On-device AI and digital twins: A synergistic approach to intelligent cyber-physical systems

作者：Antonello Barbone, Nicola Bicocchi, Matteo Martinelli, Riccardo Morandi, Marco Picone · 发表于：Future Generation Computer Systems · 年份：2025 · DOI：10.1016/j.future.2025.108068 · 被引用次数：6 · 研究领域：Digital Transformation in Industry、IoT and Edge/Fog Computing、Engineering Education and Technology

The convergence of Artificial Intelligence (AI) and the Industrial Internet of Things (IIoT) is reshaping Cyber-Physical Systems (CPSs), enabling intelligent automation, real-time decision-making, and adaptive control across diverse industrial domains. A key enabler of this transformation is On-Device AI, where training and inference occur directly on edge devices. While deploying AI models in constrained environments presents challenges-such as limited computational resources and hardware heterogeneity-the benefits of reduced latency, improved energy efficiency, and enhanced data privacy make this approach essential for next-generation CPSs. However, scaling and managing AI-enabled CPSs introduces new complexities, including efficient coordination among sensing, computation, and actuation, as well as the need for dynamic model adaptation in resource-constrained settings. Addressing these challenges requires architectural solutions that support distributed intelligence while maintaining system responsiveness and robustness. This paper investigates the use of Digital Twins (DTs) as a cyber-physical abstraction layer that enhances the deployment and management of On-Device AI. By maintaining synchronized, high-level digital representations of physical assets, DTs facilitate local AI execution, optimize resource allocation, and support low-latency decision-making. We validate our approach through experimental evaluation in a microfactory testbed, demonstrating how DTs improve li...