Cited by 296IoT and Edge/Fog ComputingInternet of Things and AIIoT Networks and Protocols

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TinyML-Enabled Frugal Smart Objects: Challenges and Opportunities

Sanchez-Iborra, Ramon, Skarmeta, Antonio F.·2020·IEEE Circuits and Systems Magazine

DOI 10.1109/mcas.2020.3005467

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Abstract

The TinyML paradigm proposes to integrate Machine Learning (ML)-based mechanisms within small objects powered by Microcontroller Units (MCUs). This paves the way for the development of novel applications and services that do not need the omnipresent processing support from the cloud, which is power consuming and involves data security and privacy risks. In this work, a comprehensive review of the novel TinyML ecosystem is provided. The related challenges and opportunities are identified and the potential services that will be enabled by the development of truly smart frugal objects are discussed. As a main contribution of this paper, a detailed survey of the available TinyML frameworks for integrating ML algorithms within MCUs is provided. Besides, aiming at illustrating the given discussion, a real case study is presented. Concretely, we propose a multi-Radio Access Network (RAT) architecture for smart frugal objects. The issue of selecting the most adequate communication interface for sending sporadic messages considering both the status of the device and the characteristics of the data to be sent is addressed. To this end, several TinyML frameworks are evaluated and the performances of a number of ML algorithms embedded in an Arduino Uno board are analyzed. The attained results reveal the validity of the TinyML approach, which successfully enables the integration of techniques such as Neural Networks (NNs), Support Vector Machine (SVM), decision trees, or Random Forest (RF) in frugal objects with constrained hardware resources. The outcomes also show promising results in terms of algorithm's accuracy and computation performance.

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Research works printed but not linked: 29

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2020, The Microsoft Cognitive Toolkit
2020, Scikit-Learn
2020, TensorFlow™
2020, Embedded Learning Library
2020, TinyGo
2020, TensorFlow Lite
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2020, MBED Platform
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Authors: Sanchez-Iborra, R., & Skarmeta, A. F.
Venue: IEEE Circuits and Systems Magazine
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2020
Volume: 20
Issue: 3
Pages: 4-18
ISSN: 0163-6812 (ISSN-L)
1531-636X
1558-0830
DOI: 10.1109/mcas.2020.3005467
OpenAlex: W3067862503
Cited by: 295
References: 36
Citations by year: 2026: 26 · 2025: 38 · 2024: 57 · 2023: 67 · 2022: 63 · 2021: 40 · 2020: 4
FWCI: 20.44
Type: journal article
Language: en
Open access: closed
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Circuits and Systems Magazine (publishedVersion)
Funders: European Commission IoTCrawler (779852)
European Commission CyberSec4Europe (830929)
European Commission Fed4IoT (814918)
Spanish Ministry of Science Innovation and Universities - PERSEIDES (TIN2017-86885-R)
Topics: Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
Physical Sciences › Engineering › Electrical and Electronic Engineering › IoT Networks and Protocols
Keywords: TinyML, microcontroller, embedded machine learning, Arduino Uno, artificial neural networks, support vector machine, random forest, decision tree, edge computing

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TinyML-Enabled Frugal Smart Objects: Challenges and Opportunities

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