Fuzzy energy management strategies for energy harvesting IoT nodes based on a digital twin concept

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10256765" target="_blank" >RIV/61989100:27240/25:10256765 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0167739X25000123" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0167739X25000123</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.future.2025.107717" target="_blank" >10.1016/j.future.2025.107717</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Fuzzy energy management strategies for energy harvesting IoT nodes based on a digital twin concept

Popis výsledku v původním jazyce

This study presents a cloud-assisted energy management strategy for energy harvesting Internet-of-Things (IoT) nodes, using a novel digital twin (DT) concept for dynamic optimization of IoT node behavior. The system is built upon a fuzzy-rule-based controller optimized through a differential evolution (DE) algorithm. DE is particularly well-suited for this application, as it is capable of optimizing the controller without requiring gradient information, allowing it to efficiently navigate the complex, nonlinear characteristics of IoT energy management problems. The optimization process tunes nine key fuzzy input coefficients to create an energy-efficient control strategy. The DT concept serves as a virtual replica of the physical IoT node, continuously synchronizing real-time data from sensors and other internal parameters, including energy harvesting rates and component health. Through this real-time feedback loop, the DT enables predictive adjustments to the control system, increasing the longevity and reliability of the IoT devices in harsh and changing environments. Compared to traditional energy management strategies, the proposed method improves energy utilization by 11%, leveraging four years of solar data collected from multiple geographical locations. Moreover, the system achieves a 12% increase in successful transmissions, ensuring greater data availability in the cloud while minimizing device failures and optimizing the use of available energy. The DT concept allows the system to simulate and predict IoT node behavior under various conditions, continuously refining the energy management strategy. This ensures not only optimal energy efficiency but also accounts for component degradation over time, offering long-term adaptability and minimizing the need for manual intervention. Thus, the synergy between the DT concept and DE optimization offers a powerful, scalable solution for managing energy-constrained IoT networks, surpassing conventional expert-designed strategies in both adaptability and performance. © 2025 The Authors

Název v anglickém jazyce

Fuzzy energy management strategies for energy harvesting IoT nodes based on a digital twin concept

Popis výsledku anglicky

This study presents a cloud-assisted energy management strategy for energy harvesting Internet-of-Things (IoT) nodes, using a novel digital twin (DT) concept for dynamic optimization of IoT node behavior. The system is built upon a fuzzy-rule-based controller optimized through a differential evolution (DE) algorithm. DE is particularly well-suited for this application, as it is capable of optimizing the controller without requiring gradient information, allowing it to efficiently navigate the complex, nonlinear characteristics of IoT energy management problems. The optimization process tunes nine key fuzzy input coefficients to create an energy-efficient control strategy. The DT concept serves as a virtual replica of the physical IoT node, continuously synchronizing real-time data from sensors and other internal parameters, including energy harvesting rates and component health. Through this real-time feedback loop, the DT enables predictive adjustments to the control system, increasing the longevity and reliability of the IoT devices in harsh and changing environments. Compared to traditional energy management strategies, the proposed method improves energy utilization by 11%, leveraging four years of solar data collected from multiple geographical locations. Moreover, the system achieves a 12% increase in successful transmissions, ensuring greater data availability in the cloud while minimizing device failures and optimizing the use of available energy. The DT concept allows the system to simulate and predict IoT node behavior under various conditions, continuously refining the energy management strategy. This ensures not only optimal energy efficiency but also accounts for component degradation over time, offering long-term adaptability and minimizing the need for manual intervention. Thus, the synergy between the DT concept and DE optimization offers a powerful, scalable solution for managing energy-constrained IoT networks, surpassing conventional expert-designed strategies in both adaptability and performance. © 2025 The Authors

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

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

2025

Kód důvěrnosti údajů

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

Název periodika

Future Generation Computer Systems 22

ISSN

0167-739X

e-ISSN

—

Svazek periodika

166

Číslo periodika v rámci svazku

May 2025

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

12

Strana od-do

nestránkováno

Kód UT WoS článku

001407317300001

EID výsledku v databázi Scopus

2-s2.0-85215440663