The anonymity and decentralization of blockchain facilitate illicit blockchain transactions, while the dispersed computing resources in IoT(Internet of Things) scenarios pose challenges like insufficient high-dimensional feature processing capability, poor unknown anomaly detection, and high model update costs for identifying illicit accounts. To address these issues, we propose FLTG-Double GAT, a dedicated monitoring method for decentralized resource scenarios. It aggregating multi-party knowledge to enable cross-organizational detection of illicit transaction, while protecting data privacy by only uploading detection model parameter. It adopt a customized feature extraction paradigm combining double GAT and PCA balances deep feature mining and efficiency, enabling high-precision modeling on edge devices. At the same time, a GRU-driven real-time update mechanism is used. This realizes lightweight adaptation of the detection model to dynamic transactions and enhances early warning capability. Experiments on the Elliptic++ dataset show it achieves 99.80% detection recall and 99.28% prediction recall with low computational complexity, verifying its scalability and practical value in large-scale distributed blockchain monitoring systems.

With the rapid proliferation of Industrial Internet of Things (IIoT) devices, massive Delay Sensitive and Computation Intensive (DSCI) tasks are generated. Traditional Mobile Edge Computing (MEC) systems face limitations like inter-cell interference at cell edges, degrading Quality of Service (QoS). To address this, Cooperative Access Edge Networks (CAEN) enable dynamic Access Point (AP) clusters for enhanced transmission reliability in IIoT. However, challenges arise from multi-user interference, bandwidth contention, dynamic environments, and heterogeneous resources, complicating joint resource orchestration. This paper proposes EdgeDiffuse, a diffusion-enhanced distributed resource orchestration algorithm, which optimizes task offloading selection, transmission power control, and computational resource allocation to minimize long term task completion time while promoting system load balancing. EdgeDiffuse enables adaptive and hierarchical coordination between user agents and edge servers. It integrates diffusion models under a Multi-Agent Deep Reinforcement Learning (MADRL) framework for improved policy exploration in high dimensional offloading decision spaces, and further uses convex optimization for server side resource allocation. Experimental results demonstrate that EdgeDiffuse achieves 28.17% reduction in task completion time, 7.40% improvement in task transmission rates, and 15.71% enhancement in load balancing compared to advanced baselines, showcasing superior performance in multi-user and resource constrained scenarios.

With the wide use of distributed energy resources, it is important to build efficient and trustworthy coordination among source, grid, load, and storage (SGLS) for the Energy Internet. Blockchain can provide a base of trust, but getting off-chain data through decentralized oracles still faces problems of low efficiency and poor reliability. To address these issues, this paper proposes a four-layer architecture that joins blockchain and oracle services. It also includes a data aggregation and checking algorithm based on threshold signatures and a reputation-based oracle selection method. The main idea of the algorithm is to find reliable nodes more efficiently by using a changing, multi-factor reputation model, and to make the aggregation process faster through preselection and threshold signatures. In this way, it keeps both reliability and efficiency in complex network settings. Simulation results show that the proposed method increases the speed of putting data on the chain and lowers delay, while enhancing the robustness of the oracle network under adverse network conditions. This work providesuseful technical support for building an efficient and dependable distributed-energy coordination infrastructure.

Internet of Things Devices (IoTDs) in remote areas connect to the Internet through Low Earth Orbit (LEO) satellites. The transmission of massive IoTDs data volumes leveraging satellites faces a critical challenge, as constrained bandwidth onboard significantly impacts the performance of time-sensitive applications. Moreover, due to the limited number of satellite-ground links and the capacity of satellite access devices, it is challenging to serve all IoTDs within the satellite network coverage. To tackle these challenges, we propose a dual-layer network architecture that integrates Satellite Internet of Things (SIoT) and Unmanned Aerial Vehicles (UAVs), where UAVs serve as transmission units and satellites serve as computing units to overcome the limitations of conventional SIoT. In the dual-layer network architecture, a dual-layer routing problem is formulated to ensure the rapid transmission of tasks, and the problem is further decomposed into UAV layer routing and LEO satellite layer routing. At the UAV layer, we propose a multitask concurrent routing strategy based on task segmentation according to path bandwidth to reduce link load and improve transmission efficiency. At the LEO satellite layer, an integrated computation-transmission routing strategy is introduced to mitigate onboard bandwidth constraints through computational capabilities, thereby reducing transmitted data volume and significantly enhancing transmission efficiency. We transform the problem into a Markov Decision Process (MDP) for each layer and solve the problem using Deep Reinforcement Learning (DRL). To enhance the performance, we introduce improvements to the route algorithm. In the UAV layer, we introduce channel state averaging to reduce algorithmic complexity. In the LEO layer, we employ Prioritized Trajectory Replay (PTR) to improve learning efficiency, while a loss constraint is introduced to enhance training stability. Simulation results demonstrate that the proposed algorithm outperforms other algorithms in terms of convergence performance and overall delay.

Communication overhead represents a primary bottleneck in distributed deep learning, impeding training scalability. Although existing gradient sparsification techniques reduce network traffic, they introduce critical limitations: they fail to optimize intra-node data paths and are incompatible with efficient, decentralized Allreduce operations. To address these issues, we propose D-DOSA, a DPU-based communication offloading framework. D-DOSA incorporates two key innovations: 1) D-DO, an architecture that establishes a direct GPU-DPU data path to offload data loading and intra-node communication from the host CPU; and 2) D-SA, a novel sparse Allreduce algorithm that, for the first time, enables compatibility between sparse tensors and high-performance, ring-based communication. We evaluated D-DOSA on a 8-node, DPU-enabled cluster using representative models including VGG, LSTM, and BERT. Experimental results demonstrate that our framework accelerates training by up to 1.32x compared to the state-of-the-art sparse training baseline, without compromising accuracy. Ultimately, D-DOSA shows that co-designing data-flow architectures and communication algorithms on the DPU resolves key bottlenecks in sparse training and presents a viable path toward scalable performance in larger systems.

Relay-based communication has become a key approach to meeting the growing demands for low latency and high reliability links in intelligent transportation and vehicle-to-everything (V2X) systems. In complex urban environments such as roads, tunnels, and dense high-rise building areas, traditional vehicle-to-vehicle (V2V) relay links are severely impeded by deep fading and multiple interference sources, which significantly degrade end-to-end performance. To enhance relay-based transmission under such harsh conditions, this paper investigates a dual reconfigurable intelligent surface (RIS)-assisted decode-and-forward (DF) relay V2V system as a representative relay-enhanced architecture. By combining two reconfigurable intelligent surfaces with a DF relay, the proposed scheme strengthens both hops of the relay link, effectively alleviating the performance bottlenecks commonly encountered in single-RIS or traditional relay schemes. The system adopts a Nakagami-mfading channel model and explicitly considers the aggregated interference at the relay and destination nodes. Based on this, we derive analytical expressions for the end-to-end outage probability and average channel capacity, employing Fox’s H function and the Gaussian-Laguerre quadrature method for precise evaluation. Additionally, an adaptive RIS reflection unit allocation algorithm is proposed to jointly optimize the total number of RIS units and their two-stage distribution under reliability constraints, thereby enhancing the efficiency of relay-based communication while reducing hardware deployment costs.

The crux of data disclosure lies in the meticulous quantification and judicious trade-off between privacy leakage and utility. Firstly, the measurement of privacy leakage is the premise of sensitive data compliance disclosure. Existing solutions are mainly based on the qualitative perspective and the group perspective, which are unable to quantitatively measure the risk of individual privacy leakage. Secondly, in terms of balancing privacy leakage and utility, existing solutions overlook uninformed disclosure scenarios. In such scenarios, the two-dimensional privacy-utility game will be reduced to the optimization of a single parameter of mutual information. To address the aforementioned drawbacks, this paper proposes a data disclosure mechanism tailored for heterogeneous privacy profiles. Specifically, we decouple the multilevel privacy leakage through mutual information. By respectively addressing the informed and uninformed disclosure, we achieve the optimization of the joint potential energy surface of privacy and utility, breaking through the dilemma of excessive protection and utility collapse in data disclosure. On this basis, we conduct a comprehensive discussion on the two data disclosure strategies, namely introducing disturbance and linear obfuscation. The results of extensive simulations verify the effectiveness of the proposed mechanism. Finally, our work shows that balancing privacy and utility in data disclosure is unfeasible with disturbance-introducing strategies. In contrast, using linear obfuscation strategies can achieve such a balance, and the optimal approach is the disclosure scheme that minimizes the data disclosure loss.

Few-shot knowledge graph completion (few-shot KGC) mines unseen knowledge by leveraging meta-learning and contrastive learning to achieve accurate predictions with limited triples. Recent studies have focused on designing distance or similarity metrics to provide better knowledge representation between entities and relations. However, three issues with negative sampling remain unexplored: 1) the construction of negative queries heavily relies on manual experience in selecting candidate tail entities, 2) the constructed negative queries may mislabel potential true facts, and 3) the varying difficulties of negative queries are ignored. To solve the above issues, in this paper, we introduce curriculum learning into few-shot KGC and propose a novel few-shot KGC framework empowered by an adaptive negative sampling mechanism, which can eliminate the dependence on any additional manual experience, reduce mislabeling, and generate negative queries with appropriate difficulty. Specifically, the proposed framework includes two alternating phases. In the negative sampling phase, we first design a novel positive-unlabeled learning based scoring function with a type-related candidates encoder and then build a variable-speed sliding window based pacing function to select negative queries with appropriate learning difficulty under current training step. In the meta-training phase, we develop an adapted triple-oriented knowledge encoder to provide accurate representation for queries. Experimental results demonstrate that the proposed framework outperforms the state-of-the-art baselines and provides negative queries with appropriate difficulty in few-shot KGC.

Open Radio Access Network (O-RAN) supports heterogeneous service coexistence through functional splitting and open interfaces, enabling traffic steering via functional orchestration and resource scheduling. However, existing studies focus on known traffic patterns and lack the ability to anticipate dynamic service demands in advance. Isolated optimization of orchestration and scheduling fails to ensure End-to-End (E2E) latency. The varying time scales and vast solution space further complicate the joint optimization. To address this, we propose a traffic twin-enabled orchestration and scheduling multi-timescale joint optimization scheme. Explicitly, we design a spatiotemporal attention-assisted Time Series Generative Adversarial Network (TimeGAN) traffic twin model (STAG-TD) to capture unknown traffic patterns. Based on twin results, we formulate a joint optimization problem and design a dual-timescale algorithm framework, including propose a Task Decomposed Dueling Double Deep Q-Network (TD3QN) algorithm to handle large-timescale orchestration, and use a Penalty-based Particle Swarm Optimization (PPSO) algorithm to manage small-timescale scheduling. Our scheme achieves a predictive joint optimization to reduce the transmission latency of services. Extensive results show our scheme outperforms state-of-the-art methods, reducing E2E latency by over 39% and increasing throughput by over 14.9%. The highly consistent results between real and twin data also demonstrate the effectiveness of the traffic twin model.

Semantic communications (SemCom) provide efficient transmission for mobile edge computing (MEC) services by extracting critical semantics from raw information. Although widely adopted in various scenarios, existing single-modal SemCom systems struggle to efficiently support edge multimodal data transmission. Additionally, mobile end users have varying communication requirements across different modalities. However, existing work lacks the ability to generate personalized communication policies tailored to diverse intents (Typically, communication policies include bandwidth allocation and modulation and coding schemes, etc.). In this paper, we propose an edge Cognitive SemCom Agent (CSCA) to facilitate edge multimodal SemCom. Specifically, CSCA leverages an edge Large AI Model (LAM) to realize modality alignment and natural language intent understanding. Moreover, we develop a communication planning module to realize the planning capability, which generates personalized wireless communication policies based on LAM’s environment and intent cognition. Particularly, to assess the efficiency of communication policies in multimodal SemCom and capture intent competition, we present a novel indicator named cognitive SemCom quality indicator (CSCQI). Then, we use the denoising diffusion probabilistic model to optimize the generation policy. Extensive experimental results demonstrate that CSCA achieves an average improvement in intent satisfaction rate and semantic accuracy by 42.19% and 29.75% respectively, while reducing communication delay by 33.40% .

The evolution of 6G networks introduces heterogeneous services with stringent computing and latency demands. However, constrained edge resources, intricate task dependencies, and dynamic network fluctuations intensify resource contention, increasing the risk of node faults and service interruption. Current fault-tolerant methodologies lack the necessary adaptability to handle the coupled complexity of task interdependencies and volatile resource states, leading to sub-optimal decisions or excessive system overhead. To address these challenges, this paper innovatively proposes TransDiffuse—an intelligent preemptive service migration framework for 6G edge networks. First, the framework employs a Transformer-GAT hybrid model to capture long-range temporal load dynamics and spatial topological constraints, enabling accurate failure prediction. Second, to navigate the trade-off between migration overhead and service robustness, we devise a diffusion-based decision module. This module efficiently explores the discrete combinatorial solution space to synthesize near-optimal service orchestration. Furthermore, a comprehensive evaluation system is constructed to validate the effectiveness of TransDiffuse. Experiments demonstrate that TransDiffuse reduces energy consumption by 32.4%, decreases task completion time by 25.6%, and improves resource balance by 18.7%, while keeping service violations below 5%. This work achieves joint optimization of energy, delay, and resource efficiency, offering a robust solution for resilient service orchestration in 6G edge networks.

Blockchain’s decentralization and immutability inherently ensure the privacy and transactional reliability of cultural resources. However, traditional global consensus mechanisms scale poorly with increasing data volume and transaction frequency. While sharding enhances blockchain scalability, current sharding-based implementations exhibit high latency and communication overhead during cross-shard transactions. In this paper, we propose F-CShard, a fast cross-shard consensus protocol that optimizes blockchain sharding and consensus for large-scale cultural resource sharing. F-CShard addresses two key challenges in existing systems: low transaction throughput and high cross-shard communication costs. Our solution incorporates four technical innovations. First, we construct a spatio-temporal correlation model based on historical transaction patterns and account geographical distribution to minimize cross-shard transactions. Second, we add a random-bit to optimize the Cuckoo Rule, thereby reducing the migratory frequency of nodes while improving system throughput and robustness. Third, we design a heartbeat-enhanced consensus protocol to decrease latency and communication overhead. Finally, we propose a cross-shard consensus protocol based on virtual accounts to simplify the processing of cross-shard transactions and ultimately improve the scalability and security of the system. Experimental results show that F-CShard outperforms X-Shard and LBF in terms of throughput and latency, and has near-linear scalability in high concurrency environments.

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.

Task scheduling decisions for multimodal large language model (MLLM) applications in multilayer computing power networks present a significant challenge, as they simultaneously balance system delay, carbon emissions, and model accuracy requirements while adapting to network conditions and varying energy availability. Thus, in this paper, we formulate the joint optimization problem of MLLM task scheduling, resource allocation, and green energy utilization to minimize system delay and carbon emissions while meeting accuracy requirements. We propose Green-Aware MAPPO, a novel approach that integrates graph attention networks (GAT) with multi-agent proximal policy optimization (MAPPO) for distributed decision-making in multilayer computing power networks. By modeling the problem as a partially observable Markov decision process (POMDP), our algorithm enables agents to capture complex resource dependencies through relation-specific attention mechanisms while maintaining high performance with limited local observations. Experiments in various network configurations demonstrate that Green-Aware MAPPO significantly outperforms baseline algorithms.

Transmitting multimodal data through semantic communication offers a promising way to enhance the quality of experiences. However, existing single-modal semantic communication systems struggle to efficiently support multimodal data transmission. Additionally, users have different communication requirements for different modalities, while existing work lacks the capability to generate personalized communication schemes tailored to diverse requirements. In this paper, we propose a cognitive multimodal semantic communication system. At its core is a cognitive semantic communication agent (CSCA) powered by edge large AI model (LAM), enabling low-latency modality alignment and natural language intent understanding. The CSCA integrates a cognitive communication planning algorithm that leverages intent cognition and environment cognition to create personalized communication schemes for users. Experimental results demonstrate that our system outperforms baseline systems in terms of semantic accuracy, intent satisfaction rate and communication latency.

Celluar traffic forecasting is critical for optimizing resources, balancing loads, and reducing costs in cellular networks. However, newly established base stations often suffer from data scarcity due to the lack of historical traffic data, and the varying traffic demands across regions and times further complicate accurate predictions. While deep learning models typically perform well in data-rich environments, their accuracy drops significantly in data-scarce areas. To tackle this challenge, we propose a Deep Attention-based Graph Transfer Network(DAGTN), a novel framework that leverages cross-domain transfer learning. The framework includes a spectral clustering algorithm based on Dynamic Time Warping (DTW) to group similar base stations, enabling effective knowledge transfer and capturing spatial dependencies. We also introduce DSAN, a deep temporal prediction model that integrates attention mechanisms and sampling strategies to capture traffic patterns more accurately. Additionally, Generative Adversarial Networks (GANs) are used for domain adaptation, aligning the distribution between source and target domains while maintaining data privacy. Our experiments show that DAGTN outperforms existing methods, improving prediction accuracy by 8.7% in RMSE, MAE, and R2. Ablation studies further validate the contribution of each component.

Artificial Intelligence Generated Content (AIGC) technology, with its highly efficient automation and intelligent algorithms, is transforming the way information is produced. However, the resource-intensive nature of large generative AI models, combined with traditional cloud-based inference approaches, leads to significant bandwidth consumption and unpredictable communication latency. On the other hand, directly deploying AIGC models on edge nodes faces challenges such as limited computational resources and storage space. To address these issues, we proposes an AIGC inference framework based on edge node collaboration, and develops an optimization model using model splitting methods. Building on this, we also designed a deep reinforcement learning algorithm incorporating Graph Attention Networks (GAT), which adaptively adjusts model partitioning points and resource scheduling strategies to effectively deploy AIGC models. Finally, simulation results show that the proposed algorithm improves task success rates by an average of 20% compared to Soft Actor-Critic (SAC) and Proximal Policy Optimization (PPO) algorithms, achieving a balance between computation and communication latency in edge environments.

With the development of immersive applications, eXtended Reality (XR) has emerged as a key application in scenarios such as Industrial Internet of Things (IIoT) and smart healthcare, where the demand for low-latency and high-bandwidth network performance is particularly urgent. To meet the low-latency requirements of multiple users in multiple XR services, service caching and wireless resource scheduling have become essential approaches. However, most existing studies focus on optimizing only one of these two aspects, ignoring the coupling relationship between the two, which makes it difficult to comprehensively improve the quality of XR services. To address this challenge, we propose a joint service caching and wireless resource scheduling method for low-latency XR delivery. By comprehensively considering user requests and service features, we formulate a joint optimization model with the objective of minimizing end-to-end latency. To solve this model, we design a Transformer-Enabled Multi-Agent ActorCritic (TMAC) algorithm. Specifically, we first introduce Graph Neural Network (GNN) to capture network features. Then, we model each XR service as an agent and design a Transformer-based actor network to make the service caching decision, while incorporating a global actor network based on Hypergraph Neural Network (HGNN) to generate resource scheduling decisions. This method achieves collaborative optimization of service caching and wireless resource scheduling while enhancing system flexibility and decision-making efficiency. Compared to various baseline methods, the proposed algorithm reduces the average service latency by approximately$\mathbf{1 5. 8 8} \boldsymbol{\%} \mathbf{- 3 1. 2 9} \boldsymbol{\%}, \mathbf{2 6. 7 3} \boldsymbol{\%} \mathbf{4 5. 0 9 \%,} \mathbf{2 9. 9 5 \%} \boldsymbol{-} \mathbf{4 9. 4 8 \%}$respectively, significantly improving the QoS (Quality of Service) and low-latency guarantee capability of XR service delivery in multi-user, multi-service scenarios.

Blockchain’s anonymity and decentralization improve financial efficiency but also facilitate illicit activities like money laundering and fraud. In the field of blockchain illicit transaction detection, researchers are confronted with several challenges, including the difficulty of analyzing high-dimensional data features, the imbalance in the quantity of training data, and the absence of certain features in the training data. This paper proposes a reinforcement learning-enhanced temporal generative adversarial networks (DRL-TGAN) model for detecting illicit transactions in blockchain. Firstly, this model adopts a dynamic feature selection strategy to achieve feature compression, saving computing resources without affecting accuracy. Secondly, we tackle the issue of training data imbalance by introducing noise during the generator training process to synthesize illicit data. Moreover, we propose a sliding window method based on TCN to capture the dynamic changes and long-term dependencies between transactions at different timesteps. We conducted the experiment using two Elliptic dataset, DRL-TGAN achieved a precision of 96.4%, outperforming existing methods in handling imbalanced and incomplete data.

With the rise of AI-Generated Content (AIGC) services in wireless networks, efficient and high-quality distributed training of Large Language Models (LLMs) has become essential for enabling the large-scale application of next generation AI technologies. However, the extensive parameters of LLMs impose significant demands on memory, computing power and communication resources in heterogeneous networks. To efficiently utilize the dispersed network resources, this paper presents a First-Pipeline- Then-Federated Learning (FPTFL) approach with a hybrid parallel scheduling strategy to facilitate the training of Transformer-based LLMs. We propose a block-wise splitting mechanism to partition the Transformer's encoder into distinct segments, which are deployed cross individual devices. The encoder parameters and intermediate smashed data are uploaded to the edge server, where the whole model is updated through federated aggregation. Particularly, we develop a fine-grained computation-efficient method based on pipeline parallelism, enabling the segments to cooperatively train the entire encoder. An optimization problem is formulated to determine the LLM segments and the number of micro-batches under network resource constraints, with the goal of minimizing the total latency of LLM training services. Simulation results demonstrate that our approach enables Transformer-based model training on resource-constrained devices, preserves model performance, and reduces waiting time.

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%.

In the growing demand for data sharing, how to realize fine-grained trusted access control of shared data and protect data security has become a difficult problem. Ciphertext policy attribute-based encryption (CP-ABE) model is widely used in cloud data sharing scenarios, but there are problems such as privacy leakage of access policy, irrevocability of user or attribute, key escrow, and trust bottleneck. Therefore, we propose a blockchain-assisted CP-ABE (B-CP-ABE) mechanism for trusted data access control. Firstly, we construct a data trusted access control architecture based on the B-CP-ABE, which realizes the automated execution of access policies through smart contracts and guarantees the trusted access process through blockchain. Then, we define the B-CP-ABE scheme, which has the functions of policy partial hidden, attribute revocation, and anti-key escrow. The B-CP-ABE scheme utilizes Bloom filter to hide the mapping relationship of sensitive attributes in the access structure, realizes flexible revocation and recovery of users and attributes by re-encryption algorithm, and solves the key escrow problem by joint authorization of data owners and attribute authority. Finally, we demonstrate the usability of the B-CP-ABE scheme by performing security analysis and performance analysis.

Vehicle road cooperation (VRC) services closely related to personal safety impose stringent requirements on reliability and real-time performance. However, the growing trend of vehicle to network (V2N) connections has significantly intensified the threat of distributed Denial of Service (DDoS) to the end-cloud communication links. To address this challenge, this article introduces a comprehensive DDoS defense architecture for VRC, named in-network DDoS-oriented Balancer, Inspector, and Filter (IDBIF). Leveraging programmable switches, IDBIF adopts an in-network mode to dynamically mitigate volumetric DDoS traffic in real-time, which coordinates three data plane functions for traffic processing: 1) load balancing; 2) packet inspection; and 3) traffic filtering. Our work faces the starved data plane resources and establishes the problem model for high reliability requirements of VRC services when subjected to DDoS attacks. Furthermore, we developed a multiagent hierarchical structure policy gradient (MAHSPG) algorithm that combines the inherent characteristics of DDoS attacks to tackle the runtime deployment of high-dimensional and heterogeneous defense function chains. Simulation experiment results show that the proposed approach alleviates the data plane resource bottleneck and improves the DDoS defense effect by 24.80%, highlighting its advantages in ensuring reliable VRC services.

With the development of the Industrial Internet of Things (IIoT), proactive fault tolerance through multi-node collaboration has emerged as a key approach to ensuring system stability. However, the distributed nature of edge environments introduces significant challenges to the robustness of existing proactive fault-tolerant systems. Outside the system, malicious nodes may disrupt the fault tolerance process, necessitating a robust collaborative mechanism to mitigate their impact. Inside the system, frequent node failures and other dynamic factors result in a highly dynamic network topology, requiring robust methods to optimize the effectiveness of fault identification and task migration decisions. In this paper, we utilize blockchain and Generative Adversarial Network (GAN) to construct a robustness enhanced proactive fault-tolerant framework. In our framework, we use blockchain for edge node supervision, and design an on-chain state lock mechanism to ensure the reliability of task migration during fault-tolerance processes. Considering QoS objectives and the credibility evaluations of blockchain on edge nodes, we construct proactive fault-tolerant task migration problem formulas and design a robust GAN-assisted proactive fault-tolerant task migration decision method based on these formulas. Finally, in an edge network built with Raspberry Pi devices, we validated the robustness of the proposed framework and the effectiveness of the proposed scheduling method. Compared with the baseline method, our method improved the task completion rate by an average of 13.8%, and reduced task completion delay and energy consumption by an average of 24.5% and 6.8%, respectively.

The combination of a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and an unmanned aerial vehicle (UAV) can further improve channel quality and extend coverage. However, the high-quality air-to-ground link is more vulnerable to eavesdropping by adversaries. In this paper, we investigate STAR-RIS-assisted covert communication in UAV non-orthogonal multiple access (NOMA) networks with a warden Willie, where Alice intends to transmit the covert signal to a near user Bob under the cover of a far user Carol via STAR-RIS. We aim to maximize the covert transmission rate by jointly optimizing the active and passive beamforming as well as the UAV location. The error detection probability and optimal detection threshold for Willie are first derived to obtain an analytic solution for the minimum detection error probability. Then, an alternating optimization algorithm is proposed to maximize the covert transmission rate under the condition of guaranteeing the communication of Carol and satisfying the covertness constraint of Bob. Specifically, the nonconvex problem is decomposed into three sub-problems by block coordinate descent, which are then solved using semidefinite relaxation and successive convex approximation. Finally, simulation results are presented to demonstrate the effectiveness of the proposed covert communication scheme for STAR-RIS assisted UAV air-ground networks.

In the industrial Internet of Things (IIoT), blockchain technology has been employed to ensure the trustworthiness of federated learning (FL) services. However, the existing framework that combines blockchain and FL suffers from poor training performance and high computational overhead due to the complex consensus mechanism. Although recent studies have explored architectures that integrate Directed Acyclic Graph (DAG) with FL, the aggregation process in DAG-based multi-branch structures still faces significant challenges due to strong statistical heterogeneity across branches. To accommodate the heterogeneity, this paper proposes a DAG-based asynchronous aggregation framework for decentralized FL services. In this framework, the local models are aggregated with global models in the DAG ledger to form a new transaction block (TB). The verified TB becomes the subsequent node of the tail node in the DAG multi-branch structure. Additionally, an FL model delivery mechanism based on improved ensemble distillation is designed. This mechanism merges the models in verified TBs from multiple branches of the DAG, enhancing the accuracy of the final delivery model without compromising system training efficiency. Extensive ablation and comparative experiments demonstrate that our proposed scheme enhances the training efficiency and accuracy of DAG-FL systems while ensuring the security and trustworthiness.

The rapid development of autonomous vehicles and smart city has led to an exponential increase in data generation within Intelligent Transportation Systems (ITS). However, comprehensive extraction and utilization of these data are severely hindered by communication and energy constraints, security and privacy concerns, vehicle mobility limitations, and spatial distribution challenges. Using 6G and Digital Twin (DT) technologies offers a promising solution to these problems. In this paper, we propose a DT-based model training architecture for vehicular networks and introduce Federated Learning (FL) to preserve data privacy. While distributed model training and parameter transmission introduce challenges in delay and energy consumption, which conflict with real-time service requirements in ITS. In addition, the quality of the data and the processing capability of each vehicle varies widely, which will affect the efficiency of data sharing and model accuracy. Therefore, it is vital to select appropriate training nodes and optimize resource allocation under the constraints of task delay and energy consumption. We formulate an optimization model to improve the selection of FL participating nodes and energy management strategies, aiming to maximize accuracy while minimizing energy consumption. We then develop a DT-assisted deep reinforcement learning (DRL) method. Experiments show that our scheme achieves higher training accuracy and energy efficiency compared to the benchmark.

With the initiation of the Internet of Everything, edge computing has emerged as a pivotal paradigm, shifting from cloud computing to better address the growing data demands and latency issues in Industrial Internet of Things (IIoT). However, securing edge computing systems remains a critical challenge as malicious attackers can compromise the IIoT systems, gain control over edge servers, and tamper with computation programs and results. Existing solutions, such as cryptographic encryption, intrusion detection, and blockchain-based methods, have been widely used to enhance security. Yet, these approaches often suffer from high computational overhead, limited adaptability to dynamic IIoT environments, and a lack of foundational trusted assurance mechanisms. Although Trusted Execution Environment (TEE)-based solutions provide a hardware-enhanced secure execution environment, they face scalability and usability challenges and cannot fully support the parallel execution requirements of multiple and diverse IIoT applications. To overcome these limitations, a novel secure edge computing system is proposed for IIoT that strengthens security from the physical layer. By establishing a computing sandbox model, we extend the trust boundaries of the TEE using a virtual Trusted Platform Module (TPM), enabling secure and efficient execution for diverse IIoT applications. The proposed approach integrates a trust guarantee mechanism with decentralized adaptive attestation, ensuring real-time integrity verification while reducing performance overhead. Through security analysis and experimental validation, it is shown that our system improves Non-Volatile Random-Access Memory (NVRAM) launch time by approximately 1,700 times compared to hardware TPM-based virtual TPM implementations, while enhancing protection against attacks such as rollback.

Large-scale models, also referred to as pretrained models, have attracted significant attention due to their outstanding performance and robust generalization. However, the high demands for data quality, stringent data privacy and security requirements, and limited computational and communication resources have imposed restrictions on the further development of large-scale models. Federated Learning (FL) is a popular machine learning framework that can effectively address this issue. However, when collaboratively training large-scale models using FL, local training and the transmission of large-scale parameters impose significant computational and communication burdens on mobile devices. This paper proposes a light-weight and high-efficiency federated learning framework (FedLH) for large-scale models. This framework divides large-scale models into semantic block-based submodels, allowing clients to transmit these submodels to the server for heterogeneous aggregation. This approach enables both communication and computational efficiency. With the proposed framework, each device can learn personalized, structured sparse models that can efficiently run on terminal devices. Experimental results demonstrate that FedLH outperforms other baseline algorithms by significantly reducing the number of training parameters and transmitted data. It also exhibits strong generalization and scalability.

With the deep integration of a new generation of information technology and physical manufacturing, equipment in all walks of life and fields has transformed to digitalization, networking and intelligence, and the Internet of Things puts forward higher requirements for ubiquitous interconnection, security, reliability, intelligence and efficiency. The data interaction of IoT terminal devices has cross-system, cross-enterprise, and cross-business requirements, but this also leads to many sensitive information in the Internet of Things network such as hidden leakage and difficulty in distinguishing the authenticity of data information. Based on the above challenges, this paper proposes a distributed authentication scheme based on DPoS consensus algorithm and a dynamic reputation evaluation mechanism based on smart contracts, which improves the authentication efficiency and the anti-attack ability of the authentication network and maintains the security and stability of the network. At the same time, the dynamic reputation evaluation results are uploaded to the blockchain storage, which not only ensures the security and immutability of data, but also provides queryable historical reputation records for subsequent terminal access authentication evaluation. Safety analysis and performance simulation experiments show that the proposed scheme has high safety and good performance.

With the rapid development of the Internet of Things (IoT), more and more IoT traffic is generated in the data network. Accurate perception of IoT traffic changes will facilitate traffic engineering decisions, thus ensuring the performance of IoT applications. However, current traffic prediction methods ignore the limitations of actual application environment. In this article, we propose an IoT traffic prediction method based on horizontal federated learning to predict traffic trends under the cooperation of the cloud and the edge side. In order to improve the accuracy of IoT traffic prediction, a traffic prediction model SMN3-CIFGA is proposed to predict IoT traffic based on traffic feature extraction in a limited hardware environment. In addition, in order to improve the communication efficiency in the distributed training process of the traffic prediction model, we propose a gradient compression algorithm based on dynamic threshold (GCADT). The experimental results demonstrate that compared with current methods, the average training time of the GCADT algorithm is reduced by about 6.21%, the transmission gradient size of the GCADT is reduced by about 66.71%, the average training time of the classification model SMN3 is reduced by about 40%, and the testing set prediction accuracy of SMN3-CIFGA can reach 97.61%.

To enable intelligent and low-cost End-to-End (E2E) network service deployment and Service Level Agreement (SLA) quality management in the two-level Cloud Radio Access Network (C-RAN), this paper studies a DTN-based SLA quality closed-loop management scheme, which mainly includes acquisition module, base module, deployment module, and monitoring module. The deployment module is responsible for constructing the service deployment optimization model with the goal of minimizing the average E2E delay of packets, and quickly obtain deployment decisions through a Weighted GraphSAGE (WGraphSAGE)-assisted Double Deep Q-network (DDQN)-based two-stage service deployment (WDTSD) algorithm. The monitoring module uses the state monitoring model based on Bayesian Convolutional Neural Network (BCNN) to complete the abnormal detection of physical devices. The modular closed-loop interaction provides a virtual environment for network service deployment, verification, monitoring, and policy revision, achieving SLA quality assurance. Extensive results validate the effectiveness of the WDTSD algorithm, state monitoring model, and DTN. WDTSD outperforms existing solutions in terms of memory overhead, computing speed, E2E delay, and service access ratio. The state monitoring model has better performance in indicators such as accuracy. The results under different data acquisition periods show that the service deployment effect is better when the DTN is closer to the physical network.

The distribution of computing resources in the Computing Power Network (CPN) is uneven, leading to an imbalance in resource supply and demand within domains, necessitating cross-domain resource scheduling. To address the cross-domain resource scheduling challenge in CPN, this paper presents an Improved Multi-Agent Actor-Critic (IMAAC) resource scheduling approach leveraging Data Processing Unit (DPU) offloading. Initially, we introduce a cross-domain resource scheduling architecture tailored for CPN by leveraging DPU offloading. Specifically, we delegate certain functionalities of the Multi-Agent Deep Reinforcement Learning (MADRL) Agent to DPUs, aiming to mitigate communication costs incurred during the generation of cross-domain scheduling decisions. Second, we introduce the parallel experience ensemble and multi-head attention mechanism in the Multi-Agent Actor-Critic (MAAC) framework to compress the state-space dimensionality of agent association across domains. Finally, we introduce the parallelized dual-policy network structure to mitigate training instability and convergence challenges within the actor and critic networks. Experimental results showcase that IMAAC achieves noteworthy reductions of 5.98%~13.56%, 23.54%~33.55%, and 41.17%~58.88% in total system delay, energy consumption, and the number of discarded tasks, respectively, compared to benchmark experiments.

Foundation models (FMs), known as pre-trained models, have garnered significant interest in Industrial Internet due to their remarkable performance and robust generalization capabilities in downstream tasks. However, with the increasing requirements of computing infrastructure and data privacy protection for large foundation models, existing learning frameworks face challenges such as data privacy leakage, poor scalability, and deployment difficulties. To address these issues, this paper proposes a novel collaborative Transformer Block (TB)-wise training framework based on Federated Learning (FL), which consists of three stages: pre-training, graph regularization, and personalized training. To tackle the challenge of statistical heterogeneity in distributed data, we design a Graph Convolutional Network (GCN)-based update operator that captures local training representations. Besides, we conduct an analysis based on feature similarity to enhance the interpretability of our algorithm. We choose popular vision Transformer models for the experiments, extensive results demonstrate that our framework can jointly train multiple clients to build a foundation model while improving the single client's personalized performance. The proposed method outperforms state-of-the-art frameworks under various data distributions and system heterogeneity settings, highlighting its robust performance.

The advancement of information and telecommunication technology has resulted in lots of computing power service providers (CSPs) sharing resources. This effectively improves the utilization of computing resources and provides opportunities for users to find best network services. However, with the increase of similar or identical services, traditional service provision mechanisms become more complicated and face more challenges. To provide on-demand services for users and trusted service sharing platform for CSPs, we propose a blockchain-based distributed network service provision (DNSP) mechanism. Based on blockchain technology, we design a distributed network service (BBDNS) architecture and introduce trusted QoS model for users and profit model for CSPs. Meanwhile, we develop a bi-objective optimization problem, called BP-MATCH, to balance benefits between users and CSPs. Then, we define DNSP mechanism based on smart contracts. Furthermore, to solve BP-MATCH, we design Kuhn-Munkres based service matching algorithm (KM-SMA) and ant colony optimization-based service matching algorithm (ACO-SMA). Finally, we conduct a simulation experiment based on the generated dataset. Simulation results show that the proposed algorithm can obtain the optimal service matching decision under certain conditions, and DNSP mechanism can guarantee the efficiency of service decisions while providing trusted distributed service.

With the rapid development of high-speed assistant driving and smart inspections, the edge network is required to provide quick and reliable service to avoid large service response delays and frequent re-transmissions caused by interruption. However, the reasonable service component caching, efficient edge collaboration and reliable cross-domain computation offloading are still key problems to be solved. Thus, we consider an AI and mobile edge computing (MEC) integrated service framework, which is highly reliable for high-speed mobile businesses, and we divide the service process into component caching phase and task offloading phase. In the first phase, we novelly define the edge collaborative service domain (ECSD) which allows multiple edge nodes to collaboratively share resources from a global perspective and design a user behavior aware service component pre-caching method to increase resource utilization. In the second phase, based on the formed ECSDs and cached service components, we present an AI-empowered cross-domain computation task offloading mechanism including task partition and backup to enhance the reliable service capability of edge networks. Simulation results verify that the proposed mechanism can jointly optimize the allocation of caching, computation, and communication resources, while improving the service response speed and resource utility of edge networks.

Utilizing unmanned aerial vehicles (UAVs) as aerial platforms to provide both sensing and communication services is envisioned as a promising paradigm, due to their inherent flexibility and maneuverability. In this paper, we propose a UAV-enabled sensing-assisted communication scheme for vehicular networks using the integrated sensing and communication (ISAC) technique. Specifically, we consider the geometry of vehicles as extended targets with multiple resolvable scatters and adjust beamwidth to cover the entire vehicle in the ISAC duration. Based on the reflected signals, the UAV can predict the state of vehicle, which is then exploited to generate tailored beams to effectively track the vehicle. To address the asymmetric sensing and communication requirements, a three-stage ISAC scheme with dynamic sensing duration and frequency is proposed according to the communication/sensing performance in real time. The initial state of vehicle is estimated in the first stage, followed by the use of ISAC wide beams in the second stage to achieve the vehicle coverage, employing an extended Kalman filtering (EKF) approach for state tracking and prediction. In the third stage, the UAV selectively transmits either an ISAC beam or a communication-only beam based on monitored sensing and communication performance metrics. Finally, simulation results are provided to evaluate the efficacy of the proposed scheme as compared to other benchmarks and also shed light on the tradeoff between communication and sensing.

Time sensitive networking (TSN) has been widely adopted and applied in many fields. The scheduling problem of TSN requires that the gate control list (GCL) is calculated according to the flow information in a given topology network. Conventional flow scheduling schemes are usually based on the given routing scheme, which limits the scheduling performance. Besides, current works mostly focus on the time trigger flows (TT). However, AVB flows exist as aperiodic flows in the industrial Internet. The integrated scheduling of these two types of flows is required to improve the overall schedulability. In this paper, a problem model of joint routing and GCL scheduling is proposed. An algorithm based on Tabu search (Tabu-RG) is proposed to solve the problem with specific design of neighborhood movement policy, neighborhood selection policy, as well as diversified function. Experimental results show that compared with the solver method, the proposed algorithm can save 75% of the time cost on the premise of ensuring the solution performance.

At the current stage, data trading is still in the initial stage, there is no mature pricing mechanism, and it is difficult to realize the effective use of data resources.In order to incentivize the better circulation of data and give full play to the value of data, it is necessary to build a pricing mechanism for data transactions between data owners and data requesters. To address the above issues, this paper proposes a blockchain-based data transaction pricing mechanism. First, for the privacy and security of data, we adopt a blockchain-based architecture to guarantee the security of the transaction data and at the same time satisfy the arbitration of data transactions. Second, we formulate a pricing scheme based on data quality to provide a basis for further game dynamic pricing; then, in this paper, in order to incentivize both parties of the transaction to actively participate in the transaction and maximize the utility of both parties as much as possible, we construct a pricing model based on the Stackelberg game; lastly, we design an improved Double Deep Q Network (DDQN) reinforcement learning algorithm to optimize the strategy of pricing in data transactions. Experiments show that the pricing mechanism proposed in this paper improves the utility of both parties in the data pricing process, and improves the total benefit by 10% compared to the DQN algorithm.

As the future trend, more and more vehicles access to the Internet of Vehicles, which means that a huge number of tasks of the vehicle terminals need to be transformed and completed on the network. Edge computing makes the tasks executed on the edge nodes near the terminal, but some vehicle terminals are at a relatively idle state and these additional computing resources are not utilized, causing great waste of resources. What is more, it is hard to highly and comprehensively satisfy the high real-time requirements of some tasks. In order to execute these tasks efficiently, we propose a dynamic edge–end computing collaboration architecture for urban IoV. In this architecture, edge nodes and vehicle terminals can cooperate with each other, which means tasks can be allocated more dynamically and flexibly. We evaluate the completion of the task by considering task latency and overhead, task transmission model, task priority, as well as edge node and vehicle terminal’s capacity when defining task comprehensive utility. Then weformulate the task allocation as an optimization problem and propose an improved quantum particle swarm optimization algorithm to solve the problem. Simulation results show that the proposed strategy have better task allocation utility than other strategies, which can effectively solve the multi task allocation problem.

The traditional centralized resource scheduling method leads to trust issues among multiple subjects carrying microservices. At the same time, in the process of service provision, single-point failure problems also occur from time to time. In order to realize the trusted provision of services, we build a blockchain-based distributed intelligent service trusted provision architecture, which uses smart contracts to realize the on-chain registration of resource information and automatic orchestration of microservices. In order to break through the bottleneck of blockchain efficiency and improve scalability, we use sharding technology to expand the blockchain. And the Raft-practical Byzantine fault-tolerance two-level consensus mechanism combining the Boneh–Lynn–Sacham (BLS) threshold signature (B-RBFT) is designed for blockchain sharding, which greatly improves throughput and reduces consensus delay while taking security into account. To meet higher Quality-of-Service (QoS) requirements, we design the microservice orchestration algorithm based on the improved double deep$Q$network (DDQN) to support microservice deployment and migration. In particular, to make the neural network converge faster, we improve the traditional DDQN framework by using double replay buffers and weighted target values. Simulation results show that our proposed algorithm has advantages in convergence speed, resource usage cost, delay, and load balancing.

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.

With the rapid development of the 5G and 6G technology, it has become an inevitable trend to share the cross-domain scattered data and enhance data value transmission. As a new data-sharing technology with intelligence and privacy computing, federated learning (FL) receives wide attention. It can realize data value delivery and data privacy protection at the same time, however, it lacks supervision in the application process, and the reliability of the calculation process and result transmission cannot be guaranteed. As a distributed ledger technology, blockchain has the trust property but lacks computing power. Therefore, we propose to extend the computing and supervision capabilities of blockchain with state channel, using state channel to create sandboxes and instantiate FL tasks in order to realize the trust supervision mechanism based on sandboxes. In this article, we establish an FL-based distributed data-sharing architecture and on the basis of the architecture we design a state channel-based distributed data-sharing trust supervision mechanism. Through theoretical analysis and experimental verification, the supervision mechanism we designed has an excellent performance in improving system security, resisting malicious attacks, and improving data model quality.

As a new trusted data sharing pattern with privacy protection, the integration mechanism of blockchain and Federated Learning has attracted extensive attention. Generally, this mechanism uses blockchain technology to supervise the original data and calculation results, which ignores the supervision of the Federated Learning model and computing process. Therefore, we introduce the concepts of the sandbox and state channel to construct a new data privacy sharing paradigm via Blockchain and Federated Learning. Under this paradigm, we use state channel to connect Blockchain and Federated Learning. And state channel is used to create a “trusted sandbox” to instantiate Federated Learning tasks in the trustless edge computing environment. Meanwhile, we also mainly solve problems about data privacy sharing in Federated Learning and system performance degradation caused by data quality. The simulation results show that the proposed method has better performance and efficiency than the traditional data sharing method.

IEEE 802.11ax, as a new generation of wireless local area network (WLAN) standard, has great development potential for multi-service access in industrial WLAN with its characteristics of low-cost, quick-deployment and high-efficiency. However, it still faces the problem of quick and efficient access point (AP) seamless handover for multi-service communications due to terminal mobility and channel degradation. To solve the problem, combining the characteristics of orthogonal frequency division multiple access (OFDMA) technology and multi-user multiple-input multiple-output (MU-MIMO) technology, this paper firstly proposes an edge computing based IEEE 802.11ax multi-service bearer mechanism by dynamically allocating channel resource units (RUs). Secondly, a multi-service AP seamless handover mechanism based on edge computing is proposed, which consists of a channel scanning delay optimization method based on virtual AP sentinel, a handover trigger judgment algorithm considering mobility and RU reallocation, a multi-objective optimized AP handover model considering load balancing, transmission rate optimization and handover cost minimization with service attributes. Finally, we propose a multi-service AP seamless handover algorithm based on improved particle swarm optimization (PSO) to get optimized handover making-decision. Simulation results show that the proposed handover mechanism can reduce the handover delay significantly while realizing seamless handover accurately in multi-service industrial WLAN.

Network function virtualization (NFV) is an emerging technology that uses virtualization technology to provide various services in enterprise networks and reduce costs. However, in cloud edge networks, effective virtual network function (VNF) configuration is particularly difficult, and the system design needs to consider the reliability and energy-saving while meeting the requirements of Quality of Service (QoS). This paper uses the binary integer programming (BIP) model to study the service function chain (SFC) orchestration problem, and designs a federated deep reinforcement learning SFC orchestration algorithm (FDOA). With this method, energy consumption can be reduced and the QoS of users can be improved. In addition, considering the limitations of local deep reinforcement learning (DRL) model training, this paper proposes a federated DRL algorithm to help obtain a more robust model, and simultaneously improve the convergence speed of the model. Among them, we introduce reputation theory during model training to evaluate the reliability of the nodes carrying the DRL model, avoiding the influence of unreliable models on the training effect. Finally, the simulation results show that FDOA has better performance in training time and end-to-end delay compared with other existing algorithms.

With the development of smart traffic and assisted driving, the mobile edge computing and artificial intelligence technologies are seen as the key solutions in the internet of vehicles. However, the limited edge network resources and leakage of vehicle private data in assisted driving process are still problems to be solved. Therefore, we design a federated learning (FL) empowered edge collaborative content caching mechanism to provide low latency and high reliable assisted driving services for vehicles. First, we build an edge collaborative cache domain to allow multiple edge nodes to jointly share the service component resources required by vehicles. Next, based on LSTM prediction model obtained by FL, we propose a service component pre-caching and placement strategy according to the predicted and real-time vehicle behavior, to realize fast and accurate content caching services. The simulation results show that the proposed mechanism can improve the performance in terms of caching hit rate, service delay and the resource utilization of edge nodes.

With the development of smart cities, the chimney construction method can no longer meet service needs. It is extremely urgent to build a unified urban brain, and the core issue is data sharing and fusion. Aiming at the problems of data island, data leakage, and high trust cost in the IoT of the smart city, a lightweight and trusted sharing mechanism (LTSM) is proposed. First, the blockchain is combined with federated learning to realize the data sharing, which not only protects the private data, but also ensures the sharing process trust. Then, a node selection algorithm based on credit value and a node evaluation algorithm based on smart contract are designed to improve the quality of federated learning. Finally, we propose an improved raft consensus to meet the delay and security requirements of the consortium blockchain in the smart city scenario. In the simulation, we evaluate the federated learning algorithm, the node selection algorithm, and the improved raft consensus, respectively. The experimental results show that the LTSM mechanism has a good application value. The federated learning model has a better accuracy, but its training time is also longer. The node selection algorithm is helpful to improve the accuracy of the federated learning model. The improved raft consensus improves the throughput.

As Internet-of-Things (IoT) service becomes richer, data sharing among different IoT systems gets popular. The traditional IoT system provides data storage and access service with the central cloud, which faces serious trust and security challenges. To provide a cross-system data sharing service, we adopt blockchain to build a multicenter data management (DM) framework and construct a trustable environment for data sharing. As regards to a security problem, attribute-based encryption (ABE) has been applied to the IoT system, but it still relies on the central server. Therefore, we design an ABE algorithm that could be used for multicenter scenario and shift DM to blockchain instead of a central server. Moreover, IoT devices always cannot afford complex encrypt computations as they have limited computing resource. To solve this, we design an obfuscating policy to shift encryption computations to the cloud instead of terminals. In this way, IoT devices could encrypt data with low computation cost. Security analysis and simulations prove that the algorithm we designed could reduce computation burdens of IoT terminals in data encryption and decryption phases effectively and safely.

As an emerging distributed machine learning (ML) method, federated learning (FL) can protect data privacy through collaborative learning of artificial intelligence (AI) models across a large number of devices. However, inefficiency and vulnerability to poisoning attacks have slowed FL performance. Therefore, a blockchain-based asynchronous federated learning (BAFL) framework is proposed to ensure the security and efficiency required by FL. The blockchain ensures that the model data cannot be tampered with while asynchronous learning speeds up global aggregation. A novel entropy weight method is used to evaluate the participating rank and proportion of the local model trained in BAFL of the devices. The energy consumption and local model update efficiency are balanced by adjusting the local training and communication delay and optimizing the block generation rate. The extensive evaluation results show that the proposed BAFL framework has higher efficiency and higher performance for preventing poisoning attacks than other distributed ML methods.

With the development of the Internet of Things, trust has become a limited factor in the integration of heterogeneous IoT networks. In this regard, we use the combination of blockchain technology and SDN/NFV to build a heterogeneous IoT network resource management model based on the consortium chain. In order to solve the efficiency problem caused by the full amount of data on the chain, we deploy light nodes and full nodes for the consortium chain. At the same time, we use the idea of identification to realize the separation of identification and resource information, build the application mode of on-chain identification and off-chain information, and realize resources endogenous trust management. We also propose a practical Byzantine fault-tolerant consensus mechanism based on reputation value to save consensus costs and improve efficiency. Combined with artificial intelligence technology, we introduce deep reinforcement learning for service function chain orchestration, and design a service function chain orchestration algorithm based on Asynchronous Advantage Actor-Critic to optimize orchestration costs. The final simulation results show that the consensus algorithm and service function chain orchestration algorithm we designed have good performance in terms of cost saving and efficiency improvement.

Blockchain technologies can enable secure computing environments among mistrusting parties. Permissioned blockchains are particularly enlightened by companies, enterprises, and government agencies due to their efficiency, customizability, and governance-friendly features. Obviously, seamlessly fusing blockchain and cloud computing can significantly benefit permissioned blockchains; nevertheless, most blockchains implemented on clouds are originally designed for loosely-coupled networks where nodes communicate asynchronously, failing to take advantages of the closely-coupled nature of cloud servers. In this paper, we propose an innovative cloud-oriented blockchain -- CloudChain, which is a modularized three-layer system composed of the network layer, consensus layer, and blockchain layer. CloudChain is based on a shared-memory model where nodes communicate synchronously by direct memory accesses. We realize the shared-memory model with the Remote Direct Memory Access technology, based on which we propose a shared-memory consensus algorithm to ensure presistence and liveness, the two crucial blockchain security properties countering Byzantine nodes. We also implement a CloudChain prototype based on a RoCEv2-based testbed to experimentally validate our design, and the results verify the feasibility and efficiency of CloudChain.

The industrial Internet of Things (IIoT) and 5G have been served as the key elements to support the reliable and efficient operation of Industry 4.0. By integrating burgeoning network function virtualization (NFV) technology with cloud computing and mobile edge computing, an NFV-enabled cloud–edge collaborative IIoT architecture can efficiently provide flexible service for the massive IIoT traffic in the form of a service function chain (SFC). However, the efficient cloud–edge collaboration, the reasonable comprehensive resource consumption, and different quality of services are still key problems to be solved. Thus, to balance the quality of IIoT services, as well as computational and communicational resource consumption, a multiobjective SFC deployment model is designed to characterize the diverse service requirements and specific network environment for the IIoT. Then, a deep-$Q$-learning-based online SFC deployment algorithm is presented, which can efficiently learn the relationship between the SFC deployment scheme and its performance through the iterative training. Simulation results demonstrate that our proposed approach outperforms others in balancing the resource consumption, accepting more SFC requests, as well as providing differentiated services for delay-sensitive IIoT traffic and resource-intensive IIoT traffic.

The Internet of Things (IoT) in smart cities collects and transmits a large amount of time–space-sensitive information to realize feedback control. It bridges the gap between the information world and the real world. With the data-based feature, the security and credibility of the IoT mainly depend on whether the source of the data is trusted. Therefore, as the data collection and transmission entity, sensing nodes should be classified and proved the trustworthiness. However, the existing works failed to classify and measure the credibility of sensing nodes multidimensional in real time. The previous trust-proof methods also cannot effectively protect the key information. To address these problems, this article first proposes a multidimensional and fine-grained dynamic measurement method in a trusted computing environment. Then, a trust classification model of sensing nodes is presented, and a grouping mechanism of different trust levels is designed to identify malicious nodes. Finally, a threshold ring signature-based trust certification scheme is proposed for data source authentication. It can adequately protect the privacy information of the attestation node and has complete anonymity and traceability. Besides, the scheme has a shorter signature and high computational efficiency, which makes it also suitable for sensing nodes with limited computing resources. The simulation results show that the scheme has better dynamic adaptability and can effectively ensure the credibility of data sources under the premise of various attacks with accessible impact on the system.

The rapid development of the Internet of Things has put forward higher requirements for the processing capacity of the network. The adoption of cloud edge collaboration technology can make full use of computing resources and improve the processing capacity of the network. However, in the cloud edge collaboration technology, how to design a collaborative assignment strategy among different devices to minimize the system cost is still a challenging work. In this paper, a task collaborative assignment algorithm based on genetic algorithm and simulated annealing algorithm is proposed. Firstly, the task collaborative assignment framework of cloud edge collaboration is constructed. Secondly, the problem of task assignment strategy was transformed into a function optimization problem with the objective of minimizing the time delay and energy consumption cost. To solve this problem, a task assignment algorithm combining the improved genetic algorithm and simulated annealing algorithm was proposed, and the optimal task assignment strategy was obtained. Finally, the simulation results show that compared with the traditional cloud computing, the proposed method can improve the system efficiency by more than 25%.

Blockchain, as an emerging distributed data management technology, has attracted extensive attention in recent years. In particular, a public blockchain network can ensure data security by addressing computationally intensive cryptographic tasks. Therefore, for node devices, sufficient computing power is required. However, mobile devices with limited computing power do not meet the conditions required by public blockchain network applications (OZEX, CoininAsia, BitRewards, etc.). To copy with the mentioned problems, nodes can offload computing tasks to edge computing services with low latency. This paper mainly focuses on the trade between edge computing providers (ECP) and nodes. We build a computational resource market model based on auction. Meanwhile, we propose two strategies to deal with two methods of offloading to achieve higher system profit. We also prove that the proposed strategy has individual rationality, authenticity under resource constraints. The simulation results have significance for administrators of a public blockchain network to improve the efficiency of computing resource allocation.

The demand for information security is growing with the changes of the times, and the authentication system is an important gateway to ensure information security. Password authentication is the most commonly used authentication method in modern network. However, because the password is easy to be cracked, we need to pay more attention to more authentication methods. In many authentications, the advantage of keystrokes and mouse authentication are more obvious; however, when researchers use mouse dynamics to authenticate, classifier training always require a large amount of data, and when the data is less, there may be inaccurate results. In this paper, a decision-level fusion method of the two classifiers is proposed, which reduces the strong dependence on data during training. In this method, the support vector machine optimized by genetic algorithm and k-nearest-neighbor algorithm are combined to get a lower error rate, which is lower than the error rate generated by the two methods alone.

Blockchain has broad application prospects in Smart City, and the technical characteristics of the blockchain itself can solve the problems of mistrust of network resource production relations and unfair distribution of revenue. However, most of the devices in Smart City are mobile devices with insufficient resources. The demand for computing power of the mining process cannot be met. For this, we introduce mobile edge computing, deploy edge servers on the edge side, and provide resources for mobile devices. We build an edge network resource allocation model for mobile blockchain to realize the effective application of blockchain technology in mobile environments. The resources required by the mining process can be obtained from neighboring resource sharing devices or edge servers. The resource allocation between adjacent devices can be modeled as a two-way auction model, and the Bayesian- Nash equilibrium is solved to determine the optimal price, while considering the trusted value of the device; the process of the mobile device acquiring resources from the edge server can be modeled as a two-stage Stackelberg game. Finally, simulation experiments show that this mechanism achieves a higher personal utility than an existing model that only considers requesting resources from an edge server.

In modern grid systems which is a typical cyber-physical System (CPS), information space and physical space are closely related. Once the communication link is interrupted, it will make a great damage to the power system. If the service path is too concentrated, the risk will be greatly increased. In order to solve this problem, this paper constructs a route planning algorithm that combines node load pressure, link load balance and service delay risk. At present, the existing intelligent algorithms are easy to fall into the local optimal value, so we chooses the deep reinforcement learning algorithm (DRL). Firstly, we build a risk assessment model. The node risk assessment index is established by using the node load pressure, and then the link risk assessment index is established by using the average service communication delay and link balance degree. The route planning problem is then solved by a route planning algorithm based on DRL. Finally, experiments are carried out in a simulation scenario of a power grid system. The results show that our method can find a lower risk path than the original Dijkstra algorithm and the Constraint-Dijkstra algorithm.

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.

The development of edge computing has alleviated the problem of limited vehicular computing capabilities in VANET. The vehicular edge computing (VEC) provide resources for the implementation of multiple intelligent services. However, the mobility of vehicles and the diversity of edge computing nodes pose huge challenges for service offloading. Deep reinforcement learning (DRL) in artificial intelligence (AI) is an effective technology to solve such challenges. Based on this scenario, we first introduce a software-defined vehicular networks (SDV) architecture that takes full advantage of the characteristics of SDN technology and can effectively and dynamically obtain a global view in VANET to facilitate the management of resources in the network. Then, we propose a new intelligent service offloading decision model, which introduces the Deep Deterministic Policy Gradient (DDPG) algorithm in DRL to solve the joint optimization of service offloading with multiple constraints. Simulation results show that the DDPG-based service offloading model has better performance and better stability than similar algorithms.

NFV separates network functions from hardware-dependent middle boxes, which can significantly reduce costs and improve network management flexibility. It has been widely used in operator networks. However, due to traffic fluctuation in the network, using virtual network functions to provide flexible services is still challenging. In addition, most VNF scaling methods are passive in nature, which may cause high latency and fail to meet the QoS requirements of services. Therefore, this paper first proposes a GRU-based traffic prediction model and scales in/out VNF instances in advance based on the prediction result. Then we design a VNF buffering mechanism to avoid frequently releasing and creating VNF instances. Furthermore, based on the scaling results of VNF, we apply a DRL algorithm called A3C to train the agent and then obtain the optimal strategy of deploying new instances. Simulation results show that compared with other methods, the proposed proactive method can respond to traffic fluctuation in advance and reduce the total operating costs.

Network Function Virtualization (NFV) technology decouples network functions from the proprietary hardware by using generalized equipment and software, which lowers the cost of network operator. However, the existing mapping mechanisms in NFV environment can't optimize the cost of deployment and improve the rationality of network resource allocation while ensuring the basic service quality requirements of users. To solve the problem, a mathematical model which looks on the assurance of quality of service and cost optimization is established in this article. The model aims at maximizing the total revenue from service chain deployment in resource-constrained network, and takes the resource demand, end-to-end delay requirement and reliability requirement of the service request as the basic constraints. Furthermore, a greedy algorithm of service chain mapping named GA+LCB is proposed to solve the problem. Simulation results show that compared with other algorithms, GA+LCB can effectively improve the success rate of receiving service requests, reduce the cost in the deployment process and achieve higher deployment benefits while ensuring the QoS requirements.

Network Function Virtualization (NFV) can support flexible services provisioning in form of Service Function Chains (SFCs) consisting of ordered Virtual Network Functions (VNFs). The end-to-end (E2E) delay of flow traversing SFC has been an important indicator, especially for delay-sensitive E2E services, but there does not exist an analytical model that can accurately evaluate it. Moreover, the complicated network and stochastic request arrival are hard to predict and model. Therefore, quantitative delay model and dynamic NFV resource allocation method are needed. In this paper, an adaptive allocation method is designed to meet E2E delay requirements. Firstly, we devise an NFV resource allocation framework based on deep reinforcement learning (DRL) that can adapt to network changes by interacting with the network. Then a dynamic queuing model is established to determine average E2E packet delay. Based on the delay, we define the network utility function and propose a minimizing delay (MD) problem. According to the continuity of the problem, we use unsupervised reinforcement and auxiliary learning (UNREAL) to obtain the optimal allocation policy. At last, extensive simulation results show that UNREAL-MD has better performance compared to state-of-the-art methods in terms of delay, throughput and network utility.

Private and public networks sharing resources for Internet of Things (IoT) network through network function virtualization (NFV) and software-defined networking (SDN) forms a heterogeneous cloud-edge environment. However, the heterogeneous cloud-edge network faces trust and adaptation issues in resource allocation. To address these two problems, we introduce consortium blockchain and deep reinforcement learning (DRL) to construct the trusted and auto-adjust service function chain (SFC) orchestration architecture. In the architecture, this article integrates the consortium blockchain into the distributed SFC orchestration model to realize trusted resource sharing. In addition, for realizing auto-adjusted service provision, this article designs a dynamic hierarchical SFC orchestration algorithm (DHSOA) based on DRL to minimize the orchestration cost and improve the quality of service. Moreover, considering the dynamics of network entities, this article proposes a time-slotted model to support dynamic service migration which adapts to the high-mobility IoT network. The simulation results show that DHSOA has better performance than the link-state routing algorithm and deep Q -network placement algorithm not only in cost saving of 15.8% and 10.1% but also in time saving of 22.0% and 10.0%.

Nowadays, the widely used Internet-of-Things (IoT) mobile devices (MDs) generate huge volumes of data, which need analyzing and extracting accurate information in real time by compute-intensive deep learning (DL) inference tasks. Due to its multilayer structure, the deep neural network (DNN) is appropriate for the mobile-edge computing (MEC) environment, and the DL tasks can be offloaded to DNN partitions deployed in MEC servers (MECSs) for speed-up inference. In this article, we first assume the arrival process of DL tasks as Poisson distribution and develop a tandem queueing model to evaluate the end-to-end (E2E) inference delay of DL tasks in multiple DNN partitions. To minimize the E2E delay, we develop a joint optimization problem model of partition deployment and resource allocation in MECSs (JPDRA). Since the JPDRA is a mixed-integer nonlinear programming (MINLP) problem, we decompose the original problem into a computing resource allocation (CRA) problem with fixed partition deployment decision and a DNN partition deployment (DPD) problem that optimizes the optimal-delay function related to the CRA problem. Next, we design a CRA algorithm based on Markov approximation and a low-complexity DPD algorithm to obtain the near-optimal solution in the polynomial time. The simulation results demonstrate that the proposed algorithms are more efficient and can reduce the average E2E delay by 25.7% with better convergence performance.

With the fast development of smart cities and 5G, the amount of mobile data is growing exponentially. The centralized cloud computing mode is hard to support the continuous exchanging and processing of information generated by millions of the Internet-of-Things (IoT) devices. Therefore, mobile-edge computing (MEC) and software-defined networking (SDN) are introduced to form a cloud-edge-terminal collaboration network (CETCN) architecture to jointly utilize the communicational and computational resources. Although the CETCN brings many benefits, there still exist some challenges, such as the unclear operation mode, low utilization of edge resources, as well as the limited energy of terminals. To address these problems, a reinforcement learning-based joint communicational-and-computational resource allocation mechanism (RJCC) is proposed to optimize overall processing delay under energy limits. In RJCC, a Q -learning-based online offloading algorithm and a Lagrange-based migration algorithm are designed to jointly optimize computation offloading across multisegments and on edge platform, respectively. The simulation results show that the proposed RJCC outperforms the delay-optimal, energy-optimal, and edge-to-terminal offloading algorithm by 42%-74% in long-term average energy consumption while maintaining relatively low delay.

As the great prevalence of various Internet of Things (IoT) terminals, how to solve the problem of isolated information among different IoT platforms attracts attention from both academia and industry. It is necessary to establish a trusted access system to achieve secure authentication and collaborative sharing. Therefore, this article proposes a distributed and trusted authentication system based on blockchain and edge computing, aiming to improve authentication efficiency. This system consists of physical network layer, blockchain edge layer and blockchain network layer. Through the blockchain network, an optimized practical Byzantine fault tolerance consensus algorithm is designed to construct a consortium blockchain for storing authentication data and logs. It guarantees trusted authentication and achieves activity traceability of terminals. Furthermore, edge computing is applied in blockchain edge nodes, to provide name resolution and edge authentication service based on smart contracts. Meanwhile, an asymmetric cryptography is designed, to prevent connection between nodes and terminals from being attacked. And a caching strategy based on edge computing is proposed to improve hit ratio. Our proposed authentication mechanism is evaluated with respect to communication and computation costs. Simulation results show that the caching strategy outperforms existing edge computing strategies by 6%-12% in terms of average delay, and 8%-14% in hit ratio.

Residential energy management (REM) is an important way to encourage users to reduce or shift energy demand with dynamic pricing. It could significantly affect the supply-demand relationship between electricity service providers (ESPs) and users, reduce energy cost and consumption, and contribute to sustainable development. To improve latency and processing performance, a three-tier edge-cloud collaborative REM (ECCREM) architecture is presented. In consideration of matching the architecture, a two-stage energy management mechanism is proposed with system reliability and resource utilization requirements taken into account. At the first stage, the interaction between real-time pricing and energy demand is modeled by a Stackelberg and Lyapunov-based pricing and energy demand joint optimization (SLPEDO) algorithm. At the second stage, two procedures, i.e., energy scheduling between a cloud tier and an access tier, and energy scheduling between an access tier and an infrastructure tier, are implemented. A priority-based demand ratio sequentially scheduling strategy is proposed to address energy scheduling in these two procedures, respectively. Simulation results show that compared with the existing demand ratio-based scheduling and equally scheduling strategies, the proposed strategy can improve overall satisfaction of users by up to 20%. In addition, energy cost can be reduced and demand fluctuation relieved.

With the development of wireless local area networks and intelligent transportation technologies, the Internet of Vehicles is considered to be an effective method to alleviate the severe situation of the current transportation system. The vehicles in the Internet of Vehicles system build the Vehicular Ad Hoc Networks through wireless communication technology and dynamically provide different services through the real-time driving information broadcast by the vehicles. Vehicle drivers can control the distance, planning the driving route, between vehicles according to the current traffic environment, which improves the overall safety and efficiency of the traffic system. Due to the particularity of the Internet of Vehicles system service, vehicles need to broadcast their location information frequently. Attackers can collect and analyze vehicle broadcast information to steal privacy and even directionally track the owner through the driving trajectory, bringing serious security risks. This paper proposes a blockchain-based privacy protection system for the Internet of Vehicles. The system combines the blockchain with the Internet of Vehicles system to design a safe and efficient two-way authentication and key agreement algorithm through encryption and signature algorithm, which also solves the central dependency problem of the traditional Internet of Vehicles system.

Recently, with wide use of internet and rapid growth of computer networks, the problem of intrusion detection in network security has become an import issue of concern. In this paper, a new intrusion detection algorithm DB-Kmeans has been introduced which combines K-means with DBSCAN. DB-Kmeans uses a new selection method of initial cluster center in K-means and set the neighborhood radius in DBSCAN to dynamic. Compared to K-means algorithm, it overcomes the shortage of sensitivity to initial centers and reduces the impact of noise points. Compared to DBSCAN algorithm, it reduces the influence of fixed neighborhood radius. The experiments on the NSL-KDD data set indicate that the proposed method is more efficient than that based on MinMax K-means algorithm. Also, the method has higher detection accuracy and lower false alarm rate.

With the development of Internet of Things (IoT), a large number of terminals and devices are connected to the network. Mobile edge computing (MEC) is proposed to assist cloud computing, to relieve the pressure of network and satisfy the requirements of delay-sensitive applications. Considering reasonable allocation of computing resources is the most important aspect corresponding to delay, this paper designs geographic clustering and collaborative scheduling (GC-CS) mechanism. This mechanism can be divided into two parts, which are the decentralized deployment of MEC servers and the resource allocation optimization in MEC. For the first part, this paper designs the load balancing based geographic clustering (LBGC) algorithm which combines the idea of greedy algorithm to realize the initial allocation of computing resources. For the second part, delay minimization oriented collaborative scheduling (DMCS) algorithm is designed to decrease the response delay without increasing system overhead. Finally, the effectiveness of the mechanism is verified by simulation in the IoT scene.

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.

Smart Grid Communication System (SGCS) needs to meet the QoS requirements of various applications in terms of latency, bandwidth and reliability. However, cloud computing owns significant latency and cannot meet the need of real-time applications, edge computing is gradually applied in SGCS. In this paper, LBPC (edge-based load-balancing algorithm based on popularity and centrality) is proposed based on edge network and achieves the deployment of computing units at the edge nodes. In the aspect of service caching, LBPC measures the popularity of requests and the centrality of nodes, and it also takes the latency needs and the cache cost into consideration. In the aspect of task offloading, LBPC calculates the cost at different neighbor nodes and chooses the best one to finish the calculation when the current node is in high-load condition. Compared with some related works, experimental results show that LBPC can effectively reduce communication latency and balance the network load.

With the rapid development of communication technologies such as 5G, vehicular information and applications are exploding. Mobile edge computing (MEC) as a new technology can transfer the information more quickly and accurately. Providing differentiated services for the information can affect the performance of the applications. In this study, based on 802.11p EDCA protocol, we propose a new differentiated service scheme called DD-EDCA (Differentiating Density Enhanced Distributed Channel Access). Firstly, we use MEC Server to estimate the road density. Secondly, different solutions have been designed according to different vehicle density environment requirements. For example, the displacement trend is considered at a low density, and the multi-hop broadcast information is pre-processed at a high density. And the schemes for dynamically adjusting EDCA parameters are designed. Simulation results show that our method reduces latency and packet loss rate, and improves the throughput.

With the development of 5G and Internet of Things technologies, some studies consider combining fog computing with mobile ad hoc networks (MANETs) to form a cloud-assisted mobile ad hoc network. But it faces many challenges, such as terminal mobility, dynamic topology, multi-hop nature in transmission, limited bandwidth and battery. So, to better utilize the resource, a clustering algorithm based on communication overhead and link stability is proposed with two sub-stages. First, in clustering stage, we design a clustering method based on multiparameter-limited overhead to select resource directory index nodes for resource information management. Then, in the maintenance stage, we present a network clustering adaptive adjustment algorithm based on link stability. At last, the simulation result shows the proposed algorithm can reduce the communication overhead and improve the stability of the system.

Mobile edge computing (MEC) is envisioned as re- expanded cloud compared to fog computing. As making mobile services computing sank to the edge of network further, performance improvement can be got on time aspect. Therefore, MEC is seemed as a potential technology for delay-sensitive applications. Based on that, a reasonable computation offloading strategy would reduce system consumption for MEC further and release its computational capability. In view of resource shortage, especially bandwidth competition problem among Small Cells and edge computational requirements of HetNet, we focus on distributed uplink offloading for MEC in macro-micro coordination scene. It mainly contains two steps. First, based on Lyapunov to solve offloading decision-making problem for users in each Small Cell. Second, complete offloading update order- making of Small Cells in Macro Cell with proposed deviation update decision algorithm (DUDA). Our strategy makes up for the lack of system stability and uplink analysis in existing research. Numerical results demonstrate the effectiveness of our strategy.

In order to reduce the cost and energy consumption in wireless sensor network's charging process, this paper proposes a Recharging Scheme based on Lifetime Prediction (RSLP) for wireless rechargeable sensor networks. First of all, based on the historical quantity of electricity variation sequence of the sensor nodes, the lifetime prediction scheme of the sensor nodes is established; and then, considering the sensor nodes need to be recharged and the Sink nodes chosen by the mobile charger (MC) according to the charging value to establish an undirected complete diagram. A Hamilton charging circuit is established by using the Gene-Expressive cuckoo algorithm to solve the charging problem of the rechargeable sensor networks. The simulation experiments show that the proposed algorithm can improve charging efficiency and reduce the mobile energy consumption.

The construction of energy-efficient network and achievement of green communication have garnered great attention as a promising way to reduce network operating costs and greenhouse gas emissions. Link sleeping and rate adaptation are proposed to reduce energy consumption when the traffic demands are at low levels. It has been observed that many networks (include ISP backbone network) exhibit regular diurnal traffic patterns, which offers the opportunity to apply link sleeping for energy saving. In this paper, we propose an online scheme called Multiple Topology Switching with Data Plane Forwarding Path Rerouting (MTSDPFPR) for energy saving. Based on the dynamic network traffic demands, MTSDPFPR switches the links to sleep mode to save energy. Then we use the GEANT network and the real traffic matrix to evaluate proposed scheme. The results show that up to 30% energy savings can be achieved.

Satellite communication network (SCN) has the capability to provide long-distance and high-quality communication services. It could play a significant role in the future networks for its high reliability and large capacity. However, SCN still needs more efficient resources allocation and dynamical traffic scheduling. As a new design paradigm, network functions virtualization (NFV) is potential to facilitate the performance of traditional networks, including SCN. Therefore, the applicability of NFV in SCN has attracted many people's attention, especially the study on service function chains (SFC). In this paper, we try to explain the problem of SFC deployment in NFV-enabled SCN and deal with it. Our main goal is to minimize the end-to-end service delay and then achieve flexible service orchestration. Based on the general NFV-enabled architectures, we build a time-varying SCN model and novel forms of SFC requests. Then we formulize this problem and propose an effective approach named SFC deployment in satellite network (SDSN). The solution is conducive to promoting the development of SCN. The simulation results show that SDSN could not only take much shorter execution time and minimize the total delay, but also has a good performance in the resource utilization and acceptance ratio.

Software-defined networking (SDN) enables a network to be programmable, which makes it easy for the network to recover from failures. Upon failure, network can revert to operational state through preprogrammed recovery strategies. However, most of existing recovery approaches do not consider storage resource consumption. Nowadays the network scale and the number of flows increase greatly, numerous flow entries are required in case of failures, but the Ternary Content Addressable Memory (TCAM) that stores flow entries is very expensive and capacity-limited. Therefore, it is significant to reduce the consumption of backup resource. In this paper, we propose a ring-based single-link failure recovery approach (RSFR) to achieve failure recovery with less flow entries. A ring is selected from the network to act as a shared backup path, based on the ring, we plan all backup paths and design switches' flow tables to improve the utilization of flow entries required for failure recovery, thus network can recover from failures with less flow entries. Simulation results show that the proposed approach has a better performance in backup resource consumption, and recovery delay is less than 50ms.

In ubiquitous stub network, it is a critical challenge to realize resource discovery and share under disconnected network topology. In this paper, a cluster-based resource discovery mechanism is proposed with resource registration, distribution and routing model. Firstly, we use resource directory index nodes to assist in resource management. Secondly, we use inter-cluster mobile terminals to support resource routing. In addition, we take the nodes contact probability into account and establish the minimum expectation delay routing standard to opportunistically route between terminals. At last, the simulation result shows this mechanism is better applied to support disconnected ubiquitous resource discovery.

In the smart grid, to improve the quality of service and to reduce the risk of network much further is the main research direction. How to choose a highly reliable, stable and low-risk routing is the most critical part of the smart grid. The factors considered by most existing algorithms do not sufficiently consider redundancy, so that optimization is not sufficient. Therefore, based on service path pressure and the special factors of the power communication network, this paper proposes a kind of multi-constrained maximally disjoint routing mechanism and states the superiority of this algorithm via experiment which is simulated on the power communication network of a certain province.

Software-defined networking (SDN) develops a logically centralized control plane from the data plane, which makes the network management more intelligent. As the network becomes larger, the control plane with one controller can no longer manage the network efficiently. Therefore, multiple controllers are needed to manage the network. However, the survivability has been a key challenge in multi-controller SDN, which is sensitive to the controller failures. Once the controller breaks down, the switches will lose connections to the controller. This leads to severe consequences. In this regard, we propose an approach to improve fault tolerance of network in face of controller failures. In our work, we also attach great importance to the survivability of connections between controller and switch under random-link failures. Propagation delay is considered in our approach. Simulation results show that our approach guarantees that the controller failures can be effectively recovered. Moreover, after the controller failure is recovered, the survivability of multi-controller SDN in face of random-link failures can be improved.

Network function virtualization is a new network architecture, where the dedicated hardware network functions can be implemented in network function instances running on general purpose hardware such as high volume servers in data centers. End-to-end services require the traffic flow go through a list of NFs in sequence, which is defined by service function chain (SFC). Multiple NFs in a SFC are often orchestrated across multiple DCs to satisfy their position or performance requirements. However, different orchestration strategies of the SFC will lead to different deployment cost, including VNF instance cost and inter-DC bandwidth cost. Besides, large number of NFV instances are deployed in micro-DCs which have limited physical resource. Therefore, in this paper we investigate a costaware strategy to orchestrate the SFCs across multiple DCs, while considering the loads of DCs. An Integer Linear Programming (ILP) model is formulated to minimize the total deployment cost. Then, we prove that the problem is NP-hard and provide a heuristic Cost-Aware SFC Orchestration algorithm (CASO) to solve it. The simulation results show that CASO orchestrates SFCs in a cost-efficient way.

Recently, densified small cell deployment with overlay coverage through Heterogeneous Networks (HetNets) has emerged as a viable solution for 5G mobile networks. Cell Outage Detection (COD) which is the essential functionality in Self-Organizing Network (SON) is designed to autonomously deal with unexpected faults. Typical methods for detecting cell outage are usually based on Manual Drive Tests (MDT). However, it is difficult to detect small cell outage by MDT measurements in HetNets, because the User Equipment (UE) served by these small cells can switch to the macro cell and keep the Reference Signal Received Power (RSRP) and Signal to Interference plus Noise Ratio (SINR) values normal. To resolve this issue, we propose a COD architecture based on the handover statistics. Our model concentrates on cell outage detection in a two-tier heterogeneous network. We process sequential handover statistics spatially and temporally in conjunction with data mining methods. Also, an improved LOF algorithm (M-LOF) is proposed to enhance the detection performance based on handover statistics. To evaluate the system performance, a set of tests has been carried out using some reasonable assumptions and network simulator we designed. The results of simulation show that our system is more effective to detect cell outage in comparison to the architecture using MDT measurements.

This paper proposes a risk assessment model of key service and optimization methods to reduce service risk in smart grid communication network. Firstly, we analyze the probability of failure of communication link and node which is induced by external factors, like natural disaster, human attack and system disturbances. Then using importance of services, links and nodes, we build the risk model of failure for key services. Further, we propose optimization methods based on Dijkstra algorithms to reduce the risk of key services. Finally, based on part of smart grid communication network topology structure from a Chinese province, the simulation results show that the risk of key services and whole network are reduced.

To meet different communication requirements, a smart grid communication framework based on a hybrid network has been widely used. Virtual networks are established for various types of services respectively, and are embedded into heterogeneous substrate networks. Based on the framework, wireless network virtual mapping algorithm based on particle swarm optimization is proposed. In this scheme, the throughput is considered as fitness function, and parameters and operations of the particles are redefined. At last, comparing to basic heuristic algorithms, the proposed algorithm can perform better in term of throughput while satisfying the reliability of real time services.

Hadoop has become a popular platform for the management of big data. To provide a healthy Hadoop platform for big data application, an HMM-based approach for performance diagnosis in Hadoop clusters is proposed. We use metrics which are collected under the normal situation to train HMM (Hidden Markov Model), then use this model to detect anomaly based on the probability, which is more accurate than other methods. Through evaluation in a controlled environment running Hadoop clusters, we find our approach can find out the real cause of performance problems in an average 84% precision and 83% recall, which is better than the method based on ARIMA and KNN (k-Nearest Neighbor).

To satisfy the QoS requirements and improve the reliability of the network, we propose a load-balancing-based fault-tolerant mapping method (LFMM) in smart grid virtual networks. The process of LFMM is divided into two stages, one is node mapping stage, and the other is link mapping stage. During node mapping stage, we present a load-balancing-based virtual node mapping (LVNM) algorithm. We choose the nodes with minimum load ratio in virtual network providing layer to map, in order to avoid the appearance of “bottleneck node”. During link mapping stage, we design a genetic-algorithm-based fault-tolerant virtual link mapping (GFVLM) algorithm to ensure the fault tolerance and reliability of the network. By this method, we select two disjoint links for each request including a primary link and a backup link. The evaluation results show that our proposed method can balance network load, have better fault tolerance ability and improve the reliability of smart grid communication networks.

OTN technology has been widely used in smart grid, which improves transmission speed, transmission efficiency and transmission capacity of the network. In order to supervise the operation of the network in real time and improve quality of service (QoS), we need to make a quantitative assessment of operation quality for OTN in smart grid. In this paper, we propose LM-BP evaluation method to assess the operation quality of OTN in smart grid. At first, we give the evaluation indicator system. Secondly, we introduce LM-BP algorithm. At last we make simulation experiments based on MATLAB with last 10 years network operation data of a province electric power OTN company in smart grid. The simulation results show that BP neutral network optimized by Levenberg-Marquardt (LM) algorithm has shorter training time, faster convergence rate and better prediction accuracy than that standard BP neutral network. Demonstrating a new method of LM-BP network in operation quality evaluation of OTN in Smart grid is feasible, effective and reasonable.

Container transportation has become the main transportation form for international freight. In this paper, the energy saving and reliability tactics are considered and we design a new E-ZBR routing algorithm based on the original ZBR routing protocol. Firstly we propose the score criterion for estimating a path, and then based on nodes' connectivity, we select two existed better routing paths through improved FCM clustering algorithm. Through clustering, we get rid of those nodes with possible failure and construct a fault tolerance routing path with more reliability and robustness. We demonstrate that E-ZBR routing protocol has higher the energy efficiency and fault tolerance.

Software Defined Networking (SDN) enables the underlying infrastructure to be abstracted from the network services and controlled by one or more controllers. If a link or a node fails, the switches that can detect the failure have to either inform controller to update flow tables or transform the data to pre-configured paths to recover the failure. However, existing failure recovery approaches mainly consider the recovery delay and packet loss, and ignore the storage resources consumption for backup paths in case of link or node failure. Moreover, the Ternary Content Addressable Memory (TCAM) that stores flow entries is expensive and limited with high-energy consumption. Thus in order to minimize the consumption of backup resources and meet the required failure recovery delay, a backup-resource based failure recovery approach is proposed. Two metrics are proposed to grade physical links, and three kinds of strategies for different graded links are provided, based on which the approach tries to use less flow entries to recover link failure and meets the required failure recovery delay, while guaranteeing the reliability of the network. Simulations show that backup-resource based approach can use as less flow entries as possible to ensure the performance of failure recovery and satisfy the required delay of important traffic at the same time. Moreover, the approach has good and steady performance in networks of different scales and connectivity.

Currently, the requirements of service quality in the electric power data network are getting higher and higher, and traffic prediction is an important premise to promote service quality. In order to accurately predict the total traffic of communication channels, a Multi-Applications Comprehensive Traffic Prediction (MACTP) model is proposed in this paper. Differing from F-ARIMA and S-ARIMA models which are used to predict the traffic of single application, the proposed MACTP model is used to predict the traffic of multi-applications conveyed in the channels. Simulation results show that MACTP model has higher accuracy and efficiency than classical prediction models, and it is suitable for electrical power data network.

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.

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.

In ubiquitous stub environments, it is a critical challenge to select an optimal set of devices to accomplish a graph-based ubiquitous service and execute it continuously. The mobility of devices, the diverse access technologies and underlying path quality have a great influence on the user experience. Thus, we put forward a hierarchical model and a novel selection function considering the synergetic effect between devices. Then we elaborate a Synergy-aware Selection Mechanism (SSM) which includes three modules: service launch, device selection and service maintenance. We design a distributed core algorithm to integrate the devices and a dynamic updating weight method. The simulation results show that Synergy-aware Selection Mechanism can select a set of executive devices to ensure the service quality, continuity and smoothness. It improves the performance in the perceived experience and the number of service reelections.

Because of the advantages of multi-hop communication, self-organizing, self-healing and reliability, wireless mesh network becomes an ideal choice for data collection. However, wireless mesh network for data collection faces challenge on communication performance of network caused by application layer data traffic. When a large number of data occurs in emergence, some mesh nodes (the last hop nodes) which are in pivotal location will face great communication pressure and probably lead to extremely data congestion, especially in smart grid. For the idea of load balancing, this paper proposes a new random switching traffic scheduling algorithm based on data collection tree. Simulation data show that the new algorithm can create a balanced data collection tree, significantly reduce the packet loss ratio of the burst data and release congestion of system.

This paper proposes a planning mechanism to design and plan the communication network for the smart distribution grid when considering economics, reliability and (n-1)-resilience. From the communications perspective, the smart distribution grid mainly consists of End Nodes (ENs) and Access Points (APs), in particular, a distribution grid planning problem focus on deciding which end nodes are to be enabled as access points. In order to solve the problem, an optimization problem is formulated first, which minimizes the cost of installing APs, meanwhile, the constraints of reliability and (n-1)-resilience should be satisfied simultaneously. Then, an approach based on improved genetic algorithm (GA) is developed to solve the proposed problem. Finally, simulation results in MATLAB testify that the proposed planning mechanism is capable to deal with diverse network size and planning effectively with high flexibility and scalability.

One of the key technologies of smart grid is an efficient, reliable and secure two-way communication system for meter data collection. Because of the advantages of muti-hop communication, self-organizing, self-healing and reliability, wireless muti-hop communication technology becomes an ideal choice for smart grid meter data collection. However, forming wireless mesh network with advanced electricity devices (smart meters) which have the communication capabilities for meter data collection faces challenge on communication performance of network caused by application layer data traffic. When a large number of data occur in emergence, some smart meters (the last hop nodes) which are in pivotal location will face great communication pressure and probably lead to extremely data congestion. With the idea of load balancing, this paper proposes a new random switching traffic scheduling algorithm based on meter data collection tree. Simulation data show that the new algorithm can create a balanced meter data collection tree, significantly reduce the packet loss ratio of the burst data and release congestion of system.

In ubiquitous stub environment, several mobile devices can work cooperatively to provide efficient and reliable service. But device movement or failure usually causes service interruption. How to recover the service path quickly and enhance the user experience greatly is an important problem. This paper presents a novel recovery strategy to resolve the problem. First of all, the strategy conducts the local service recovery process. If it fails, the strategy will use the global service recovery process. Several key factors affecting recovery process are adopted in the strategy. Compared with other similar strategies in simulation experiment, the failure rate and service time of this strategy is lower than the others. So this novel strategy has a better performance.

In the open MANETs, nodes with different goals expect to benefit from others, but are unwilling to share their own resources. These selfish behaviors have posed increasing research challenges for cooperation. Negotiation as a key form of interaction for two or more parties enables nodes to announce their contradictory demands and seek to an agreement by concession. In the paper, the Service Negotiation model for Selfish nodes in the MANETs (SNSM) combines the policies of imitating rivals' behaviors and fast-approaching reserve prices presented to generate mutual offer and counter-offer for service bargaining. Specially, the model provides three types of changing rates of bids to speculate the rivals' behaviors. In addition, we improve the Weber-Fechner's law to self-adjust the deadline in the negotiation. Simulation results demonstrate our model has superior performances in increasing the negotiation efficiency, achieving mutual benefits between the service buyer and seller.