Computational Cost and Implementation Analysis of a Wavelet-Based Edge Computing Method for Energy-Harvesting Industrial IoT Sensors

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Computational Cost and Implementation Analysis of a Wavelet-Based Edge Computing Method for Energy-Harvesting Industrial IoT Sensors

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Computational Cost and Implementation Analysis of a Wavelet-Based Edge Computing Method for Energy-Harvesting Industrial IoT Sensors

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20202 - Communication engineering and systems

Projekt

<a href="/cs/project/EF16_019%2F0000867" target="_blank" >EF16_019/0000867: Centrum výzkumu pokročilých mechatronických systémů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Access

ISSN

2169-3536

e-ISSN

—

Svazek periodika

12

Číslo periodika v rámci svazku

18 December 2024

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

15

Strana od-do

193607-193621

Kód UT WoS článku

001383065500003

EID výsledku v databázi Scopus

2-s2.0-85212648981