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Leveraging Machine Learning for WBANs

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Distributed Computing for Emerging Smart Networks (DiCES-N 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1564))

Abstract

Wireless Body Area Networks are considered as an effective solution for a wide range of healthcare, military and sports applications. These applications are responsible for gathering and managing a huge amount of heterogeneous data from the human body or the surrounding environment in both real and non-real time manners. Relevant information for various fields can be extracted from the raw data. Recently, Machine learning has been extensively explored for real-time big data processing. Thus, the machine learning techniques are very useful for the big data analytic process. In this paper, we discuss the importance of the machine learning techniques use in the fusion and the treatment of the WBAN data, useful for different fields of applications.

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Notes

  1. 1.

    AAL: Ambient Assisted Living.

  2. 2.

    PDA: Personal Digital Assistant.

  3. 3.

    QoS: Quality of Service.

  4. 4.

    DCT: Discrete Cosine Transform.

  5. 5.

    LBP: Local Binary Pattern.

  6. 6.

    UCA: unsupervised coloring algorithm.

  7. 7.

    AR: autoregressive.

  8. 8.

    SINR: signal to interference plus noise ratio.

  9. 9.

    BNC: Body Node Coordinator.

  10. 10.

    BN: Body Node.

  11. 11.

    RL-CAA: reinforcement learning - channel assignment algorithm.

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Authors and Affiliations

  1. ENSI, University of Manouba, Manouba, Tunisia

    Rim Negra

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  1. Rim Negra
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Correspondence to Rim Negra .

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Editors and Affiliations

  1. University of Carthage, Zarzouna Bizerte, Tunisia

    Imen Jemili

  2. University of Bordeaux, Bordeaux, France

    Mohamed Mosbah

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Negra, R. (2022). Leveraging Machine Learning for WBANs. In: Jemili, I., Mosbah, M. (eds) Distributed Computing for Emerging Smart Networks. DiCES-N 2022. Communications in Computer and Information Science, vol 1564. Springer, Cham. https://doi.org/10.1007/978-3-030-99004-6_3

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  • DOI: https://doi.org/10.1007/978-3-030-99004-6_3

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