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

@article{Mohammadi2018DeepLF,
  title={Deep Learning for IoT Big Data and Streaming Analytics: A Survey},
  author={Mehdi Mohammadi and Ala Al-Fuqaha and Sameh Sorour and Mohsen Guizani},
  journal={IEEE Communications Surveys \& Tutorials},
  year={2018},
  volume={20},
  pages={2923-2960}
}
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. [...] Key Method 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…Expand
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References

SHOWING 1-10 OF 241 REFERENCES
Big Data Analytics Platforms for Real-Time Applications in IoT
TLDR
In this chapter, the analytics requirements of IoT applications are motivated using several practical use cases, the trade-offs between processing latency and data volume capacity of contemporary big data platforms are characterized, and the critical role that Distributed Stream Processing and Complex Event Processing systems play are discussed.
Towards a Big Data Analytics Framework for IoT and Smart City Applications
TLDR
This chapter shows how an integrated Big Data analytical framework for Internet of Things and Smart City application could look like and presents an initial version of such a framework mainly addressing the volume and velocity challenge.
Efficient embedded learning for IoT devices
TLDR
This work highlights 3 approaches viz. machine learning accelerators, approximate computing and post-CMOS technologies that demonstrate significant promise in bridging the efficiency gap and may be instrumental in enabling machine learning based IoT applications to enter the mainstream.
CEML: Mixing and moving complex event processing and machine learning to the edge of the network for IoT applications
TLDR
This paper presents the Complex Event Machine Learning framework, a set of tools for automatic distributed machine learning in (near-) real-time, automatic continuous evaluation tools, and automatic rules management for deployment of rules for a deployment at the edge of the network instead of the cloud.
Mobile big data analytics using deep learning and apache spark
TLDR
An overview and brief tutorial on deep learning in mobile big data analytics and discusses a scalable learning framework over Apache Spark that speeds up the learning of deep models consisting of many hidden layers and millions of parameters.
Data Fusion and IoT for Smart Ubiquitous Environments: A Survey
TLDR
The aim of this paper is to review literature on data fusion for IoT with a particular focus on mathematical methods (including probabilistic methods, artificial intelligence, and theory of belief) and specific IoT environments (distributed, heterogeneous, nonlinear, and object tracking environments).
Semisupervised Deep Reinforcement Learning in Support of IoT and Smart City Services
TLDR
This paper proposes a semisupervised DRL model that fits smart city applications as it consumes both labeled and unlabeled data to improve the performance and accuracy of the learning agent and utilizes variational autoencoders as the inference engine for generalizing optimal policies.
Deep learning applications and challenges in big data analytics
TLDR
This study explores how Deep Learning can be utilized for addressing some important problems in Big Data Analytics, including extracting complex patterns from massive volumes of data, semantic indexing, data tagging, fast information retrieval, and simplifying discriminative tasks.
Big IoT data mining for real-time energy disaggregation in buildings
TLDR
This work proposes a hybrid approach, which combines sparse smart meters with machine learning methods, and shows how this method may be used to accurately predict and identify the energy flexibility of buildings unequipped with smart meters, starting from their aggregated energy values.
Cognitive computation and communication: A complement solution to cloud for IoT
TLDR
COGNICOM+ concept, a brain-inspired software-hardware paradigm, to support IoT's future growth and developed 4 research directions - flexible radio, convolutional neural network accelerator, compressed deep learning, and game theory for reasoning and collaboration - within COGNICom+.
...
1
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