in the VPN service management is studied in detail. The implementation of the VPN SMS using CORBA is also given.