Trends Challenges and Future Directions for Intelligent IoT and Deep Learning: A Review

The Internet of Things (IoT) has become a transformative paradigm, enabling the use of connected, smart devices across diverse sectors like healthcare, transportation, and industry. With the rapid increase in IoT devices, an enormous volume of data is generated, presenting challenges in real-time processing, analysis, and decision-making. Traditional data handling methods often struggle to manage this complexity. Deep learning offers powerful capabilities for extracting insights from large, diverse datasets, making it a suitable solution for IoT data challenges. This review explores recent scientific progress in combining deep learning techniques with IoT frameworks. It highlights key applications, ranging from smart homes to industrial automation. The study also examines technical challenges such as limited computational resources, security issues, and deployment complexities. Various deep learning architectures adapted for IoT are analysed. The need for edge computing and lightweight models is emphasized. Future research opportunities in scalable and secure intelligent IoT systems are identified. Overall, this work provides a comprehensive overview of trends and innovations in intelligent IoT powered by deep learning.