• Arvind Kumar Pandey

Applying analytics to such data streams to find new information, anticipate future insights, and make control choices is a critical step that distinguishes IoT as a viable business model and a technology that improves quality of life. We provide a comprehensive overview of utilizing a class of sophisticated machine learning methods known as Deep Learning (DL) to assist analytics and learning in the IoT domain in this article. We begin by defining IoT data properties and identifying two main machine learning treatments for IoT data, namely IoT big data analytics and IoT streaming data analytics. We also go through why deep learning is a potential method for achieving the required insights in these kinds of data and applications. The possibility of utilizing new deep learning methods for IoT data analytics is then explored, along with its benefits and drawbacks. We provide a thorough overview of several DL designs and methods. We also examine and describe significant published research projects that used deep learning in the IoT area. Also addressed are smart IoT devices with DL as part of their intelligence base. The impact of DL deployment on fog and cloud centers in support of IoT applications is also examined. Finally, we discussed various difficulties as well as future research areas. We emphasize the lessons gained from our experiments and a review of current research at the conclusion of each segment.

Deep Learning, Deep Neural Network, Internet of Things, IoT Big Data, Fast Data Analytics, Cloud-Based Analytics.

"IoT-Related Deep Learning Streaming Analytics and Big Data", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 3, page no.46-51, March-2019, Available :http://www.jetir.org/papers/JETIRFT06011.pdf

"IoT-Related Deep Learning Streaming Analytics and Big Data", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 3, page no. pp46-51, March-2019, Available at : http://www.jetir.org/papers/JETIRFT06011.pdf