Intelligent and Efficient IoT Through the Cooperation of TinyML and Edge Computing
Open accessAbstract
The coordinated integration of heterogeneous TinyML-enabled elements in highly distributed Internet of Things (IoT) environments paves the way for the development of truly intelligent and context-aware applications. In this work, we propose a hierarchical ensemble TinyML scheme that permits system-wide decisions by considering the individual decisions made by the IoT elements deployed in a certain scenario. A two-layered TinyML-based edge computing solution has been implemented and evaluated in a real smart-agriculture use case, permitting to save wireless transmissions, reduce energy consumption and response times, at the same time strengthening data privacy and security.
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The device in [125] helps farmers monitor greenhouse gases coming from the soil.
“This device helps farmers to monitor greenhouse gases coming from the soil.”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [125]
Sections 4.2 and 4.3.1 describe sensors used to assess plants’ moisture and nutrient needs and guide irrigation or fertigation. Section 4.3.1 specifies the monitored parameters as air temperature, soil moisture, soil pH, and soil electrical conductivity—not greenhouse gases emitted from soil. No page numbers are available in our copy. (LLM, conf=0.86)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredAuthors in [125] proposed a low-cost device that aims to recognize different types of gas (NH3, CH4, N2O).
“The authors in [125] proposed a low+cost device that aims to recognize different types of gas (NH3, CH4, N2O).”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [125]
Section 4.3.1 describes the proposed sensors as measuring air temperature and soil conditions to decide whether plants need irrigation or fertigation. Section 4.2 likewise identifies the proposed application as greenhouse irrigation, not recognition of NH3, CH4, or N2O. (LLM, conf=0.85)
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Flagged first, then by citations
Research works printed but not linked: 2
- de Prado, M., Donze, R., Capotondi, A., Rusci, M., Monnerat, S., Benini, L., Pazos, N. (2020). Robust Navigation with TinyML for Autonomous Mini-Vehicles. arXiv preprint: arXiv:2007.00302. · arXiv
- Warden, P., Situnayake, D. (2019). TinyML: Machine Learning with Tensorflow Lite on Arduino and Ultra-Low- Power Microcontrollers. O'Reilly UK Ltd. 978-1492052043.
Cited by
Sorted by citations
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 · see citing works
“The authors in [125] proposed a system where IoT devices and edge nodes work together to monitor the status of the plants in a greenhouse to make decisions about the operation of sprinklers.”
Background
- Authors
- Sanchez-Iborra, R., Zoubir, A., Hamdouchi, A., Idri, A., & Skarmeta, A.
- Affiliations
- Université Mohammed VI Polytechnique (MA)
- Mohammed V University (MA)
- Venue
- Informatica
- Publisher
- Vilnius University Press
- Published
- 2023
- Pages
- 147-168
- ISSN
- 0868-4952 (ISSN-L)
- 1822-8844
- OpenAlex
- W4320148327
- Cited by
- 28
- References
- 44
- Citations by year
- 2026: 5 · 2025: 11 · 2024: 8 · 2023: 4
- FWCI
- 3.11
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Informatica (publishedVersion, cc-by)
- Funders
- Ministerio de Ciencia, Innovación y Universidades (PID2020-112675RB-C44, PID2020)
- European Commission (101070473, 857202)
- 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
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Keywords
- TinyML, edge computing, Internet of Things, hierarchical ensemble, decentralized decision-making, smart agriculture, energy efficiency, data privacy, data security, context-aware systems
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