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Advancing TinyML in IoT: A Holistic System-Level Perspective for Resource-Constrained AI

Author

Listed:
  • Leandro Antonio Pazmiño Ortiz

    (Escuela de Formación de Tecnólogos, Escuela Politécnica Nacional, Quito 170525, Ecuador)

  • Ivonne Fernanda Maldonado Soliz

    (Escuela de Formación de Tecnólogos, Escuela Politécnica Nacional, Quito 170525, Ecuador)

  • Vanessa Katherine Guevara Balarezo

    (Escuela de Formación de Tecnólogos, Escuela Politécnica Nacional, Quito 170525, Ecuador)

Abstract

Resource-constrained devices, including low-power Internet of Things (IoT) nodes, microcontrollers, and edge computing platforms, have increasingly become the focal point for deploying on-device intelligence. By integrating artificial intelligence (AI) closer to data sources, these systems aim to achieve faster responses, reduce bandwidth usage, and preserve privacy. Nevertheless, implementing AI in limited hardware environments poses substantial challenges in terms of computation, energy efficiency, model complexity, and reliability. This paper provides a comprehensive review of state-of-the-art methodologies, examining how recent advances in model compression, TinyML frameworks, and federated learning paradigms are enabling AI in tightly constrained devices. We highlight both established and emergent techniques for optimizing resource usage while addressing security, privacy, and ethical concerns. We then illustrate opportunities in key application domains—such as healthcare, smart cities, agriculture, and environmental monitoring—where localized intelligence on resource-limited devices can have broad societal impact. By exploring architectural co-design strategies, algorithmic innovations, and pressing research gaps, this paper offers a roadmap for future investigations and industrial applications of AI in resource-constrained devices.

Suggested Citation

  • Leandro Antonio Pazmiño Ortiz & Ivonne Fernanda Maldonado Soliz & Vanessa Katherine Guevara Balarezo, 2025. "Advancing TinyML in IoT: A Holistic System-Level Perspective for Resource-Constrained AI," Future Internet, MDPI, vol. 17(6), pages 1-23, June.
    • Handle: RePEc:gam:jftint:v:17:y:2025:i:6:p:257-:d:1677007
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    References listed on IDEAS

    as
    1. Youssef Abadade & Nabil Benamar & Miloud Bagaa & Habiba Chaoui, 2024. "Empowering Healthcare: TinyML for Precise Lung Disease Classification," Future Internet, MDPI, vol. 16(11), pages 1-14, October.
    2. 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.
    3. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
    Full references (including those not matched with items on IDEAS)

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