Advanced Deep Neural Network Models for Heterogeneous and Real-Time IoT Data Analytics

The proposed framework integrates deep learning architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Transformer-based models, to process structured, semi-structured, and unstructured IoT data streams. The framework emphasizes real-time analytics, feature extraction, anomaly detection, predictive modeling, and intelligent decision-making while ensuring scalability and adaptability in dynamic environments. Experimental evaluations demonstrate that advanced DNN models significantly improve prediction accuracy, processing speed, and robustness compared to conventional machine learning methods. The findings highlight the potential of deep neural networks to address key challenges in IoT ecosystems, enabling smarter applications in healthcare, smart cities, industrial automation, transportation, and environmental monitoring.

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