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

With the development of smart grids and the increasing demand for electric energy consumption, electricity load forecasting has become more and more important in electric energy management. However, the differences in electricity load patterns between different regions lead to data heterogeneity, which may seriously affect the model performance of traditional federated learning methods in electricity load forecasting. Meanwhile, the resource heterogeneity between clients will further decrease the efficiency and the accuracy of forecasting. To this end, we propose a spatio–temporal aware personalized federated learning (PFL) framework for electricity load forecasting to improve the forecasting accuracy and training speed so as to enhance the real-time responsiveness and system stability of the power grid. First, to solve the data heterogeneity, we design a collaborative training domain (CTD) construction method based on spatio–temporal features. Then, on the basis of constructed CTDs, we propose a spatio–temporal convolutional network (STCN)-based layered PFL method to address the resource heterogeneity, which can be divided into personalized layers and generalized layers according to temporal and spatial static features separately. In addition, we design a hierarchical aggregation mechanism and an adaptive edge model aggregation adjustment mechanism to optimize the training process. Experimental results show that the method proposed in this article outperforms other methods in terms of model accuracy and convergence speed.

Adapters, one of the most important parameter-efficient fine-tuning (PEFT) methods, achieve state-of-the-art (SOTA) performance through manual architecture design. To unlock the full potential of adapter tuning, we introduce the AutoAdapter framework, designated to design novel adapter architectures automatically. First, we discuss the adapter design choices and define a search space. Second, we propose CDARTS, an contribution-based differentiable neural architecture search (NAS) method, to enhance the search results. We conduct comprehensive experiments and analysis on the GLUE and SuperCLUE benchmark tasks, demonstrating that AutoAdapter effectively designs novel adapters that outperform recent baseline PEFT methods.

The complexity and dynamism of modern networks pose significant challenges to network management services. Existing technologies often exhibit latency in migrating services after encountering problems. However, adopting a proactive approach through anomaly detection before migration enables the early reservation of resources, thereby ensuring overall performance and stability. This paper proposes a Network Management Service Composition (NMSC) migration method leveraging anomaly detection. The method includes an anomaly detection approach using a Transformer model with a time decay mechanism and a multi-agent reinforcement learning algorithm enhanced by a graph attention autoencoder. First, a time decay mechanism is designed to enhance the self-attention mechanism, allowing the model to capture long-term dependencies while maintaining high sensitivity to recent events. Second, a critic network, enhanced by a graph attention autoencoder, enables the multi-agent algorithm to comprehend the interaction dynamics among agents during computation. Experimental results demonstrate that the proposed anomaly detection algorithm significantly outperforms existing algorithms. Furthermore, the service composition migration algorithm exhibits superior performance in terms of reward value and migration delay, thus proving its effectiveness and feasibility.

Augmented Reality (AR) applications are latency-sensitive and contain significant heterogeneous content, such as mixed static objects and interactive data. Relying solely on real-time edge caching makes it difficult to meet the latency requirements of AR, disrupting user’s immersive experience. In addition, the operator can motivate terminal caching foreground content and reduce transmission costs through device-to-device (D2D). Therefore, we proposes a cloud-edge-terminal collaborative proactive caching and differentiated delivery mechanism of heterogeneous content, which reduces service response latency and improves comprehensive revenue through efficient edge collaboration methods, accurate heterogeneous content pre-caching strategies, and differentiated delivery mechanisms. Firstly, we synthetically considers AR user’s service response latency and operator’s comprehensive revenue, proposing a user behavior and resource-aware edge collaborative service domain construction method to improve the collaborative service capability of edge nodes. Then, it proposes a pre-caching algorithm for heterogeneous content based on foreground/background content separation, user preference prediction, and storage space partitioning to improve cache utilization in the edge network. In particular, a D2D-assisted differentiated delivery strategy is designed to improve service response speed and overall revenue. The numerical results show that the proposed mechanisms are better than other solutions and can improve cache hit rates and operator’s comprehensive revenue.

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.

With the advancement and widespread adoption of Artificial Intelligence (AI) and Internet of Things (IoT) technology, machine learning (ML) can be leveraged in au-tomated factories to enable intelligent robot fault detection and recognition. As an emerging distributed machine learning framework, federated learning (FL) enables collaborative model training while safeguarding the privacy of user data. However, FL encounters various challenges, including the lack of stable energy supply to maintain continuous training and the existence of malicious terminals in the system. These situations could result in issues such as free-riding attacks and robots running out of energy during the training process. Hence, this paper presents a reliability and energy balanced computation resource allocation mechanism in FL system for IoT businesses. Firstly, a training latency and model contribution based reputation evaluation model is proposed to reveal the reliability of IoT terminals. Then, a training client reliable selection and delayed admission strategy is proposed, which takes into account both the reputation and energy consumption, aiming at enabling terminals with high-quality data and low energy capacity to keep contributing to the model during the later training stage. Especially, we integrate reputation and computing capability of IoT terminals to decide the freshness level of the allocated FL global model, which can effectively defend against free-riding attacks. The simulation results verify that the proposed mechanism outperforms non-delayed/partial-delayed admission mechanisms in terms of model quality and system stability.

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%.

To both take the advantage of wired and wireless networks, the burgeoning mobile edge computing (MEC) technology is integrated into fiber-wireless (FiWi) network to support the cost-effective deployment of Internet of Things (IoT). However, the trusted model training, efficient task computing, reasonable comprehensive energy consumption and different quality of services, are still the key problems to be solved. Thus, we introduce the federated reinforcement learning (FRL) to the framework to jointly optimize the accessing mode selection, computation offloading decision and transmission power allocation without the leakage of users’ privacy. Then, we further design a twolayer FRL algorithm based on reputation value to respectively realize the protection of user privacy and efficient optimization of the global model. The simulation results demonstrate that our proposed method outperforms others in balancing energy consumption, reducing service delay, as well as providing differentiated services.

To provide intelligent and accurate assistant driving services in smart city, as well as ensure the security and tamper proof of vehicle data, this paper build a deep reinforcement learning (DRL) and main-side blockchain empowered service framework. By storing driving data and vehicle information on the sidechain, while deploying index information on the mainchain, the main-side blockchain structure can enhance the scalability of blockchain, decrease the communication overhead, improve consensus efficiency, and avoid the leakage of data between different sidechains. However, the resource limited vehicles on sidechain cannot process numerous computation-intensive mining tasks in time, resulting in high service delays. Thus, this paper integrate mobile edge computing with blockchain system to design a double-layer mining service offloading mechanism, allowing the edge nodes and neighboring vehicles to form a cooperative mining network and collaboratively participate in mining process with specific offloading rates. The first layer uses Asynchronous Advantage Actor-critic (A3C) algorithm to efficiently offload partial mining task from the task vehicle to the road side unit (RSU), and the second layer applies double auction to specifically obtain the offloading rates from RSU to multiple service vehicles. Simulation results demonstrate that, our proposed mechanism outperforms other compared algorithms in the average profit and consensus delay.

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.

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.

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.

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.

With the development of intelligent transportation, the concept of vehicular networks is becoming more and more popular. In the vehicular networks, vehicle nodes have large scale, strong mobility, and there are malicious attackers. The open network environment and the diversity of communication types make the vehicle information vulnerable to various types of attacks, and the user's privacy information is facing the risk of being leaked at any time. In view of the security threats in the Internet of vehicles (IoV), this paper proposes a blockchain-based trusted authentication service processing framework for vehicular networks, designs a block generation model, stores the privacy information in the blockchain system deployed in the edge layer, and proposes a delay and throughput-oriented mobility-aware blockchain resource allocation (MABRA) algorithm. Also, we use the deep reinforcement learning (DRL) algorithm to optimize the total delay and transaction throughput of the system. Finally, the simulation results show the effectiveness of the MABRA algorithm under different parameter settings.

The mobile edge computing (MEC) technology is introduced into the traditional mobile cloud computing (MCC) mode to form a cloud-edge-terminal collaborative network architecture (CETCN) to jointly process the massive data and improve the operating efficiency. However, there are still some problems such as high energy consumption of some edge nodes and limited terminal energy in this network architecture. In order to solve the above problems, this paper proposes a computation offloading algorithm based on sleep mechanism. Firstly, time delay and energy consumption models of cloud layer, edge layer and terminal layer are built according to the requirements of different scenes of smart city. Then, the communication and computing resources of each network segment are allocated jointly with the optimization goals of time delay and energy consumption, and the edge server sleep mechanism is used to further improve the resource utilization of the edge nodes. The simulation results show that the proposed algorithm can reduce the network delay and energy consumption, and has practicability.

Aiming at the problem that existing association rule mining algorithms cannot quickly mine faulty association rules in the current perception layer of electric power sensor networks, an improved eclat mining algorithm fast_eclat is proposed. The algorithm combines the characteristics of sparse data and large number of transactions at the perception layer of the power sensor network, and adopts a set intersection strategy based on pruning cross-counting, which reduces the computational complexity and improves the computational efficiency of the algorithm, which can more effectively deal with fault association rules. Comparative analysis through simulation experiments shows that the fast_eclat algorithm has better performance in the face of sparse data and large number of transactions.

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

As a new intelligent technology of identity recognition., face recognition is an important means of security authentication. However, in real life, the face may be blocked by hats, masks and other occlusions, resulting in the existing face recognition algorithms and methods cannot be applied in practical applications. In order to solve the above problems, this paper proposes a partial occlusion face recognition method based on infrared thermal imaging to locate acupoints. Firstly, the face image is collected and the face features are obtained by Principal component analysis (PCA) algorithm. Secondly, the infrared thermal imaging technology is introduced adaptively by judging the occlusion status of the face, and then the individual facial acupoints are located by BP neural network algorithm to assist in the effective recognition of partially occluded faces. The results of simulation show that this recognition method proposed can increase the accuracy of partial occlusion face recognition, which has practical value.

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

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

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%.

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.

With the development of the energy Internet, power communication services are heterogeneous, and different power communication services have different business priorities. The power communication services with different priorities have different requirements for network bandwidth and real-time performance. For traditional unified service, a scheduling method cannot meet these service requirements at the same time, and electric power communication network cannot guarantee the quality of service. Therefore, how to make full use of the time-varying characteristics of communication resources to meet the business needs of different priorities and achieve the goals of high resource utilization and transmission quality has become one of the urgent problems in the power communication network. For this reason, in order to adapt to the real-time congestion of the network, we have designed a packet scheduling method based on the dynamic adjustment of service priority, which dynamically adjusts the priority of the power service on the node; in addition, an evaluation method for the trust value of wireless forwarding nodes is introduced to improve the security of data transmission; and finally, we valuate the channel quality to establish a reasonable and efficient packet scheduling mechanism for services of different priorities. Simulation results show that this method improves the communication performance of high-priority services and improves the spectrum resource utilization of the entire system.

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.

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.

In recent years, with the construction of large-scale urban gas pipelines, accidents such as fires, explosions caused by gas pipelines have been increasing. The catastrophic effect of these accidents on people and their properties highlights the urgent need to develop appropriate safety strategies for those who are in risks. However, there are no effective measures to identify gas pipelines with hidden danger due to occupation or crossover. The aim of this study is to quickly identify a large number of gas pipelines with hidden dangers at one time and locate the positions of the targets that cause such hidden dangers. This paper proposed a rapid hidden danger detection method for determining geographic information of hidden danger targets and their associated gas pipelines. It consists of three main parts: an overview of the rapid detection method, the general identification process and the identification of hidden danger targets with their associated gas pipelines. This paper proposed matrix-based "hidden danger identification area", "potential hidden danger target area" and "primer number identification method" to find all hidden danger points at one time. This method contributes to the safety of cities and becomes an important part of smart cities.

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.

To satisfy the quality of service requirements of various electricity information interactive applications in power fiber to the home network, we propose a QoS-based optical network unit group planning algorithm. This method uses several ONUs to converge data from all kinds of sensors in a network and then transmit it to OLT through the optical fiber composite low-voltage cable. Process of this method is divided into two stages. First, based on QoS requirements of a network including packet delay, packet error probability and packet loss rate, the number of ONUs in a certain area is solved by a series of mathematical methods such as queuing theory. Then, locations of ONUs are planned by using a tabu-search-algorithm-based ONU location planning method to achieve the lowest power consumption of ONUs. Evaluation results show that our proposed method can guarantee QoS of a network and reduce total power consumption effectively.

Based on double-fiber-tangent rings we propose a high-reliable wavelength-division-multiplexing optical access network. With the proposed architecture, data convergence can be realized and fiber link can be protected well. Because of fiber failure occurring no matter in primary ring and sub-ring, optical line terminal and remote nodes need to be switched automatically to protection mode. The RN designed as tangent point can realize wavelengths adding and dropping between PR and SR, and it also can expand the network conveniently and smoothly with more wavelengths in the network. What is more, the design of DFTR can enhance the survivability and self-healing ability of the network. The analysis and simulation demonstrate the proposed scheme has the strong survivability and it will be a good choice for large-scale access network in the future.

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.

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.

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