An Investigative Analysis of Adaptive Consensus Mechanisms for Distributed Blockchain Systems
DOI:
https://doi.org/10.4108/eetiot.11144Keywords:
Adaptive Consensus, Blockchain Technology, Hybrid Consensus, Dynamic Consensus, Consensus Mechanisms, Proof of Work, Proof of Stake, Byzantine Fault Tolerance, Scalability, SecurityAbstract
INTRODUCTION: Blockchain technology has achieved widespread adoption across diverse application domains, yet its foundational consensus mechanisms remain largely static and may not suited to the dynamic, heterogeneous conditions of modern decentralized networks. While adaptive consensus has emerged as a promising solution, a comprehensive and systematic framework for classifying, evaluating, and selecting adaptive mechanisms remains notably absent.
OBJECTIVES: This survey addresses this critical gap by introducing a structured taxonomy of adaptive consensus mechanisms for distributed blockchain systems, underpinned by a systematic review of performance evidence and real- world deployment experiences.
METHODS: Following PRISMA 2020 guidelines, we conducted a systematic search of 1,675 records from Scopus (2020– 2025), ultimately including 68 peer-reviewed studies. We analyze four principal categories of adaptive consensus namely dynamic parameter adjustment, consensus algorithm switching, hybrid mechanisms, and AI/ML-enhanced protocols across five key performance dimensions: throughput, scalability, energy efficiency, security level, and implementation complexity. Case studies of SABEC for UAV coordination, 6G cognitive radio spectrum management, and supply chain networks illustrate real-world deployment trade-offs.
RESULTS: Our comparative analysis reveals fundamental performance–security trade-offs inherent to each adaptive category. Dynamic parameter adjustment mechanisms offer low implementation complexity but limited scalability gains; algorithm-switching approaches achieve high throughput (100–1,000 TPS) at the cost of very high implementation complexity; hybrid schemes such as HyFlexChain demonstrate 112.5+ TPS under BFT mode with high security but remain at the prototype stage; and AI/ML-enhanced protocols show theoretical promise yet face critical security challenges including adversarial attacks and model poisoning that undermine system trustworthiness. The maturity assessment confirms that higher implementation complexity consistently correlates with limited real-world deployment.
CONCLUSION: Our findings demonstrate that no universal adaptive consensus mechanism exists for blockchain applications. This structured, evidence-grounded survey provides an effective methodology for designing, evaluating, and selecting adaptive consensus solutions for diverse decentralized system deployments.
Downloads
References
hukla, A., Jirli, P., Mishra, A., & Singh, A. K. (2024). An overview of blockchain research and future agenda: Insights from structural topic modeling. Journal of Innovation & Knowledge, 9(4), 100605. https://doi.org/10.1016/j.jik.2024.100605
[2] Pineda, M., Jabba, D., Nieto-Bernal, W., & Pérez, A. (2024). Sustainable Consensus Algorithms Applied to Blockchain: A Systematic Literature Review. Sustainability, 16(23), 10552.https://doi.org/10.3390/su162310552
[3] Shen, Z., Qu, Q., & Chen, X. B. (2025). Blockchain Consensus Mechanisms: A Comprehensive Review and Performance Analysis Framework. In Electronics (Switzerland) (Vol. 14, Issue 17). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/electronics14173567
[4] Zhuravel, S., Shpur, O., &Klymash, M. (2024). Adaptive Consensus Algorithms: Designing for Durability against Unstable Network Connections. International Journal of Computing, 23(4), 574-582.
[5] Dogan, H., & Setzer, A. (2025). SABEC: Secure and Adaptive Blockchain-Enabled Coordination Protocol for Unmanned Aerial Vehicles (UAVs) Network.
[6] Natraj, N. A., Midhunchakkaravarthy, J. J., & Mishra, B. K. (2024). A Quantitative Framework for the Selection of Hybrid Consensus Mechanisms in Blockchain-IoT Systems. EAI Endorsed Transactions on Internet of Things, 11.
[7] Haddaway, N. R., Page, M. J., Pritchard, C. C., & McGuinness, L. A. (2022). PRISMA2020: An R package and Shiny app for producing PRISMA 2020-compliant flow diagrams, with interactivity for optimised digital transparency and Open Synthesis Campbell Systematic Reviews, 18, e1230. https://doi.org/10.1002/cl2.1230
[8] Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system.
[9] 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.
[10] Adu-Manu, K. S., & Adjetey, C. (2025). PoAD: A Scalable and Energy-Efficient Consensus Algorithm for Smart Contract Execution in Decentralized Systems. Concurrency and Computation: Practice and Experience, 37(18–20). https://doi.org/10.1002/cpe.70197
[11] Ramos-Cruz, B., Andreu-Perez, J., Quesada-Real, F. J., & Martínez, L. (2025). Fuzzychain: An equitable consensus mechanism for blockchain networks. Journal of Network and Computer Applications, 241. https://doi.org/10.1016/j.jnca.2025.104204
[12] Saad, S. M. S., &Radzi, R. Z. R. M. (2020). Comparative review of the blockchain consensus algorithm between proof of stake (pos) and delegated proof of stake (dpos). International Journal of Innovative Computing, 10(2). https://doi.org/10.11113/ijic.v10n2.272
[13] Yakubu, M. M., Hassan, F. B., Danyaro, K. U., Junejo, Z., Siraj, M., Yahaya, S., & Abdulsalam, K.(2024). A Systematic Literature Review on Blockchain Consensus Mechanisms' Security: Applications and Open Challenges. Computer Systems Science & Engineering, 48(6).
[14] Wuthier, S., & Chang, S. Y. (2021, July). Proof-of-work network simulator for blockchain and cryptocurrency research. In 2021 IEEE 41st International Conference on Distributed Computing Systems (ICDCS) (pp. 1098- 1101). IEEE.
[15] Ren, S., Lee, C., Kim, E., & Helal, S. (2022). Flexico: An efficient dual-mode consensus protocol for blockchain networks. PLOS ONE, 17. https://doi.org/10.1371/journal.pone.0277092
[16] Butean, A., Pournaras, E., Tara, A., Turesson, H., &Ivkushkin, K. (2020). Dynamic consensus: Increasing blockchain adaptability to enterprise applications. In Applied Informatics and Cybernetics in Intelligent Systems: Proceedings of the 9th Computer Science On- line Conference 2020, Volume 3 9 (pp. 433-442). Springer International Publishing.
[17] Hussein, Z., Salama, M., & El-Rahman, S. (2023). Evolution of blockchain consensus algorithms: a review on the latest milestones of blockchain consensus algorithms. Cybersecurity, 6, 1-22. https://doi.org/10.1186/s42400-023-00163-y.
[18] Wu, Y., Song, P., & Wang, F. (2020). Hybrid Consensus Algorithm Optimization: A Mathematical Method Based on POS and PBFT and Its Application in Blockchain. Mathematical Problems in Engineering. https://doi.org/10.1155/2020/7270624.
[19] Wei, Y., Xu, Q., & Peng, H. (2024). An enhanced consensus algorithm for blockchain. Scientific reports, 14(1), 17701. https://doi.org/10.1038/s41598-024-68120-4.
[20] Wen, F., Yang, L., Cai, W., & Zhou, P. (2020). DP-Hybrid: a two-layer consensus protocol for high scalability in permissioned blockchain. 2 (pp. 57-71). Springer Singapore. https://doi.org/10.1007/978-981-15- 9213-3_5
[21] Ferreira, H. J. P. C. (2024). Hyflexchain: A Permissionless Decentralized Ledger with Hybrid and Flexible Consensus Plane (Master's thesis, Universidade NOVA de Lisboa (Portugal)).
[22] Venkatesan, K., &Rahayu, S. B. (2024). Blockchain security enhancement: an approach towards hybrid consensus algorithms and machine learning techniques. Scientific Reports, 14(1), 1149 https://doi.org/10.1038/s41598-024-51578-7
[23] Saadat, K., Wang, N., & Tafazolli, R. (2023). AI- Enabled Blockchain Consensus Node Selection in Cluster-Based Vehicular Networks. IEEE Networking Letters, 5, 115-119. https://doi.org/10.1109/lnet.2023.3238964.
[24] Nishad, D., Verma, V., Rajput, P., Gupta, S., Dwivedi, A., & Shah, D. (2025). Adaptive AI-enhanced computation offloading with machine learning for QoE optimization and energy-efficient mobile edge systems. Scientific Reports, 15. https://doi.org/10.1038/s41598- 025-00409-4.
[25] Cai, W., Jiang, W., Xie, K., Zhu, Y., Liu, Y., & Shen, T. (2020). Dynamic reputation–based consensus mechanism: Real-time transactions for energy blockchain. International Journal of Distributed Sensor Networks, 16(3). https://doi.org/10.1177/1550147720907335
[26] Hu, Q., Yan, B., Han, Y., & Yu, J. (2021). An Improved Delegated Proof of Stake Consensus Algorithm. Procedia Computer Science, 187, 341–346. https://doi.org/10.1016/j.procs.2021.04.109
[27] HussainiWindiatmaja, J., Hanggoro, D., Salman, M., & Sari, R. F. (2023). PoIR: A Node Selection Mechanism in Reputation-Based Blockchain Consensus Using Bidirectional LSTM Regression Model. Computers, Materials & Continua, 77(2). 10.32604/cmc.2023.041152
[28] Al-Matari, N. Y., Zahary, A. T., & A Al-Shargabi, A. (2024). A survey on advancements in blockchain-enabled spectrum access security for 6G cognitive radio IoT networks. Scientific reports, 14(1), 30990. https://doi.org/10.1038/s41598-024-82126-y
[29] Maksymyuk, T., Gazda, J., Volosin, M., Bugar, G., Horvath, D., Klymash, M., & Dohler, M. (2020). Blockchain-empowered framework for decentralized network management in 6G. IEEE Communications Magazine, 58(9), 86-92.
[30] Zambianco, M., & Verticale, G. (2020, August). Spectrum allocation for network slices with inter- numerology interference using deep reinforcement learning. In 2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications (pp. 1-7). IEEE.
[31] Qamar, F., Siddiqui, M. U. A., Hindia, M. N., Hassan, R., & Nguyen, Q. N. (2020). Issues, challenges, and research trends in spectrum management: A comprehensive overview and new vision for designing 6G networks. Electronics, 9(9), 1416.
[32] Pathak, V., Sharma, A., Rathore, M. S., & Pandya, R. J. (2024, June). Reputation-based Resource Allocation Prioritization in 6G: A Coalitional Game Theoretic Approach. In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-6). IEEE.
[33] Cuellar, D., Sallal, M., & Williams, C. (2024). BSM-6G: Blockchain-based dynamic Spectrum management for 6G networks: addressing interoperability and scalability. IEEE Access
[34] Vu, N., Ghadge, A., &Bourlakis, M. (2021). Blockchain adoption in food supply chains: a review and implementation framework. Production Planning & Control, 34(6), 506–523.https://doi.org/10.1080/09537287.2021.1939902
[35] Singh, R. K., Mishra, R., Gupta, S., & Mukherjee, A. A. (2023). Blockchain applications for secured and resilient supply chains: A systematic literature review and future research agenda. Computers & Industrial Engineering, 175, 108854. https://doi.org/10.1016/j.cie.2022.108854
[36] Franco, C. W., Benitez, G. B., de Sousa, P. R., Neto, F. J. K., & Frank, A. G. (2024). Managing resources for digital transformation in supply chain integration: The role of hybrid governance structures. International Journal of Production Economics, 278, 109428.
[37] Xue, L., Yang, W., Chen, W., & Huang, L. (2022). STBC: a Novel Blockchain-based spectrum trading solution. IEEE Transactions on Cognitive Communications and Networks, 8, 13–30
[38] Dansana, D. et al. (2024). BSMACRN: design of an efficient blockchain-based security model for improving attack-resilience of Cognitive Radio Ad-hoc networks. IEEE Access, 12, 10047–10058.
[39] Ghode, D. J., Jain, R., & Soni, G. (2020, December). Challenges of adoption of blockchain technology in supply chain: an overview. In International Conference on Evolution in Manufacturing (pp. 157-165). Singapore: Springer Nature Singapore.
[40] Svantesson, D. (2020). Data localisation trends and challenges: Considerations for the review of the Privacy Guidelines. OECD Digital Economy Papers, (301).
[41] Imane, L., Noureddine, M., Driss, S., & Hanane, L. Y. (2023). Towards blockchain-integrated enterprise resource planning: A pre-implementation guide. Computers, 13(1), 11.
[42] Xie, M., Liu, J., Chen, S., & Lin, M. (2023). A survey on blockchain consensus mechanism: research overview, current advances and future directions. International Journal of Intelligent Computing and Cybernetics, 16(2), 314-340.
[43] Xiong, H., Chen, M., Wu, C., Zhao, Y., & Yi, W. (2022). Research on progress of blockchain consensus algorithm: A review on recent progress of blockchain consensus algorithms. Future Internet, 14(2), 47.
[44] Li, R. (2024, October). Comparative Analysis and Future Directions of Consensus Algorithms in Blockchain Technology. In 2024 2nd International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2024) (pp. 333-344). Atlantis Press
[45] Wu, J., Dong, M., Ota, K., Li, J., & Yang, W. (2020). Application-Aware Consensus Management for Software-Defined Intelligent Blockchain in IoT. IEEE Network, 34, 69-75. https://doi.org/10.1109/mnet.001.1900179
[46] Wu, C., Mehta, B., Amiri, M. J., Marcus, R., & Loo, B. T. (2022). AdaChain: A learned adaptive blockchain. arXiv preprint arXiv:2211.01580.
[47] Song, A., & Zhou, C. (2024). FlexBFT: A Flexible and Effective Optimistic Asynchronous BFT Protocol. Applied Sciences (Switzerland), 14(4). https://doi.org/10.3390/app14041461
[48] Lu, Y., Liu, C., Kong, L., & Niu, X. (2025). ATBFT-automatically switch consensus protocol. Blockchain: Research and Applications, 6(1). https://doi.org/10.1016/j.bcra.2024.100255
[49] Nijsse, J., & Litchfield, A. (2020). A taxonomy of blockchain consensus methods. Cryptography, 4(4), 32.
[50] Yusof, S. H., Zahilah, R., & Othman, S. H. (2024). Blockchain consensus for resources constraint devices: a hybrid approach using PoA, DPoS and threshold cryptography Journal of Engineering and Technology (JET), 15(2).
[51] Bhumichai, D., Smiliotopoulos, C., Benton, R., Kambourakis, G., &Damopoulos, D. (2024). The convergence of artificial intelligence and blockchain: The state of play and the road ahead. Information, 15(5), 268.
[52] Antunes, M., Oliveira, L., Seguro, A., Veríssimo, J., Salgado, R., & Murteira, T. (2022). Benchmarking Deep Learning Methods for Behaviour-Based Network Intrusion Detection. Informatics, 9, 29. https://doi.org/10.3390/informatics9010029.
[53] Khoa, T., Son, D., Hoang, D., Trung, N., Quynh, T., Nguyen, D., Ha, N., & Dutkiewicz, E. (2022). Collaborative Learning for Cyberattack Detection in Blockchain Networks. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 54, 3920-3933. https://doi.org/10.1109/TSMC.2024.3374280.
[54] Qiu, C., Ren, X., & Mai, T. (2021). Deep Reinforcement Learning Empowered Adaptivity for Future Blockchain Networks. IEEE Open Journal of the Computer Society, 2, 99-105. https://doi.org/10.1109/ojcs.2020.3010987.
[55] Xiao, R., Cao, Y., & Xia, B. (2025). Adaptive Tip Selection for DAG-Shard-Based Federated Learning with High Concurrency and Fairness. Sensors, 25(1). https://doi.org/10.3390/s25010019
[56] Feng, C., Celdr'an, A., Von Der Assen, J., Beltr'an, E., Bovet, G., & Stiller, B. (2024). DART: A Solution for Decentralized Federated Learning Model Robustness Analysis. Array, 23, 100360. https://doi.org/10.48550/arXiv.2407.08652.
[57] Konstantinidis, K., Vaswani, N., & Ramamoorthy, A. (2022). Detection and Mitigation of Byzantine Attacks in Distributed Training. IEEE/ACM Transactions on Networking, 32, 1493-1508. https://doi.org/10.1109/TNET.2023.3324697.
[58] Zhang, P., Ding, S., & Zhao, Q. (2023). Exploiting Blockchain to Make AI Trustworthy: A Software Development Lifecycle View. ACM Computing Surveys, 56, 1 - 31. https://doi.org/10.1145/3614424.
[59] Tiamiyu, D., Aremu, S., Emmanuel, I., Ihejirika, C., Adewoye, M., & Ajayi, A. (2024). Interpretable Data Analytics in Blockchain Networks Using Variational Autoencoders and Model-Agnostic Explanation Techniques for Enhanced Anomaly Detection. International Journal of Scientific Research in Science and Technology. https://doi.org/10.32628/ijsrst24116170.
[60] Sun, T., & Yu, W. (2020). A Formal Verification Framework for Security Issues of Blockchain Smart Contracts. Electronics. https://doi.org/10.3390/electronics9020255.
[61] Abellán Álvarez, I., Gramlich, V., &Sedlmeir, J. (2024, April). Unsealing the secrets of blockchain consensus: A systematic comparison of the formal security of proof-of- work and proof-of-stake. In Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing (pp. 278-287).
[62] Liu, A., Chen, J., He, K., Du, R., Xu, J., Wu, C., ... & Ma, J. (2024). Dynashard: Secure and adaptive blockchain sharding protocol with hybrid consensus and dynamic shard management. IEEE Internet of Things Journal.
[63] Asif, R., & Hassan, S. (2023). Shaping the future of Ethereum: exploring energy consumption in Proof-of- Work and Proof-of-Stake consensus. Frontiers Blockchain, 6. https://doi.org/10.3389/fbloc.2023.1151724.
[64] Schwarz-Schilling, C., Neu, J., Monnot, B., Asgaonkar, A., Tas, E., & Tse, D. (2021). Three Attacks on Proof-of- Stake Ethereum. IACR Cryptol. ePrint Arch., 2021, 1413. https://doi.org/10.1007/978-3-031-18283-9_28.
[65] Wang, B., Li, Z., & Li, H. (2020). Hybrid consensus algorithm based on modified proof-of-probability and DPoS. Future Internet, 12(8). https://doi.org/10.3390/FI12080122
[66] Naeem, Mena. (2024). Adaptive Consensus Mechanism for Energy-Efficient IoT Networks via Power Consumption as a Key Factor. International Journal of Computer Science and Information Security, Vol. 22 No.5. 5.
[67] Pang, Y. (2020). A New Consensus Protocol for Blockchain Interoperability Architecture. IEEE Access, 8, 153719-153730. https://doi.org/10.1109/access.2020.3017549.
[68] Gol, D. A., &Gondaliya, N. (2024). Blockchain: A comparative analysis of hybrid consensus algorithm and performance evaluation. Computers and Electrical Engineering, 117. https://doi.org/10.1016/j.compeleceng.2023.108934
[69] Ahn, J., Yi, E., & Kim, M. (2024). Blockchain consensus mechanisms: A bibliometric analysis (2014–2024) using vosviewer and r bibliometrix. Information, 15(10), 644.
[70] He, Y., Wang, Y., Qiu, C., Lin, Q., Li, J., & Ming, Z. (2021). Blockchain-Based Edge Computing Resource Allocation in IoT: A Deep Reinforcement Learning Approach. IEEE Internet of Things Journal, 8, 2226- 2237. https://doi.org/10.1109/jiot.2020.3035437
[71] Gan, B., Wang, Y., Wu, Q., Zhou, Y., & Jiang, L. (2022). EIoT-PBFT: A multi-stage consensus algorithm for IoT edge computing based on PBFT. Microprocess. Microsystems, 95, 104713. https://doi.org/10.1016/j.micpro.2022.104713
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Smita Sachin Bhore, Natraj N.A.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 4.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.
