Enhancing Adaptive Smart System Orchestration using Post-Quantum Transformer-Driven Semantic Sensing in 6G Digital Twin Frameworks

This paper presents a transformer-infused semantic sensing ecosystem that integrates post-quantum signatures with 6G-enabled digital twins to enable adaptive orchestration in next-generation smart systems. Conventional IoT architectures struggle with semantic understanding across heterogeneous sensor streams, vulnerability to quantum attacks, and synchronization delays between physical and digital representations. The proposed platform deploys transformer models optimized for multi-modal sensor fusion to extract contextually rich semantic features from raw measurements, feeding these insights into digital twins synchronized over 6G networks with microsecond precision. Post-quantum lattice-based signatures ensure data integrity and authentication across the high-velocity sensing-orchestration pipeline, resisting both classical and quantum adversaries. The adaptive orchestration engine leverages twin predictions and semantic context to generate control policies that optimize system performance under dynamic conditions. Evaluation across industrial, urban, and autonomous transport scenarios demonstrates 3.8× improvement in semantic inference accuracy, 92% reduction in twin synchronization error, and 28% latency reduction compared to baseline architectures, while maintaining quantum-resistant security guarantees. The framework establishes a blueprint for secure, semantically-aware smart ecosystems capable of real-time adaptive orchestration at 6G scale.