Blockchain technology: Applied to big data in collaborative edges

End users are now encircled by an ever-increasing volume of information from edge devices relevant to a range of stakeholders, thanks to the introduction of edge computing in a variety of application domains. However, because of their distrust, these edge devices are unable to communicate significan...

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Bibliographic Details
Published in:Measurement. Sensors Vol. 24; p. 100521
Main Authors: Saluja, Kamal, Gupta, Sunil, Vajpayee, Amit, Debnath, Sanjoy Kumar, Bansal, Ankit, Sharma, Neha
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.12.2022
Elsevier
Subjects:
Big data
Block chain
Collaborative edges
Consensus mechanism
Transaction offloading
Collaborative edges
Big data
Block chain
Transaction offloading
Consensus mechanism
ISSN:2665-9174, 2665-9174
Online Access:Get full text
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Summary:End users are now encircled by an ever-increasing volume of information from edge devices relevant to a range of stakeholders, thanks to the introduction of edge computing in a variety of application domains. However, because of their distrust, these edge devices are unable to communicate significant amounts of data. The non-repudiation and non-tampering features of the block chain are used in this study to provide trust in collaborative edges. To address the limited processing capabilities of edge devices, create a block chain-based huge data sharing architecture in cooperative edges. Then, for high computational reduction, propose a Proof-of-Collaboration consensus technique, in which edge devices participate in block formation by giving their PoC credits. Furthermore, a useless transaction filter technique was proposed for transaction offloading, drastically decreasing the block chain's storage space in edges. Comprehensive tests are carried out to illustrate our proposal's better performance. Using this learning opportunity an environment friendly block chain sustainable infrastructure be created for developing countries.
ISSN:2665-9174
2665-9174
DOI:10.1016/j.measen.2022.100521