Deep learning for internet of things in fog computing: Survey and Open Issues

Abstract

In recent years, the internet of things is getting very popular where it arose in several areas such as education, and healthcare to enhance our live. This popularity has led to an increase number of IoT devices and thus generates massive volume of data. However, this data requires efficient methods of analysis to provide intelligent services. Recently, the deep learning can meet the requirements of IoT data analysis by providing techniques for large scale data analysis and meaningful feature extraction. The deep learning implementation is traditionally delivered to cloud computing due to its high compute resources provisioning. However, given the sheer volume of IoT data, the cloud computing fall to meet the IoT requirements, it presents many issues in term of time response, large data transmission, energy consumption, etc. To address this challenges the fog computing, new layer between cloud computing and internet of things devices, appears. So, moving the implementation of deep learning to fog computing can achieve the requirements of internet of things systems and enhance their performances. In this paper, we introduce deep learning for internet of things, next the application of deep learning in internet of things. We address fog computing for the internet of things. Finally, we present the deep learning in fog computing.