IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v16y2024i5p156-d1386409.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    Renault Group, Technocentre, 1 Avenue du Golf, 78084 Guyancourt, 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)

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.

Suggested Citation

  • Cyril Naves Samuel & François Verdier & Severine Glock & Patricia Guitton-Ouhamou, 2024. "A Fair Crowd-Sourced Automotive Data Monetization Approach Using Substrate Hybrid Consensus Blockchain," Future Internet, MDPI, vol. 16(5), pages 1-27, April.
    • Handle: RePEc:gam:jftint:v:16:y:2024:i:5:p:156-:d:1386409
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/16/5/156/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/16/5/156/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:16:y:2024:i:5:p:156-:d:1386409. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.