Deep Learning Techniques in Big Data-Enabled Internet-of-Things Devices

Abstract

Due to the development in various tools and deep learning (DL) techniques that might be helpful in evaluating Internet of Things (IoT) big data, the integration of the IoT with DL has magnetically drawn the attention of the research community. On the other hand, as the number of IoT installations at the ground level grows, so does the number of data sources that are continuously generated. This gives rise to IoT big data, which can be employed in a variety of fields when examined using DL tools and methodologies. The proposed study provides a comprehensive overview and deep dive into the field of DL techniques that can be used for IoT big data analytics. Tables are used to identify, review, and summarize various articles on IoT-DL integration. There is also a rundown of big data-enabled IoT devices. DL is used to illustrate several new methodologies for IoT big data analytics, emphasizing the importance of DL in the context. Benefits and challenges in using DL techniques for IoT big data are discussed. Lastly, some recent work published on DL is explored to illustrate the advantages and disadvantages of using DL techniques.