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Securing Advanced Metering Infrastructure Using Blockchain for Effective Energy Trading

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Applied Machine Learning and Data Analytics (AMLDA 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1818))

Abstract

To effectively regulate the energy supply, Advanced Metering Infrastructure (AMI) deployment is gaining momentum in various parts of the world. Energy distributors, service providers, and consumers must work together to address several issues. All the transactions need to be documented properly and securely. Third parties can be trusted in those transactions by using blockchain. With the deployment of Advanced Metering Infrastructure (AMI) and distributed ledgers, blockchain may aid in safeguarding and facilitating the movement of data. This paper discusses the viability of utilizing blockchain for advanced metering infrastructure, along with the security risks and threat landscape. For the exchange of energy, we have proposed an Ethereum-based blockchain model, and results are discussed for the scenario when an independent house’s smart meter and distribution system operator trades energy.

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Authors and Affiliations

  1. VIT Bhopal University, Bhopal, India

    Yuvraj Singh, Agni Datta & Shishir Kumar Shandilya

  2. Sagar Institute of Research and Technology, Bhopal, India

    Smita Shandilya

Authors
  1. Yuvraj Singh
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  2. Agni Datta
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  3. Smita Shandilya
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  4. Shishir Kumar Shandilya
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Corresponding author

Correspondence to Yuvraj Singh .

Editor information

Editors and Affiliations

  1. Vardhaman College of Engineering, Hyderabad, India

    M. A. Jabbar

  2. Autonomous University of Tamaulipas, Ciudad Victoria, Mexico

    Fernando Ortiz-Rodríguez

  3. Autonomous University of Tamaulipas, Ciudad Victoria, Mexico

    Sanju Tiwari

  4. University of Paris-Est Creteil, Vitry sur Seine, France

    Patrick Siarry

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Singh, Y., Datta, A., Shandilya, S., Shandilya, S.K. (2023). Securing Advanced Metering Infrastructure Using Blockchain for Effective Energy Trading. In: Jabbar, M.A., Ortiz-Rodríguez, F., Tiwari, S., Siarry, P. (eds) Applied Machine Learning and Data Analytics. AMLDA 2022. Communications in Computer and Information Science, vol 1818. Springer, Cham. https://doi.org/10.1007/978-3-031-34222-6_3

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  • DOI: https://doi.org/10.1007/978-3-031-34222-6_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-34221-9

  • Online ISBN: 978-3-031-34222-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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