A Fair Crowd-Sourced Automotive Data Monetization Approach Using Substrate Hybrid Consensus Blockchain

Cyril Naves Samuel (), François Verdier (), Severine Glock and Patricia Guitton-Ouhamou
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Cyril Naves Samuel: Laboratoire d’Electronique, Antennes et Télécommunications/National Centre for Scientific Research Unité Mixte de Recherche, Electronic Department, Campus Sophia Tech, Université Côte d’Azur, 930 Routes Des Colles, 06410 Nice, France
François Verdier: Laboratoire d’Electronique, Antennes et Télécommunications/National Centre for Scientific Research Unité Mixte de Recherche, Electronic Department, Campus Sophia Tech, Université Côte d’Azur, 930 Routes Des Colles, 06410 Nice, France
Severine Glock: Renault Group, Technocentre, 1 Avenue du Golf, 78084 Guyancourt, France
Patricia Guitton-Ouhamou: Renault Group, Technocentre, 1 Avenue du Golf, 78084 Guyancourt, France

Future Internet, 2024, vol. 16, issue 5, 1-27

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.

Keywords: blockchain; substrate; hybrid consensus; Polkadot; authority round; BABE; GRANDPA (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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