Cross-Domain Data Optimization Framework for Enhancing AI Model Generalization in IoT-Driven Environments

proliferation of Internet of Things (IoT) devices across diverse industries has generated massive, heterogeneous datasets. However, the effectiveness of Artificial Intelligence (AI) models trained on such data remains limited due to data inconsistency, domain specificity, and high preprocessing requirements. This paper proposes a novel Cross-Domain Data Optimization (CDDO) framework that systematically groups and segregates IoT data before AI training. Behavioral grouping (on/off, threshold-based, conditional) and domain feature segregation (shared vs. exclusive) help structure raw data into a reusable, adaptable format. Experiments conducted across automotive, healthcare, and agricultural domains show improved generalization, reduced preprocessing time, and enhanced model accuracy. The proposed method offers a scalable, low-cost strategy for developing robust AI systems