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<p>The rapid development of Internet of Vehicles presents critical challenges in secure storage and sharing of massive vehicular data. Traditional blockchain’s full-node storage paradigm incurs prohibitive overhead in resource-constrained vehicular environments, while existing coded blockchain schemes struggle with IoV’s dynamic network topology and fluctuating node population. This paper proposes an adaptive rateless coded blockchain architecture tailored for dynamic IoV environments, employing systematic Raptor codes for block compression, VRF-based lightweight consensus, and dynamic parameter adjustment that adapts redundancy in real-time without re-encoding existing blocks. The scheme achieves an average compression ratio of 97.88% while ensuring decoding failure probability below <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(10^{-12}\)</EquationSource> </InlineEquation>, significantly reducing storage overhead and demonstrating robustness in dynamic vehicular networks.</p>

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Adaptive rateless coded blockchain for dynamic IoV scenarios

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月26日

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  • Shuo Zhang,
  • Yuan-Yuan Song,
  • Ming-Gang Zheng,
  • Jie Li,
  • Han Liang

摘要

The rapid development of Internet of Vehicles presents critical challenges in secure storage and sharing of massive vehicular data. Traditional blockchain’s full-node storage paradigm incurs prohibitive overhead in resource-constrained vehicular environments, while existing coded blockchain schemes struggle with IoV’s dynamic network topology and fluctuating node population. This paper proposes an adaptive rateless coded blockchain architecture tailored for dynamic IoV environments, employing systematic Raptor codes for block compression, VRF-based lightweight consensus, and dynamic parameter adjustment that adapts redundancy in real-time without re-encoding existing blocks. The scheme achieves an average compression ratio of 97.88% while ensuring decoding failure probability below \(10^{-12}\) , significantly reducing storage overhead and demonstrating robustness in dynamic vehicular networks.