A Standardization-Driven Study of Generative AI Sensor Fusion in Secure Digital Twin–Based Cyber-Physical Systems

Abstract

The accelerating convergence of cyber-physical systems, sensor networks, edge computing, and artificial intelligence has catalyzed the emergence of secure digital twin ecosystems as foundational infrastructures for next-generation autonomous, industrial, and socio-technical applications. Within this evolving landscape, generative artificial intelligence–based sensor fusion has begun to redefine how heterogeneous sensor data are synthesized, validated, and operationalized across distributed environments. This article presents an extensive, publication-ready research study that theorizes, contextualizes, and critically examines generative AI sensor fusion as an enabling mechanism for secure, reliable, and standardization-aligned digital twin ecosystems. Anchored explicitly in the framework proposed by Hussain et al. (2026), published in IEEE Communications Standards Magazine, this study positions generative AI not merely as a data augmentation tool but as a probabilistic reasoning engine capable of synchronizing cyber and physical states under uncertainty.

The article systematically integrates interdisciplinary scholarship spanning wireless sensor networks, synthetic data generation, edge computing, autonomous systems, and international standards such as ISO and 3GPP. Through an exhaustive theoretical elaboration, the research reconstructs the historical evolution of sensor fusion methodologies, traces the epistemic shift from deterministic models to probabilistic and generative paradigms, and interrogates the security implications of AI-mediated perception in digital twins. Methodologically, the study adopts a text-based analytical synthesis approach, combining comparative literature analysis, conceptual modeling, and interpretive reasoning to derive emergent insights without reliance on mathematical formalism or visual artifacts.

The results section advances a descriptive interpretation of how generative sensor fusion enhances fault detection, synchronization fidelity, and resilience in digital twin ecosystems, particularly when deployed at the edge in latency-sensitive contexts. The discussion extends these findings by situating them within broader scholarly debates on synthetic data validity, trust in AI-generated representations, and the governance challenges posed by standardization alignment. By articulating limitations, counter-arguments, and future research trajectories, this article contributes a comprehensive intellectual foundation for researchers, standards bodies, and system architects seeking to operationalize secure digital twins in complex cyber-physical domains.