TinyML-Enabled Frugal Smart Objects: Challenges and Opportunities
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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TinyML processes data locally in microcontroller devices that have low cost and can apply AI locally on the device itself to process the data with high performance.
“TinyML processes data locally in microcontroller devices that have low cost and can apply AI locally on the device itself to process the data with high performance [23].”
From TinyML: Enabling of Inference Deep Learning Models on Ultra-Low-Power IoT Edge Devices for AI Applications (2022) · cites this paper as [23]
When data is transmitted to the cloud, it is prone to transmission errors, cyber-attacks such as eavesdropping or man-in-the-middle issues, and may be compromised or lost.
“When the data is transmitted to the cloud, it is prone to transmission errors, cyber-attacks e.g., eavesdropping or man-in-the-middle issues. Thereby, the transmitted data may be compromised or lost.”
From TinyML: Enabling of Inference Deep Learning Models on Ultra-Low-Power IoT Edge Devices for AI Applications (2022) · cites this paper as [23]
IoT devices that work on MCUs rely on batteries or energy harvesting and consume less energy compared to powerful processors and GPUs.
“Since IoT devices that work on MCUs rely on batteries or even energy harvesting, they consume less energy in comparison with powerful processors and Graphics Processing Units (GPUs) that demand a great amount of power.”
From TinyML: Enabling of Inference Deep Learning Models on Ultra-Low-Power IoT Edge Devices for AI Applications (2022) · cites this paper as [23]
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- Authors
- Sanchez-Iborra, R., & Skarmeta, A. F.
- Venue
- IEEE Circuits and Systems Magazine
- Published
- 2020
- Volume
- 20
- Issue
- 3
- Pages
- 4-18
- ISSN
- 0163-6812 (ISSN-L)
- 1531-636X
- 1558-0830
- 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
- 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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