Deep Learning for IoT Big Data and Streaming Analytics: A Survey
2017, arXiv (Cornell University)
Abstract
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. We also analyze and summarize major reported research attempts that leveraged DL in the IoT domain. The smart IoT devices that have incorporated DL in their intelligence background are also discussed. DL implementation approaches on the fog and cloud centers in support of IoT applications are also surveyed. Finally, we shed light on some challenges and potential directions for future research. At the end of each section, we highlight the lessons learned based on our experiments and review of the recent literature.
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- Mehdi Mohammadi (S'14) received his B.S. degree in Computer Engineering from Kharazmi University, Tehran, Iran in 2003 and his M.S. degree in Com- puter Engineering (Software) from Sheikhbahaee University, Isfahan, Iran in 2010. He received his Ph.D. degree in Computer Science from Western Michigan University (WMU), Kalamazoo, MI, USA. His research interests include Internet of Things, IoT data analytics, Machine Learning, and Cloud Computing. He served as reviewer for multiple journals including IEEE Internet of Things Jour- nal, IEEE Communications Magazine, IEEE Communications Letters, IEEE Transactions on Emerging Topics in Computational Intelligence, Wiley's Security and Wireless Communication Networks Journal and Wiley's Wireless Communications and Mobile Computing Journal. He was the recipient of six travel grants from the National Science Foundation (NSF). Ala Al-Fuqaha (S'00-M'04-SM'09) received his M.S. from the University of Missouri-Columbia and Ph.D. from the University of Missouri-Kansas City. Currently, he is a Professor and director of NEST Research Lab at the Computer Science Department of Western Michigan University. His research inter- ests include the use of machine learning in general and deep learning in particular in support of the data- driven and self-driven management of large-scale deployments of IoT and smart city infrastructure and services, Wireless Vehicular Networks (VANETs), cooperation and spectrum access etiquettes in cognitive radio networks, and management and planning of software defined networks (SDN). He is a senior member of the IEEE and an ABET Program Evaluator (PEV). He served on editorial boards and technical program committees of multiple international journals and conferences. Sameh Sorour (S'98, M'11, SM'16) is an As- sistant Professor at the Department of Electrical and Computer Engineering, University of Idaho. He received his B.Sc. and M.Sc. degrees in Electrical Engineering from Alexandria University, Egypt, in 2002 and 2006, respectively. In 2011, he obtained his Ph.D. degree in Electrical and Computer Engineer- ing from University of Toronto, Canada. After two postdoctoral fellowships at University of Toronto and King Abduallah University of Science and Tech- nology (KAUST), he joined King Fahd University of Petroleum and Minerals (KFUPM) in 2013 before moving to University of Idaho in 2016. His research interests lie in the broad area of advanced communications/networking/computing/learning technologies for smart cities applications, including cyber physical systems, internet of things (IoT) and IoT-enabled systems, cloud and fog networking, network coding, device-to- device networking, autonomous driving and autonomous systems, intelligent transportation systems, and mathematical modelling and optimization for smart systems.