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Cited by 1,471Internet of Things and AIIoT and Edge/Fog ComputingData Stream Mining Techniques

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

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Mohammadi, Mehdi, Al-Fuqaha, Ala, Sorour, Sameh, Guizani, Mohsen·2018·IEEE Communications Surveys & Tutorials
DOI 10.1109/comst.2018.2844341
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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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  • Traditional IoT AI functions placed on data centers or cloud servers are not scalable to exponential growth of IoT devices and high data distribution.

    “In traditional IoT systems [39], [40], AI functions are often placed on data centers or cloud servers, which is obviously not scalable to the exponential growth of IoT devices and the high data distribution of large-scale IoT networks.”

    From Federated Learning for Internet of Things: A Comprehensive Survey (2021) · cites this paper as [39]

    Section VI (page unavailable) says IoT analytics tasks are currently performed in the cloud and that moving some analytics closer to devices can avoid transmitting large amounts of data to distant cloud nodes. It does not say cloud-based AI is inherently unable to scale with IoT growth or distributed data; the Enabling Technologies and Platforms subsection instead calls scalable cloud-centric analytics for massive IoT data an important research area. (LLM, conf=0.86)

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  186. 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).
  187. 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.

Cited by

Sorted by citations

Authors
Mohammadi, M., Al-Fuqaha, A., Sorour, S., & Guizani, M.
Affiliations
  • Western Michigan University (US)
  • University of Idaho (US)
ORCID
Venue
IEEE Communications Surveys & Tutorials
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2018
Volume
20
Issue
4
Pages
2923-2960
ISSN
  • 1553-877X (ISSN-L)
  • 2373-745X
DOI
10.1109/comst.2018.2844341
OpenAlex
W2772457693
Cited by
1,471
References
27
Citations by year
2026: 79 · 2025: 147 · 2024: 164 · 2023: 168 · 2022: 213 · 2021: 281 · 2020: 246 · 2019: 135 · 2018: 38
FWCI
282.41
Type
journal article
Language
en
Open access
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations
  • IEEE Communications Surveys & Tutorials (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
  • Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
  • Physical Sciences › Computer Science › Artificial Intelligence › Data Stream Mining Techniques
Keywords
Internet of Things, deep learning, streaming analytics, deep learning architectures, fog computing, cloud computing, real-time data streams

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