Generative Artificial Intelligence–Driven Sensor Fusion Architectures For Secure Digital Twin Ecosystems In Cyber-Physical Healthcare And Environmental Monitoring Systems

Abstract

The accelerating convergence of cyber-physical systems, artificial intelligence, and networked sensor infrastructures has generated unprecedented opportunities for real-time modeling, prediction, and control across healthcare, environmental science, and industrial automation. At the core of this convergence lies the digital twin paradigm, which provides a continuously synchronized virtual representation of physical systems that can be used for diagnosis, simulation, and decision support. However, as digital twins become increasingly dependent on heterogeneous, distributed, and often unreliable sensor data, fundamental challenges emerge in the areas of security, trust, synchronization, and fault tolerance. These challenges are particularly acute in safety-critical domains such as medical monitoring and environmental surveillance, where erroneous data fusion or adversarial interference can have severe consequences. Recent advances in generative artificial intelligence and probabilistic sensor fusion offer a powerful yet still theoretically underdeveloped pathway for addressing these challenges by enabling digital twins to reason about uncertainty, infer missing or corrupted data, and maintain alignment with physical reality in the presence of noise, attacks, or partial observability.

This article develops a comprehensive theoretical and methodological framework for generative AI-driven sensor fusion in secure digital twin ecosystems. The analysis is grounded in contemporary research on cyber-physical systems, wearable and implantable sensors, wireless sensor networks, edge computing, and artificial intelligence for healthcare and environmental monitoring. Central to the framework is the integration of generative probabilistic models with standardized synchronization and reliability mechanisms, enabling digital twins to function not merely as passive mirrors of the physical world but as active inferential agents capable of reconstructing and validating sensor data streams. This perspective is aligned with the standardization-oriented approach articulated by Hussain and colleagues in their work on generative AI sensor fusion for secure digital twin ecosystems, which emphasizes ISO and 3GPP compliance, probabilistic logic, and fault detection as foundational pillars of trust in cyber-physical environments (Hussain et al., 2026).

Through an extensive critical synthesis of the literature on sensor technologies, wireless communication, and intelligent data processing, the article demonstrates how generative AI can mitigate long-standing limitations of traditional fusion algorithms, such as brittleness to missing data, poor scalability, and vulnerability to adversarial manipulation. The proposed methodology articulates how multimodal biosensors, environmental sensors, and triboelectric energy-harvesting devices can be integrated into a secure, self-adapting digital twin architecture using edge-cloud coordination and probabilistic reasoning. The results section interprets how such architectures would improve reliability, detection of anomalies, and operational continuity across diverse application domains, while the discussion situates these findings within broader theoretical debates on autonomy, control, and trust in intelligent cyber-physical systems. By synthesizing insights from sensor engineering, network theory, and artificial intelligence, this work contributes a robust conceptual foundation for the next generation of secure digital twins, highlighting both their transformative potential and the critical challenges that must be addressed to realize it.