BigDataFr recommends: Deep Learning for IoT Big Data and Streaming Analytics: A Survey

[…] Subjects: Learning (cs.LG); Artificial Intelligence (cs.AI); Databases (cs.DB); Networking and Internet Architecture (cs.NI)

In the era of the Internet of Things (IoT), an enormous amount of sensing devices collect and/or generate various sensory data over time for a wide range of fields and applications. Based on the nature of the application, these devices will result in big or fast/real-time data streams. Applying analytics over such data streams to discover new information, predict future insights, and make control decisions is a crucial process that makes IoT a worthy paradigm for businesses and a quality-of-life improving technology. In this paper, we provide a thorough overview on using a class of advanced machine learning techniques, namely Deep Learning (DL), to facilitate the analytics and learning in the IoT domain. We start by articulating IoT data characteristics and identifying two major treatments for IoT data from a machine learning perspective, namely IoT big data analytics and IoT streaming data analytics. We also discuss why DL is a promising approach to achieve the desired analytics in these types of data and applications. The potential of using emerging DL techniques for IoT data analytics are then discussed, and its promises and challenges are introduced. We present a comprehensive background on different DL architectures and algorithms. […]

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By Mehdi Mohammadi, Ala Al-Fuqaha, Sameh Sorour, Mohsen Guizani
Source: arxiv.org