A survey on machine learning in Internet of Things: Algorithms, strategies, and applications

@article{Messaoud2020ASO,
  title={A survey on machine learning in Internet of Things: Algorithms, strategies, and applications},
  author={Seifeddine Messaoud and Abbas Bradai and Syed Hashim Raza Bukhari and Pham Tran Anh Quang and Olfa Ben Ahmed and Mohamed Atri},
  journal={Internet Things},
  year={2020},
  volume={12},
  pages={100314}
}

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References

SHOWING 1-10 OF 326 REFERENCES

Machine learning and data analytics for the IoT

This paper critically review how IoT-generated data are processed for machine learning analysis and highlights the current challenges in furthering intelligent solutions in the IoT environment and proposes a framework to enable IoT applications to adaptively learn from other IoT applications.

Edge Machine Learning: Enabling Smart Internet of Things Applications

A step forward has been taken to understand the feasibility of running machine learning algorithms, both training and inference, on a Raspberry Pi, an embedded version of the Android operating system designed for IoT device development.

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

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 and discusses why DL is a promising approach to achieve the desired analytics in these types of data and applications.

Data Fusion and IoT for Smart Ubiquitous Environments: A Survey

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).
...