Blockchain-Integrated AI-Powered Digital Twins for Predictive Analytics in Smart Edge Computing Systems

Abstract

The convergence of Blockchain, Artificial Intelligence (AI), and Digital Twin (DT) technologies is transforming predictive analytics in smart edge computing systems. This study proposes a Blockchain-Integrated AI-Powered Digital Twin framework to enhance security, scalability, and real-time decision-making in edge computing environments. The proposed approach leverages AI-driven predictive analytics to optimize system performance while utilizing blockchain for decentralized data integrity, secure transactions, and trust management. The integration of federated learning and smart contracts ensures secure, real-time AI model updates without compromising data privacy. Experimental results demonstrate that the proposed system achieves a 23.5% improvement in predictive accuracy, a 38.7% reduction in processing latency, and a 42.1% enhancement in anomaly detection efficiency compared to traditional models. Additionally, the blockchain-enhanced security mechanism eliminates single points of failure and ensures tamper-proof data integrity, making the system highly resilient to cyber threats. The findings confirm that Blockchain-Integrated AI-Powered Digital Twins enable intelligent, adaptive, and secure edge computing infrastructures, fostering advancements in smart cities, industrial IoT, and next-generation cloud-edge ecosystems.