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<p>The increasing integration of IoT and big data in smart dairy farming creates challenges in securing data and managing access control. This study introduces a security framework combining big data analytics, blockchain, and expressive ontology-based access control to protect sensitive agricultural data. The model uses Gated Recurrent Units (GRU) and Fully Convolutional Networks (FCN) for trust evaluation of IoT devices, improving anomaly detection and threat mitigation. Lightweight encryption, asymmetric key exchange, and SHA-256 hashing secure communication at the edge. An ontology-based decentralized oracle mechanism ensures dynamic access control using Verifiable Credentials (VCs) and Decentralized Identifiers (DIDs). The framework, enhanced by edge computing, reduces latency and data load between nodes and cloud infrastructure. Findings show a 27% improvement in transaction throughput, demonstrating the system’s efficiency, scalability, and enhanced security. This solution advances secure, intelligent systems in precision agriculture, addressing data authentication and access control challenges.</p>

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Big data authentication and security management in smart dairy farming using blockchain with expressive ontology-based access control mechanism

  • 期刊论文

  • 发表日期：

    2026年6月17日

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  • V. Manibabu,
  • M. Gomathy

摘要

The increasing integration of IoT and big data in smart dairy farming creates challenges in securing data and managing access control. This study introduces a security framework combining big data analytics, blockchain, and expressive ontology-based access control to protect sensitive agricultural data. The model uses Gated Recurrent Units (GRU) and Fully Convolutional Networks (FCN) for trust evaluation of IoT devices, improving anomaly detection and threat mitigation. Lightweight encryption, asymmetric key exchange, and SHA-256 hashing secure communication at the edge. An ontology-based decentralized oracle mechanism ensures dynamic access control using Verifiable Credentials (VCs) and Decentralized Identifiers (DIDs). The framework, enhanced by edge computing, reduces latency and data load between nodes and cloud infrastructure. Findings show a 27% improvement in transaction throughput, demonstrating the system’s efficiency, scalability, and enhanced security. This solution advances secure, intelligent systems in precision agriculture, addressing data authentication and access control challenges.