Sustainable Secure Blockchain Assisted AIoT and Green Multiconstraints Supply Chain System

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10258524" target="_blank" >RIV/61989100:27240/25:10258524 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/10918965" target="_blank" >https://ieeexplore.ieee.org/document/10918965</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/JIOT.2025.3548037" target="_blank" >10.1109/JIOT.2025.3548037</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Sustainable Secure Blockchain Assisted AIoT and Green Multiconstraints Supply Chain System

Popis výsledku v původním jazyce

In this era, digital technologies such as artificial intelligence, the Internet of Things (IoT) and blockchain are gaining popularity in research and academia. The supply chain management application is the key to achieving many benefits from AIoT and blockchain technology. However, these technologies have many issues, such as sustainability, a green environment, and multiconstraints (e.g., time, energy, cost, and CO2) for supply chain management applications. This article presents sustainable, secure blockchain-assisted AIoT and green multiconstraint supply chain systems. Initially, we present a secure and sustainable methodology that securely validates the supply chain management system data. For the green environment, we consider the problem a combinatorial problem consisting of different constraints such as time, energy, cost, and carbon dioxide (CO2). To solve this problem for supply chain management jobs, we present a multiconstraint genetic algorithm deep convolutional neural network (MCGA-DCNN) algorithm methodology. The objective is to reduce total processing time, total processing energy consumption, cost, and the CO2 environment as a green environment for supply chain management jobs. The genetic algorithm is evolutionary, where the fitness function optimizes the multiconstraint weights at the runtime based on DCNN and provides the optimal solutions for jobs. Simulation results show that MCGA-DCNN minimized the time, energy, cost, and CO2 and securely validated all transactions for all supply chain management jobs compared to existing schemes.

Název v anglickém jazyce

Sustainable Secure Blockchain Assisted AIoT and Green Multiconstraints Supply Chain System

Popis výsledku anglicky

In this era, digital technologies such as artificial intelligence, the Internet of Things (IoT) and blockchain are gaining popularity in research and academia. The supply chain management application is the key to achieving many benefits from AIoT and blockchain technology. However, these technologies have many issues, such as sustainability, a green environment, and multiconstraints (e.g., time, energy, cost, and CO2) for supply chain management applications. This article presents sustainable, secure blockchain-assisted AIoT and green multiconstraint supply chain systems. Initially, we present a secure and sustainable methodology that securely validates the supply chain management system data. For the green environment, we consider the problem a combinatorial problem consisting of different constraints such as time, energy, cost, and carbon dioxide (CO2). To solve this problem for supply chain management jobs, we present a multiconstraint genetic algorithm deep convolutional neural network (MCGA-DCNN) algorithm methodology. The objective is to reduce total processing time, total processing energy consumption, cost, and the CO2 environment as a green environment for supply chain management jobs. The genetic algorithm is evolutionary, where the fitness function optimizes the multiconstraint weights at the runtime based on DCNN and provides the optimal solutions for jobs. Simulation results show that MCGA-DCNN minimized the time, energy, cost, and CO2 and securely validated all transactions for all supply chain management jobs compared to existing schemes.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20200 - Electrical engineering, Electronic engineering, Information engineering

Projekt

—

Návaznosti

O - Projekt operacniho programu

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

IEEE Internet of Things Journal

ISSN

2327-4662

e-ISSN

—

Svazek periodika

12

Číslo periodika v rámci svazku

19

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

15

Strana od-do

39392-39406

Kód UT WoS článku

001579048500001

EID výsledku v databázi Scopus

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