Digital Twins: Systematic Review Maps the Computer Engineering Core of a Booming Technology

Digital twins have become one of the most talked-about ideas in modern engineering: living computational replicas of physical machines, power grids, and factories that update in near real time, predict what will happen next, and even take control actions. Yet a new systematic review argues that the field’s explosive growth has hidden a serious imbalance. Most scholarship on digital twins has been written from mechanical, electrical, or business perspectives, while the computer engineering foundations—embedded processors, real-time schedulers, edge-cloud networks, and on-device artificial intelligence—that actually determine whether a twin can run reliably have remained largely unexamined.

That gap is now addressed by a systematic literature review published in Frontiers in the Internet of Things by Nayyar R. Kibria and Javeed Kittur of the University of Oklahoma. Using the PRISMA 2020 framework, the researchers searched seven academic databases, screened 541 records, and retained 45 peer-reviewed publications that explicitly engage with digital twins in computer engineering contexts. They then applied inductive thematic analysis, distilling the literature into six major themes that together map how digital twin systems are designed, deployed, and evaluated from a computing perspective.

The timing of the analysis is telling. The 45 articles span 2017 to early 2026, but 87 percent were published between 2022 and 2025, with a peak of 13 articles in 2025. First authors came from 18 countries, led by the United States and China with six papers each, followed by India and the United Kingdom. Nearly all contributions appeared in archival peer-reviewed journals rather than conference proceedings, a signal that digital twin work in computer engineering is maturing from preliminary demonstrations into sustained, citable scholarship.

Where are digital twins actually being used? Among the 20 studies with clearly specified application domains, power and energy systems dominated at 55 percent, covering smart grids, networked microgrids, substations, lithium-ion batteries, and direct-current microgrids. Industry 4.0, manufacturing, and predictive maintenance accounted for another 20 percent, while networking, extended reality, smart retail, and cross-domain surveys filled out the remainder. The concentration in energy reflects a practical reality: grids are cyber-physical systems where a twin that models both electricity flow and information flow can justify its computational cost.

The review’s first theme, computer engineering foundations, examines the local machinery of twinning: embedded and real-time execution platforms, hardware-software co-design, and synchronization. Standout studies include runtime period adaptation of real-time control tasks under fixed-priority scheduling, a multi-agent-system-based cyber-physical system model spanning nine layers from physical hardware to application software, and a Web of Things architecture called the Relativistic Digital Twin that aims to weave twins into the IoT ecosystem while managing latency and consistency. Collectively, these works show that a digital twin is not a visualization dashboard but an end-to-end systems engineering problem.

The second theme, networked and distributed infrastructure, captured 15 articles and highlights how 5G, 6G, edge computing, and cloud orchestration shape twin performance. Exemplars include Digital Twin Edge Networks toward 6G, digital-twin-aided intelligent offloading with edge selection in mobile edge computing, and secure, latency-aware twin-assisted resource scheduling for 5G-enabled distribution grids. A recurring insight is bidirectional: wireless networks both carry twin data and are themselves twinned for proactive optimization. The reviewers note, however, that most proposed architectures are validated only in simulation or controlled prototypes, not in longitudinal field deployments.

Intelligence is the third and fastest-growing theme. Here, machine learning transforms twins from mirrors into decision engines. One study pairs a semiempirical capacity model with metaheuristically optimized machine learning—using algorithms such as the Improved Grey Wolf Optimizer with AdaBoost—validated by ten-fold cross-validation on NASA battery aging data to predict lithium-ion discharge capacity with very low error. Another trains a reinforcement learning controller entirely inside a digital twin of a DC microgrid, avoiding the safety risks of exploratory actions on live hardware, and then demonstrates improved disturbance response on a real testbed. A third, EcoEdgeTwin, uses deep reinforcement learning to jointly optimize quality of experience, latency, and energy in 6G-era edge networks, treating the twin as an active control system rather than a passive replica.

Security and dependability form the fourth theme, where eight articles position twins as defenders rather than targets. Studies describe IoT-based twins that help networked microgrids withstand coordinated false data injection and denial-of-service attacks, probabilistic twin representations that store object states as probability distributions for risk-sensitive control under uncertainty, and a user-preference-based trust computation model for federated clouds. The reviewers also flag a notable blind spot: distributed ledger technologies could provide tamper-evident data exchange and cross-organization auditability, but they introduce latency and storage costs that must be weighed against real-time requirements.

The fifth theme, modeling, simulation, and testing, is the largest with 25 articles, reflecting how much work goes into making twins trustworthy. Reference architectures like C2PS organize sensing, data handling, and compute services into reusable building blocks, while a systematic review of digital-twin-based testing for cyber-physical systems treats the twin as a test asset, raising questions about test oracles and model visibility. Perhaps the most striking finding is an absence: no single theoretical framework appeared repeatedly across studies, meaning the field has not yet converged on a unified design methodology, and standardized validation metrics for synchronization accuracy, timing jitter, and real-world transfer remain scarce.

The review closes with a research agenda grounded in engineering trade-offs. The best digital twin, the authors conclude, is rarely the highest-fidelity model—it is the model most verifiable under operational constraints of timing, bandwidth, energy, and safety. They call for hardware-aware twin design, safe continual-learning pipelines that can survive distribution shift, interoperability treated as a measurable engineering target rather than a limitation paragraph, and reusable artifacts such as co-simulation testbeds, benchmarks, and open-source reference implementations. As twins edge toward agentic behavior powered by transformers and large language models, those guarantees will only become more critical. For a technology pitched as the nervous system of smart infrastructure, the review’s message is clear: the field must now prove its reliability on real, constrained hardware, not just in slides and simulators.

Subject of Research: Digital twin technologies in computer engineering

Article Title: The emerging role of digital twin technologies in computer engineering research: a systematic literature review

Article References: The emerging role of digital twin technologies in computer engineering research: a systematic literature review. (n.d.). https://doi.org/10.3389/friot.2026.1870191

Image Credits: AI Generated

DOI: 10.3389/friot.2026.1870191

Keywords: digital twins, computer engineering, cyber-physical systems, Internet of Things, edge computing, 6G, on-device AI, systematic literature review, security, interoperability, real-time systems, predictive maintenance