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TinyML Algorithms for Big Data Management in Large-Scale IoT Systems

Author

Listed:
  • Aristeidis Karras

    (Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece)

  • Anastasios Giannaros

    (Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece)

  • Christos Karras

    (Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece)

  • Leonidas Theodorakopoulos

    (Department of Management Science and Technology, University of Patras, 26334 Patras, Greece)

  • Constantinos S. Mammassis

    (Department of Industrial Management and Technology, University of Piraeus, 18534 Piraeus, Greece)

  • George A. Krimpas

    (Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece)

  • Spyros Sioutas

    (Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece)

Abstract

In the context of the Internet of Things (IoT), Tiny Machine Learning (TinyML) and Big Data, enhanced by Edge Artificial Intelligence, are essential for effectively managing the extensive data produced by numerous connected devices. Our study introduces a set of TinyML algorithms designed and developed to improve Big Data management in large-scale IoT systems. These algorithms, named TinyCleanEDF, EdgeClusterML, CompressEdgeML, CacheEdgeML, and TinyHybridSenseQ, operate together to enhance data processing, storage, and quality control in IoT networks, utilizing the capabilities of Edge AI. In particular, TinyCleanEDF applies federated learning for Edge-based data cleaning and anomaly detection. EdgeClusterML combines reinforcement learning with self-organizing maps for effective data clustering. CompressEdgeML uses neural networks for adaptive data compression. CacheEdgeML employs predictive analytics for smart data caching, and TinyHybridSenseQ concentrates on data quality evaluation and hybrid storage strategies. Our experimental evaluation of the proposed techniques includes executing all the algorithms in various numbers of Raspberry Pi devices ranging from one to ten. The experimental results are promising as we outperform similar methods across various evaluation metrics. Ultimately, we anticipate that the proposed algorithms offer a comprehensive and efficient approach to managing the complexities of IoT, Big Data, and Edge AI.

Suggested Citation

  • Aristeidis Karras & Anastasios Giannaros & Christos Karras & Leonidas Theodorakopoulos & Constantinos S. Mammassis & George A. Krimpas & Spyros Sioutas, 2024. "TinyML Algorithms for Big Data Management in Large-Scale IoT Systems," Future Internet, MDPI, vol. 16(2), pages 1-29, January.
    • Handle: RePEc:gam:jftint:v:16:y:2024:i:2:p:42-:d:1326236
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    References listed on IDEAS

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    1. Kimmi Kumari & Mrunalini M., 2022. "A Framework for Analysis of Incompleteness and Security Challenges in IoT Big Data," International Journal of Information Security and Privacy (IJISP), IGI Global, vol. 16(2), pages 1-13, April.
    2. Maged Mohammed & Ramasamy Srinivasagan & Ali Alzahrani & Nashi K. Alqahtani, 2023. "Machine-Learning-Based Spectroscopic Technique for Non-Destructive Estimation of Shelf Life and Quality of Fresh Fruits Packaged under Modified Atmospheres," Sustainability, MDPI, vol. 15(17), pages 1-24, August.
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