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A Survey on Data Availability in Layer 2 Blockchain Rollups: Open Challenges and Future Improvements

Author

Listed:
  • Muhammad Bin Saif

    (Department of Computer Science, University of Verona, 37134 Verona, Italy
    These authors contributed equally to this work.)

  • Sara Migliorini

    (Department of Computer Science, University of Verona, 37134 Verona, Italy
    These authors contributed equally to this work.)

  • Fausto Spoto

    (Department of Computer Science, University of Verona, 37134 Verona, Italy
    These authors contributed equally to this work.)

Abstract

Layer 2 solutions have emerged in recent years as a valuable alternative to increase the throughput and scalability of blockchain-based architectures. The three primary types of Layer 2 solutions are state channels, sidechains, and rollups. The rollups are particularly promising, allowing significant improvements in transaction throughput, security, and efficiency, and have been adopted by many real-world projects, such as Polygon and Optimistic. However, the adoption of Layer 2 solutions has led to other challenges, such as the data availability problem, where transaction data processed off-chain must be posted back on the main chain. This is crucial to prevent data withholding attacks and ensure all participants can independently verify the blockchain state. This paper provides a comprehensive survey of existing rollup-based Layer 2 solutions with a focus on the data availability problem and discusses the major advantages and disadvantages of them. Finally, an analysis of open challenges and future research directions is provided.

Suggested Citation

  • Muhammad Bin Saif & Sara Migliorini & Fausto Spoto, 2024. "A Survey on Data Availability in Layer 2 Blockchain Rollups: Open Challenges and Future Improvements," Future Internet, MDPI, vol. 16(9), pages 1-16, August.
    • Handle: RePEc:gam:jftint:v:16:y:2024:i:9:p:315-:d:1467221
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    Cited by:

    1. Mahmoud AlJamal & Rabee Alquran & Ayoub Alsarhan & Mohammad Aljaidi & Mohammad Alhmmad & Wafa’ Q. Al-Jamal & Nasser Albalawi, 2024. "A Robust Machine Learning Model for Detecting XSS Attacks on IoT over 5G Networks," Future Internet, MDPI, vol. 16(12), pages 1-18, December.

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