The continuous development of network technology has driven the emergence of smart devices, and the demand for smart devices interconnection has increased sharply, which requires the identity of devices to be authenticated to carry out secure communication. The traditional certificate-based identity authentication scheme can no longer meet the authentication requirements of massive devices. As an authority that issues and manages certificates, Certificate Authority (CA) creates data islands of intra-domain certificates, increasing the complexity of cross-domain authentication. In order to improve the efficiency of cross-domain authentication, this paper introduces blockchain technology, which can establish trust in an untrusted environment. We propose a multi-CA-based authentication architecture to establish distributed trust and share cross-domain certificate information among multiple domains. On this basis, we design a simplified identity authentication scheme to quickly complete cross-domain identity authentication and reduce authentication overhead. To further improve the efficiency of cross-domain authentication, a cross-domain certificate revocation mechanism is designed. The scheme has passed the formal security analysis, and the simulation results show that the cross-domain authentication scheme is efficient.

In this paper, in order to fully investigate the great potential of increasingly powerful vehicles and their predictable mobility in improving dynamic network performance, we develop a V2V task offloading algorithm for SVCNs (Software-defined Vehicular Cooperative Networks). First of all, a three-layer SVCNs architecture is built with vehicle edge layer, SDN control layer and cloud layer from bottom to top, moreover, at SDN control layer, there are multiple SDN controllers responsible for data collection and resource scheduling, and a dual LSTM-based vehicle trajectory prediction model is also designed with the aim of achieving a spatiotemporal trajectory prediction process. We then formulate an optimization problem and further show our V2V task offloading algorithm design, which consists of a distributed V2V (dV2V) offloading sub-algorithm and a centralized V2V (cV2V) offloading sub-algorithm. Finally, we test our trajectory prediction model with TensorFlow, and simulate our V2V task offloading algorithm on NS3 platform, results show that not only our prediction model obtains the highest accuracy, but also our V2V task offloading design outperforms the comparison schemes in terms of average service time, task success ratio and algorithm convergence with various vehicle density and motion modes.

The rapid development of digital technology is reshaping the architecture of the Industrial Internet of Things (IIoT). The traditional architecture cannot process vast amounts of data exchanges and provide entities with trust. The future IIoT is expected to be a decentralized architecture in which blockchain and digital twin-driven IIoT can enable trusted data exchanges. However, this architecture cannot obtain huge amounts of external real-time data and isolated data. Moreover, it cannot handle complex industrial computing tasks. Therefore, we combine oracle with decentralized learning to propose a novel IIoT-oriented digital twin architecture. We also propose an effective decentralized collaboration mechanism to support external data and resources exchanges. Moreover, we propose a novel computing collaboration mechanism to expand the learning capabilities of the industrial ecology. Experiments show that our proposed paradigm has less processing time, a more stable process, and better learning ability compared to other paradigms.

Network traffic classification is a key foundation of traffic management and network security. With the development of traffic encryption technologies and more attention given to user privacy, traditional rule-based and payload-based traffic classification methods have become less effective. To address this problem, recent studies have introduced deep learning-based methods. However, most of these studies do not consider both the flow-level and packet-level characteristics, which we believe are significant in network traffic classification. To further improve the accuracy of traffic classification, this paper proposed DM-HNN, a hybrid neural network based on dual-mode features. First, we treat the packet length sequence as the flow-level feature and the initial byte of the packet as the packet-level feature. Then, we diverge into two paths to analyze the dual-mode features using neural networks. Finally, we combine the two-path features and output the final classification results. We have performed the experiments on public datasets, the results comparing to single-mode and dual-mode traffic classifiers indicate that DM-HNN can achieve excellent performance and has certain effectiveness.

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 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 paper 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.