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Machine Learning for Blockchain and IoT Systems in Smart Cities: A Survey

Author

Listed:
  • Elias Dritsas

    (Athena Research and Innovation Center, Industrial Systems Institute (ISI), 26504 Patras, Greece)

  • Maria Trigka

    (Athena Research and Innovation Center, Industrial Systems Institute (ISI), 26504 Patras, Greece)

Abstract

The integration of machine learning (ML), blockchain, and the Internet of Things (IoT) in smart cities represents a pivotal advancement in urban innovation. This convergence addresses the complexities of modern urban environments by leveraging ML’s data analytics and predictive capabilities to enhance the intelligence of IoT systems, while blockchain provides a secure, decentralized framework that ensures data integrity and trust. The synergy of these technologies not only optimizes urban management but also fortifies security and privacy in increasingly connected cities. This survey explores the transformative potential of ML-driven blockchain-IoT ecosystems in enabling autonomous, resilient, and sustainable smart city infrastructure. It also discusses the challenges such as scalability, privacy, and ethical considerations, and outlines possible applications and future research directions that are critical for advancing smart city initiatives. Understanding these dynamics is essential for realizing the full potential of smart cities, where technology enhances not only efficiency but also urban sustainability and resilience.

Suggested Citation

  • Elias Dritsas & Maria Trigka, 2024. "Machine Learning for Blockchain and IoT Systems in Smart Cities: A Survey," Future Internet, MDPI, vol. 16(9), pages 1-15, September.
    • Handle: RePEc:gam:jftint:v:16:y:2024:i:9:p:324-:d:1472902
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    References listed on IDEAS

    as
    1. Sisi Zhou & Kuanching Li & Lijun Xiao & Jiahong Cai & Wei Liang & Arcangelo Castiglione, 2023. "A Systematic Review of Consensus Mechanisms in Blockchain," Mathematics, MDPI, vol. 11(10), pages 1-27, May.
    2. Esmat, Ayman & de Vos, Martijn & Ghiassi-Farrokhfal, Yashar & Palensky, Peter & Epema, Dick, 2021. "A novel decentralized platform for peer-to-peer energy trading market with blockchain technology," Applied Energy, Elsevier, vol. 282(PA).
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