Massive content delivery will become one of the most prominent tasks of future B5G/6G communication. However, various multimedia applications possess huge differences in terms of object oriented (i.e., machine or user) and corresponding quality evaluation metric, which will significantly impact the design of encoding or decoding within content delivery strategy. To get over this dilemma, we firstly integrate the digital twin into the edge networks to accurately and timely capture Quality-of-Decision (QoD) or Quality-of-Experience (QoE) for the guidance of content delivery. Then, in terms of machine-centric communication, a QoD-driven compression mechanism is designed for video analytics via temporally lightweight frame classification and spatially uneven quality assignment, which can achieve a balance among decision-making, delivered content, and encoding latency. Finally, in terms of user-centric communication, by fully leveraging haptic physical properties and semantic correlations of heterogeneous streams, we develop a QoE-driven video enhancement scheme to supply high data fidelity. Numerical results demonstrate the remarkable performance improvement of massive content delivery.

Camera networks are essential to constructing fast and accurate mapping between virtual and physical space for digital twin. In this paper, with the aim of developing energy-efficient digital twin in 6G, we investigate real-time video analytics based on cameras mounted on mobile devices with edge coordination. This problem is challenging because 1) mobile devices are with limited battery life and lightweight computation capability, and 2) the captured video frames of mobile devices are continuous changing, which makes the corresponding tasks arrival uncertain. To achieve energy-efficient video analytics in digital twin, by taking energy consumption, analytics accuracy, and latency into consideration, we formulate a deep reinforcement learning based mobile device and edge coordination video analytics framework, which can utilized digital twin models to achieve joint offloading decision and configuration selection. The edge nodes help to collect the information on network topology and task arrival. Extensive simulation results demonstrate that our proposed framework outperforms the benchmarks on accuracy improvement and energy and latency reduction.

Digital twin is an essential enabling technology for 6G connected vehicles. Through high-fidelity mobility simulation, digital twin is expected to make accurate prediction about the vehicle trajectory, and then support intelligent applications such as safety monitoring and self-driving for connected vehicles. However, it is observed that even if a digital twin model is perfectly derived, it might still fail to predict the trajectory due to tiny measurement noise or delay in the initial vehicle locations. This paper aims at investigating the sources of unpredictability of digital twin. Take the car-following behaviors in connected vehicles for case study. The theoretical analysis and experimental results indicate that the predictability of digital twin naturally depends on its system complexity. Once a system enters a complex pattern, its long-term states are unpredictable. Furthermore, our study discloses that the complexity is determined, on the one hand, by the intrinsic factors of the target physical system such as the driver's response sensitivity and delay, and on the other hand, by the crucial parameters of the digital twin system such as the sampling interval and twining latency.

The integration of digital twin (DT) and 6G edge intelligence provides accurate forecasting for distributed resources control in smart park. However, the adverse impact of model poisoning attacks on DT model training cannot be ignored. To address this issue, we firstly construct the models of DT model training and model poisoning attacks. An optimization problem is formulated to minimize the weighted sum of the DT loss function and DT model training delay. Then, the problem is transformed and solved by the proposed Multi-timescAle endogenouS securiTy-aware DQN-based rEsouRce management algorithm (MASTER) based on DT-assisted state information evaluation and attack detection. MASTER adopts multi-timescale deep Q-learning (DQN) networks to jointly schedule local training epochs and devices. It actively adjusts resource management strategies based on estimated attack probability to achieve endogenous security awareness. Simulation results demonstrate that MASTER has excellent performances in DT model training accuracy and delay.

In this paper, to deal with the heterogeneity in federated learning (FL) systems, a knowledge distillation (KD) driven training framework for FL is proposed, where each user can select its neural network model on demand and distill knowledge from a big teacher model using its own private dataset. To overcome the challenge of train the big teacher model in resource limited user devices, the digital twin (DT) is exploit in the way that the teacher model can be trained at DT located in the server with enough computing resources. Then, during model distillation, each user can update the parameters of its model at either the physical entity or the digital agent. The joint problem of model selection and training offloading and resource allocation for users is formulated as a mixed integer programming (MIP) problem. To solve the problem, Q-learning and optimization are jointly used, where Q-learning selects models for users and determines whether to train locally or on the server, and optimization is used to allocate resources for users based on the output of Q-learning. Simulation results show the proposed DT-assisted KD framework and joint optimization method can significantly improve the average accuracy of users while reducing the total delay.

In order to improve the comprehensive defense capability of data security in digital twins (DTs), an information security interaction architecture is proposed in this paper to solve the inadequacy of data protection and transmission mechanism at present. Firstly, based on the advanced encryption standard (AES) encryption, we use the keystore to expand the traditional key, and use the digital pointer to avoid the key transmission in a wireless channel. Secondly, the identity authentication technology is adopted to ensure the data integrity, and an automatic retransmission mechanism is added for the endogenous properties of the wireless channel. Finally, the software defined radio (SDR) platform composed of universal software radio peripheral (USRP) and GNU radio is used to simulate the data interaction between the physical entity and the virtual entity. The numerical results show that the DTs architecture can guarantee the encrypted data transmitted completely and decrypted accurately with high efficiency and reliability, thus providing a basis for intelligent and secure information interaction for DTs in the future.