Latency performance modelling in hyperledger fabric blockchain: Challenges and directions with an IoT perspective

Enare Abang, J ORCID: https://orcid.org/0009-0002-5836-4176, Takruri, H ORCID: https://orcid.org/0000-0001-6677-9190, Al-Zaidi, R ORCID: https://orcid.org/0000-0002-2198-7794 and Al-Khalidi, M ORCID: https://orcid.org/0000-0002-1655-8514 (2024) Latency performance modelling in hyperledger fabric blockchain: Challenges and directions with an IoT perspective. Internet of Things, 26. 101217. ISSN 2542-6605

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Abstract

Blockchain is a decentralized and distributed ledger technology that enables secure and transparent recording of transactions across multiple participants. Hyperledger Fabric (HLF), a permissioned blockchain, enhances performance through its modular design and pluggable consensus. However, integrating HLF with enterprise applications introduces latency challenges. Researchers have proposed numerous latency performance modelling techniques to address this issue. These studies contribute to a deeper understanding of HLF's latency by employing various modelling approaches and exploring techniques to improve network latency. However, existing HLF latency modelling studies lack an analysis of how these research efforts apply to specific use cases. This paper examines existing research on latency performance modelling in HLF and the challenges of applying these models to HLF-enabled Internet of Things (IoT) use cases. We propose a novel set of criteria for evaluating HLF latency performance modelling and highlight key HLF parameters that influence latency, aligning them with our evaluation criteria. We then classify existing papers based on their focus on latency modelling and the criteria they address. Additionally, we provide a comprehensive overview of latency performance modelling from various researchers, emphasizing the challenges in adapting these models to HLF-enabled IoT blockchain within the framework of our evaluation criteria. Finally, we suggest directions for future research and highlight open research questions for further exploration.