AI-Standardized Secure Digital Twins for Smart Home Ecosystems

Abstract

Digital Twins (DTs) are emerging as foundational components of next-generation communication systems, offering real-time synchronization, predictive modeling, and autonomous control across consumer electronics. However, their integration with AI/ML, edge–cloud infrastructures, and 5G/6G networks significantly enlarges the attack surface, raising concerns over security, privacy, and interoperability. This article introduces a secure and AI-standardized DT framework for the Internet of Consumer Electronics (ICE), embedding privacy-preserving machine learning, semantic interoperability, and compliance with ISO/IEC, ITU-T, and 3GPP standards. A hybrid edge–cloud prototype, validated on a real-world smart home dataset, achieved 27.4% energy savings and high prediction accuracy $\left ({ \mathrm {R}^{2}=0.97}\right)$ , while incorporating encryption, federated learning, and fine-grained access control. Beyond performance gains, the article critically examines challenges in AI trustworthiness, privacy, and cross-domain standardization. Future research directions are outlined to advance standardized, secure DT ecosystems that can seamlessly operate across consumer, healthcare, and industrial domains within 5G/6G environments.