The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
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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In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
Citations
Cites
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- 2020, Darknet Convolutional Neural Networks
- 2020, The Microsoft Cognitive Toolkit
- 2020, Scikit-Learn
- 2020, TensorFlow™
- 2020, Embedded Learning Library
- 2020, TinyGo
- 2020, TensorFlow Lite
- 2020, PyTorch
- 2020, Teensyduino
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- 2020, Linaro
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- 2020, uTensor
- 2020, Open neural network exchange
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- doyu, 2019, TinyML as a Service and the challenges of machine learning at the edge
- 2020, Weka-porter
- 2020, MicroML
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Cited by
Sorted by citations
A review on TinyML: State-of-the-art and prospects
Ray, P. P. (2022) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2021.11.019 · Cited by 460
A Comprehensive Survey on TinyML
Abadade, Y., Temouden, A., Bamoumen, H., Benamar, N., Chtouki, Y., & Hafid, A. S. (2023) · IEEE Access · doi:10.1109/access.2023.3294111 · Cited by 366
TinyML Meets IoT: A Comprehensive Survey
Dutta, L., & Bharali, S. (2021) · Internet of Things · doi:10.1016/j.iot.2021.100461 · Cited by 365
Towards the fully automated monitoring of ecological communities
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“Furthermore, specialised microprocessors and models are beginning to emerge to efficiently run in low power settings (Lou et al., 2020; Sanchez-Iborra & Skarmeta, 2020).”
Machine Learning for Microcontroller-Class Hardware: A Review
Saha, S. S., Sandha, S. S., & Srivastava, M. B. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3210773 · Cited by 300
“While prior surveys [3] [4] [6] [7] present a qualitative review”
BackgroundInfluential
Metadata
- 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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