With the rapid development of industrial digitalization and intelligence, there is an urgent need to accurately depict the physical world in digital space, and, in turn, regulate and optimize the behavior of physical entities based on massive data collection and analysis. As a technology that combines virtual space and physical space, digital twin can satisfy all of the above needs, and has attracted widespread attention. Due to the promising application prospects of digital twins, both academia and industry have launched research in this field, and related studies have been conducted from different perspectives. Accordingly, some articles summarizing the existing work have also been published, but they are all from a single perspective, lacking a systematic introduction and summary. Based on this, this paper conducts a comprehensive review of the existing work on digital twins from four perspectives: data, model, network and application, and strives to gain a better understanding of the development of the field from the physical to the virtual and back to the physical. Meanwhile, current research challenges and future directions for the development of digital twins are all discussed.

Digital Twins (DTs) aim to achieve real-time integration between the virtual and physical worlds, demonstrating significant application potential in emerging fields such as smart cities and intelligent transportation. However, the reliance of DT on ultra-low latency and high computational resources poses considerable challenges for their deployment at edge nodes. Additionally, wireless signal interference in densely built environments further complicates edge deployments. To address these issues, this paper proposes an IRS-enhanced digital twin edge network architecture to improve the wireless channel quality for devices. Based on this architecture, considering device mobility and network dynamics, a digital twin dynamic placement strategy based on multi-objective deep reinforcement learning (MODRLDF) is proposed. The problem is formulated as a constrained multi-objective optimization problem, and a multi-objective double deep Q-learning method with a dynamic penalty mechanism (MODDQN-HER) is introduced. Unlike existing multi-objective reinforcement learning algorithms, the proposed strategy enhances training efficiency through hindsight experience replay and adaptively handles constraints via a dynamic penalty mechanism. It further obtains the optimal solution to the placement problem by balancing the interaction cost and service duration of device digital twins. Experimental results demonstrate that compared to existing approaches, the proposed DT placement strategy in this paper achieves a maximum reduction of 15.3% in average synchronization delay of devices. Moreover, it achieves a maximum reduction of 27.1% in DT migration rate and an average increase of 10.1% in service duration.

Digital twin (DT) migration technology is pivotal for ensuring seamless synchronization between physical entities and their virtual counterparts. However, in complex urban environments, high device mobility, dynamic network topology, and uncertain wireless channel conditions often lead to suboptimal migration decisions. Such inefficiencies exacerbate resource contention and diminish migration timeliness, thereby increasing DT state deviation and compromising service quality. To address these challenges, this paper proposes an AP group-enhanced digital twin edge network model, where multi-antenna access points (APs) are integrated as relay nodes to optimize channel quality and transmission reliability. Building on this, a Group-Collaborative Multi-Agent Proximal Policy Optimization (GC-MAPPO) migration strategy is presented. The strategy formulates the migration problem as a partially observable Markov decision process (POMDP). Specifically, the K-means++ clustering algorithm is first employed to construct optimal AP collaborative groups for mobile devices; subsequently, the MAPPO algorithm is utilized to derive optimal migration policies in dynamic environments. Experimental results demonstrate that, compared to existing baselines, the proposed GC-MAPPO scheme reduces the average synchronization delay by 6.69% to 37.05% and decreases DT state deviation by 29.24% to 82.91%.