Deep Learning in IoT: Advancing Automation, Security, and Smart Services

Abstract

The rapid growth of the Internet of Things (IoT) has significantly improved intelligent automation across domains such as smart healthcare, industrial automation, and smart cities. However, conventional IoT systems continue to face challenges related to limited automation intelligence, evolving cyber threats, high communication latency, and data privacy. To address these limitations, this paper proposes an Attention-Augmented Deep Learning IoT (ADL-IoT) framework that integrates Attention Mechanisms, Graph Neural Networks (GNNs), and Federated Learning (FL) to enable adaptive automation, topology-aware threat detection, and privacy-preserving collaborative learning. The attention module dynamically prioritizes critical IoT data streams for efficient resource allocation, while the GNN module models communication relationships among connected devices to accurately identify cyber threats and network intrusions. Federated Learning facilitates decentralized model training without sharing raw data, thereby preserving data privacy while maintaining high model performance. Experimental evaluation demonstrates that the proposed ADL-IoT framework achieves 95.8% automation accuracy, 94.6% detection precision, 122 ms average latency, and faster convergence than CNN-LSTM, GNN-only, and Transformer-only baseline models. These results demonstrate that ADL-IoT provides an effective, scalable, and secure solution for next-generation intelligent IoT environments.