• Prerana Gupta
• Mehul Manani

The widespread growth of the Internet of Things (IoT) has led to the creation of vast and intricate datasets, requiring advanced analytical methods to extract valuable insights. Deep Learning (DL) has emerged as a transformative approach for tackling the complexities of IoT data, providing strong capabilities for data processing, recognizing patterns, and enabling intelligent decision-making. This survey paper offers a thorough review of deep learning is integrated into IoT systems, exploring various deep learning models and their diverse applications across areas such as smart homes, healthcare, and cybersecurity. We present a detailed comparison of different approaches and architectures, highlighting their strengths and limitations. Furthermore, the paper identifies key challenges and suggests future research directions to advance the field, including optimizing models for devices with limited resources, improving data privacy and security, and fostering explainable AI within IoT contexts. This work aims to be a valuable resource for researchers and professionals interested in the intersection of deep learning and the Internet of Things.

: Deep Learning, Internet of Things (IoT), Machine Learning, Neural Networks, Data Analytics, Smart Cities, Cybersecurity, Healthcare, Survey.

"Deep Learning in IoT Systems: A Comprehensive Survey", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 4, page no.78-84, April-2020, Available :http://www.jetir.org/papers/JETIR2004646.pdf

"Deep Learning in IoT Systems: A Comprehensive Survey", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 4, page no. pp78-84, April-2020, Available at : http://www.jetir.org/papers/JETIR2004646.pdf