Digital Twin for Pre-Deployment Validation of AI-Driven Safety-Critical Industrial Edge Control Loops

Sara Cavallero; Federico Tonini; Tony Chahoud; Giampaolo Cuozzo; Davide Borsatti; Walter Cerroni; Maurizio Fodrini; Riccardo Marini

cs.NIOct 8, 2026

Digital Twin for Pre-Deployment Validation of AI-Driven Safety-Critical Industrial Edge Control Loops

Authors: Sara Cavallero, Federico Tonini, Tony Chahoud, Giampaolo Cuozzo, Davide Borsatti, Walter Cerroni, Maurizio Fodrini, Riccardo Marini

Organizations: National Laboratory of Wireless Communications of CNIT (Wilab, CNIT), Italy · DEI, University of Bologna, Italy · WiLab CNIT, Italy · Fibercop

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.

Figures & tables

Fig. 1: Proposed Digital Twin architecture. The industrial scenario (process, assets, and network) and the DT domain (Process, Network, and Decision Modules) are bridged by a shared data infrastructure. This infrastructure enables a two-way flow: it pulls real-world measurements directly into the DT and, in turn, distributes the DT ’s services, including the synthetic data generated by its modules, to all external domains.

Fig. 2: Block diagram of the industrial PoC .

$t$	$\mathbf{a}(t)$	$g_{z}(t)$	$\mathbf{p}(t)$	$\theta(t)$	Label
$1$	$\mathbf{a}(1)$	$g_{z}(1)$	$\mathbf{p}(1)$	$\theta(1)$	Non-Fault
$2$	$\mathbf{a}(2)$	$g_{z}(2)$	$\mathbf{p}(2)$	$\theta(2)$	Non-Fault
$\vdots$	$\vdots$	$\vdots$	$\vdots$	$\vdots$	Non-Fault
$t_{f}-A$	$\mathbf{a}(t_{f}-A)$	$g_{z}(t_{f}-A)$	$\mathbf{p}(t_{f}-A)$	$\theta(t_{f}-A)$	Fault
$t_{f}-A+1$	$\mathbf{a}(t_{f}-A+1)$	$g_{z}(t_{f}-A+1)$	$\mathbf{p}(t_{f}-A+1)$	$\theta(t_{f}-A+1)$	Fault
$\vdots$	$\vdots$	$\vdots$	$\vdots$	$\vdots$	Fault

TABLE I: Example of one labeled time-series.

Fig. 3: Example of the Fault prediction using the trained CNN: the model anticipates the spill event from time series data.

Fig. 4: End-to-end communication and processing flow considered in the delay model. Blue arrows denote uplink sensor-data transmission, while red arrows represent downlink actuation commands.

Fig. 5: Real-world validation of the industrial PoC. The left image shows the outcome when the PoC is inactive (i.e., the water bottle falls due to a sudden obstruction), whereas the right image shows that the proposed solution prevents this event by accurately predicting the spill and activating the gripper in advance thanks to the high accuracy of the CNN model and the low latency provided by the OAI-based 5G network.

Symbol	Description	Value
$T_{\rm S}$	Motion data periodicity	100 ms
$T_{\rm P,UE}$	UE processing time	2 ms
$T_{\rm P,gNB}$	gNB processing time	4 ms
$T_{\rm CN}$	CN delay [ 21 ]	2 ms
$T_{\rm TCP}$	TCP overhead	7 ms
$T_{\rm T}$	CN-application server connection delay	1 ms

TABLE II: Input parameters setting.

	PoC		DT
	#1	#2	#1	#2
$R_{\rm T}$ [ms]	66.16	65.24	69.12 $\pm$ 6.13	69.21 $\pm$ 6.32
$T_{\rm C}$ [ms]	24.59	24.77	24.30 $\pm$ 0.80	24.25 $\pm$ 0.78
$P_{\rm S}$ [dBm]	-77	-79	-79 $\pm$ 0.04	-81 $\pm$ 0.08

TABLE III: Comparison of the KPIs between the PoC and the DT for two blockage events. For the DT , results are reported as mean

\pm

standard deviation over

N_{E}=50

, while

N_{E}=10

for the PoC .

Fig. 7: Locations of the two blockage positions used for both PoC experiments and DT evaluations. Blockage #1 is placed near the gNB , while Blockage #2 is farther.

Fig. 8: Effects of different margins ( $M$ ) and application requirements ( $T_{\rm I}$ ) on the time budget for $T_{\rm T}$ . Reference delay values for different cloud solutions are reported as horizontal lines.

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Digital twins have emerged as a foundational technology within the context of Industry 4.0, offering a paradigm for the real-time virtual representation of physical systems. However, managing their growing complexity, particularly in distributed industrial environments, requires intelligent architectures capable of autonomous decision-making, dynamic adaptability, and inter-agent coordination. This systematic review explores the intersection between Multi-Agent Systems and Digital Twins, with a particular focus on predictive maintenance applications in resource-constrained contexts. Through a critical analysis of over 547 papers published in high-impact journals (IEEE Transactions, Nature, Elsevier, MDPI), we establish a taxonomy of existing hybrid architectures, identify persistent technological bottlenecks, and formulate three open research questions concerning: (i) the deployment of artificial intelligence on resource-constrained microcontrollers, (ii) distributed multi-node coordination via lightweight communication protocols, and (iii) the hierarchical orchestration of Digital Twins toward smart factory control integrating residual life estimation and explainable Artificial Intelligence. The results of this analysis reveal that, despite significant progress, no existing system offers an integrated embedded-distributed hierarchical solution that simultaneously meets the requirements of Industry 5.0.

Abstract

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