Vehicle digital twin (VDT) can support multiple different vehicle services through updating, and the updating of VDT faces two fundamental issues: one is the isolation of VDT, that is VDTs can run various services without being affected by others; the other one is the timeliness of VDT updates for low latency services. In this paper, we first propose to ensure the isolation of VDTs with the assistance of intelligent reflecting surface (IRS) and network slicing (NS), and obtain better update time of the VDTs within limited resources. Specifically, we divide resources for VDTs with different update requirements to ensure the isolation of VDTs and the resources required for update. On the other hand, considering that the communication performance between vehicles and base stations is affected by urban building density, we propose using an intelligent controller to achieve intelligent control of the physical channel by adjusting the phase shift of passive reflective elements to ensure better transmission performance during VDTs updating. Secondly, considering the dynamic variability of vehicles and the environment, we propose an improved deep reinforcement learning algorithm based on the actor-critic framework to allocate communication, computing resources, and adjust the phase shift of the IRS. Finally, a large number of simulation results indicate that our proposed algorithm performs better than the benchmark algorithms.

Network function virtualization (NFV) and software-defined networking (SDN) provide high-quality services to users of the Internet of Things (IoT). However, dynamic changes in network traffic and service function chain (SFC) resource requirements may result in virtual network function (VNF) migration issue and real-time triggering of VNF migration can cause service delay issues in SDN/NVF-enabled IoT networks. In this article, we propose a digital twin (DT)-assisted VNF migration strategy to effectively address this issue. The digital twin of VNF (DT-VNF) is integrated with a multitask DT migration model based on bidirectional-gated recurrent units (DTMBi-GRUs) to achieve accurate resource demands prediction. Based on this, the VNF migration strategy is formulated in advance to avoid network performance degradation. We focus on the post-migration effects on services, networks, and DTs, so migration plans for DT-VNF and a reassociation scheme are formulated to enable real-time monitoring of post-migration VNF by DT-VNF. Then, an optimization problem is formulated to minimize average network energy consumption, network resource differences, and SDN synchronization delay in order to obtain optimal strategies. In addition, considering the problem’s complexity, it is decoupled into the VNF migration problem and the DT association and migration problem. The collaborative solution involves employing the multiagent proximal policy optimization (MAPPO) and asynchronous advantage actor–critic (A3C). Simulation results confirm the superiority of the proposed algorithms over baseline algorithms.

Digital twins (DTs), as an effective technology for remote monitoring and management of devices, enhances the intelligence of the industrial Internet of Things (IIoT). Nonetheless, the unreliable and delayed transmission of sensory data in wireless access networks hinders the accurate reflection of DTs on the physical world. In this article, we present an intelligent dual time-scale network slicing strategy utilizing the long-term and short-term trends of network, aiming to make fuller use of network resources and improve the synchronization information accuracy of DTs. Specifically, within the dual time scale slicing framework, this strategy collaboratively optimize slice scaling and sensory information synchronization for DTs, aiming to maximize sensory information satisfaction and minimize the cost of slice reconfiguration and synchronization. First, at large time scales, we utilize slices to provide isolation and address deployment issues for DTs with different Quality of Service (QoS) requirements. At small time scales, we aim to enhance the adaptability of estimation tasks to dynamic environments through more flexible wireless resource allocation, further improving communication performance, and establishing DTs that closely resemble physical entities. Furthermore, to solve optimization problems at different time scales, we propose a two-layer deep reinforcement learning (DRL) framework to achieve efficient network resource interactions, in which the lower-layer control algorithms utilize the prioritized experience replay (PER) mechanism to accelerate the convergence speed. Finally, simulation results validate the effectiveness of the proposed strategy.

Network function virtualization (NFV) and software defined network (SDN) technologies enable flexible traffic scheduling and improve the efficiency of physical resources. However, for latency-sensitive Industrial Internet of Things(IIoT) services in Industry 4.0 and beyond, the inability of the SDN controller to synchronize the resource demand information of virtual network functions (VNFs) in a timely manner can lead to delays in VNF mapping and scheduling strategies. To address this issue, we propose a digital twin-assisted VNF mapping and scheduling algorithm that combines digital twin to assist the SDN controller in collecting data of VNFs. Firstly, we designed a digital twin-assisted and SDN/NFV-based network slicing architecture. Secondly, to reduce the synchronization delays of digital twins of VNFs, we propose a twin service node reassociation mechanism. Next, a digital twin-assisted VNF mapping and scheduling model is constructed under constraints such as CPU, storage, bandwidth resources, and quality of service (QoS) to maximize the service provider’s profit. Finally, we propose digital twin-assisted VNF mapping and scheduling algorithms based on greedy and tabu search to solve the problem. Leveraging digital twins, the SDN controller can obtain accurate resource demand information of VNFs, thereby solving the delay issue in VNF mapping and scheduling strategies. Simulation results indicate that the proposed algorithms yield favorable outcomes in terms of total profit, network service acceptance rate, average system delay of digital twins, and QoS satisfaction.

Considering the limited resources, user mobility and unpredictable driving environment in intelligent driving, this paper studies the optimal training efficiency of federated learning for distributed training of collision warning services with the assistance of digital twin (DT). DT is emerging as one of the most promising technologies to make the digital representation of physical components for better prediction, analysis, and optimization of various services in intelligent driving. we first propose a DT-enabled collision warning framework, including physical network layer, digital twin layer, and application layer. Then, for the cooperative training of multi-level warning models combining gate recurrent unit (GRU) and support vector machine (SVM) in the digital twin layer, we propose semi-asynchronous federated learning with adaptive adjustment of parameters (SFLAAP) scheme. We aim at minimizing the training delay of collision warning model by dynamically adjusting the training parameters according to real-time training state and resource conditions of digital space, specifically the local training times and the number of local nodes participating in the aggregation, while ensuring the accuracy of the model. Considering the complexity of the target problem, we propose parameter adjustment algorithm based on asynchronous advantage actor-critic (A3C). Experiments on the classical dataset show high effectiveness of the proposed algorithms. Specifically, SFLAAP can reduce the completion time by about 12% and improve the learning accuracy by about 1%, compared with the state-of-the-art solutions.

An efficient Intrusion Detection System (IDS) model is essential for the protection of Unmanned Aerial Vehicles (UAVs) networks against network intrusion. However, when designing IDS models using distributed data collected by UAVs, it is crucial to ensure the security and privacy of the data. Moreover, most IDS models only focus on one-time learning and lack continuous learning capabilities. To address this, we present a Federated Continuous Learning framework with a Stacked Broad Learning System (FCL-SBLS) that utilizes Digital Twin Network (DTN) to enable quick and continuous learning on new data. To enhance the efficiency and quality of the IDS model during training and aggregation, we adopt an asynchronous federated learning architecture. Additionally, we introduce a Deep Deterministic Policy Gradient (DDPG)-based UAV selection scheme assisted by DTN to aid in global IDS model aggregation. This approach ensures that the IDS model can effectively and efficiently learn from distributed data while preserving the privacy and security of the data. The presented algorithm is validated using the CIC-IDS2017 dataset, and the simulation results reveal that our algorithm achieves higher efficiency and accuracy than the existing FL scheme.

The edge of the Internet of Things (IoT), which consists of unmanned aerial vehicles (UAVs), is vulnerable to network intrusion because software and wireless connections are used extensively in the IoT. Designing an efficient intrusion detection system (IDS) model is imperative. However, when creating IDS models with distributed data collected by UAVs, it is necessary to take precautions to protect the data’s security and privacy. Furthermore, most of the IDS models are focused on one-time learning but not on continuous learning. To this end, we propose a federated continuous learning framework with a stacked broad learning system (FCL-SBLS) based on the digital twin network (DTN), which can learn and train the IDS model on new data quickly and continuously. In order to improve the efficiency and quality of the IDS model when training and aggregation, we employ an asynchronous federated learning (FL) architecture, and a deep deterministic policy gradient (DDPG)-based UAV selection scheme assisted by DTN is proposed to help the global IDS model aggregation. The presented algorithm is validated using the CIC-IDS2017 data set, and the simulation results reveal that our algorithm achieves higher efficiency and accuracy than the existing FL scheme.

Network slicing (NS) provides customized services to users of the Internet of Things (IoT) by creating logical virtual networks, and NS combined with multiaccess edge computing (MEC) can significantly minimize the latency for delay-sensitive service. Therefore, it is important to research how to employ NS to achieve low latency for delay-sensitive service in MEC-enabled IoT. In this article, we propose a paradigm of dynamic cooperative slicing based on the digital twin network (DTN) to achieve low latency for delay-sensitive service. Specifically, we first build a DTN for the MEC-enabled IoT, and build basic models and function models, including prediction and decision making in DTN. Then, we realize dynamic cooperative slicing through the built basic models and function models. Second, with the assistance of the ubiquitous computing resources in MEC-enabled IoT based on DTN, we construct joint optimization problem of communication resources, computing resources, and collaboration proportion with the objective of ensuring low delay of delay-sensitive service while maximizing the long-term utility of operators. Third, considering that the different MEC servers participating in the cooperation in each time slot lead to different action spaces in different time slots, we propose a deep deterministic policy gradient algorithm with variable action space, called VADDPG, which draws on the idea of action masking and introduces the action adjustor to realize the hard control of action space. Finally, a large number of simulations demonstrate that the proposed algorithm outperforms the benchmark algorithms in terms of both the long-term utility of operators and the delay obtained by slicing.

As a three-layer association of Internet of Things Equipment (IoTE)–network slicing (NS)–base station (BS) in radio access network (RAN) slicing, handoff control, and resource allocation has become an important but complicated issue. In addition, the centralized controller has a difficult grasping the network situation in real time. In view of this, the problem of handoff control in the RAN slicing is investigated in the digital twin network (DTN), with the goal of maximizing the long-term utility about user satisfaction and handoff cost. Then, an improved algorithm based on the actor–critic framework is suggested, which is called HCRA. Specifically, the actor component contains neural networks for handoff control and an optimizer for resource allocation, and then the critic component evaluates the handoff and resource allocation actions of the actor component to guide the optimization of actions in the actor component. The simulation results show that HCRA can obtain better performance than benchmark algorithms.