Next-Generation ECE Hardware Framework for IoT Using Generative AI and Edge Intelligence for Real-Time Smart Applications

Abstract

The rapid growth of Internet of Things (IoT) applications has increased the demand for efficient and low-latency data processing, especially in real-time smart environments. Traditional cloud-based systems often face challenges such as high latency, bandwidth limitations, and privacy concerns, making them less suitable for time-sensitive applications. This study aims to develop a next-generation ECE hardware framework that integrates edge intelligence and generative techniques to enable efficient real-time decision-making in IoT systems. The proposed framework combines multi-sensor IoT data processing with a lightweight edge-based model and generative data augmentation to improve system performance. An IoT-based environmental dataset containing temperature, humidity, air quality, and behavioral indicators is used for experimentation. The methodology includes preprocessing techniques such as normalization, smoothing, and feature engineering, followed by GAN-based data augmentation to address data imbalance. A compact machine learning model is deployed on edge hardware using quantization and pruning for efficient inference. Experimental results demonstrate that the proposed system achieves an accuracy of 93.8%, precision of 92.6%, recall of 91.9%, and F1-score of 92.2%, with an AUC of 0.95. The optimized model reduces inference latency to approximately 4–5 ms, outperforming conventional cloud-based and baseline edge models in both efficiency and responsiveness. In conclusion, the proposed framework provides a scalable and energy-efficient solution for real-time IoT applications. It effectively balances performance and resource utilization, making it suitable for deployment in smart healthcare, smart cities, and industrial monitoring systems.