Sustainable Secure Blockchain Assisted AIoT and Green Multiconstraints Supply Chain System
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Sustainable Secure Blockchain Assisted AIoT and Green Multiconstraints Supply Chain System
Original language description
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.
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
20200 - Electrical engineering, Electronic engineering, Information engineering
Result continuities
Project
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Continuities
O - Projekt operacniho programu
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Internet of Things Journal
ISSN
2327-4662
e-ISSN
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Volume of the periodical
12
Issue of the periodical within the volume
19
Country of publishing house
US - UNITED STATES
Number of pages
15
Pages from-to
39392-39406
UT code for WoS article
001579048500001
EID of the result in the Scopus database
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