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Machine Learning Analytic-Based Two-Staged Data Management Framework for Internet of Things.

Omar Farooq1, Parminder Singh1,2, Mustapha Hedabou2

  • 1School of Computer Science and Engineering, Lovely Professional University, Phagwara 144411, India.

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|March 11, 2023
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Summary
This summary is machine-generated.

This study introduces the Machine Learning Analytics-based Data Classification Framework (MLADCF) to manage Internet of Things (IoT) data efficiently. MLADCF optimizes resource constraints, reducing energy consumption and extending battery life for connected devices.

Keywords:
IoTdata management frameworkedge-cloudresource-constrained

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Area of Science:

  • Computer Science
  • Data Science
  • Network Engineering

Background:

  • Internet of Things (IoT) applications involve numerous connected nodes with strict resource constraints (battery, processing, storage).
  • Standard data management methods are insufficient for these highly constrained and numerous IoT environments.
  • Machine learning offers a promising approach to address these complex management challenges.

Purpose of the Study:

  • To design and implement a novel framework for efficient data management in IoT applications.
  • To address the limitations of existing methods in managing resource-constrained IoT networks.
  • To improve the overall performance and longevity of IoT devices.

Main Methods:

  • Developed the Machine Learning Analytics-based Data Classification Framework (MLADCF).
  • MLADCF employs a two-stage approach combining a regression model and a Hybrid Resource Constrained KNN (HRCKNN).
  • The framework learns from real-world IoT application analytics for adaptive management.

Main Results:

  • MLADCF demonstrated proven efficiency across four diverse datasets compared to existing approaches.
  • The framework significantly reduced global network energy consumption.
  • Extended battery life for connected IoT nodes was observed.

Conclusions:

  • MLADCF provides an effective solution for data management in resource-constrained IoT environments.
  • The proposed framework enhances the efficiency and sustainability of IoT networks.
  • Machine learning integration is crucial for optimizing future IoT data management strategies.