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

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.

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% .

As the in-network computing (INC) paradigm evolves, efficient scheduling of dependent tasks within complex network systems becomes increasingly crucial. The network needs to handle high-level resource demands while adhering to strict latency requirements. Deterministic delay constraints are particularly critical in applications that rely on directed acyclic graphs (DAGs). To address this challenge, we first propose a deterministic delay-aware task scheduling optimization problem over INC to maximize resource utilization and ensure task acceptance. We accurately establish the complex deterministic delay constraint through traffic arrival and service curves and utilize network calculus for conversion to facilitate solving. Then, we further transform the task optimization problem into MDP and develop a deep reinforcement learning (DRL) algorithm that combines graph neural network (GNN) and delay-aware proximal policy optimization (DPPO) to solve it, called the Deterministic Delay-aware Task Scheduling (DDTS) scheme. It utilizes multilayer GNN to handle task dependencies and applies the DPPO algorithm to introduce deterministic delay penalty factors to evaluate policy operations, achieving optimal task scheduling. The simulation results demonstrate the significant advantages of the DDTS scheme over existing algorithms and task scheduling schemes in terms of task acceptance rate and resource utilization.

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.

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.

Multimodal event extraction task aims to identify event types and arguments from visual and textual representations related to events.Due to the high cost of multimedia training data, previous methods mainly focused on weakly alignment of excellent unimodal encoders.However, they ignore the conflict between event understanding and image recognition, resulting in redundant feature perception affecting the understanding of multimodal events.In this paper, we propose a multimodal event extraction strategy with a multi-level redundant feature selection mechanism, which enhances the event understanding ability of multimodal large language models by leveraging knowledge editing techniques, and requires no additional parameter optimization work.Extensive experiments show that our method outperforms the state-ofthe-art (SOTA) baselines on the M2E2 benchmark.Compared with the highest baseline, we achieve a 34% improvement of Precision on event extraction and a 11% improvement of F1 on argument extraction.

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.

Due to network congestion, the uplink communication of local models is slow and unpredictable in cloud-based Federated Learning (FL), which will make it difficult to achieve the goal of Hyper Reliable Low Latency Communication (HRLLC) in the 6G era. To minimize communication latency and also to achieve a larger range of user participation, Hierarchical Federated Learning (HFL) has been proposed in academia. Nevertheless, HFL still faces many challenges, such as time-varying channels, user mobility, and data heterogeneity. To address these difficulties, we design a dynamic cell association scheme for multi-cell over-the-air computation-based HFL (MC-AirCompFL). This dynamic strategy innovatively integrates the channel state information (CSI) driving mechanism with the data distribution distance sensing technique to achieve dual-dimensional cooperative optimization. Firstly, we analyze the convergence behavior of MC-AirCompFL at different global communication rounds. Secondly, to relieve the pressure from imbalanced data and nonideal wireless channels, we minimize the optimality gap and data distributed distance by jointly optimizing the cell association and transmission power at user equipment (UE) and the de-noising factors at base stations(BSs). Finally, numerical results based on the MNIST datasets validate the superiority of the proposed scheme over the traditional cell association strategy in the multi-cell FL.

As edge computing, AI data centers, and supercomputing systems continue to expand, the challenge of deterministic computing power routing has become increasingly prominent. In response, this paper proposes a new hierarchical reinforcement learning algorithm that combines node clustering with routing optimization. Our approach employs a hybrid clustering method that integrates Gaussian Mixture Models (GMM) and K-Means algorithms to categorize computing nodes into distinct groups based on their operational states and past performance. This framework uses reinforcement learning techniques to optimally match deterministic applications with the relevant node clusters, while modeling the selection of nodes and path planning as a Stackelberg game problem. We solve this game-theoretical problem using a dual-agent reinforcement learning architecture, enhanced by graph neural networks to boost generalization. Additionally, we incorporate a shortest-path-based link attention mechanism to speed up model convergence. Our proposed solution addresses the shortcomings of traditional methods, which often treat node selection and path planning separately. This integrated approach leads to more efficient use of resources and better satisfies deterministic transmission needs.

The increasing demand for computational resources from AI, blockchain, and other technologies requires efficient task scheduling within the Computing Power Network (CPN). However, data centers in CPN often face energy inefficiency and high carbon emissions due to the uneven availability of clean energy. While some research has focused on machine learning-based energy prediction, existing methods often overlook the need for integrating these predictions into large-scale, real-world computational task scheduling frameworks. Furthermore, traditional computational task scheduling methods also fall short in addressing the inherent volatility of renewable energy supplies, limiting their effectiveness in scenarios that require real-time adaptation. This paper proposes a method that integrates clean energy prediction with computational task scheduling, employing a neural network for energy forecasting and an enhanced heuristic algorithm for task scheduling. Simulation results demonstrate that the proposed approach significantly outperforms the baseline, achieving over 95 % clean energy utilization with minimal fluctuations and reduced carbon emissions. These results underscore the potential of predictive task scheduling to improve energy efficiency in data centers, aligning with global sustainability goals.

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 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.

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.

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.

To support the escalating traffic demands in the 6G era, the novel computing paradigm of in-network computing (INC), where tasks can be processed on the forwarding path, is emerging with enhanced network performance and improved service quality. Considering the large network scale with high dynamics, effective task scheduling in INC paradigm becomes imperative but challenging. In this work, we investigate the task scheduling in INC paradigm to minimize both the task delay and network energy consumption, while considering constraints on task latency, traffic dynamics, and available communication and computing resources. We first construct a novel computing and communication model considering the traffic variation in network nodes on the transmission path. To solve the task scheduling problem, we propose an algorithm, named as NBFNDRL, which is a deep reinforcement learning (DRL) algorithm based on Neural Bellman Ford networks (NBFNet). NBFNet can learn high-dimensional correlated graph structural information, utilize message-passing mechanisms to represent changes in traffic between adjacent nodes, predict scheduling paths, and provide a basis for DRL decision-making. The DRL agent trains and updates NBFNet through interaction with the environment. Finally, we present simulation results to demonstrate the effectiveness of our proposed approach in comparison to benchmark algorithms and various computing paradigms.

The reconfigurable intelligent surface (RIS) is a green and promising technology that provides passive beamforming through a large amount of low-power reflecting elements, to realizes expected coverage extension and interference signal suppression. In this paper, we investigate a self-sustainable RIS-empowered D2D communication network, where the RIS first harvests energy from the D2D signals, and then uses energy collected to sustain its passive beamforming operation. We aim to characterize the energy efficiency (EE) maximization under imperfect channel state information conditions by jointly optimizing the transmit precoding in both two stages, RIS passive beamforming design, and energy harvesting time allocation. An efficient alternating optimization algorithm is proposed to deal with the difficult non-convex optimization problem. Specifically, transmit precoding is optimized by using the Dinkelbach's method, Lagrangian dual transform, quadratic transform and S-procedure. The penalty convex-concave procedure is adopted to solve the optimal phase shift of RIS. A closed-form expression for the optimal energy harvesting duration is derived. The simulation results show that the proposed scheme further enhances the EE compared with the active RIS and no RIS schemes in various scenarios.

Segment Routing (SR) is a flexible and efficient source-routing technology. It can forward traffic along arbitrary paths in the network and has good scalability without the maintenance of routing information at intermediate nodes. These advantages make SR widely used in data centers, WANs, MANs, and other networks, particularly in the context of parallel and distributed processing. However, current SR traffic engineering has some shortcomings in terms of network load balancing, failure resiliency, and complete use of SR characteristics, which prevents better optimization of comprehensive network performance. In this paper, we propose a heuristic traffic engineering algorithm (2-SRLS) based on a two-segment routing model (2-SR), which incorporates a traffic-splitting strategy, adjacency segments and failure resiliency. The algorithm supports the technical characteristic of adjacency segments in SR with a flexible source node traffic splitting strategy and can efficiently recover from single-link failures. Experimental results show that our algorithm is close to the theoretical optimum in terms of the performance of reducing the maximum link utilization and has a 10%-16% improvement in active link coverage compared to existing algorithms. The running time is reduced by 70% compared to existing heuristics. In addition, the proposed algorithm can effectively recover from single-link failures on the basis of guaranteed the stability of the maximum link utilization (MLU).

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%.

The emergence of integrated positioning, communication, and computing (IPC2) technology has paved the way for advanced capabilities in physical-digital spatial positioning, intelligent communication, and computing. This article delves into an in-depth exploration of a federated learning-assisted multidimensionality fusion IPC2 system. Within this system, edge nodes collaboratively harness their locally distributed multidimensionality positioning and communication data to coordinate edge computing resources for model training. Throughout the process of fully distributed collaborative training, we focus on addressing two specific security concerns: 1) data tampering and 2) model tampering attacks. In pursuit of bolstering the system’s resilience against potential attacks, we introduce a novel federated-blockchain edge learning (FLBC) framework. This framework capitalizes on the inherent features of the blockchain, namely, its nontampering and traceability attributes. In addition, we present a meticulously designed verification algorithm tailored for the parameters aggregation process. Specifically, an aggregation algorithm is developed to enhance the efficiency and accuracy of the training model’s fitting. To assess the effectiveness of our proposed approach, comprehensive simulations are conducted using an openly accessible wireless artificial intelligence (AI) data set. The outcomes of these simulations clearly demonstrate that the proposed scheme adeptly combats data tampering attacks initiated by multiple malicious nodes and high-intensity model tampering attacks, all while maintaining minimal accuracy loss.

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.

In the era of 5G and beyond, different service scenarios have put forward rich and differentiated requirements for the carrier network. The emergence of flexible Ethernet technology has met the needs of high-speed transmission and flexible bandwidth configuration. However, the current FlexE transmission mechanism based on the 5Gbit/s granularity creates a massive waste of resources in multi-granularity hard isolation services. To optimize the utilization of slot resources, we propose a new FlexE calendar slot allocation mechanism based on a novel hybrid-granularity model. This mechanism encompasses traffic prediction and a calendar slot allocation method based on a FlexE hybrid-granularity model. The former adapts the slot allocation process to the fluctuations in client flows through accurate traffic prediction. The latter adopts a hybrid-granularity slot allocation algorithm based on dynamic programming to ensure a high isolation of the service transmissions and to improve the utilization of slots. A comparison with the existing schemes shows that under experiments with different periodic regularities, the proposed method can increase the slot utilization by 71.5%-77.9%, and under experiments with diverse client granularity distributions, the proposed method can increase the slot utilization by 59.2%-76.7%.

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.

The 4G/5G era in the past decades has witnessed the vigorous development of Hybrid Analog and Digital Beamforming (HBF) technologies in the field of communications under cellular Multiple Input Multiple Output (MIMO) systems. As an evolution, the B5G/6G has strong visions of high-accurate positioning capabilities other than the communication quality, thus a beam alignment method towards positioning enhancement is also urgently desired in cellular systems. To this end, this paper proposed a HBF method for positioning enhancement in cellular MIMO systems. We first derive the Fisher Information for multiple-path assisted positioning as the performance criterion of positioning under a wideband channel with both precoder and combiner considered. Then a HBF strategy is proposed to optimize such criterion over multiple resources involving the transmitting power, beam and frequency dimensions, which is referred to asSensing Beamforming. Furthermore, a Newton based heuristic method is proposed for the estimation of sensing elements (e.g. angle of arrival) from multiple paths, and the positioning results are obtained by a proposed multiple-path assisted positioning method considering the multiple path clutters in the environment. The results indicate that the proposed method can enhance the positioning performance with the accurate estimation of sensing elements.

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.

In the upcoming B5G/6G era, devices will generate a amount of heterogeneous data at the network edge. As a paradigm for implementing distributed and privacy-preserving machine learning (ML), Federated Learning (FL) has drawn great attention to secure data sharing in edge networks. However, FL takes too much time and communication resources to train and transmit model parameters, which is unaffordable for edge devices with limited capabilities. To achieve a trade-off between resource and efficiency, it is crucial to select appropriate training nodes. While existing works about node selection focus on the resources allocation and pay less attention to the node mobility and seamless service. In this paper, we considering mobility, computation capability, and transmission power of training nodes to minimize the FL system cost. We propose an algorithm and mechanism respectively for different scenarios of node speed. An algorithm based on Deep Reinforcement Learning (DRL) matches with stationary and low-speed training nodes. A heuristic mechanism is used for nodes with high mobility. Simulation results show that the proposed schemes select appropriate training nodes effectively, and reduce the system cost by up to 20%.

Abstract In recent years, Named Data Network (NDN) has become a popular network architecture because of high resource utilization, strong security and high transmission efficiency. Meanwhile, mobile multimedia communication has become the mainstream with the popularization and application of smart terminals. Most of the research on NDN mobility is focused on consumer mobility without taking producer mobility into account. To solve the delay and high cost carried by producer moving, we propose a Double-Lead content search algorithm based on neighbor and proxy and a location prediction algorithm based on traffic features. We use a neural network model to predict a new location of producers and calculate route before switching, which can save the rerouting latency in advance when predicting accurately. In a few cases of inaccurate predictions, we select different search methods according to the distance of the producer’s movement, to complete the Double-Lead search between the producer and the consumer. Experimental results show that DLPNDN can reduce the delay and traffic overhead well in NDN when the producer moves.

Sixth generation (6G) is committed to providing a fully connected world. The deployment and application of the 6G technology in the Internet of Things (IoT) can efficiently collaborate IoT resources and realize resource sharing, which mainly encourages IoT development. However, due to the lack of trust between IoT resources, security and privacy become the main challenges. As an emerging technology, blockchain can solve the trust-absence issues and provide more benefits, but the introduction of blockchain also brings problems for IoT resource collaboration and sharing. This article first proposes a blockchain-enabled edge resource-sharing (BEERS) architecture by combining blockchain and edge computing technology. Then based on a typical resource sharing and collaboration scenario, the joint optimization problem of resource scheduling and task assignment (JRSTA) is constructed. We decompose JRSTA into a primal problem and a master problem and design a greedy-based task assignment (GBTA) algorithm to solve the primal problem. Based on the GBTA algorithm, the resource scheduling and task assignment (RSTA) algorithm is developed. Next, we design a layered parallel edge RSTA (LPRSTA) mechanism to improve practicability. Finally, we analyze the security and the performance of our proposed architecture and algorithms. The results show that the proposed architecture can support the secure collaboration of IoT resources, and the proposed algorithm can achieve effective JRSTA.

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.

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

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.

With the development of 5G/6G networks, the number of wireless users is growing exponentially, and the application scenarios are increasingly diversified. Using unmanned aerial vehicles as base stations (UAV-BSs) to serve ground users has become a trend for wide area coverage and capacity enhancement for rapid access of service in 6G networks. However, as UAV-BSs have limited energy or battery storage, solutions to optimize energy efficiency while providing high-quality services are necessary. Therefore, this paper mainly concentrates on the energy-efficient deployment of coverage-aimed UAV-BSs (Co-UAV-BSs) and capacity-aimed UAV-BSs (Ca-UAV-BSs) for the coverage and capacity enhancement of ground communication under disaster areas or burst data traffic. First, Co-UAV-BSs are deployed with DQN algorithm to to get the UAV-BSs’ optimal flight paths, which mainly adopted to detect out of service users in such areas. Then the users are completely clustered based on the detection results. After that, Co-UAV-BSs and Ca-UAV-BSs are deployed hierarchically based on the user distribution and sought to optimize the energy efficiency with acceptable user services. Still, DQN algorithm and the A3C algorithm are used for obtaining all the UAV-BSs’ location deployment and users’ best connections. The simulation results show that the dynamic flying path requires less energy than the fixed path for user detecting. For the coverage and capacity enhancement, it reveals the solution we proposed could provide high-quality service for users with high energy efficiency comparing to traditional algorithms.

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.

5G and beyond network will support vertical industry applications, and the resource requirements of each service vary widely. The introduction of network slices provides great flexibility to the network, which can realize the differentiated customization requirements of service. However, while determining how to intelligently orchestrate the network slices is an important challenge, current solutions rarely treat multiple customized requirements of delay, bandwidth, load balancing, and slice isolation. In this article, network slice orchestration is considered from the perspective of slice isolation and cloud-edge collaboration. First, differentiated isolation level requirements are restricted to constraints, the customized isolation is realized. Second, bandwidth is saved and network latency is reduced via the collaboration of cloud and edge data centers. In addition, exclusive orchestration optimization objectives that match various service needs are proposed to distinguish the specific requirements of different slices. Finally, two deep reinforcement learning-based algorithms are proposed. The experimental results demonstrate that the proposed algorithms can optimize the objectives while ensuring differentiated isolation levels. For typical slices, the proposed algorithms respectively reduce bandwidth consumption by about 29% and 64%, reduce slice delay by about 14% and 70%, and optimize load balancing by about 17% and 23%.

The demand for satellite relay service is increasing, while the satellite network resources are limited and unevenly distributed, which pose a great challenge to task scheduling of tracking and data relay satellites. Most existing relay scheduling models are based on static antenna setup time, which has limitations in practical applications and leads to ineffective utilization of satellite resources. This paper models the task scheduling problem based on dynamic antenna setup time and splittable tasks, which maximizes the total scheduled task number and minimizes the total antenna setup time. We also propose a two-stage insertion heuristic to solve the problem. The experimental results show that the proposed algorithm can significantly improve the total scheduled task number, total antenna setup time and effective time window utilization compared with traditional methods.

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.

[J1C2 Presentation Abstract at IEEE SERVICES 2022 for IEEE Transactions on Services Computing DOI 10.1109/TSC.2019.2955486]

Multiaccess edge computing (MEC) provides users with better Quality of Experience (QoE) via offloading tasks to the nearby edge. However, the emergence of new Internet of Things applications with multiple tasks and repeated requests brings redundant computation and transmission to the edge. Meanwhile, the current offloading method based on deep reinforcement learning (DRL) has low sampling efficiency and slow convergence issues for training in a changing environment. Therefore, improving QoE of computation offloading services is still the ultimate challenge. In this article, we devise a collaboration of computing and cache resources among multiple edge nodes, which could reduce redundant computation and transmission. Specifically, we formulate a cache-assisted computation offloading process as a QoE-aware utility maximization problem based on multidimensional indicators. Then, we propose a cache-assisted collaborative task offloading and resource allocation strategy to solve it. This strategy is decomposed into two subproblems. First, to determine and obtain task cache state, we propose a collaborative task caching algorithm, which can improve the hit rate of tasks while balancing network overhead. Second, to acquire offloading and resource allocation decisions efficiently, we propose a metareinforcement learning-based cache-assisted computation offloading method (MCCOM), which can achieve rapid offloading decisions with a few gradient updates and samples. The optimization problem was transformed into multiple Markov decision processes (multiple MDPs). The improved learning process includes metapolicy learning that adapts to multiple Markov decision processes (MDPs) and policy learning for a specific MDP based on metapolicy. Simulation results show that our proposed method outperforms baselines in terms of QoE indicators while achieving rapid convergence and decisions.

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.

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

With the development of complex networks and with increasing service demands, service use is becoming more complex and the composition of services is becoming more complicated. In the XaaS (X as a Service) environment, users only care about the QoE of a service and do not care about the composition process of the service. Therefore, it is important to evaluate the reliability of the entire service. In this article, we use Petri Net as a basis for modeling the composition of services. In addition, we consider the problems of shared resources and common cause faults. Both of these problems can cause network congestion and regional failures. We use distance to assess the effects of regional faults and queuing theory to simulate the network congestion process. Moreover, in the simulation, we verify the impacts of regional failures and network congestion on service reliability. We choose the Tree-Based Search algorithm and the Semi-Markov Model as comparison algorithms. The results of our algorithm are related to service time. Our algorithm can timely reflect the impact of regional failure or network congestion, and it can feedback different evaluation results according to environmental changes. Therefore, our algorithm is more comprehensive and has better performance.

The power system is an infrastructure for industrial manufacturing, and its availability is relevant to industrial systems. The smart grid combines communication systems with sensing devices to provide intelligent management tools for fault monitoring and processing of the power grid. Regional failures caused by natural disasters can have a large impact on the power system. To reduce the impact of disasters, an efficient network repair strategy is needed. This article focuses on the emergency repair strategy of a power communication network under disaster conditions and seeks to ensure the operation of the power system with fewer repairs. Combined with the analysis of cascading failures and regional failures, a communication network fast repair algorithm is proposed. The coupled network is constructed in the simulation part to verify the algorithm. The results show that with a limited number of node repairs, the algorithm can ensure the highest percentage of workable nodes.

With the development of Industry 4.0, an increasing number of industrial Internet of Things (IIoT) mobile devices (MD), which constantly transmit data at any time, are working on the production line. However, due to node movement, signal attenuation, or physical obstacles, data must rely on the transmission of relay nodes to finally reach the destination node. Based on this scenario, in this article, we propose a cooperative link prediction model (CLPM) using a stacked denoising autoencoder (SDAE) to predict links of the IIoT-based MDs at the next moment through historical link information. The layer structure of the SDAE model is partitioned so that the local MD and edge servers can cooperatively process the link prediction tasks. Experimental results show that our proposed CLPM outperforms others in terms of prediction performance and execution delay.

With the development of information technology, the network consists of various proprietary hardware devices, and the use of these devices brings problems. To solve problems, network function virtualization is proposed, which decouples the software and hardware in the network, and deploys the existing network function devices to a common physical platform. However, network virtualization needs will inevitably face reliability problems during resource virtualization and service function chain deployment. This article proposes a service function chain reliability evaluation method and reliability optimization algorithm. The composition relationship and reliability influencing factors of service function chain were analyzed, including resource preemption, common cause failure, fault recovery and redundant backup. The service function chain was modeled as a Petri net model, and reliability evaluation results related to execution time were obtained. Based on the reliability assessment results, a VNF migration strategy is designed, with reliability as the optimization goal while considering costs. Simulation results show that, compared with the reliability optimization strategy based on backup, our algorithm costs less and reduces the impact of resource preemption on service reliability.

In the network function virtualization (NFV) environment, network services are usually provided in the form of service function chains (SFCs), which defines the link order of virtual network functions required in service requests and are mapped to the physical network. Although NFV facilitates the flexible provision of network services, service interruptions may occur as a result of software and hardware failures. Current solutions mostly use the backup method to ensure the reliability of SFCs. However, these methods ignore the SFC construction phase that has an impact on reliability. Besides, the resource efficiency still requires improvement. To address these issues, reliability-oriented SFC construction and backup problems are investigated in this work. First, an instance-sharing and reliable construction algorithm (ISRCA) is proposed to aggregate multiple SFCs into a service function graph (SFG), and perform reliability screening for the SFG set. After mapping the SFG to the physical network, a node-ranking algorithm with centrality and reliability (NRCR) is proposed for backup node selection and backup instance deployment to improve the reliability of SFCs that have not met the requirements. Experimental results demonstrate that under the premise of ensuring reliability, the proposed backup method can reduce the consumption of bandwidth resources by about 11.7%, when combined with the proposed construction method, it can further reduce the backup resources by 13.9%.

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.

With the rapid development of data centers, the energy consumption brought by more and more data centers cannot be underestimated. How to intelligently manage software‐defined data center networks to reduce network energy consumption and improve network performance is becoming an important research subject. In this paper, for the flows with deadline requirements, we study how to design the rate‐variable flow scheduling scheme to realize energy‐saving and minimize the mean completion time (MCT) of flows based on meeting the deadline requirement. The flow scheduling optimization problem can be modeled as a Markov decision process (MDP). To cope with a large solution space, we design a DDPG‐EEFS algorithm to find the optimal scheduling scheme for flows. The simulation result reveals that the DDPG‐EEFS algorithm only trains part of the states and gets a good energy‐saving effect and network performance. When the traffic intensity is small, the transmission time performance can be improved by sacrificing a little energy efficiency.

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.

The cloud computing market is dynamic, distributed, and lacks central authorization. In this environment, cloud resource providers are vulnerable to deception and cloud resources may be abused. How to implement efficient and feasible trusted negotiations with users to expand Benefits is an urgent issue. Based on SLA (Service Level Agreement), this paper proposes a trusted negotiation method to optimize cloud resource allocation from the perspective of cloud resource providers. In a nutshell, it firstly quantifies each indicator based on the total amount of cloud resources requested by the user and the corresponding price, the user's comprehensive credit, and the total amount of resources corresponding to each SLA level, then filters the users who meet the requirements. Next knapsack algorithm and the greedy algorithm based on dynamic programming are used to predict the allocation of cloud resources respectively. Finally, the allocated users are negotiated to reach a transaction. This article takes the resource allocation price, negotiated price, and negotiated success rate as the evaluation index. The simulation results show that compared with the greedy algorithm, the algorithm in this paper has higher resource allocation price, negotiated price and negotiated success rate under different numbers of users, and can effectively realize the optimal allocation of cloud resources.

Network Function Virtualization (NFV) is an important shift in telecommunication service provisioning. It enables the decoupling of network element functions and dedicated hardware devices. How to economically place and chain Virtual Network Functions (VNFs) according to the requirements of Service Functions Chains (SFCs) are the challenges for NFV orchestration. In this paper, we consider the offline deployment issue from the perspective of sharing VNF instance to improve resource utilization and reduce total placement costs. Firstly, we generalize the problem as a Facility Location Problem and propose a Mixed Integer Linear Programming (MILP) model. Besides, our model can be dynamically configured according to the different deployment preferences. Then we propose a heuristic algorithm based on the Steiner Tree Problem and Markov Decision Process (MDP). We evaluate our heuristic algorithm by comparing with the optimal solution of MILP and a classic graph based algorithm. The results show that the difference of the deployment costs between our algorithm and the optimal solution is less than 3%. However, the execution time can be significantly reduced by 57.4%.

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.

Cloud computing technologies can not satisfy the requirements of applications on the mobile terminals because of their disadvantages in delay, link load and energy. So Mobile Edge Computing (MEC) is proposed as a kind of novel computing technology. As an important research direction of MEC, service migration methods still have limitations that they cannot learn migration paths and be adaptive in dynamic situation and user movement. In this paper, we propose a novel service migration policy method based on reinforcement learning. We firstly investigate user movement, four different edge network situations and traditional migration policies. Then we formulate the system requirements by Satisfiability Modulo Theory (SMT) logic to acquire the migration policy space. We further propose a dynamic-awareness deep Q-learning algorithm to select paths from the policy space iteratively and conduct dynamic awareness to adjust learning rate adaptively. Meanwhile, the optimal convergence of our algorithm is proved theoretically. Finally, the experimental results highlight the effectiveness as migration successful rate, service interruption time and load balance of our method compared to the other solutions.

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.

The introduction of edge computing (EC) in intelligent driving allows the vehicle to offload tasks to the EC server closer to the vehicle side, creating a new paradigm for task offloading and resource allocation. The movement of the vehicle, the time sensitivity of the processing data, and the resource allocation of the EC server have become bottlenecks of the rapid development of intelligent driving. In this article, we jointly considered the problems of the network economy and resource allocation. In order to eliminate dependence on third parties, we propose a resource transaction architecture based on the blockchain. Moreover, we propose the dynamic allocation algorithm of edge resources (DAERs) based on the double auction mechanism to maximize the satisfaction of users and service providers of edge computing (SPs), where the DAER algorithm is implemented in the form of smart contracts in the blockchain architecture. In particular, we propose the state search algorithm that can improve the prediction accuracy of the staged destination of the vehicle to help allocate resources reasonably. Through simulation experiments, we verify the superior performance of the DAER algorithm in terms of resource utilization rate and the satisfaction of both parties participating in the auction.

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 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.

To satisfy ultra-high data volume and traffic density transmission requirements, millimeter wave (mmWave) and device-to-device (D2D) communication technology will be widely used in 5G mobile communication networks. In scenarios where mmWave small cell and D2D transmission coexist, D2D links mostly reuse frequency resources of the small cell to obtain higher spectral efficiency. However, this will make D2D impose great interference to mmWave small cell. This paper designs a Stackelberg game based interference control scheme with full frequency reuse in the context of D2D underlaying mmWave small cell network. The scheme aims to optimize the transmit power of D2D links, alleviate the interference caused by D2D communication to the mmWave small cell and take full advantage of the bandwidth of the millimeter band. Simulation results show that the proposed scheme converges rapidly, keeps signal to interference plus noise ratio (SINR) in a high range and achieves excellent throughput performance.

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 wide application of the Internet of Things, the number of network edge devices is increasing rapidly, resulting in huge network traffic that brings huge challenges to the current network. Aiming at the problem of heavy network load, researchers proposed some caching strategies. However, current strategies have some limitations. These existing caching strategies are usually global within the whole network. Most of them are to reduce the network delay and allow users to obtain content more quickly and easily. This paper proposes a network caching strategy based on edge and cloud coordination (ECC).The strategy divides the caching network into two parts (core and edge) to discuss different caching strategies. By choosing reasonable caching strategies in the core network and the edge network to implement different caching goals for different areas. Besides, the paper sets up PN nodes for coordinating and optimizing the cache resources between the edge and the core. Experimental results show that ECC has significant advantages in Server Load Reduction Ratio, Average Hop Reduction Ratio and Cache Redundancy compared with existing methods.

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 in 5G network is emerging as a very promising computation architecture by pushing computation and storage closer to end users with both strategically deployed and opportunistic processing and storage resources. Baidu cloud provides network services which can be deployed in 5G network recently. The network services such as weather forecast service and city road map service are typical applications for smart city. We analysis Baidu website data in this paper by our data mining method and related software. Clustering, outlier detection, prediction, and statistical methods are used to evaluate these smart city services, and the analysis result give suggestions to improve design and development of our 5G services (API website).

Due to the limited network resources and onboard processing capacity of the LEO/MEO double-layer satellite communication network, calculating the routing table in advance leads to heavy communication load, and rerouting results in large delay loss. Hence, this paper proposes a Priority- and-Failure-Probability-based Routing (PFPR) algorithm for LEO/MEO double-layer satellite communication networks. We combine virtual node and virtual topology strategies to eliminate satellite mobility, considers service classification and link failure probability to better fulfill the QoS requirements of different services. In addition, we introduce network virtualization technology. By using the method of common mapping of disjoint primary and backup links, it solves the delay problem of rerouting. The simulation results show that the PFPR algorithm proposed can reduce packet dropout rate, and service delay, and improve service throughput for different services, especially delay-sensitive services.

Various problems arise in the maintenance of communication networks. For example, on-site maintenance personnel have insufficient work experience. Devices used for maintenance work have limited computing resources and battery life. Moreover, most maintenance systems still use the centralized single processing mode of traditional cloud computing, which increases the data center computing pressure and slows the data flow. To overcome these problems, we propose a communication network edge maintenance system based on smart wearable technology and introduce computation offloading technology for mobile edge computing (MEC). Before offloading, we propose a multimerged computing sorting segmentation (MCSS) algorithm to divide a part of the task to offload. When making an offloading decision, we access a suitable MEC service node for each user with the lowest transmission cost and establish a related model. We use an improved Kuhn-Munkras (KM) algorithm that considers fairness among users to solve this model. After that, we propose a dynamic energy-efficiency awareness strategy. When tasks are processed locally, we optimize the CPU clock frequency. When tasks are offloaded, we adaptively allocate the transmission power. Finally, we conduct a simulation experiment. The results demonstrate that the proposed scheme can reduce the transmission cost and improve the performance, thereby increasing the level of on-site maintenance work.

With the explosion of smart devices and the gradual maturation of mobile systems, mobile crowdsensing (MCS) is playing more and more important roles in our daily life. In traditional MCS with a centralized framework, participants directly send perceived information to the task provider alone. This framework greatly increases the burden of cloud-based servers and cannot make full use of the increasing computation and storage capabilities of Internet of Things devices. To offload the computing and storage burden from traditional MCS architecture, a distributed MCS architecture was proposed in this paper, in which participants exchange sensing information with each other rather than forward it to central servers to complete a task together. Then, a diffusion Kalman filtering algorithm with quantized information exchange (QDKF) was proposed to solve the dynamic real-time estimate problem and limited communication resources in distributed MCS, where nodes exchange their quantized observations with neighbors to reduce the consumption of computing and storage resources. To prove the convergence and stability of the QDKF algorithm, an in-depth analysis of the algorithm uncertainty was reported to completely characterize the proposed solution. Moreover, the proposed algorithm achieves a superior performance by simulation.

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

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.

Wireless-based neighborhood area network (NAN) plays an increasingly important role in smart grid (SG) since the rapidly emerging smart services and rising number of terminals in grid put forward higher demand for NAN. Considering the differential business demands in NAN, we focus on the wirelessly uplink resource allocation, which allows a trade-off between network throughput and service fairness. For more flexible and coordinated allocation, this paper introduces the cloud-radio access network infrastructure into NAN with orthogonal frequency division multiplexing passive optical network (OFDM-PON) as the fronthaul link, and proposes a corresponding uplink resource allocation method that balances the network throughput and allocation fairness. By utilizing a hybrid intelligent optimization algorithm, composed by adaptive genetic algorithm and binary particle swarm optimization, the optimal throughput-fairness trade-off solution can be obtained with a good convergence ability. Simulation results demonstrate the advantages of our proposed method in both improving network throughput and achieving the trade-off between throughput and fairness.

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.

The existing network risk control mechanisms are lack of scientific and normative decision-making and rely too much on subjective judgments, which brings a great uncertainty on network risk management. In this paper, a risk control decision-making mechanism of power data network based on grey relation is put forward, and puts emphasis on the prior risk control based on the prediction results. This mechanism first constructs a matrix of positive and negative ideal measures according to the risk control objective. Then, the grey relation coefficient matrix between the candidate and ideal measures is calculated to evaluate the similarity between measures. Finally, we define the grey relation projection coefficient to evaluate the degree of closeness between the candidate measure and the positive ideal measure and the degree of deviation between the candidate measure and the negative ideal measure. Simulation results show that this mechanism can make timely and accurate decision-making of network risk control measures.

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

With the development of Internet of Things, there are more and more devices and applications at the edge of the smart grid. To enhance the quality of service, further processing of the smart grid to achieve load balancing is regarded as a critical step. The most interesting element in smart grid communications is data itself regardless of the data source. The emergency of Content-Centric network (CCN) just meets the demands and addresses the problems. First we model the smart grid with Content-Centric Network, and concentrate on the edge communication. Then we propose a load-aware potential-based routing (LAPBR) algorithm and evaluate its performances. The simulations results demonstrate the stability and robustness of LAPBR.

With the development of the Internet of Things, more and more devices can access the network through wireless access. And the wireless access of vehicles, which constitutes an edge network, can provide real-time road information and significant traffic state. Thus, it has gradually got the public attention. In order to offer the better quality of service (QoS) in the vehicular network, we propose a multi-priority bionic competition mechanism to implement service differentiation and resource allocation. Firstly, we deduce a context metric (CM) through fuzzy inference, which relates the urgency degree of a vehicle to its environment. Vehicle traffic is re-prioritized into four access categories (ACs) combined with the CM and transmission data types. Then, we propose the bionic competition model based on 802.11e EDCA protocol. This model considers the competition in the same level ACs and the competition among different level ACs, allocates different transmission rate and bandwidth for different ACs, which greatly improve the network throughput and bandwidth utilization. Finally, the simulation results show that our method improves the throughput, reduces the mean delay and packet loss rate.

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.

Cognitive-radio-based smart grid networks have been studied recently as an efficient way to overcome radio spectrum shortages, especially in wireless Neighborhood Area Network (NAN). In this paper, we propose the optimal spectrum allocation strategy of cognitive radio NAN Gateway (NGW), which also acts as a spectrum collector by radio sensing and leasing from the providers for a fee. Since the service terminals in grid are heterogeneous based on different QoS requirements and willingness to pay, this paper uses differential pricing and admission control for different terminals to improve the benefits of NGW. The decision-making process for spectrum collection and allocation is modeled as a 4-stage Stackelberg gaming, where the optimal decision of radio sensing, spectrum leasing, admission control and differential pricing are deducted through a reverse derivation. A novel corresponding algorithm is also given to solve these optimal solutions efficiently. The numerical results verify the theoretical work sufficiently, meanwhile some obvious meaningful conclusions are drawn from the observation of numerical experiments.

This paper studies the multi-layer structure of coupled power network based on the problem of fault cascade and unreasonable network design in the network virtualization environment (NV). Based on the complex network theory, we propose a network optimization algorithm: PNGA (Primary Nodes Group Algorithm). The objective of PNGA is promoting the robustness of the entire network. In the simulation experiment, this paper analyzes the network modeling and topological characteristics of a three-tier power grid in NV. We use the degree sorting algorithm as the control group which is widely used in power grid. Under different attack strategies, we investigate the performance of different algorithms and the state of fault generation. The results of the simulation we performed in this paper have shown that PNGA is superior to the rest of the algorithm in suppressing faults.

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.

To make an accurate evaluation of node importance in the power grid communication network, we propose an algorithm based on the communication topology layer and the power grid layer to evaluate the importance of the nodes. On the basis of the node contraction algorithm [7], the cut point is assigned a higher weight to reflect the difference between the key nodes and the non-key nodes. Simultaneously, combined with the characteristics of the power grid, the power factor, power service and node failure probability are added to evaluate node importance of power grid communication network objectively. Compared with the node contraction algorithm, the results show that the algorithm in this paper can better distinguish the importance of nodes, and has great reference value for the evaluation of node importance in the power grid communication network. Finally, the algorithm is applied to node risk analysis. By optimizing power service routing, average node risk of entire network can reduce significantly. Therefore, the reliability of network is improved.

Opportunistic offloading can be utilized to offload computing tasks and traffic data in Mobile Edge Computing (MEC). To improve the ratio of successful data offloading and reduce unnecessary data redundancy in opportunistic forwarding process, some methods of evaluating a device’s forwarding capability are proposed. However, most of these methods do not consider the temporal impact from device mobility and the efficiency influence from the capability computation process. To settle these problems, we proposed a Transient‐cluster‐based Capability Evaluation Method (TCEM) to evaluate a device’s data forwarding capability. The TCEM can be divided into two steps. The first step aims to reduce computational complexity by evaluating a device’s possibility of contacting the destination within a time constraint based on the transient cluster generated by our proposed Transient Cluster Detection Method (TCDM). The second step is to calculate a device’s probability of directly and indirectly forwarding data to the destination. The probability as a metric of evaluating a device’s forwarding capability can be used in different data forwarding strategies. Simulation results demonstrate that the TCEM‐based data forwarding strategy outperforms other data forwarding strategies from the aspect of the proportion of the data delivery ratio to the data redundancy.

An attractive architecture called heterogeneous cloud radio access networks (H‐CRAN) becomes one of the important components of 5G networks, which can provide ubiquitous high‐bandwidth services with flexible network construction. However, massive access nodes increase the risk of cell outages, leading to negative impact on user‐perceived QoS (Quality of Service) and QoE (Quality of Experience). Thus, cell outage management (COM) became a key function proposed in SON (Self‐Organized Networks) use cases. Based on COM, cell outage detection (COD) will be resolved before cell outage compensation (COC). Currently few studies concentrate on COD for 5G H‐CRAN, and we propose self‐organized COD architecture and approach for it. We firstly summarize current COD solutions for LTE/LTE‐A HetNets and then introduce self‐organized architecture and approach suitable for H‐CRAN, which includes COD architecture and procedures, and corresponding key technologies for it. Based on the architecture, we take a use case with handover data analysis using modified LOF (Local Outlier Factor) detection approach to detect outage for different kinds of cells in H‐CRAN. Results show that the proposed approach can identify the outage cell effectively.

With the multi-rate transmission and variable bandwidth switching technology, the Elastic Optical Networks (EONs) have many advantages to satisfy current network traffic. Compared with the traditional optical network, the EONs improve the spectrum utilization and increase the network capacity. So the EONs gradually become the key point of next generation optical transport networks. Obviously, the restoration mechanism in EONs has become thefocus of network operators' attention. This paper presents a dynamic restoration scheme based on software defined network (SDN) framework and an improved regional fault-tolerant routing and spectrum allocation algorithm (RSA). Using the SDN framework, we can greatly reduce recovery time and avoid configuration contentions. On this basis, we introduce the improved regional fault-tolerant RSA algorithm. The proposed RSA algorithm can decrease restoration blocking probability and relief effects caused by regional failures. The performance of the proposed dynamic restoration is evaluated in terms of restoration blocking probability and recovery time under different network loads, and compared against other schemes.

Network function virtualization (NFV) is a powerful emerging technique with widespread applicability. It provides Network Functions (NFs) through software virtualization techniques that decouple software and hardware. Some connected network functions constitute a service function chain (SFC). Therefore, the deployment of SFCs is much agile and simple. However, this leads to more service failure. The service failure includes service availability failure and service quality degradation. Aiming at the problem that the existing service function chain detection methods have high detection cost and cannot locate the failure accurately. This paper presents a method based on minimum detection cost. The method consists of failure detection and failure localization. In failure detection, we calculate detection paths according to the topology of network functions to avoid duplicate probing of links between network functions. In failure localization, we locate service availability failure and service quality degradation respectively and add timestamp fields to network service header to analyze locations of service quality degradation. Experiments show that the method reduces active detection cost and improves the recall and false-positive of service failure localization.

Software-defined networking (SDN) develops a logically centralized control plane by abstracting the underlying network forwarding devices, which makes the control of network traffic more flexible and more intelligent. In SDN, a switch can only work according to the rule of the flow tables received from its controller. Once the controller breaks down, the switch cannot transmit the incoming data packet which cannot be matched in the flow table. The SDN network can be severely affected by the controller failure. In this regard, we are committed to design a proper backup approach for SDN controllers to reduce the loss brought by controller failures. Besides, we attach great importance to the survivability of the control network under network failures. In this paper, we first formulate the survivability of control network. Then we propose a backup approach for controllers based on the survivability model. The network delay is considered in the backup approach. Simulation is conducted to verify the validity and efficiency of our approach. Results show our backup approach guarantees that the controller failures can be effectively recovered. Comparison results between our approach and other existing approaches prove that the approach can effectively reduce the link loss brought by network failures when the backup controller replaces the failed controller to manage the network.

In network virtualization environment, multiple virtual networks share the same resource of a physical network. Since the physical resources of a substrate network is limited, it is necessary to improve the utilization of physical resources. Considering the resource requirement of a virtual network may change over its lifetime, we propose a prediction-based resource management mechanism. To increase the utilization of the substrate network, we can adjust the resource allocated to the virtual network based on the result of prediction. Additionally, in order to avoid the result of prediction deviates from the real requirement, we compare our prediction result with the collection of the resource utilization at real time to ensure the correctness of our result. The simulation results show that our approach can increase the utilization of the physical resource and improve the virtual network acceptance ratio while ensuring the requirement of the virtual networks.

Life-time represents the effective survival time of network, which is significant when measuring the performance of wireless sensor networks (WSNs). Therefore, it is so important to extend network life-time by planning appropriate path based on energy consumption and remaining energy of wireless sensors. In this paper, a path planning method of WSNs based on service priority is proposed, and a customized Dijkstra algorithm is used to solve this problem. This method minimizes the total energy consumption of network while balancing remaining energy of all nodes in network, and through the sacrifice of network delay in exchange for extension of life-time. The simulation results show that our method not only prolongs network life-time compared to shortest-path algorithm but also improves network reliability.

With the today's the rapid developing of wireless mobile networks, various types of mobile devices have emerged and a variety of applications have been developed. People's desire for more convenient life and more efficient collaboration may be coming true in this era. Meanwhile there are lots of security challenges in wireless mobile networks. To resist conventional attacks and obtain stronger securities in the scenarios of mobile emergency services, we propose a privacy-preserving authenticated key agreement protocol with smart cards which requires no verification tables stored by the server and provides both perfect forward security and user anonymity. Emergency notifications are modeled as emergency message codes that are provided with security and anonymity by the protocol.

Combining VANET with ICN (Information Centric Network), this paper proposes a new FIB (Forwarding Information Base) selecting algorithm-ECRMLET (Efficient Content Routing Model Based on Link Expiration Time). To build stable routings and reduce network traffic, our ECRMLET has the following designs: 1) we modify the structure of PIT (Pending Interest Table) by adding two domains: receive time and tolerance time; 2) we introduce the algorithm of LET (Link Expiration Time) to help with the content routing selection in FIB; 3) ECRMLET also gets the link availability probability to be auxiliary information for our algorithm.

The delivery of services typically requires packets to be steered through a sequence of middleboxes to improve network security and performance. One constraint on the deployment of services is that middleboxes are tightly coupled to the physical network topology. As a result, ensuring successful deployment requires error-prone and complex low-level configurations. Software-Defined Networking (SDN) can eliminate the need to configure network devices manually to deploy services. However, in terms of steering middlebox-specific traffic in data plane, applying the existing capabilities supported by OpenFlow protocol may lead to incorrect forwarding decisions when there is a loop in the route used to steer traffic. In this paper, we present an implementation using tagging to discriminate different instances of the same packet arriving at the same ingress port on the same switch (i.e. the existence of the loop). Moreover, we propose an algorithm to judge the existence of the loop in a physical sequence of switches and decide which switches are responsible for adding tags. The experimental result demonstrates that our implementation can properly steer traffic through a specific sequence of middleboxes even when there are loops in forwarding path.

Vulnerability assessment and optimization for wide area monitoring, protection and control system (WAMPAC) can enhance the robustness and sustainability of network. However, current assessment methods are incomplete and optimization methods ignore dynamic process. A comprehensive vulnerability assessment and optimization method is proposed. Firstly, for assessment, a comprehensive vulnerability indicator is designed to assess vulnerability of nodes and edges in the network integrating static and dynamic aspects. And then, to relieve unbalanced vulnerability distribution in the network, a routing optimization method is proposed by reconfiguring service routes on the edge with high vulnerability. Finally, the simulation is taken under a real system. Vulnerability assessment with the defined indicator is executed, and its correctness is proved as well. Then with the optimization method, the network vulnerability can be balanced, which takes on effective theoretical and practical significance.

Software-defined networking (SDN) uses a centralized control plane to manage the whole network. If the scale of the network is large, it is necessary to divide it into multiple domains. Since the network scale becomes larger, the probability of failure occurrences is higher. Therefore, it is important to guarantee the control plane resilience in multi-domain SDN. However, the existing approaches cannot store the network state in real time, and do not consider the backup controllers placement problem in multi-domain SDN. In order to ensure the resilience of the control plane in multi-domain SDN, we propose a sharing data store and backup controllers based approach. Sharing data store is used to ensure that each master controller has a view of the whole network and data store can save the network state during the failure time. The sharing backup controllers are used to guarantee the resilience of control plane with minimum cost. Simulations show that our approach can use as less backup controllers as possible to ensure the resilience of control plane.

Fault-tolerant topology control is a critical problem in WSNs. It is important for improving network lifetime and reliability. In this paper, we present a novel algorithm FTMRT, which ensures Fault Tolerance by constructing a Multi-Routing Tree. We firstly construct a multi-routing tree of the initial topology, which ensures there are at least k-disjoint paths from each sensor to the set of supernodes. And then each sensor adjusts its transmission power according to the multi-routing tree to form the fault-tolerant network topology. In the topology maintenance phase, topology reconstruction is invoked each time there are some node fail and the supernode connectivity is broken. The effectiveness of the proposed algorithm is validated through simulation experiments.

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.

The wireless sensors deployed at the highway can ensure the safety of the traveling vehicles. However, the ribbon deployed wireless sensor network in the roadside infrastructure can easily to generate energy hole. Cooperative communication between sensors and vehicles is an effective way to improve this situation and reduce the energy consumption of the sensors. A cooperative relay selection algorithm based on residence time (CRSR) and cooperative relay selection algorithms based on prediction (CRSP) are proposed in this paper. In order to improve the data transfer amount of the vehicle, residence time is considered in CRSR when sensors select the vehicles. To further improve energy efficiency CRSP considers arrival time of the vehicle to store collected data in the delay tolerance situation. Energy is also considered in CRSP to reduce the energy consumption and ease energy hole. Simulation results show that the CRSR and CRSP methods can reduce the energy consumption and prolong sensor network lifetime than the traditional algorithm.

LTE-based Public Safety Network (PSN) is a wireless communication network which can provide efficient and reliable communication in disasters or emergencies for disaster relief and public protection. Therefore, ensuring that network congestion will not happen in PSN during an emergency is becoming increasingly important. LTE-based PSN is easy to be congested because part of spectrum resources is compressed to guarantee priority requirements of public safety users. In this paper, we develop a new method namely Selective Admission Control (SAC) mechanism to manage the radio bearers access to the commercial radio for Public Safety (PS) in LTE-based PSN. In the case of emergency, we select the traffic bearer with minimum estimated load increment accessing to the LTE-based PSN. The channel quality of new bearers should be taken into account, which means that in congestion, users who arrive earlier with poor channel quality will be rejected to reserve sufficient resources for users who arrive later with good channel quality. The simulation results show that the SAC mechanism can improve throughput by 36% and lower the rejection rate by 73% at most than reference method based on non-selective access control model, as a result effectively avoiding the network congestion and improving the utilization of spectrum resources for public safety communication.

This paper proposes a novel Gain-Aware Uplink-Downlink(GAUD) jointly resource allocation scheme to maximize the Device-to-Device(D2D) throughput while guaranteeing Quality of Service (QoS) of cellular users. We formulate the global optimization problem as a mixed integer nonlinear programming problem and decompose it into three sub-problems. Firstly, a method of jointly uplink and downlink reuse mode selection is proposed. Based on the throughout gain, each D2D pair is appropriately assigned by either downlink or uplink frequency resource to reuse. Then a heuristic scheduling is designed for fairness channel allocation in order to form D2D users as much as possible. At last, the Lagrangian dual algorithm is developed to solve the optimal power allocation. The simulation results show that our proposed jointly downlink-uplink resource reusing scheme can make the system throughput increased by about 35% and 50% higher than the scheme based on Only Downlink and Only Uplink resource reusing.

Activity recognition is a hot topic of research that is widely adopted by many applications such as fall detection of elderly people. Emerging passive RFID (radio-frequency identification) is creating huge opportunity for wearable devices to achieve activity recognition. However, performance of activity recognition is constrained by RFID localization accuracy and low quality of data streams characterized by sparsity and noise. In this paper, we present a novel activity recognition system, called RTagCare, which is a low-cost, unobtrusive and lightweight RFID based system. The RTagCare system leverage RFID localization technology, 3D-accelerometer base human activity identification and data mining algorithm to overcome traditional activity recognition system issues. RTagCare has been implemented and deployed in a test environment. As a result, RTagCare generally performs well to recognize human activity with high performance (F-score >94%).

Information Centric Networking (ICN) is a new kind of network architecture centered on content data. The ICN improves the efficiency of data transmission by the longest matching routing mechanism based on the content name prefix of the request interest packets, however, the multipath forwarding performance also resulted in the redundancy of the network content. The existing ICN forwarding strategy does not take into account the selection problem of routings when a content hit multiple Faces. This paper proposes a routing forwarding strategy based on Shapley value. We add a forwarding value table, which is used to calculate the number of content routing and the number of face to forwarding. The table stores the request delay of content routing and the busy degree of the next hop nodes. The content routing number and forwarding nodes of the next hop forwarding are decided by the alliance game. Simulations show that our strategy can improve the cache hit ratio, reduce server load and reduce the average request delay compared with full forwarding strategy, it improves the network performance in total.

In order to meet the requirements of traffic data fusion for real-time urban traffic state estimation, a new kind of fusion structure model is proposed. This fusion model consists of both spatial fusion and temporal fusion. First we use the power average operator as spatial fusion method. Then we propose a temporal correlation based data compression (TCDC) algorithm, based on segment linear regression (SLR) algorithm. Extensive simulation results demonstrate the effectiveness and correctness of TCDC algorithm, as well as TCDC's advantage over SLR on overall performance.

Failure instances in distributed computing systems (DCSs) have exhibited temporal and spatial correlations, where a single failure instance can trigger a set of failure instances simultaneously or successively within a short time interval. We investigate an effective approach to predict correlated failures of computing elements (CEs) in DCSs. Correlated-failure patterns are modeled using the concept of probabilistic shared risk groups (PSRG). Firstly, we design a new structure for PSRG, named SPSRG, to describe features of correlated failures. Then we exploit an association rule mining technique in a parallel way to generate and update our SPSRG using information of CE-failure states. Finally, we propose a correlated failure prediction approach to evaluate the probabilities of upcoming failures from the SPSRG. The experimental results show that the proposed approach outperforms other approaches in failure prediction performance in terms of precision, recall and F-measure. Moreover, it allows employing customizable thresholds by which the trade-off between precision and recall can be adjusted for various requirements.

With the rapid development of cellular network systems, the operators need more experience to deal with complicated network management system and wide range of Key Performance Indicators (KPIs). There are many indicators related to each other due to the definition or communication process. But several implicit associations still exist among these KPIs. This paper proposes an approach to figure out the implicit linear relationship among indicators clearly in which a new clustering technique is used for distinguishing different relationships. Data analysis using real network data shows that the approach can well divide data into clusters, and each cluster can effectively reflect the relationship between indicators.

Energy efficiency in future green cellular wireless networks poses a challenge to operators and researchers. An effective method of providing energy savings (ES) in base stations (BSs) is to switch off idle BSs or put them into sleep mode and subsequently migrate the traffic loads to active BSs in their neighborhood. The intrinsic issue related to such methods applied to heterogeneous networks (HetNets) is that the optimal selections of various types of BSs during both energy-saving and coverage-compensating (CC) processes are difficult to determine. In an attempt to resolve the insufficiency of existing strategies, we propose a novel traffic-aware self-organizing ES mechanism that enables efficient resource allocation and interference management in multi-level networks. To further develop optimal energy conservation procedures for BSs, a new constraint model is proposed. We employ coverage gaps and over-provisioning as the optimization objectives and analyze the effects of their weights. The performance in terms of energy savings is evaluated in an urban Long-Term Evolution (LTE) scenario with different types of BSs. The simulation results show that the proposed mechanism can maximize energy efficiency in heterogeneous cellular networks while guaranteeing the quality of service. This algorithm is autonomous in terms of decision making and execution.

Software defined network (SDN) develops a centralized control plane to manage the whole network. If the scale of the network is large, it is necessary to deploy multiple distributed controllers. In SDN, a switch can only work by relying on flow tables received from its controller. Therefore, controller placement is an important problem to keep the switches working efficiently and improve the reliability of the control network, which consists of controllers, switches and the communication paths between them. However, the existing controller placement approaches are not effective or do not consider the network reliability and the required delay between switches and controllers at the same time. In order to ensure the reliability of the control network and meet the required propagation delay, a min-cover based controller placement approach is proposed. Two metrics are proposed to measure the reliability of a control network, and the definitions of neighborhood and min-cover are provided, based on which the approach try to use less controllers to achieve the reliability and low delay of the control network while guaranteeing the manageability of the network. Simulations show that min-cover based approach can use as less controllers as possible to ensure the reliability of control network and satisfy the required delay at the same time. Moreover, the approach has steadily good performance in networks of different scales and connectivity.

Recent research activities have recognized the essentiality of node mobility for the creation of stable, scalable and adaptive clusters with good performance in mobile ad hoc networks (MANETs). In this paper, we propose a distributed clustering algorithm based on the group mobility and a revised group mobility metric which is derived from the instantaneous speed and direction of nodes. Our dynamic, distributed clustering approach use Gauss Markov group mobility model for mobility prediction that enables each node to anticipate its mobility relative to its neighbors. In particular, it is suitable for reflecting group mobility pattern where group partitions and mergence are prevalent behaviors of mobile groups. We also take the residual energy of nodes and the number of neighbor nodes into consideration. The proposed clustering scheme aims to form stable clusters by reducing the clustering iterations even in a highly dynamic environment. Simulation results show that the performance of the proposed framework is superior to two well-known clustering approaches, the MOBIC and DGMA, in terms of average number of clusterhead changes.

This paper discusses how to assess video Quality of Experience (QoE) with image feature information which includes texture and saliency information. In order to compress and transmit the feature information, wavelet transform is conducted and the high-frequency component histograms are fitted using generalized Gaussian distribution. At end user side, the video distortion is measured by using Kullback-Leibler Divergence (KLD) and therefore MOS is evaluated using neural network fitting. The LIVE Video Quality Database is used for testing the performance of proposed method. result confirms that the proposed method is competitive and suitable for assessing the QoE of real-time video service.

Fault detection in wireless sensor networks is a crucial and challenging task. Many detection approaches relying on specific rules or inference models have been proposed to distinguish faulty sensors by exploring spatial-temporal correlations among sensor readings. However, these approaches may require high communication overhead or computational cost, and many potential faulty sensors that may not generate anomalous sensor readings remain undetected. In this paper, we propose a metric-correlation-based distributed fault detection (MCDFD) approach. It is motivated by the fact that the correlations between sensor nodes' system metrics usually perform regularly, whereas abnormity of such correlations indicates failures. MCDFD explores sensor nodes' internal metric correlations using correlation value matrixes. An improved cumulative summation (CUSUM) algorithm is used to track gradual changes or abrupt changes. Once any changes occur in correlation value time sequences, potential failures can be detected. The apply of metric correlations has made MCDFD with high-energy efficiency and low computational complexity, since no communication overhead is incurred and CUSUM algorithm is simple for computation. Simulation results demonstrate MCDFD performs well in respects of higher detection accuracy and lower false positive rate even under high node failure ratios and dense distribution conditions.

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.

Network virtualization has been considered as an enabling technology for future network, through which multiple heterogeneous virtual networks can run on a shared infrastructure. In order to study and test the network management mechanism of future network, we develop and implement a network operation simulation platform of the network virtualization environment. The platform mainly simulates the double-layer network topology and the virtual network embedding in the network virtualization environment. In addition, the running status and the fault of networks can also be simulated. The validation results show that our platform can effectively emulate the network virtualization environment. It has three advantages: (i) Simulating Double-layer network model. (ii) Running virtual network embedding experiments graphically and supporting comparison among different embedding algorithms. (iii) Simulating the faults of both substrate and virtual networks and emulating the detection results based on the simulated faults.

Multi-domain virtual network embedding (MVNE) aims to embed a virtual network (VN) across multiple physical domains while minimizing the embedding cost. A key phrase of MVNE is VN partitioning which partitions a VN into multiple physical domains. Since the MVNE problem is NP-hard, we provide a heuristic VN partitioning approach named VNP-PSO based on the Particle Swarm Optimization (PSO) to increase the efficiency of VN partitioning. The VNP-PSO algorithm generates a near-optimal solution of VN partitioning through the evolution process of the particles. The simulation results show that our proposal can increase the efficiency of VN partitioning and decrease the embedding cost of MVNE.

Survivable virtual network mapping (SVNM) has been extensively investigated to guarantee that the mapped virtual network (VN) works normally against substrate failures. The existing studies of SVNM mainly focus on single node or single link failure. Since natural disasters usually cause severe substrate failures in geographic regions, some work addressing SVNM against regional failures has been studied. However, the current approaches only solve the mapping problem against single regional failure. When there are multiple regional failures aroused by natural disasters, such approaches are not effective. In this paper, we first design a regional failure model with the knowledge of risk assessment. Then we propose two effective mapping algorithms based on the disaster-prediction scheme with the regional failure model. One is the minimum link risk prior selection algorithm and the other is the asymmetric parallel flow allocation algorithm. Simulation results show that both approaches can reduce the capacity loss of virtual networks caused by regional failures and can effectively increase the average VN acceptance ratio.

Network virtualization has been regarded as a core attribute of Future Internet. To improve the quality of virtual network, it is important to diagnose the faulty components quickly and accurately. Recently more and more researches focus on end-user fault diagnosis, which can fit incomplete knowledge and dynamic challenges. In this paper, we present a fault diagnosis system called DiaEO in virtual network. It improves the present end-user fault diagnosis methods by screening evidences before analyzing to reduce the time-consuming. Besides that, DiaEO also improves the anti-noise ability of the system. The simulation results show that the proposed method can keep high accuracy and ameliorate time performance.

It's a challenge to effectively assess Quality of Experience (QoE) for TCP video streaming with network performance parameters, to resolve this problem, an objective hierarchical Evaluation for Transmission Control Protocol (TCP) video streaming is proposed under video playback scenarios. QoE assessment for TCP video streaming is resolved into two sub-steps. In the first place, in consideration of video playback performance parameters affecting QoE, the authors demonstrate three novel application-layer metrics. Further, impact of network status on video playback performance is investigated and the authors propose high level network-layer parameters. Then the correlation between the network-layer parameters and application-layer metrics is characterized through analysis and inference. In the secondly place, subjective tests are conducted to evaluate QoE from application-layer metrics. Ultimately, the authors validate analysis and model by simulations and experiments in real networks. The experimental study shows that the proposed method performs well in assessing QoE of TCP video streaming.

In this paper we introduce a new collaboration paradigm to achieve content discovery with infrastructure fixed on many autonomous geographical regions in mobile wireless networks. In this paradigm, mobile users, physically located in the regions, send a request to the infrastructure and obtain contents using their mobile devices. To achieve the paradigm, we design an incentive mechanism with a repeated cooperative game-theoretic approach. This approach stimulates mobile users that are selfish and often reluctant to consume their energy for providing any information or services to make their own contents available to someone else. Additionally, we discuss game ingredients in our approach, including the assessed value, the battery charge level and the signal strength level. We find all Nash equilibria in our approach and obtain the ratio of temporal discounting. The results via our simulations show that our approach can effectively motivate mobile users to share their contents in terms of the ratio.

Fault detection plays a crucial role in wireless sensor networks (WSNs). Many fault detection approaches requiring a priori knowledge of network faults have been proposed to distinguish faulty sensors by exploring spatial-temporal correlations among sensor readings. However, many faulty sensors that may not generate anomalous sensor readings, and potential failures with unknown types and symptoms remain undetected. In this paper, we propose a Metric-Correlation-Based Fault Detection (MCFD) approach using clustering analysis. It is motivated by the fact that the system metric correlations of most fault-free sensors usually show strong similarities, whereas different patterns of such correlations indicate potential failures. MCFD explores internal metric correlations inside sensors using correlation value views. An improved Neighbor-based Local Density Clustering Analysis (NLDCA) algorithm based on the Neighbor-based Local Density Factor (NLDF) is applied in spatial domain detection to cluster similar correlation value views together, thus potential faulty sensors with abnormal views not belonging to any cluster can be detected. Simulation results demonstrate that MCFD approach performs well in respects of higher detection accuracy and lower false positive rate even under high node failure ratios and dense distribution conditions.

Due to off-the-shelf hardware and software applications integrated with distinct manufactures are widely used, networked computing systems incur high risk of failures and exceptions. Failures play a crucial role and must be timely handled to ensure system survivability and reliability. This paper focuses on on-line failure prediction for networked computing systems using system runtime data. We propose a failure prediction approach based on cloud theory (CT) and hidden Markov model (HMM). This approach expands the HMM, training with the CT. Additionally, we define the parameter ω as the correlations between various indices and failures, taking account of multiple runtime indices in networked computing systems. And we use multiple dimensions to describe failure prediction in detail, by extending parameters in HMM. In order to reduce computing cost in model training phase, we exploit the likelihood and membership degree computing algorithms in CT, instead of traditional HMM algorithms. Finally, the results from our simulations show the feasibilities and effectiveness of our approach. The experiments show that the execution time of the proposed failure prediction is reduced in terms of promised prediction performance.

Mobile social network service (MSNS) provides daily services for the user and can also be used in emergencies, such as natural disasters. How to conduct service composition and recovery among mobile devices quickly and efficiently is one of the important research areas of MSNS. This paper puts forward a comprehensive strategy applied to MSNS during natural disasters. When communication facilities are limited, several devices can work cooperatively to provide users with reliable composite service, also known as the service composition process. In addition, when some of the devices fail and the composite service interrupts, the presented recovery process reconstructs a service path quickly. Composition and recovery cost functions are used in the two processes separately. The goal is to find the service path or the recovery path with minimal cost function value in each process that satisfies the quality-of-service requirement. The simulation results show that the proposed strategy not only reduces the interrupt number and recovery time but also improves the success rate of the service request, making the performance of this strategy better than that of the other similar strategies.

Service search has been introduced to exploit heterogeneous resources of distributed devices on the purpose of supplying ubiquitous services in ubiquitous stub environments, especially in MANETs. However, due to the characteristics of infrastructure-less, devices' limited resources, and dynamic topology caused by the mobility of devices, service search faces great risk of failure. Usually, clusters are the main way of organizing the devices in MANETs. Therefore, an effective clustering algorithm is necessary to ensure the reliability of service search. A maximized reliability clustering algorithm (MRCA) is proposed. We present predicted battery supporting time, CPU computing power, connecting degree and predicted velocity of devices, select the best devices as cluster heads. We combine the four factors together using FAHP algorithm. The simulations show that the MRCA can prolong cluster headers and members' valid time, reduce the consumed energy in the cluster's life cycle. This proves that MRCA can improve the reliability of cluster.

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.

A good web selection algorithm can provide the most suitable service for users. However, known for its slow convergence rate and proneness of oscillation in its learning process, the traditional error back propagation neural network algorithm cannot be applied in the service selection scenarios of actual smart distribution grid. In order to meet the requirements of telecommunication technology for smart distribution grid and improve the quality of telecommunication service, this paper proposes an improved error back propagation algorithm, in which the learning factor can be self-adjusted with every iteration. The simulation results show an optimization of the training speed and an oscillation reduction in the learning process with the new algorithm, thus obvious optimizing the web services selection in smart distribution grid.

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.

The performance and reliability of services relies on the network virtualization environment's capabilities to effectively detect and diagnose faults in both substrate and virtual network. However, Network Virtualization Environment (NVE) brings to fault diagnosis new challenges such as inaccessible substrate network information and multi-layer faults. To solve the above issues, a Multi-layer Fault Diagnosis Method (MFDM) is proposed. A layer-by-layer strategy is used to resolve the problem of inaccessible substrate network information. And a filtering algorithm is proposed to distinguish the multi-layer faults in the network virtualization environment. At last, a contribution-based hypothesis selection algorithm is proposed to infer the most possible faults. Simulations and experimental results show that MFDM has a higher performance in the accuracy ratio, false-positive ratio.

In the management of WSNs, the mechanism of fault detection and location based on active probing has been widely applied. The main optimal direction of active probing is to maximize the coverage of nodes in the network by sending minimal set of probes from probe stations. Therefore, before probing, selecting optimizational station set to improve reachable rates of probed nodes has significant influence on detection effect of active probing. In this paper, we propose an optimizational strategy of probe station selection (PSS) by Genetic Algorithm (GA) and achieve the improvement of confirmed achievable rates of probed nodes. Meanwhile, lower runtime cost and more reasonable usage of energy are reached.

The main role of wireless sensor networks is to collect environmental data. As the sensor nodes are vulnerable and work in unpredictable environments, sensors are possible to fail and return unexpected response. Therefore, fault detection and recovery are important in wireless sensor networks. In this paper, we propose a fault detection algorithm based on support vector regression, which predicts the measurements of sensor nodes by using historical data. Credit levels of sensor nodes will be determined by a contrast between predictions and actual measured values. In this paper we also propose a fault recovery algorithm according to the node credit levels combined with genetic algorithm. The simulation results demonstrate that the algorithms we propose work well in failure detection rate, fault recovery speed and energy consumption.

Survivable virtual network embedding (SVNE) aims at embedding a virtual network (VN) in a way, that after being affected by substrate failures, the VN is still operating. Based on the single node failure assumption, that at any time there can be at most one failed substrate node, the existing studies for the SVNE against substrate node failures back up VNs with a maximum resource sharing. However, multiple node failures do happen in reality, thus those methods are not always effective. In this paper, we propose a topology-aware VN embedding approach to enhancing the survivability against multiple node failures. We make use of the topology attributes to provide each substrate node with multiple potential failover choices, based on which a recoverability-based VN embedding algorithm and a profit-driven VN remapping algorithm are presented. Simulation results show that the proposed approach can achieve rational resource allocation and effectively increase the long term business profit to the infrastructure provider.

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.

We address the problem of inferring the network link loss rates using end-to-end measurements, which can also be formulated as network tomography. As we have known that most tomography problems are rank-deficit. One kind of method uses multiple probe measurements to acquire more information about the system that may generate much additional overhead; the other method imposes unrealistic assumption on the system. To address the issue that most network tomography methods cannot take into account both accuracy and efficiency, a novel link loss rate inference algorithm is proposed. In this paper, we get all identifiable links and then we utilize the information of these determined links to acquire the global distribution of the system. Moreover we partition all links in the network into several subsets. For each group, nonlinear programming is used to get the optimization solution of link loss rate. Finally, we evaluate our method and two former representative methods by the simulation. The results demonstrate that our method not only reduces the probe costs and the running time to a low level, but also makes a great improvement on the accuracy. Furthermore, our method can also perform well in more congested and large networks.

Network virtualization has been regarded as a core attribute of the Future Internet. In a Network Virtualization Environment (NVE), heterogeneous virtual networks can share the same physical infrastructure regardless of their different topologies, demands, protocols and so on. In this case, the Survivable Virtual Network Embedding (SVNE) problem becomes increasingly critical to overcome the failure of physical infrastructure. Backup resources needed to provide survivability of virtual network undoubtedly increase the challenge of resources efficiency of SVNE. In this paper, we study the SVNE problem and propose a method of allocating bandwidth resources based on load balancing of the physical resources and a strategy of reconfiguring backup resources. Simulation experiments show that load balancing based method has a higher performance in the long term acceptance ratio, revenues and utilization of substrate links. And the reconfiguration of backup resources is cost-efficient and also helpful to increase the acceptance ratio.

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.

A novel dynamic multi-stage ESM (Energy-Saving Management) mechanism based on BS (Base Station) cooperation is proposed. The mechanism firstly introduces a local OP (Opposite Pair) cooperation method taking account of geographic topology and then divides time period into four domains. In time domains, regional dynamic multi-stage algorithms and efficient performance evaluation model for the mechanism is analyzed as well. The mechanism is simulated under a practical LTE BS deployment. Results show that 25.1% of regional energy can be saved at most. Still better coverage, interference, and throughput performance can be obtained comparing to other algorithms.

In a composite service for multiple users, users that locate in the different network position are related to network parameters that change dynamically. Therefore, we need a service composition method that can not only handle many user requests, but also adapt to the change of the current network parameters. We use queuing theory and reliability theory to model services, and propose a runtime service composition method. The method obtains multiple service execution paths for each kind of user requests, and chooses the proper candidate service in runtime according to the current network state. The results show that our method is effective and can adapt to the changes of the network parameters.

Because the quality of experience (QoE) of video is affected by the content type of the video, this paper firstly establishes a video content classification mechanism. Based on the content types and the objective parameters of bitstream layer and application layer, which have influences on video QoE, a multi-factor QoE evaluation method for H.264/AVC encoded video is proposed. The experimental study shows that the proposed method performs well in assessing the video QoE.

Service composition has been introduced to exploit heterogeneous resources of distributed nodes for purpose of supplying ubiquitous services in ubiquitous stub environments, especially in MANETs. However, due to the characteristics of infrastructure-less, the limited resources of the nodes and dynamic topology caused by the mobility of nodes, service composition faces great risk of failure. Therefore, service recovery handling failure is crucial to guarantee composite service's successful execution. In this paper, we propose a novel recovery selection function which incorporates device effective rate, individual capability and cooperative capability. Then we elaborate an original heuristic thought-based backup recovery algorithm: Dynamic Local Backup Recovery Algorithm (DLBRA). Finally, simulation results demonstrate that the proposed strategy ensures high performance, effectively guarantees the sustainability and significantly reduces the response time of the composite service.

Plenty of web services are emerging in clouds. They are distributed, heterogeneous, autonomous and dynamic. These characteristics may make a composite service unstable and inflexible. To adapt to this environment, we propose a machine learning strategy that is developed for and applied to web service composition. This way, the composition framework continually learns which web service candidates are currently best suited to be selected and composed to fulfill more complex tasks. Since the learning process is not stopped, the framework is able to adapt its composition strategies to changing conditions in dynamic environments. A case study is given and the learning algorithm is evaluated and compared to the results of related work, which shows that our method improves the success rate of service composition.

In this paper, we introduce a network performance anomaly detection and localization method based on active probing, aiming at avoiding waste of unnecessary probes and reducing detecting time by decreasing selecting rounds in detection phase. We propose a method of classifying detection strategies in order to find a balance between extra calculation and link load. Also we optimized the procedures of one of the strategies so that instead of finding a local optimal solution, we get a global optimal approach. An algorithm that can adapt to multi anomaly link networks is proposed and several issues during detection phase were being discussed. Finally we simulate a former representative algorithm and our improved method on different network topologies. The results show that our improved algorithm outperforms the former one in both probe selecting rounds during detection phase by 10%.

The definition of network management interface is generally divided into three phases, requirements, analysis and design. In analysis phase, UML is used as the modeling language to present technology independent models, and these models can be mapped into multiple technology specific models. With the development of Web Services applied in network management domain, it is required to define Web Services-based information models in design phase. This paper applied MDA approach to realize models transformation. As the main work, this paper proposed detailed mapping rules describing how to map the existing source UML models into the target Web Services-based models. An automatic transformation approach using XSLT was proposed to implement the mapping rules, and experiments were made for verification. The proposed mapping rules improved the defects in related work. Furthermore, the work of this paper could help resolve some issues in current models transformation work which is still being accomplished manually by standard developers.

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

Since wireless sensor network has limited resources, it's important to design its task allocation algorithm reasonably to reduce energy consumption. The contract net is simple and flexible so that it can meet the needs of the wireless sensor network. In this paper, we introduce the improved C-MEANS algorithm to cluster nodes to decrease the number of bidders, and at the same time, the LMS algorithm is adopted to predict the bid value of the nodes. The simulation results show that the energy consumption and traffic flow are reduced, and the bid value more accurately reflects the status of the node when allocated tasks, which increased the complete rate of network tasks.

The uncertainty of change and low-utilization of human resource make change management more difficult to implement in today's competitive IT management environment. To fill this gap, in this paper a novel strategy is proposed for scheduling human resource to change activities concerning both runtime and business impact. Strategy is heuristic-based to obtain (1) ordering of human operators by mining potential individual skills, and (2) ordering of change activities based on improved critical path method, and then scheduling humans to activities with business constraints. Note that, before the activities are ordered, the splitting-regrouping of the complicated change workflow should be considered first by the concepts of computable dependency and cluster. The approach has been validated by a small but realistic case. Results showed that, compared to previous studies, our novel strategy is more suitable for highly parallel time-critical scenarios.

Existing network link estimation methods generally assume that the network link status in the measurement is stationary, but this assumption is not always true in the real network. Thus they cannot provide desired estimation accuracies. To address the problem, in this paper, we propose a new methodology, which can accurately infer the packet loss rates of all links in the network and locate the non-stationary links. Through software simulation, we compare our method with a former inference algorithm (LIA). Experimental results show that the new algorithm can provide higher inference accuracy within the same computing time.

We address the problem of inferring link loss rates from unicast end-to-end measurements. Different from previous tomographic techniques, we provide a method to partition all links in the network into several subsets-loss inferences can be performed independently among each subset. We also design a approach, based on the independence of links, to infer the loss rates of individual links in each subset with high accuracy. Compared with two previous representative approaches: LIA and Netscope (the most two accurate algorithms as far as we know) by both analytical and experimental tools, our method mainly has the following strengths: 1) Lower cost. Our method only makes use of single measurement (2% of probe cost of previous methods) on each independent path; 2) More accurate. Even in the network with 30% lossy links, our method accurately identifies 96% of the lossy links, with the false positive rate of 3%, which is a great improvement over the existing alternatives; 3) More scalable. Our algorithm runs much faster than previous ones, with bounded inference error, especially for the networks with more lossy links.

To solve the abnormal coverage problems caused by unreasonable network parameter settings more effectively, an automated coverage optimization scheme based on downtilt-adjustment of base stations in wireless access networks is proposed. After detecting and analyzing the abnormal coverage situation, simulated annealing algorithm is adopted by the scheme to figure out an optimal downtilt-adjustment solution for each base station. And then each base station can effectively adjust its electronic downtilt according to the solution to optimize the coverage. The whole process is completed without human intervention. Simulation results show that the proposed automated coverage optimization scheme can improve the wireless network coverage quality. Moreover, weak coverage and excessive coverage problems can be solved effectively.

Coalition formation is an essential component for in wireless sensor network (WSN). Most of current coalition formation algorithms have focused on disjoint coalitions. We develop an improved ant colony algorithm to solve the overlapping coalition formation(OCF) problem in multiple coalitions in WSN domain. In this improved ant colony algorithm, we bring in mutation operation and elite strategy from genic algorithm. By doing this, it will improve the pheromone update strategy and allow sensors to allocate different parts of their resources to serve different coalitions simultaneously.

Since there is no central management center in the MANETs, nodes need to self-manage service provided by other nodes. To pursue maximal utilities, they should be able to negotiate autonomously for services, e.g., packets transmission, information share. While some selfish mobile nodes in MANETs are unwilling to provide services for other users, it directly leads to serious decline in network performances. Hence an effective negotiation mechanism is required to stimulate them to cooperation. In this paper, we present a service-oriented negotiation model between selfish nodes in view of one-to-many application scenarios, driven by the basic intuition that negotiators tend to maximize their individual payoffs while ensuring that an agreement is reached. We develop the genetic algorithm to make the negotiation more adaptive in the MANETs. The simulation results show that the algorithm reduces energy consumption and communication traffic in deed.

With the development of Mobile Communication Network, flat network architecture has become a study focus. The flat traffic transmission can effectively lighten the burden on the operators' core network. The LIPA (Local IP Access) and SIPTO (Selected IP Traffic Offload) proposed by 3GPP are the typical technologies in the horizontal structure evolution. Current offload policy for LIPA&SIPTO either uses a coarse control bringing high cost to both of operators and mobile terminals, or performs rather complex in practice. This paper proposes a flexible and effective offload policy mechanism (the traffic offload mechanism based on policy of bearer granularity, TOMBOBP) to support the LIPA&SIPTO solution in H(e)NB subsystem. By matching the traffic information to the defined policy in the H(e)NB, it performs offload evaluation very well.

Identifying and classifying different network applications is very important for trend analysis, dynamic access control, network security and traffic engineering, while traffic classification is able to classify applications effectively. Current popular methods of traffic classification mainly include machine learning algorithm based on supervised or unsupervised and the method based load. In practical applications, the above methods have high complexity or low accuracy degree, so we propose a semi-supervised support vector machine method only based on flow statistics to identify and classify network applications. In this method, SVM, "constant" flow and co-training algorithm are the key core to obtain a classifier rapidly. The classifier got by this method has three advantages contrast to the previous classical methods: 1) high classification degree; 2) high generalization performance; 3) rapid computational performance. As a proof of concept, we implement the classification algorithm based on open-resource, and show the characteristics and feasibility of our method in the campus and resident network.

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.

We suggest a method of bottleneck diagnosis with more excellent performance. There are some problems with current diagnosis methods, such as extra network packets and sensitivity to time changes. In this paper, for UDP network, we propose a new method of bottleneck diagnosis based on the concept of network utility maximization. This bottleneck diagnosis method overcomes the disadvantages of current methods, since it totally depends on mathematic models and method, instead of sending and processing large numbers of packets. The problem is modeled as a geometric program problem and the link loss rates are computed by solving the maximization problem. In addition, the method is expanded to random input rates, allowing network managers to control the network performance more flexibly. At last, experiments are carried out to compare the new method with the method proposed by Shetty et al [1]. The results indicate that our method has better performance in both networks with fixed rates and with random rates.

In WSN, Clustering Routing Algorithm can effectively reduce network energy consumption and prolong network lifetime well. But existed Clustering Routing Algorithms are usually location-based, where data correlations are not considered. There is still data redundancy in the terminal. This paper proposes a data correlation-based virtual clustering approach. It integrates the advantages of clustering technique and data correlation. Nodes that are good data correlated will be partitioned in the same virtual cluster. The experimental results show that the proposed algorithm can reduce the amount of messages sent by the nodes, and reduce the energy consumption. The network lifetime is prolonged as well.

With the development of high-speed networks, the challenge of effectively analyzing the massive data source for anomaly detection and diagnosis is yet to be resolved. This paper proposes a new flow-based anomaly detection method based on summary data structures and combinations of traffic features. Using IPFIX flow records as input, parallel sketches are established for chosen traffic features respectively. For each sketch, we use Holt-Winters forecasting technique to achieve their forecast sketches and deviation matrixes. When the deviation exceeds a certain threshold, sub-alarms will be generated. According to the characteristics of various attacks and combinations of traffic features, sub-alarms can be merged into final alarms. While sketches of flows are being constructed, destination addresses are recorded in linked lists which are used to locate victims by a series of set operations. This method can not only detect the existence of anomalies in near real time, but can roughly indicate the anomaly types and locate abnormal addresses.

Fault detection of wireless sensor networks has been studied intensively in recent years under the assumption that manager nodes are default probe stations. However, some additional fault detection tasks will make so busy the manager node to be failed more quickly. Moreover, at the first beginning period most sensor networks are working normally without any dead nodes and the probing work are actually ineffective. Thus it is important to study the problems of electing the probe stations and probing frequency. This paper presents a probability-based fault detection algorithm to elect sensor nodes as probe stations by considering the probability distribution of sensor nodes and the fault distribution information (accord with Pareto principle) of sensor networks. The dynamic adjusting rule for probing frequency is also proposed in this paper. The simulation demonstrated that the algorithm and the rule can prolong the lifetime of sensor network only sacrificing very few fault detected rate. The Pareto principle that a small number of clusters contain most of the faults has also been demonstrated, and this principle has been applied to electing probing stations. The results obtained in this paper provide useful guideline to fault management in wireless sensor networks.

This paper studies task allocation in cluster-based Wireless Sensor Network (WSN) using negotiation model. We study both the negotiation reasoning model and the negotiation protocol for task allocation to achieve energy efficiency while balancing nodes energy. Reasoning model determines the offer generate scheme and gives control over negotiation process. A time depending Boulware function is used as the concession strategy in reasoning model to balancing efficiency and utility. Contract net protocol is used as negotiation protocol to regulate the interaction style of nodes. The goals of this study are: 1) energy efficiency task allocation; 2) maintaining energy balance of nodes in WSN after the task to prolong the network life cycle. Experimental results using this cluster-based negotiation model task allocation approach verify its performance.

Traffic matrices are very essential for many network engineering tasks: for instance, load balancing, capacity planning, routing protocol configuration. However, measuring these traffic matrices directly is difficult and costly. Hence many methods have been proposed to estimate these traffic matrices based on link load measurements and other more easily available data. This paper presents an iterative algorithm to estimate traffic matrix without differentiating the access links and the peering links and our algorithm get a similar performance with the Minimal Mutual Information method which requires that difference. Experiments on real backbone network data have also demonstrated that our algorithm is accurate and robust to measurement noise.

Due to the efficiency and adaptability, the active probing technique has become an attractive tool for fault localization in large and complex computer networks. It performs diagnosis by appropriately selecting the probes and analyzing the results. However, selecting an optimal probe set in such environment has been proven to be NP-hard problem. And, even the current approximate methods that can achieve near-optimal solutions have exponential computing time with the network size. To address this issue, we utilize the properties of conditional independence and directed-separation of Bayesian network, and propose a novel methodology which is used to estimate the approximate conditional independence of probes. According to the methodology, the model can be divided into several approximate independent subsets, on which the probes could be selected respectively. Furthermore, by integrating the methodology with a former representative probe selection algorithm which is called BPEA, we design a new efficient probe selection algorithm. Several experiments are given afterwards to show how our algorithm outperforms BPEA. And we also present that our algorithm can be used in large-scale computer networks while the former one can not. Moreover, the methodology can be applied to other probing based techniques as well.

Active probing is an effective tool for monitoring networks. By measuring probing responses, we can perform fault diagnosis actively and efficiently without instrumentation on managed entities. In order to reduce the traffic generated by probing messages and the measurement infrastructure costs, an optimal set of probes is desirable. However, the computational complexity for obtaining such an optimal set is very high. Existing works assume single-fault scenarios, apply only to small size networks, or use simplistic methods that are vulnerable to noises. In this paper, by exploiting the conditionally independent property in Bayesian networks, we prove a theorem on the information provided by a set of probes. Based on this theorem and structure property of Bayesian networks, we propose two approaches which can effectively reduce the computation time. A highly efficient adaptive probing algorithm is then presented. Compared with previous techniques, experiments have shown that our approach is more efficient in selecting an optimal set of probes without degrading diagnosis quality in large scale and noisy networks.

Mobility Model for mobile users is an important part of simulation of the wireless mobile network's resource management and optimization. The Manhattan Mobility Model proposed by ETSI is highly versatile. However, its random direction selecting strategy lacks of objectives, which means the scene of hotspot attracting user mobility could not be simulated accurately. In order to simulate the user mobility attracted by hotspots, a novel Hotspot Attraction Driven User Mobility Model (HADUMM) is proposed based on the Manhattan Mobility Model, and a Direction Deciding Algorithm (DDA) is designed. The HADUMM's realistic significance is evaluated.

Network monitoring is essential for assessing performance issues, identifying and locating problems. There is increasing interest in passive monitoring of flows at multiple locations within a distributed network. In order to figure out how to place monitors under cost-effective and budget constraints within the network, a new approach is presented in this paper, which solves the problem of monitoring stations selection and the tradeoff between monitoring cost and reward. Using the method from combinatorial optimization on submodular of information gain, the approach firstly proves that joint entropy and information gain in network models satisfy submodularity under certain conditions, and then an approximate algorithm is proposed to solve optimizing problem of conditional entropies. On the basis, monitoring stations selected by our solution are much better than existing classical criterion. The simulation results validate the approach, demonstrating the solution improving monitoring quality, accuracy and computation time.

In a clustering-based MANETs, task allocation has posed increasing research challenges because the needs of management and coordination are accentuated by complicated demands of cluster members. A self-adaptive method of task allocation is designed to facilitate self-planning and self-negotiation for nodes during tasks being distributed and executed. The method is composed of two parts: for one part, the cluster head works out an integrated schedule for tasks, including selecting different sets of execution nodes and defining their functions according to task types. Cooperative group towards synergetic task is formed by policies of filtering and voting. Assignment modes based on either polling or mobile agents are also involved, the latter adopts an improved Ant Colony Optimization (ACO) algorithm to plan a migration path. For another, if a cluster member fails to accomplish a task, it could negotiate as a tenderee with other nodes using a revised contract net protocol. In addition, we employ a stimulation mechanism of distributing virtual task experience in connection with QoS guarantees to offer compensation for nodes' energy consumption and extra load. Simulation results demonstrate performance benefits of our self-adaptive method can efficaciously alleviate load of the cluster head, balance loads of nodes in consideration of energy restriction, and prolong the lifecycle of the cluster.

Future LTE RAN will benefit from a significant degree of self-organization. Autonomic Load Balancing (ALB) is considered as an important function of self-organization for LTE RAN. A novel distributed method to achieve ALB for LTE RAN, AFWBM (Autonomic Flowing Water Balancing Method), is presented, which works by AFWBM module. The eNBs with AFWBM modules can detect their load conditions depending on self-monitoring actions. When overload conditions are detected, eNBs can adjust their HOM (handover hysteresis margin) and trigger handover behaviors of users automatically to balance load. Simulation results have demonstrated that by AFWBM, load of eNBs can be balanced and system capacity can be improved significantly.

The modern society has come to rely heavily on IT services. To improve the quality of IT services it is important to quickly and accurately detect and diagnose their faults which are usually detected as disruption of a set of dependent logical services affected by the failed IT resources. The task, depending on observed symptoms and knowledge about IT services, is always disturbed by noises and dynamic changing in the managed environments. We present a tool for analysis of IT services faults which, given a set of failed end-to-end services, discovers the underlying resources of faulty state. We demonstrate empirically that it applies in noisy and dynamic changing environments with bounded errors and high efficiency. We compare our algorithm with two prior approaches, Shrink and Maxcoverage, in two well-known types of network topologies. Experimental results show that our algorithm improves the overall performance.

The research of service management information model can bring forward the following benefits: unified service planning and provisioning, consistency among the functionality models in the service supply chain and correctness of the mapping between management requirements and management functions. In this paper, we have proposed a lifecycle-based generic modeling method on service management information, which adopts a requirement mapping in a top-down manner to define managed objects in the segments of service lifecycle. The model can be referenced as a meta-model to direct the development of service management systems.

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