A hybrid self-evolving edge-AI framework for scalable digital process twins in enterprise systems
Abstract
Edge-centric digital process twins struggle with slow adaptation and real-time compliance. We propose a self-evolving Edge-AI architecture with five components. The Hierarchical Neuro-Symbolic Verification Graph integrates symbolic rules and neural graphs, reducing latency by 35% while maintaining over 97% compliance. Federated Evolutionary Drift Adaptation improves drift response by 28% and F1 score by 15% using evolutionary operators. The Multi-Resolution Spatio-Temporal Causal Inference Network enables 40% earlier fault detection via causal separation. The Quantum-Inspired Edge Reinforcement Optimiser speeds convergence by 30% and boosts effectiveness by 12%. The Cross-Layer Digital Twin Consistency Ledger ensures tamper-proof state integrity with
Fields of Study & Subjects
Digital Transformation in Industry
Business Process Modeling and Analysis
IoT and Edge/Fog Computing
Publication details
- Journal / source
- Enterprise Information Systems
- Publisher
- Informa UK Limited
- Volume
- 20
- Issue
- 8
- Publication date
- 14 April 2026
- Language
- EN