Digital twins are becoming a key concept for communication and networked systems. A digital twin keeps a live digital copy of a physical asset, process or network and uses it for monitoring, diagnosis, prediction, optimization and closed-loop control. Today, digital twins are studied for wireless and sixth-generation (6G) networks, Industrial Internet of Things (IIoT) systems, edge–cloud infrastructures and larger settings, such as Digital Twin Networks (DTNs), Digital Twin Edge networks (DITENs) and the Internet of Digital Twins (IoDTs).
Recent research and survey studies show that several core problems are still open. Digital twins must handle heterogeneous, high-rate data streams from sensors, logs, simulations and external sources, and support both batch and streaming analytics. Their models need to be accurate but efficient, combining physics-based models with data-driven and hybrid approaches, often using Artificial Intelligence (AI) and Machine Learning (ML). Twins must stay synchronized with their physical counterparts under strict latency and reliability targets, while computation and communication are coordinated across edge, fog and cloud resources. As deployments grow, security, privacy, trust and governance become central, together with clear metrics such as twinning rate, age-of-twin, fidelity, decision accuracy, latency, overhead and recovery time.
This special issue, on Digital Twins, invites contributions that address these challenges for communication and networked systems. We welcome original research papers, surveys and position papers on foundations and architectures for digital twins, as well as data, communication and computing platforms that support them. Also welcome are AI and learning-based modelling and decision-making, and security, privacy, trust and resilience for twin systems. We especially encourage work that defines and uses explicit metrics, and those that build or evaluate experimental platforms, testbeds, simulators or real deployments. Interdisciplinary submissions are also welcome; for example linking communication networks with industrial systems, transportation, smart cities, energy, healthcare and other critical infrastructures, or studying the impact of digital twins on standardization, regulation and policy.
5G, 6G, artificial intelligence (AI), cloud computing, data fusion, digital twin, digital twin edge network (DITEN),digital twin network (DTN), edge computing, industrial Internet of things (IIoT), Internet of digital twins (IoDTs), machine learning (ML), network management, performance metrics, privacy, resilience, security, standardization, testbeds, trust
