Thermoelectric energy harvesting for internet of things devices using machine learning: A review
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10252726" target="_blank" >RIV/61989100:27240/23:10252726 - isvavai.cz</a>
Result on the web
<a href="https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12259" target="_blank" >https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12259</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1049/cit2.12259" target="_blank" >10.1049/cit2.12259</a>
Alternative languages
Result language
angličtina
Original language name
Thermoelectric energy harvesting for internet of things devices using machine learning: A review
Original language description
Initiatives to minimise battery use, address sustainability, and reduce regular maintenance have driven the challenge to use alternative power sources to supply energy to devices deployed in Internet of Things (IoT) networks. As a key pillar of fifth generation (5G) and beyond 5G networks,IoT is estimated to reach 42 billion devices by the year 2025. Thermoelectric generators (TEGs) are solid state energy harvesters which reliably and renewably convert thermal energy into electrical energy. These devices are able to recover lost thermal energy, produce energy in extreme environments, generate electric power in remote areas, and power micro-sensors. Applying the state of the art, the authorspresent a comprehensive review of machine learning (ML) approaches applied in combination with TEG-powered IoT devices to manage and predict available energy. The application areas of TEG-driven IoT devices that exploit as a heat source the temperature differences found in the environment, biological structures, machines, and other technologies are summarised. Based on detailed research of the state of the art in TEG-powered devices, the authors investigated the research challenges, applied algorithms and application areas of this technology. The aims of the research were to devise new energy prediction and energy management systems based on ML methods, create supervised algorithms which better estimate incoming energy, and develop unsupervised and semi-supervised approaches which provide adaptive and dynamic operation. The review results indicate that TEGs are a suitable energy harvesting technology for low-power applications through their scalability, usability in ubiquitous temperature difference scenarios, and long operating lifetime. However, TEGs also have low energy efficiency (around 10%) and require a relatively constant heat source.
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
20201 - Electrical and electronic engineering
Result continuities
Project
<a href="/en/project/FW03010194" target="_blank" >FW03010194: Development of a System for Monitoring and Evaluation of Selected Risk Factors of Physical Workload in the Context of Industry 4.0.</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
CAAI Transactions on Intelligence Technology
ISSN
2468-6557
e-ISSN
2468-2322
Volume of the periodical
Neuveden
Issue of the periodical within the volume
2023-07-27
Country of publishing house
US - UNITED STATES
Number of pages
21
Pages from-to
—
UT code for WoS article
001026580300001
EID of the result in the Scopus database
2-s2.0-85165212008