A Fair Crowd-Sourced Automotive Data Monetization Approach Using Substrate Hybrid Consensus Blockchain.
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| Title: | A Fair Crowd-Sourced Automotive Data Monetization Approach Using Substrate Hybrid Consensus Blockchain. |
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| Authors: | Samuel, Cyril Naves, Verdier, François, Glock, Severine, Guitton-Ouhamou, Patricia |
| Source: | Future Internet; May2024, Vol. 16 Issue 5, p156, 27p |
| Subject Terms: | BLOCKCHAINS, DATA privacy, MONETIZATION, CLOUD computing, CONSORTIA, CLOUD storage |
| Abstract: | This work presents a private consortium blockchain-based automotive data monetization architecture implementation using the Substrate blockchain framework. Architecture is decentralized where crowd-sourced data from vehicles are collectively auctioned ensuring data privacy and security. Smart Contracts and OffChain worker interactions built along with the blockchain make it interoperable with external systems to send or receive data. The work is deployed in a Kubernetes cloud platform and evaluated on different parameters like throughput, hybrid consensus algorithms AuRa and BABE, along with GRANDPA performance in terms of forks and scalability for increasing node participants. The hybrid consensus algorithms are studied in depth to understand the difference and performance in the separation of block creation by AuRa and BABE followed by chain finalization through the GRANDPA protocol. [ABSTRACT FROM AUTHOR] |
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| Database: | Complementary Index |
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