Abstract
Industrial environments are increasingly characterized by the tight interaction among physical processes, communication infrastructures, and intelligent applications. In this context, Digital Twins (DTs) have emerged as a key technology for system analysis and optimization. However, existing DT solutions typically focus either on industrial processes or communication networks, while lacking an integrated and application-aware perspective. To fill this gap, this paper proposes a modular DT framework for industrial environments that jointly models physical processes, wireless communications, and application logic within a unified architecture. The feasibility of the proposed framework is experimentally validated through a real-world Proof-of-Concept (PoC) implemented in the BI-REX pilot line, involving a 5G-connected Autonomous Mobile Robot (AMR) transporting hazardous liquids and remotely controlled by an AI-driven application. The proposed DT is used to reproduce the behaviour of the real deployment and to investigate the impact of different placements of the AI application, including on-premise, edge, and remote cloud execution scenarios. Experimental results demonstrate a close agreement between DT predictions and PoC measurements in terms of both network-level metrics, such as Reference Signal Received Power (RSRP) and latency, and end- to-end application metrics, including application-level Round- Trip-Time (RTT). Moreover, the analysis shows how inaccuracies of network modeling can critically affect the feasibility of latency-sensitive industrial control loops, highlighting the potential of integrated DTs as tools for the pre-deployment design and validation of next-generation industrial systems.
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Publication details
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- Open access
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Cite this article
APA 7
Cavallero, S., Tonini, F., Chahoud, T., Cuozzo, G., Borsatti, D., Cerroni, W., Fodrini, M., & Marini, R. (2026). Digital Twin for Pre-Deployment Validation of AI-Driven Safety-Critical Industrial Edge Control Loops. https://omanscience.com/en/articles/digital-twin-for-pre-deployment-validation-of-ai-driven-safety-critical-industrial-edge-control-loops
MLA 9
Cavallero, Sara, et al. "Digital Twin for Pre-Deployment Validation of AI-Driven Safety-Critical Industrial Edge Control Loops." https://omanscience.com/en/articles/digital-twin-for-pre-deployment-validation-of-ai-driven-safety-critical-industrial-edge-control-loops.
Chicago (author–date)
Cavallero, Sara, Federico Tonini, Tony Chahoud, Giampaolo Cuozzo, Davide Borsatti, Walter Cerroni, Maurizio Fodrini, and Riccardo Marini. 2026. "Digital Twin for Pre-Deployment Validation of AI-Driven Safety-Critical Industrial Edge Control Loops." https://omanscience.com/en/articles/digital-twin-for-pre-deployment-validation-of-ai-driven-safety-critical-industrial-edge-control-loops.
Harvard
Cavallero, S., Tonini, F., Chahoud, T., Cuozzo, G., Borsatti, D., Cerroni, W., Fodrini, M. and Marini, R. (2026) 'Digital Twin for Pre-Deployment Validation of AI-Driven Safety-Critical Industrial Edge Control Loops', Available at: https://omanscience.com/en/articles/digital-twin-for-pre-deployment-validation-of-ai-driven-safety-critical-industrial-edge-control-loops.
Vancouver
Cavallero S, Tonini F, Chahoud T, Cuozzo G, Borsatti D, Cerroni W, et al. Digital Twin for Pre-Deployment Validation of AI-Driven Safety-Critical Industrial Edge Control Loops. https://omanscience.com/en/articles/digital-twin-for-pre-deployment-validation-of-ai-driven-safety-critical-industrial-edge-control-loops
IEEE
S. Cavallero, F. Tonini, T. Chahoud, G. Cuozzo, D. Borsatti, W. Cerroni, M. Fodrini, and R. Marini, "Digital Twin for Pre-Deployment Validation of AI-Driven Safety-Critical Industrial Edge Control Loops," https://omanscience.com/en/articles/digital-twin-for-pre-deployment-validation-of-ai-driven-safety-critical-industrial-edge-control-loops.