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K-Nearest Neighbour Algorithm for Classification of IoT-Based Edge Computing Device

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Artificial Intelligence for Cloud and Edge Computing

Part of the book series: Internet of Things ((ITTCC))

Abstract

The world’s population has boomed with the billions of connected devices in our households, towns, factories, hospitals, and so on. Limited-resource applications communicate with the world and users. To evaluate meaningful behavior to execute specific predictions and make decisions, several of these technologies are built on machine learning (ML) procedures. Hence, the need to integrate intelligence using machine learning algorithms on end devices is important. Implementing machine learning on edge devices enhances and makes it possible to perform computations near to the data sources. Therefore, the objective of this investigation is to provide a method that guarantees the implementation of low-performance ML techniques on hardware in the Internet of Things model, creating means for IoT awareness. The study employed the use of the KNN ML algorithm for the implementation, and a confusion matrix in terms of accuracy was used to evaluate the system. The result of the experiment shows an 85% accuracy which outperformed other methods that have been suggested and compared within the literature. However, this study proves to be relevant and can be adopted for better efficiency in IoT and edge/cloud computing applications.

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Authors and Affiliations

  1. Department of Computer Science, Landmark University, Omu Aran, Nigeria

    Micheal Olaolu Arowolo & Roseline Oluwaseun Ogundokun

  2. Department of Computer Science and Communication, Ostfold University College, Halden, Norway

    Sanjay Misra

  3. Covenant University, Ota, Nigeria

    Jonathan Oluranti

  4. Department of Computer Science, Kwara State University, Malete, Nigeria

    Akeem Femi Kadri

Authors
  1. Micheal Olaolu Arowolo
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  2. Roseline Oluwaseun Ogundokun
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  3. Sanjay Misra
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  4. Jonathan Oluranti
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  5. Akeem Femi Kadri
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Corresponding author

Correspondence to Roseline Oluwaseun Ogundokun .

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Editors and Affiliations

  1. Department of Computer Science and Communication, Ostfold University College, Halden, Norway

    Sanjay Misra

  2. School of Computer Science and Engineering, Vellore Institute of Technology, Tamil Nandu, India

    Amit Kumar Tyagi

  3. Dipartimento di Informatica, Universita' degli Studi di Milano, Milano, Italy

    Vincenzo Piuri

  4. Faculty of Information and Communication Technology, University of Malta, Msida, Malta

    Lalit Garg

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Arowolo, M.O., Ogundokun, R.O., Misra, S., Oluranti, J., Kadri, A.F. (2022). K-Nearest Neighbour Algorithm for Classification of IoT-Based Edge Computing Device. In: Misra, S., Kumar Tyagi, A., Piuri, V., Garg, L. (eds) Artificial Intelligence for Cloud and Edge Computing. Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-030-80821-1_8

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  • DOI: https://doi.org/10.1007/978-3-030-80821-1_8

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