Digital twin network (DTN) is an emerging network that utilizes digital twin (DT) technology to create the virtual twins of physical objects. DTN realizes co-evolution between physical and virtual spaces through DT modeling, communication, computing, data processing technologies. In this article, we present a comprehensive survey of DTN to explore the potentiality of DT. First, we elaborate key features and definitions of DTN. Next, the key technologies and the technical challenges in DTN are discussed. Furthermore, we depict the typical application scenarios, such as manufacturing, aviation, healthcare, 6G networks, intelligent transportation systems, and urban intelligence in smart cities. Finally, the new trends and open research issues related to DTN are pointed out.

Mobile edge computing (MEC) is a promising paradigm to provide cloud-computing capabilities in close proximity to mobile devices in fifth-generation (5G) networks. In this paper, we study energy-efficient computation offloading (EECO) mechanisms for MEC in 5G heterogeneous networks. We formulate an optimization problem to minimize the energy consumption of the offloading system, where the energy cost of both task computing and file transmission are taken into consideration. Incorporating the multi-access characteristics of the 5G heterogeneous network, we then design an EECO scheme, which jointly optimizes offloading and radio resource allocation to obtain the minimal energy consumption under the latency constraints. Numerical results demonstrate energy efficiency improvement of our proposed EECO scheme.

Computation offloading services provide required computing resources for vehicles with computation-intensive tasks. Past computation offloading research mainly focused on mobile edge computing (MEC) or cloud computing, separately. This paper presents a collaborative approach based on MEC and cloud computing that offloads services to automobiles in vehicular networks. A cloud-MEC collaborative computation offloading problem is formulated through jointly optimizing computation offloading decision and computation resource allocation. Since the problem is non-convex and NP-hard, we propose a collaborative computation offloading and resource allocation optimization (CCORAO) scheme to solve it, and design a distributed computation offloading and resource allocation (DCORA) algorithm for CCORAO scheme that achieves the optimal solution. The simulation results show that the proposed algorithm can effectively improve the system utility and computation time, especially for the scenario where the MEC servers fail to meet demands due to insufficient computation resources.

In Internet of Vehicles (IoV), data sharing among vehicles for collaborative analysis can improve the driving experience and service quality. However, the security and privacy issues hinder data providers from participating in the data sharing process. In addition, due to the intermittent and unreliable communications in IoV, the efficiency and reliability of data sharing need to be further enhanced. In this paper, we propose a new architecture for data sharing based on blockchain and federated learning. To enhance the efficiency and security of data sharing, we develop a hybrid blockchain architecture which consists of the permissioned blockchain and the local Directed Acyclic Graph (DAG). Moreover, we propose an asynchronous federated learning scheme by adopting Deep Reinforcement Learning (DRL) for node selection to improve the efficiency. The reliability of shared data is also guaranteed by integrating learned models into blockchain and executing a two-stage verification. Numerical results show that the proposed data sharing scheme provides both higher learning accuracy and convergence efficiency.

Cloud-based vehicular networks are a promising paradigm to improve vehicular services through distributing computation tasks between remote clouds and local vehicular terminals. To further reduce the latency and the transmission cost of the computation off-loading, we propose a cloud-based mobileedge computing (MEC) off-loading framework in vehicular networks. In this framework, we study the effectiveness of the computation transfer strategies with vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication modes. Considering the time consumption of the computation task execution and the mobility of the vehicles, we present an efficient predictive combination-mode relegation scheme, where the tasks are adaptively off-loaded to the MEC servers through direct uploading or predictive relay transmissions. Illustrative results indicate that our proposed scheme greatly reduces the cost of computation and improves task transmission efficiency.

Emerging technologies such as digital twins and 6th Generation mobile networks (6G) have accelerated the realization of edge intelligence in Industrial Internet of Things (IIoT). Federated learning has been regarded as a promising solution in wireless networks. However, unreliable communication channels and limited resources hinder its application in IIoT. In this paper, we introduce Digital Twin Wireless Networks (DTWN) to migrate real-time data processing and computation to the edge plane. Then, we propose a blockchain empowered federated learning framework running in the DTWN for collaborative computing. To balance the learning accuracy and time cost of the proposed scheme, we formulate an optimization problem for edge association by jointly considering digital twin association, training data batch size, and bandwidth allocation. We exploit multi-agent reinforcement learning to find an optimal solution. Numerical results on real-world dataset show that the proposed scheme yields improved efficiency and reduced cost compared to benchmark learning method.