Research Article
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Year 2025, Volume: 5 Issue: 2, 132 - 144
https://doi.org/10.57019/jmv.1623586

Abstract

References

  • Dubey, R., Gunasekaran, A., Childe, S. J., Fosso Wamba, S., Roubaud, D., & Foropon, C. (2021). Empirical investigation of data analytics capability and organizational flexibility as complements to supply chain resilience. International Journal of Production Research, 59(1), 110–128.
  • Moosavi, J., Naeni, L. M., Fathollahi-Fard, A. M., & Fiore, U. (2021). Blockchain in supply chain management: A review, bibliometric, and network analysis. Environmental Science and Pollution Research, 1–15.
  • Dubey, R., Gunasekaran, A., Bryde, D. J., Dwivedi, Y. K., & Papadopoulos, T. (2020). Blockchain technology for enhancing swift-trust, collaboration and resilience within a humanitarian supply chain setting. International Journal of Production Research, 58(11), 3381–3398.
  • Queiroz, M. M., Telles, R., & Bonilla, S. H. (2020). Blockchain and supply chain management integration: A systematic review of the literature. Supply Chain Management: An International Journal, 25(2), 241–254.
  • Tu, M. (2018). An exploratory study of internet of things (IoT) adoption intention in logistics and supply chain management: A mixed research approach. The International Journal of Logistics Management, 29(1), 131–151.
  • Parker, S., Wu, Z., & Christofides, P. D. (2023). Cybersecurity in process control, operations, and supply chain. Computers & Chemical Engineering, 108169.
  • Simon, J., & Omar, A. (2020). Cybersecurity investments in the supply chain: Coordination and a strategic attacker. European Journal of Operational Research, 282(1), 161–171.
  • Gordon, L. A., Loeb, M. P., Lucyshyn, W., & Zhou, L. (2015). The impact of information sharing on cybersecurity underinvestment: A real options perspective. Journal of Accounting and Public Policy, 34(5), 509–519.
  • Huang, C. D., Hu, Q., & Behara, R. S. (2008). An economic analysis of the optimal information security investment in the case of a risk-averse firm. International Journal of Production Economics, 114(2), 793–804.
  • Wu, Y., Feng, G., Wang, N., & Liang, H. (2015). Game of information security investment: Impact of attack types and network vulnerability. Expert Systems with Applications, 42(15–16), 6132–6146.
  • Vanov, D. (2018). Revealing interfaces of supply chain resilience and sustainability: A simulation study. International Journal of Production Research, 56(10), 3507–3523.
  • Ali, I., & Gölgeci, I. (2019). Where is supply chain resilience research heading? A systematic and co-occurrence analysis. International Journal of Physical Distribution & Logistics Management.
  • Ponomarov, S. Y., & Holcomb, M. C. (2009). Understanding the concept of supply chain resilience. The International Journal of Logistics Management.
  • Tukamuhabwa, B. R., Stevenson, M., Busby, J., & Zorzini, M. (2015). Supply chain resilience: Definition, review and theoretical foundations for further study. International Journal of Production Research, 53(18), 5592–5623.
  • Beck, J., Birkel, H., Spieske, A., & Gebhardt, M. (2023). Will the blockchain solve the supply chain resilience challenges? Insights from a systematic literature review. Computers & Industrial Engineering, 185, 109623.
  • Bayramova, A., Edwards, D. J., & Roberts, C. (2021). The role of blockchain technology in augmenting supply chain resilience to cybercrime. Buildings, 11(7), 283.
  • Azmi, N. A., Sweis, G., Sweis, R., & Sammour, F. (2022). Exploring implementation of blockchain for the supply chain resilience and sustainability of the construction industry in Saudi Arabia. Sustainability, 14(11), 6427.
  • Fadi, O., Bahaj, A., Zkik, K., El Ghazi, A., Ghogho, M., & Boulmalf, M. (2025). Smart contract anomaly detection: The contrastive learning paradigm. Computer Networks, 111121.
  • Zkik, K., Sebbar, A., Fadi, O., Mustapha, O., & Belhadi, A. (2023). A graph neural network approach for detecting smart contract anomalies in collaborative economy platforms based on blockchain technology. In 2023 9th International Conference on Control, Decision and Information Technologies (CoDIT) (pp. 1285–1290). IEEE.
  • Sengupta, J., Ruj, S., & Bit, S. D. (2020). A comprehensive survey on attacks, security issues and blockchain solutions for IoT and IIoT. Journal of Network and Computer Applications, 149, 102481.
  • Novo, O. (2018). Blockchain meets IoT: An architecture for scalable access management in IoT. IEEE Internet of Things Journal, 5(2), 1184–1195.
  • Aggarwal, S., Chaudhary, R., Aujla, G. S., Kumar, N., Choo, K.-K. R., & Zomaya, A. Y. (2019). Blockchain for smart communities: Applications, challenges and opportunities. Journal of Network and Computer Applications, 144, 13–48.
  • Berger, C., & Reiser, H. P. (2018). Webbft: Byzantine fault tolerance for resilient interactive web applications. In Distributed Applications and Interoperable Systems: 18th IFIP WG 6.1 International Conference, DAIS 2018 (pp. 1–17). Springer.
  • An, A. C., Diem, P. T. X., Van Toi, T., & Binh, L. D. Q. (2019). Building a product origins tracking system based on blockchain and POA consensus protocol. In 2019 International Conference on Advanced Computing and Applications (ACOMP) (pp. 27–33). IEEE.
  • Lepore, C., Ceria, M., Visconti, A., Rao, U. P., Shah, K. A., & Zanolini, L. (2020). A survey on blockchain consensus with a performance comparison of PoW, PoS and Pure PoS. Mathematics, 8(10), 1782.
  • Saleh, F. (2021). Blockchain without waste: Proof-of-stake. The Review of Financial Studies, 34(3), 1156–1190.
  • Hjalmarsson, F. P., Hreiðarsson, G. K., Hamdaqa, M., & Hjálmtýsson, G. (2018). Blockchain-based e-voting system. In 2018 IEEE 11th International Conference on Cloud Computing (CLOUD) (pp. 983–986). IEEE Computer Society.
  • Li, W., Feng, C., Zhang, L., Xu, H., Cao, B., & Imran, M. A. (2020). A scalable multi-layer PBFT consensus for blockchain. IEEE Transactions on Parallel and Distributed Systems, 32(5), 1146–1160.
  • Honnavalli, P. B., Cholin, A. S., Pai, A., & Anekal, A. D. (2020). A study on recent trends of consensus algorithms for private blockchain network. In International Congress on Blockchain and Applications (pp. 31–41). Springer.
  • De Angelis, S., Aniello, L., Baldoni, R., Lombardi, F., Margheri, A., & Sassone, V. (2018). PBFT vs proof-of-authority: Applying the CAP theorem to permissioned blockchain. arXiv preprint arXiv:1901.07160.
  • Sukhwani, H., Martínez, J. M., Chang, X., Trivedi, K. S., & Rindos, A. (2017). Performance modeling of PBFT consensus process for permissioned blockchain network (Hyperledger Fabric). In 2017 IEEE 36th Symposium on Reliable Distributed Systems (SRDS) (pp. 253–255). IEEE.
  • Xiang, H., Ren, Z., Zhou, Z., Wang, N., & Jin, H. (2020). Alphablock: An evaluation framework for blockchain consensus protocols. arXiv preprint arXiv:2007.13289.
  • Ekparinya, P., Gramoli, V., & Jourjon, G. (2019). The attack of the clones against proof-of-authority. arXiv preprint arXiv:1902.10244.
  • Openethereum. (2024). Parity Ethereum. https://github.com/openethereum/parity-ethereum.
  • Rouhani, S., & Deters, R. (2017). Performance analysis of Ethereum transactions in private blockchain. In 2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS) (pp. 70–74). IEEE.
  • Islam, M. M., Merlec, M. M., & In, H. P. (2022). A comparative analysis of proof-of-authority consensus algorithms: Aura vs Clique. In 2022 IEEE International Conference on Services Computing (SCC) (pp. 327–332). IEEE.
  • Christyono, B. B. A., Widjaja, M., & Wicaksana, A. (2021). Go-Ethereum for electronic voting system using Clique as proof-of-authority. TELKOMNIKA (Telecommunication Computing Electronics and Control, 19(5), 1565–1572.
  • Saltini, R., & Hyland-Wood, D. (2019). Correctness analysis of IBFT. arXiv preprint arXiv:1901.07160.
  • Mylrea, M., & Gourisetti, S. N. G. (2018). Blockchain: Next generation supply chain security for energy infrastructure and NERC critical infrastructure protection (CIP) compliance. In Resilience Week.
  • Raikwar, M., Gligoroski, D., & Velinov, G. (2020). Trends in development of databases and blockchain. In 2020 Seventh International Conference on Software Defined Systems (SDS) (pp. 177–182). IEEE.
  • Kiffer, L., Rajaraman, R., & Shelat, A. (2018). A better method to analyze blockchain consistency. In Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security (pp. 729–744).
  • Longo, R., Podda, A. S., & Saia, R. (2020). Analysis of a consensus protocol for extending consistent subchains on the Bitcoin blockchain. Computation, 8(3), 67.
  • Cachin, C., & Vukolić, M. (2017). Blockchain consensus protocols in the wild. arXiv preprint arXiv:1707.01873.
  • Oyinloye, D. P., Teh, J. S., Jamil, N., & Alawida, M. (2021). Blockchain consensus: An overview of alternative protocols. Symmetry, 13(8), 1363.
  • Applied Protocol Research. (2024). Blockchain simulator. https://github.com/appliedprotocolresearch/blockchain-simulator
  • Zkik, K., Sebbar, A., Nejjari, N., Lahlou, S., Fadi, O., & Oudani, M. (2023). Secure model for records traceability in airline supply chain based on blockchain and machine learning. In Digital Transformation and Industry 4.0 for Sustainable Supply Chain Performance (pp. 141–159). Springer.
  • Fadi, O., Lahlou, S., Bahaj, A., Zkik, K., El Ghazi, A., & Boulmalf, M. (2025). An integrated framework for securing IoT networks with blockchain and AI. In Empowering IoT with Big Data Analytics (pp. 199–211). Elsevier.

RBFT: Resilience-Oriented Blockchain Consensus Protocol

Year 2025, Volume: 5 Issue: 2, 132 - 144
https://doi.org/10.57019/jmv.1623586

Abstract

Blockchain resilience is the capacity of a blockchain system to proactively adapt to and recover from disruptions. A resilient blockchain remains operational and effective even in the face of unexpected challenges such as security breaches, system failures, or other unforeseen events. Moreover, consensus protocols play a fundamental role in blockchain networks by providing transparency, traceability, and trust, thereby addressing fundamental system weaknesses. However, before adopting these protocols in real-world applications, it is crucial to evaluate their specific features and how they influence the resilience of blockchain. In this study, a new consensus protocol that is resilience-oriented is developed. To achieve this, a comprehensive analysis of existing consensus protocols is conducted, focusing on identifying key metrics essential for evaluating blockchain resilience and ensuring long-term sustainability. The proposed RBFT protocol has demonstrated enhanced resilience within the blockchain network, primarily due to three key mechanisms: a weak coordinator model, weighted validation, and tolerance for late nodes. Furthermore, RBFT outperformed existing consensus protocols in both latency and throughput, showcasing its robustness against increasing numbers of faulty nodes and confirming its scalability under growing network demands. This research aims to assist managers and organizations in adopting blockchain technology, particularly by integrating suitable consensus protocols, improving the resilience of blockchain, as well as its adoptive networks.

References

  • Dubey, R., Gunasekaran, A., Childe, S. J., Fosso Wamba, S., Roubaud, D., & Foropon, C. (2021). Empirical investigation of data analytics capability and organizational flexibility as complements to supply chain resilience. International Journal of Production Research, 59(1), 110–128.
  • Moosavi, J., Naeni, L. M., Fathollahi-Fard, A. M., & Fiore, U. (2021). Blockchain in supply chain management: A review, bibliometric, and network analysis. Environmental Science and Pollution Research, 1–15.
  • Dubey, R., Gunasekaran, A., Bryde, D. J., Dwivedi, Y. K., & Papadopoulos, T. (2020). Blockchain technology for enhancing swift-trust, collaboration and resilience within a humanitarian supply chain setting. International Journal of Production Research, 58(11), 3381–3398.
  • Queiroz, M. M., Telles, R., & Bonilla, S. H. (2020). Blockchain and supply chain management integration: A systematic review of the literature. Supply Chain Management: An International Journal, 25(2), 241–254.
  • Tu, M. (2018). An exploratory study of internet of things (IoT) adoption intention in logistics and supply chain management: A mixed research approach. The International Journal of Logistics Management, 29(1), 131–151.
  • Parker, S., Wu, Z., & Christofides, P. D. (2023). Cybersecurity in process control, operations, and supply chain. Computers & Chemical Engineering, 108169.
  • Simon, J., & Omar, A. (2020). Cybersecurity investments in the supply chain: Coordination and a strategic attacker. European Journal of Operational Research, 282(1), 161–171.
  • Gordon, L. A., Loeb, M. P., Lucyshyn, W., & Zhou, L. (2015). The impact of information sharing on cybersecurity underinvestment: A real options perspective. Journal of Accounting and Public Policy, 34(5), 509–519.
  • Huang, C. D., Hu, Q., & Behara, R. S. (2008). An economic analysis of the optimal information security investment in the case of a risk-averse firm. International Journal of Production Economics, 114(2), 793–804.
  • Wu, Y., Feng, G., Wang, N., & Liang, H. (2015). Game of information security investment: Impact of attack types and network vulnerability. Expert Systems with Applications, 42(15–16), 6132–6146.
  • Vanov, D. (2018). Revealing interfaces of supply chain resilience and sustainability: A simulation study. International Journal of Production Research, 56(10), 3507–3523.
  • Ali, I., & Gölgeci, I. (2019). Where is supply chain resilience research heading? A systematic and co-occurrence analysis. International Journal of Physical Distribution & Logistics Management.
  • Ponomarov, S. Y., & Holcomb, M. C. (2009). Understanding the concept of supply chain resilience. The International Journal of Logistics Management.
  • Tukamuhabwa, B. R., Stevenson, M., Busby, J., & Zorzini, M. (2015). Supply chain resilience: Definition, review and theoretical foundations for further study. International Journal of Production Research, 53(18), 5592–5623.
  • Beck, J., Birkel, H., Spieske, A., & Gebhardt, M. (2023). Will the blockchain solve the supply chain resilience challenges? Insights from a systematic literature review. Computers & Industrial Engineering, 185, 109623.
  • Bayramova, A., Edwards, D. J., & Roberts, C. (2021). The role of blockchain technology in augmenting supply chain resilience to cybercrime. Buildings, 11(7), 283.
  • Azmi, N. A., Sweis, G., Sweis, R., & Sammour, F. (2022). Exploring implementation of blockchain for the supply chain resilience and sustainability of the construction industry in Saudi Arabia. Sustainability, 14(11), 6427.
  • Fadi, O., Bahaj, A., Zkik, K., El Ghazi, A., Ghogho, M., & Boulmalf, M. (2025). Smart contract anomaly detection: The contrastive learning paradigm. Computer Networks, 111121.
  • Zkik, K., Sebbar, A., Fadi, O., Mustapha, O., & Belhadi, A. (2023). A graph neural network approach for detecting smart contract anomalies in collaborative economy platforms based on blockchain technology. In 2023 9th International Conference on Control, Decision and Information Technologies (CoDIT) (pp. 1285–1290). IEEE.
  • Sengupta, J., Ruj, S., & Bit, S. D. (2020). A comprehensive survey on attacks, security issues and blockchain solutions for IoT and IIoT. Journal of Network and Computer Applications, 149, 102481.
  • Novo, O. (2018). Blockchain meets IoT: An architecture for scalable access management in IoT. IEEE Internet of Things Journal, 5(2), 1184–1195.
  • Aggarwal, S., Chaudhary, R., Aujla, G. S., Kumar, N., Choo, K.-K. R., & Zomaya, A. Y. (2019). Blockchain for smart communities: Applications, challenges and opportunities. Journal of Network and Computer Applications, 144, 13–48.
  • Berger, C., & Reiser, H. P. (2018). Webbft: Byzantine fault tolerance for resilient interactive web applications. In Distributed Applications and Interoperable Systems: 18th IFIP WG 6.1 International Conference, DAIS 2018 (pp. 1–17). Springer.
  • An, A. C., Diem, P. T. X., Van Toi, T., & Binh, L. D. Q. (2019). Building a product origins tracking system based on blockchain and POA consensus protocol. In 2019 International Conference on Advanced Computing and Applications (ACOMP) (pp. 27–33). IEEE.
  • Lepore, C., Ceria, M., Visconti, A., Rao, U. P., Shah, K. A., & Zanolini, L. (2020). A survey on blockchain consensus with a performance comparison of PoW, PoS and Pure PoS. Mathematics, 8(10), 1782.
  • Saleh, F. (2021). Blockchain without waste: Proof-of-stake. The Review of Financial Studies, 34(3), 1156–1190.
  • Hjalmarsson, F. P., Hreiðarsson, G. K., Hamdaqa, M., & Hjálmtýsson, G. (2018). Blockchain-based e-voting system. In 2018 IEEE 11th International Conference on Cloud Computing (CLOUD) (pp. 983–986). IEEE Computer Society.
  • Li, W., Feng, C., Zhang, L., Xu, H., Cao, B., & Imran, M. A. (2020). A scalable multi-layer PBFT consensus for blockchain. IEEE Transactions on Parallel and Distributed Systems, 32(5), 1146–1160.
  • Honnavalli, P. B., Cholin, A. S., Pai, A., & Anekal, A. D. (2020). A study on recent trends of consensus algorithms for private blockchain network. In International Congress on Blockchain and Applications (pp. 31–41). Springer.
  • De Angelis, S., Aniello, L., Baldoni, R., Lombardi, F., Margheri, A., & Sassone, V. (2018). PBFT vs proof-of-authority: Applying the CAP theorem to permissioned blockchain. arXiv preprint arXiv:1901.07160.
  • Sukhwani, H., Martínez, J. M., Chang, X., Trivedi, K. S., & Rindos, A. (2017). Performance modeling of PBFT consensus process for permissioned blockchain network (Hyperledger Fabric). In 2017 IEEE 36th Symposium on Reliable Distributed Systems (SRDS) (pp. 253–255). IEEE.
  • Xiang, H., Ren, Z., Zhou, Z., Wang, N., & Jin, H. (2020). Alphablock: An evaluation framework for blockchain consensus protocols. arXiv preprint arXiv:2007.13289.
  • Ekparinya, P., Gramoli, V., & Jourjon, G. (2019). The attack of the clones against proof-of-authority. arXiv preprint arXiv:1902.10244.
  • Openethereum. (2024). Parity Ethereum. https://github.com/openethereum/parity-ethereum.
  • Rouhani, S., & Deters, R. (2017). Performance analysis of Ethereum transactions in private blockchain. In 2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS) (pp. 70–74). IEEE.
  • Islam, M. M., Merlec, M. M., & In, H. P. (2022). A comparative analysis of proof-of-authority consensus algorithms: Aura vs Clique. In 2022 IEEE International Conference on Services Computing (SCC) (pp. 327–332). IEEE.
  • Christyono, B. B. A., Widjaja, M., & Wicaksana, A. (2021). Go-Ethereum for electronic voting system using Clique as proof-of-authority. TELKOMNIKA (Telecommunication Computing Electronics and Control, 19(5), 1565–1572.
  • Saltini, R., & Hyland-Wood, D. (2019). Correctness analysis of IBFT. arXiv preprint arXiv:1901.07160.
  • Mylrea, M., & Gourisetti, S. N. G. (2018). Blockchain: Next generation supply chain security for energy infrastructure and NERC critical infrastructure protection (CIP) compliance. In Resilience Week.
  • Raikwar, M., Gligoroski, D., & Velinov, G. (2020). Trends in development of databases and blockchain. In 2020 Seventh International Conference on Software Defined Systems (SDS) (pp. 177–182). IEEE.
  • Kiffer, L., Rajaraman, R., & Shelat, A. (2018). A better method to analyze blockchain consistency. In Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security (pp. 729–744).
  • Longo, R., Podda, A. S., & Saia, R. (2020). Analysis of a consensus protocol for extending consistent subchains on the Bitcoin blockchain. Computation, 8(3), 67.
  • Cachin, C., & Vukolić, M. (2017). Blockchain consensus protocols in the wild. arXiv preprint arXiv:1707.01873.
  • Oyinloye, D. P., Teh, J. S., Jamil, N., & Alawida, M. (2021). Blockchain consensus: An overview of alternative protocols. Symmetry, 13(8), 1363.
  • Applied Protocol Research. (2024). Blockchain simulator. https://github.com/appliedprotocolresearch/blockchain-simulator
  • Zkik, K., Sebbar, A., Nejjari, N., Lahlou, S., Fadi, O., & Oudani, M. (2023). Secure model for records traceability in airline supply chain based on blockchain and machine learning. In Digital Transformation and Industry 4.0 for Sustainable Supply Chain Performance (pp. 141–159). Springer.
  • Fadi, O., Lahlou, S., Bahaj, A., Zkik, K., El Ghazi, A., & Boulmalf, M. (2025). An integrated framework for securing IoT networks with blockchain and AI. In Empowering IoT with Big Data Analytics (pp. 199–211). Elsevier.
There are 47 citations in total.

Details

Primary Language English
Subjects Information Security and Cryptology
Journal Section Research Articles
Authors

Oumaima Fadi 0000-0003-0666-517X

Karim Zkik This is me 0000-0002-8485-8455

Adil Bahaj This is me 0000-0001-7842-8726

Abdellatif El Ghazi This is me 0000-0003-1990-4993

Mohammed Boulmalf This is me 0009-0002-9760-0215

Early Pub Date June 23, 2025
Publication Date
Submission Date January 20, 2025
Acceptance Date May 21, 2025
Published in Issue Year 2025 Volume: 5 Issue: 2

Cite

APA Fadi, O., Zkik, K., Bahaj, A., El Ghazi, A., et al. (2025). RBFT: Resilience-Oriented Blockchain Consensus Protocol. Journal of Metaverse, 5(2), 132-144. https://doi.org/10.57019/jmv.1623586

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