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

Leandro Antonio Pazmiño Ortiz (), Ivonne Fernanda Maldonado Soliz and Vanessa Katherine Guevara Balarezo
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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

Future Internet, 2025, vol. 17, issue 6, 1-23

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.

Keywords: artificial intelligence; IoT; edge computing; resource-constrained devices; TinyML; federated learning; model compression; security and privacy (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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