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Trustworthy Transaction Spreading Using Node Reliability Estimation in IoT Blockchain Networksoa mark
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Publication Year
2022-09-01
Publisher
MDPI
Citation
Applied Sciences (Switzerland), Vol.12
Keyword
GCNIoT blockchainIPFSQ-learning
All Science Classification Codes (ASJC)
Materials Science (all)InstrumentationEngineering (all)Process Chemistry and TechnologyComputer Science ApplicationsFluid Flow and Transfer Processes
Abstract
Featured Application: Blockchain-powered IoT Applications, such as agricultural applications, logistics, and smart factory data management. Blockchain network architecture is a promising technology for constructing highly secure Internet of Things (IoT) networks. IoT networks typically comprise various sensors and actuators. Blockchain network technology can be applied to secure control robots in smart factories or reliable drone deliveries in smart cities. The wide spread of transactions and shared smart contracts across blockchain networks guarantees ultimate network security. A typical wired blockchain network maintains sufficient redundancy within a stable configuration. However, IoT blockchain networks exhibit unavoidable instability. The dynamic configuration changes caused by flexible node membership make it impossible to achieve the same level of redundancy as a stable network. A trustworthy transaction spreading method provides practical transaction sharing for dynamic IoT networks. We propose a Q-learning framework and a graph convergence network (GCN) to search for the proper spreading path of each transaction. The proposed Q-learning framework determines the next spreading hop using node features. The GCN determines the reliable area based on the Q-learning results. The discovered reliable area guides the proper spreading path of transactions to the destination node. In addition, the proposed trustworthy transaction spreading was implemented over an InterPlanetary File system (IPFS). The IPFS-powered experiments confirmed the practicability of the proposed transaction spreading mechanism.
ISSN
2076-3417
Language
eng
URI
https://dspace.ajou.ac.kr/dev/handle/2018.oak/32909
DOI
https://doi.org/10.3390/app12178737
Fulltext

Type
Article
Funding
This work was partially supported by a grant from the Institute for Information and Communications Technology Promotion (IITP) funded by the Korean Government (Ministry of Science and Information Technology) (Manufacturing S/W platform based on digital twin and robotic process automation) under grant 2021000292. This work was also supported by a National Research Foundation of Korea (NRF) grant from the Korean Government (Ministry of Science and Information Technology) under Grant 2020R1F1A1049553.
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Kim, Jae-Hoon김재훈
Department of Industrial Engineering
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