Integrating digital twins for optimized off-chain processing in decentralized systems

Digital Twins are becoming essential for optimizing decentralized systems through real-time data processing and off-chain analytics. However, existing solutions often face challenges such as high latency, limited scalability, and weak synchronization between physical and virtual entities. This study introduces a novel framework that integrates DTs with blockchain to enhance off-chain processing efficiency while preserving security and data integrity. Using IoT sensor data and a test environment built with Ganache, Flask, and Python-based optimization models, the proposed system achieved throughput levels of up to 500 transactions per second and reduced latency by 50% compared to a traditional baseline, with processing times consistently under 150 m. It maintained a transaction success rate above 90% under moderate load (30–40 concurrent Digital Twins) and approximately 70% with 100 concurrent Digital Twins under high-stress conditions. The framework also demonstrated strong resource efficiency, averaging 50% CPU and 40% memory usage, while reducing energy consumption by 33%–41% across low, moderate, and high load scenarios. A comprehensive cost-effectiveness analysis revealed a 35.2% reduction in total cost of ownership (TCO) over a 3-year period, with a return on investment (ROI) of 140%. Security assessments confirmed resilience against common attack vectors. These results highlight the system’s potential as a scalable, secure, energy-efficient, and economically viable solution for decentralized applications in smart cities and industrial IoT environments.