Exploring the Landscape of Deep Learning Techniques for IoT Data: A Systematic Literature Review

The rapid evolution of software, hardware, and internet technology has enabled the proliferation of internet-connected sensor tools that gather information and observations from the physical world. The IoT comprises billions of intelligent devices, extending physical and virtual boundaries. However, traditional data processing methods face significant challenges in handling the vast volume and variety of IoT data. This paper systematically reviews. These devices generate vast amounts of data daily, with diverse applications crucial for generating new knowledge, identifying future trends, and making informed decisions. This underscores IoT's value and enhances technology. Deep learning (DL) has significantly enhanced IoT and mobile applications, demonstrating promising outcomes. Its data-driven, anomaly-based approach for detecting emerging threats positions it well for IoT intrusion detection. This paper proposes a comprehensive framework leveraging DL techniques to address data processing challenges in IoT environments and enhance intelligence and application capabilities. Furthermore, this study systematically reviews and categorizes existing deep learning techniques applied in IoT, identifies critical challenges in IoT data processing, and provides actionable insights to inspire further research in this domain. It discusses the introduction of IoT and its data processing challenges and explores various DL approaches applied to IoT data. Significant DL efforts in IoT are surveyed and summarized, focusing on datasets, features, applications, and challenges to inspire further advancements in this field.