Analyzing Blockchain Consensus Mechanisms for Internet of Things Networks

Abstract

The consensus mechanism is the main component of blockchain technology, which allows multiple nodes to agree on a consistent view of data within the blockchain network. A carefully selected algorithm, based on which the consensus of transactions occurs, can provide the network with such properties as fault tolerance and immutability. Currently, it is relevant to apply the blockchain (with all its advantages) to Internet of Things (IoT) systems, which are gaining more and more popularity every year. IoT systems are used in areas important to society such as healthcare, economics, agriculture, transport, and are also used in various forms of social security (smart cities, logistics, product tracking, parcels, etc.). Data integrity and consistency are extremely important in these areas, because hardware and software failure or discrediting the data may harm the company and its customers using IoT devices. In addition, the blockchain has become the basis for decentralized networks. The main difficulty of implementing blockchain in IoT is the lack of computing resources of these "smart devices". It follows from this that traditional consensus algorithms, for example, Proof of Work, are not applicable, as they are extremely resource-intensive. This article provides a comparative analysis of popular consensus mechanisms according to the list of developed criteria. Based on the results obtained, conclusions are drawn that help in choosing the most appropriate consensus mechanisms for applicability in IoT systems, and the conditions necessary for their integration are determined. The possibility of implementing both PoW and PoS algorithms in IoT systems using consensus algorithms specially developed for them, such as Microchain and Proof of Supply Chain Share, is also considered.

Author Biographies

Maxim Olegovich Melnikov, Bunin Yelets State University

Postgraduate student of the Chair of Mathematical Modeling, Computer Technologies and Information Security, Institute of Mathematics, Natural Science and Technology

Elena Viktorovna Igonina, Bunin Yelets State University

Associate Professor, Head of the Chair of Mathematics and Methods of its Teaching, Institute of Mathematics, Natural Science and Technology, Cand. Sci. (Phys.-Math.), Associate Professor

References

1. Singh S., Sharma P.K., Yoon B., Shojafar, M., Cho G.H., Ra I.H. Convergence of blockchain and artificial intelligence in IoT network for the sustainable smart city. Sustainable Cities and Society. 2020;63:102364. https://doi.org/10.1016/j.scs.2020.102364
2. Werner R., Lawrenz S., Rausch A. Blockchain Analysis Tool of a Cryptocurrency. In: Proceedings of the 2020 2nd International Conference on Blockchain Technology (ICBCT '20). New York, NY, USA: Association for Computing Machinery; 2020. p. 80-84. https://doi.org/10.1145/3390566.3391671
3. Min H. Blockchain technology for enhancing supply chain resilience.Business Horizons.2019;62:35-45. https://doi.org/10.1016/j.bushor.2018.08.012
4. Dujak D., Sajter D. Blockchain Applications in Supply Chain. In: Kawa A., Maryniak A. (eds.) SMART Supply Network. EcoProduction. Cham: Springer; 2019. p. 21-46. https://doi.org/10.1007/978-3-319-91668-2_2
5. Sternberg H.S., Hofmann E., Roeck D. The Struggle is Real: Insights from a Supply Chain Blockchain Case. Journal of Business Logistics.2021;42:71-87. https://doi.org/10.1111/jbl.12240
6. Casado-Vara R., Prieto J., Prieta F.D., Corchado J.M. How blockchain improves the supply chain: Case study alimentary supply chain. Procedia Computer Science. 2018;134:393-398. https://doi.org/10.1016/j.procs.2018.07.193
7. Salimitari M., Chatterjee M., Fallah Y.P. A Survey on Consensus Methods in Blockchain for Resource-constrained IoT networks. Internet of Things. 2020;11:100212. https://doi.org/10.1016/j.iot.2020.100212
8. Conti M., Sandeep Kumar E., Lal C. Ruj S. A survey on security and privacy issues of bitcoin. IEEE Communications Surveys & Tutorials. 2018;20(4):3416-3452. https://doi.org/10.1109/COMST.2018.2842460
9. Kim J.-Y., Lee J., Moon S.-M. Trie-Hashimoto: State Trie-Based Proof-of-Work Mining for Optimizing Blockchain Storage. IEEE Access. 2024;12:18315-18329. https://doi.org/10.1109/ACCESS.2024.3360379
10. Huang J., Kong L., Chen G., Wu M.Y. Towards Secure Industrial IoT: Blockchain System With Credit-Based Consensus Mechanism. IEEE Transactions on Industrial Informatics. 2019;15(6):3680-3689. https://doi.org/10.1109/TII.2019.2903342
11. Andola N., Venkatesan S., Verma S. PoEWAL: A lightweight consensus mechanism for blockchain in IoT. Pervasive and Mobile Computing. 2020;69:101291. https://doi.org/10.1016/j.pmcj.2020.101291
12. Wen Y., Lu F., Liu Y., Cong P., Huang X. Blockchain Consensus Mechanisms and Their Applications in IoT: A Literature Survey. In: Qiu M. (eds.) Algorithms and Architectures for Parallel Processing. ICA3PP 2020. Lecture Notes in Computer Science. Vol. 12454. Cham: Springer; 2020. P. 564-579. https://doi.org/10.1007/978-3-030-60248-2_38
13. Esgin M.F., Kuchta V., Sakzad A., Steinfeld R., Zhang Z., Sun S., Chu S. Practical Post-quantum Few-Time Verifiable Random Function with Applications to Algorand. In: Borisov N., Diaz C. (eds.) Financial Cryptography and Data Security. FC 2021. Lecture Notes in Computer Science. Vol. 12675. Berlin, Heidelberg: Springer; 2021. p. 560-578. https://doi.org/10.1007/978-3-662-64331-0_29
14. Gilad Y., Hemo R., Micali S., Vlachos G., Zeldovich N. Algorand: Scaling Byzantine Agreements for Cryptocurrencies. In: Proceedings of the 26th Symposium on Operating Systems Principles (SOSP '17). New York, NY, USA: Association for Computing Machinery; 2017. p. 51-68. https://doi.org/10.1145/3132747.3132757
15. Galindo D., Liu J., Ordean M., Wong J.M. Fully Distributed Verifiable Random Functions and their Application to Decentralised Random Beacons. In: 2021 IEEE European Symposium on Security and Privacy (EuroS&P). Vienna, Austria: IEEE Computer Society; 2021. p. 88-102. https://doi.org/10.1109/EuroSP51992.2021.00017
16. Zhang S., Lee J.H. Analysis of the main consensus protocols of blockchain.ICT Express.2020;6(2):93-97. https://doi.org/10.1016/j.icte.2019.08.001
17. Tsang Y.P., Choy K.L., Wu C.H., Ho G.T., Lam H.Y. Blockchain-Driven IoT for Food Traceability with an Integrated Consensus Mechanism. IEEE Access. 2019;7:129000-129017. https://doi.org/10.1109/ACCESS.2019.2940227
18. Averin A. Cheskidov P. Review of Existing Consensus Algorithms Blockchain. In: 2019 International Conference "Quality Management, Transport and Information Security, Information Technologies" (IT&QM&IS). Sochi, Russia: IEEE Computer Society; 2019. p. 124-127. https://doi.org/10.1109/ITQMIS.2019.8928323
19. Xu R., Chen Y., Blasch E., Chen G. Microchain: A Hybrid Consensus Mechanism for Lightweight Distributed Ledger for IoT. arXiv:1909.10948. 2019. https://doi.org/10.48550/arXiv.1909.10948
20. Polge J., Robert J., Traon Y.L. Permissioned blockchain frameworks in the industry: A comparison.ICT Express.2021;7(2):229-233. https://doi.org/10.1016/j.icte.2020.09.002
21. Zhang P., Schmidt D.C., White J., Dubey A. Chapter Seven – Consensus mechanisms and information security technologies. Advances in Computers. 2019;15:181-209. https://doi.org/10.1016/bs.adcom.2019.05.001
22. Silvano W.F., Marcelino R. Iota Tangle: A cryptocurrency to communicate Internet-of-Things data. Future Generation Computer Systems. 2020;112:307-319. https://doi.org/10.1016/j.future.2020.05.047
23. Li J., Li N., Peng J., Cui H., Wu Z. Energy consumption of cryptocurrency mining: A study of electricity consumption in mining cryptocurrencies. Energy. 2019;168:160-168. https://doi.org/10.1016/j.energy.2018.11.046
24. Tirado-Andrés F., Rozas A., Araujo A. A Methodology for Choosing Time Synchronization Strategies for Wireless IoT Networks. Sensors.2019;19(16):3476. https://doi.org/10.3390/s19163476
25. Auhl Z., Chilamkurti N., Alhadad R., Heyne W. A Comparative Study of Consensus Mechanisms in Blockchain for IoT Networks. Electronics. 2022;11(17):2694. https://doi.org/10.3390/electronics11172694
Published
2024-03-31
How to Cite
MELNIKOV, Maxim Olegovich; IGONINA, Elena Viktorovna. Analyzing Blockchain Consensus Mechanisms for Internet of Things Networks. Modern Information Technologies and IT-Education, [S.l.], v. 20, n. 1, p. 92-100, mar. 2024. ISSN 2411-1473. Available at: <http://sitito.cs.msu.ru/index.php/SITITO/article/view/1007>. Date accessed: 13 sep. 2025. doi: https://doi.org/10.25559/SITITO.020.202401.92-100.
Section
The Internet of Things (IoT): standards, communication and IT