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The burgeoning development of next-generation technologies, especially the Industrial Internet of Things (IIoT), has heightened interest in predictive maintenance (PdM). Accurate failure forecasting and prompt responses to downtime are essential for improving the industrial efficiency. Traditional PdM methods often suffer from high false alarm rates and inefficiencies in complex environments. This paper introduces a predictive maintenance framework using identity resolution and a transformer model. Devices receive unique IDs via distributed identifiers (DIDs), followed by a state awareness model to assess device health from sensor signals. A sequence prediction model forecasts future signal sequences, which are then used with the state awareness model to determine future health statuses. Combining these predictions with unique IDs allows for the rapid identification of facilities needing maintenance. Experimental results show superior performance, with 99% accuracy for the state awareness model and a mean absolute error (MAE) of 0.062 for the sequence prediction model, underscoring the effectiveness of the framework.

In order to reduce the processing volume of multi-camera real-time video data in smart city scenarios, a video collaborative analysis method based on machine learning algorithms at the edge was proposed.Firstly, for the important objects detected by each camera, different key windows were designed to filter the region of interest (RoI) in the video, reduce the video data volume and extract its features.Then, based on the extracted data features, the same objects in the videos from different cameras were annotated, and a strategy for calculating the association degree value between cameras was designed for further reducing the video data volume.Finally, the GC-ReID algorithm based on graph convolutional network (GCN) and re-identification (ReID) was proposed, aiming at achieving the collaborative analysis of multi-camera videos.The experimental results show that proposed method can effectively reduce the system latency and improve the video compression rate while ensuring the high accuracy, compared with the existing video analysis methods.

In order to solve the problem of the low data transmission efficiency of the blockchain network, an optimization method of blockchain transmission efficiency was proposed to optimize the network topology and forwarding strategy.First, the trusted value function was designed to calculate the trusted value of blockchain nodes, and a tree topology was constructed by comprehensively considering the trusted value and transmission time.Furthermore, the forwarding path selection strategy was designed based on the tree topology, with the minimum overall concurrent transmission time as the goal, and the forwarding table of the node about the forwarding order of its neighbor nodes was established.To reduce the impact of the node changed on the tree topology, a topology dynamic optimization strategy was proposed to adjust tree topology locally.The forwarding path selection strategy enabled the entire data transmission process to have the minimum transmission time, and the dynamic optimization strategy avoided reconfiguration of the entire network topology and effectively shortened the data transmission time.The simulation results show that, compared with the weight-first algorithm, the transmission time of the proposed method is reduced by about 20%, and the data transmission efficiency is significantly improved.

The software defined network separates the control plane from the data plane to achieve flexible traffic scheduling, which can use network resources more efficiently.However, with the increase of the number of flow entries, load rate, the number of connected hosts, and other factors, the forwarding efficiency of the SDN switch will be reduced, which will affect the end-to-end transmission delay.To solve the above problems, the forwarding efficiency aware traffic scheduling algorithm based on deep reinforcement learning was proposed.First, the switch state was integrated into the perception model, and the mapping relationship between switch state information and forwarding efficiency was established based on neural network.Then, combined with network state and traffic information, traffic scheduling policy was generated through deep reinforcement learning.Finally, the expert samples generated by the shortest path and load balance algorithms could guide the model training, which enabled the model to learn knowledge from expert samples to improve performance and accelerated the training process.The experimental results show that the proposed algorithm not only reduces the average end-to-end transmission delay by 15.31%, but also ensures the overall load balance of the network, compared with other algorithms.

For the cross domain scheduling problem of time-sensitive network, a software-defined cross-domain scheduling mechanism for time-sensitive networking was proposed, which was based on three kinds of wide area clock synchronization technology to ensure nanosecond inter-domain time synchronization.Then, a time slot conflict-free cross domain scheduling model was proposed to solve the problem of time slot alignment caused by inter-domain link delay.The time slot conflict-free constraints were used to strictly limit the arrival time of cross-domain traffic at the edge node.Finally, the gate control list of fusion domains for global scheduling was calculated online.The simulation results show that the model can schedule hundreds of time-sensitive traffic in a typical cross-domain remote control scenario, and has a feasible algorithm execution time in seconds.

In order to solve the problems of the low transaction success rate and network imbalance of the payment channel network, an efficient routing strategy of blockchain-based payment channel network was proposed.This strategy established a dedicated payment channel for the high-priority services according to the service type and service priority, and divided the conventional business into multiple transaction unit.Furthermore, a channel balanced routing algorithm was designed to route each transaction unit, which could reduce the number of transactions on the blockchain and maintain long-term stable operation of the off-chain payment channel, as well as improve the transaction success rate.In addition, in order to avoid the temporary shortage of funds and unavailability of channels due to a certain link occupied by multiple transactions simultaneously, a transaction queuing mechanism in the payment channel network was designed.This mechanism established the forwarding rules for transactions by calculating the escrow amount between the node that transactions arrived and the next hop node, where the channel balanced routing algorithm was used to calculate the new forwarding path for the nodes that could not carry out capital injection within the queuing threshold.The simulation results show that the proposed strategy could improve the transaction success rate and realize the equilibrium of the payment channel network.

As an essential piece of infrastructure supporting cyberspace security technology verification, network weapons and equipment testing, attack defense confrontation drills, and network risk assessment, Cyber Range is exceptionally vulnerable to distributed denial of service (DDoS) attacks from three malicious parties. Moreover, some attackers try to fool the classification/prediction mechanism by crafting the input data to create adversarial attacks, which is hard to defend for ML-based Network Intrusion Detection Systems (NIDSs). This paper proposes an adversarial DBN-LSTM method for detecting and defending against DDoS attacks in SDN environments, which applies generative adversarial networks (GAN) as well as deep belief networks and long short-term memory (DBN-LSTM) to make the system less sensitive to adversarial attacks and faster feature extraction. We conducted the experiments using the public dataset CICDDoS 2019. The experimental results demonstrated that our method efficiently detected up-to-date common types of DDoS attacks compared to other approaches.

Blockchain is a kind of distributed ledger technology that upgrades to a complete storage system by adding logic control functions such as intelligent contracts.With the changes of its classification,service mode and application requirements,the core technology forms of Blockchain show diversified development.In order to understand the Blockchain ecosystem thoroughly,a hierarchical technology architecture of Blockchain was proposed.Furthermore,each layer of blockchain was analyzed from the perspectives of basic principle,related technologies and research progress in-depth.Moreover,the technology selections and characteristics of typical Blockchain projects were summarized systematically.Finally,some application directions of blockchain frontiers,technology challenges and research prospects including Smart Cities and Industrial Internet were given.

With the development of the internet of things (IoT),5G and industrial technology,industrial IoT has become a new research field.Due to the specific requirements of delay,security and stability in industrial production,the traditional DNS has limitations to meet the needs of industrial IoT because of its single identity object,rigid resolution results,and weak security.In this context,how to provide efficient,flexible and secure resolution services for universal objects has become a hot area worldwide.Firstly,we discussed the design principles and key supporting technologies of the identity resolution system in industrial IoT.Then summarized and compared the existing identity resolution systems in detail.Next,the new identity resolution schemes in academic research were introduced.Finally,the core problems and the future research trend in this field were discussed.

Network virtualization allows multiple isolated virtual networks (VNs) to share the same substrate network (SN). VN embedding (VNE) algorithms can efficiently allocate the limited SN resources to VNs and assign a unique identifier to each VN. However, the fixed bit width of VN identifier in the packet header limits the number of VNs, and extending the bit width leads to the increase of the network traffic. In this paper, we consider the label-combination method to generate VN identifiers by combining the link-grained labels with location information. This method requires the efficient allocation of labels, but the existing VN embedding works only consider the CPU and bandwidth resources. To address this issue, we propose a novel embedding model that considers the label, CPU and bandwidth resource constraints. Furthermore, two window-based heuristic algorithms called VNE-LIA and VNE-iLIA using the greedy algorithm and the proximity principle are presented to solve the VNE problem. The simulation experiments show that our proposed algorithms increase the number of VN identifiers and the revenue to cost ratio under the different resource conditions of SN.

In smart grids, the access verification of a large number of intelligent gateways and terminal devices has become one of the main concerns to ensure system security. This means that smart grids need a new key management method that is safe and efficient and has a low computational cost. Although a large number of scholars have conducted relevant research, most of these schemes cannot balance the computational overhead and security. Therefore, we propose a lightweight and secure key management method, having a low computational overhead, based on blockchain for smart grids. Firstly, we redesigned the architecture of the smart grid based on blockchain and completed the division of various entities. Furthermore, we designed a pairing-free certification authenticated group key agreement method based on blockchain under the architecture. Finally, we achieved higher security attributes, and lower authentication delay and computational overhead, compared to the traditional schemes, as shown in performance analysis and comparison.

Recent researches on blockchain have been greatly concerned by academia and industry,while the communication based on TCP/IP protocol was not enough for broadcasting a large volume of data in blockchain technology.Therefore,a novel node model supporting push service for blockchain technology and a special procedure reading-writing the table of the node model were designed based on the named-data networking,which was a distributed network architecture supporting data transmission naturally.And then the information transmission architecture of blockchain technology via named-data networking was proposed.With the aggregation of the requests and data caching,this architecture could reduce the traffic redundancy and accelerate the communication speed.Meanwhile,a use case of bitcoin based on the proposed architecture was given,in order to better understand the architecture.A numerical simulation was used to verify the performance advantages of the proposed scheme.In addition,some related future research directions were presented.

Blockchain is becoming more and more popular in various fields. Since the information transmission mode of the blockchain is data broadcasting, the traditional TCP/IP network cannot support the blockchain system well, but the Named-Data Networking (NDN) could be a good choice because of its multi-path forwarding and intra-network caching functions. In this article, we propose a new blockchain information transmission acceleration strategy (AITS) combining with graph theory and probability theory based on the NDN architecture. We select some more important nodes in the network as “secondary nodes”, and give them more bandwidth and cache space to assist the NDN network in data transmission. In order to select the correct node as the secondary node, we present a method to calculate the number of secondary nodes, and give the function to calculate the importance of each node. The simulation results show that in complex networks, the proposed method has superior performance in accelerating information transmission and reducing data overhead.

Nowadays, the Internet is facing unprecedented challenges, including scalability, security, management, mobil-ity, content distribution capability, and energy saving, etc., which are difficult to solve through incremental development. In such context, it is a hot field of global focus to develop an innovative future network architecture. Firstly, The global progress in this field is introduced. Then the main current research results are described. Finally, the development trends and core problems in this field are discussed. It is expected to be helpful for the domestic research in the related fields.

The effect of ohmic contact conductance on the distribution of surface and bulk currents in semiconductor planar devices was investigated. The results showed that current distribution was significantly affected by the contact conductance and the ratio between the shunt currents from probes one to two, and from one to three were mainly dominated by the contact and surface conductivities in the sample.