Computational Cost and Implementation Analysis of a Wavelet-Based Edge Computing Method for Energy-Harvesting Industrial IoT Sensors
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10256406" target="_blank" >RIV/61989100:27240/24:10256406 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/abstract/document/10806679" target="_blank" >https://ieeexplore.ieee.org/abstract/document/10806679</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ACCESS.2024.3519715" target="_blank" >10.1109/ACCESS.2024.3519715</a>
Alternative languages
Result language
angličtina
Original language name
Computational Cost and Implementation Analysis of a Wavelet-Based Edge Computing Method for Energy-Harvesting Industrial IoT Sensors
Original language description
The rapid advancement of Industrial Internet of Things (IIoT) has heightened the need for efficient data processing and transmission, particularly in energy-constrained environments. This study introduces a novel wavelet-based edge computing methodology designed specifically for low-power IIoT sensors using energy harvesting. Unlike existing implementations that rely on computationally complex instructions, this approach optimizes the wavelet transform (WT) for resource-limited microcontrollers (MCUs) without sacrificing data quality. By leveraging a lightweight assembly-level WT implementation, the proposed solution significantly reduces computational costs and energy consumption. A comprehensive analysis performed on ARM Cortex-M7 MCU on an industrial vibration dataset demonstrates energy savings of assembly language (ASM) up to 87% with discrete wavelet transforms (DWT) and 32.1% with fast wavelet transforms (FWT), compared to C-based implementations. This work is distinct in its ability to dynamically adjust data transmission levels based on available energy, ensuring robust operation in batteryless IIoT environments. Moreover, the method offers flexibility in signal reconstruction, supporting scalable compression ratios and facilitating long-term predictive maintenance applications, making it a pioneering step in sustainable industrial monitoring. © 2013 IEEE.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20202 - Communication engineering and systems
Result continuities
Project
<a href="/en/project/EF16_019%2F0000867" target="_blank" >EF16_019/0000867: Research Centre of Advanced Mechatronic Systems</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Access
ISSN
2169-3536
e-ISSN
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Volume of the periodical
12
Issue of the periodical within the volume
18 December 2024
Country of publishing house
US - UNITED STATES
Number of pages
15
Pages from-to
193607-193621
UT code for WoS article
001383065500003
EID of the result in the Scopus database
2-s2.0-85212648981