Deep Learning Models for Intelligent IoT Ecosystems
K. Vijayakumari, Jasmine Beulah Gnanadurai, S. Usharani, K. Velusamy · 2026
The integration of deep learning (DL) with the Internet of Things (IoT) is redesigning digital ecosystems, igniting innovations in fields ranging from smart cities to health care and industrial automation. The capability of deep learning (DL) for real-time sensing, signal processing, anomaly detection, and predictive maintenance is increasing to allow an independence of IoT systems. However, adopting DL models in IoT systems requires overcoming the limitations, such as computational restrictions, data heterogeneity, and real-time processing requirements. This chapter focuses on a detailed examination of intelligent IoT ecosystems, focusing on the role of deep learning architectures and frameworks to enhance capacities of data processing and decision-making. This chapter covers training and implementing deep learning models in distributed IoT networks, using edge devices for real-time analytics, and investigating deep reinforcement learning for adaptive IoT systems. The integration of deep learning across IoT communication group, from the physical to the application layers, is evaluated, with priorities on performance evaluation and optimization strategies. This chapter’s main feature is an innovative impact of deep learning in constructing scalable, intelligent, and resilient IoT ecosystems by discussing modern advancements, difficulties, and future prospects.