Selecting Consensus Algorithm Integrations in a DAG-based Blockchain for IoT Using Genetic Algorithms
DOI:
https://doi.org/10.5753/jisa.2026.5926Keywords:
Blockchain, Internet of Things, Consensus Algorithms, Information SecurityAbstract
The Internet of Things (IoT) drives technological advances across various sectors by enabling seamless communication among smart devices. However, significant challenges remain regarding the integrity and reliability of the data stored by these devices. Traditional blockchain solutions, such as those based on Proof of Work (PoW), are generally unsuitable for IoT applications due to their high computational resource demands. Although approaches combining multiple consensus algorithms have emerged as alternatives to optimise performance and security, determining the best combination for each scenario remains an open problem. This paper proposes a strategy based on Genetic Algorithms (GAs) to adaptively select and combine consensus algorithms, thus improving blockchain efficiency in IoT environments. The approach was evaluated on a test blockchain, OmniBlock, implemented using a Directed Acyclic Graph (DAG) and designed specifically for evaluation purposes in IoT applications. OmniBlock supports multiple consensus algorithms, including Proof of Authority (PoA), Proof of Stake (PoS), Proof of Work (PoW), Practical Byzantine Fault Tolerance (PBFT), Raft, and others. The consensus algorithm combination is chosen based on performance attributes. All combinations of consensus algorithms evaluated in this work were suggested by GAs; the practical feasibility of each is analyzed empirically. Experimental results indicate that the evolutionary optimization-based strategy performs better across most of the combinations suggested by the GAs.
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Abellán Álvarez, I., Gramlich, V., and Sedlmeir, J. (2024). Unsealing the secrets of blockchain consensus: A systematic comparison of the formal security of proof-of-work and proof-of-stake. arXiv e-prints, pages arXiv-2401. DOI: 10.48550/arXiv.2401.14527.
Aghroud, M., Oualla, M., and El Bermi, L. (2023). The criteria for adapting a blockchain consensus algorithm to iot networks. In Computer Sciences & Mathematics Forum, volume 6, page 9. MDPI. DOI: 10.3390/cmsf2023006009.
Bach, L. M., Mihaljevic, B., and Zagar, M. (2018). Comparative analysis of blockchain consensus algorithms. In 2018 41st international convention on information and communication technology, electronics and microelectronics (MIPRO), pages 1545-1550. Ieee. DOI: 10.23919/mipro.2018.8400278.
Bhushan, B., Sahoo, C., Sinha, P., and Khamparia, A. (2021). Unification of blockchain and internet of things (biot): requirements, working model, challenges and future directions. Wireless Networks, 27:55-90. DOI: 10.1007/s11276-020-02445-6.
Bowman, M., Das, D., Mandal, A., and Montgomery, H. (2021). On elapsed time consensus protocols. In Progress in Cryptology-INDOCRYPT 2021: 22nd International Conference on Cryptology in India, Jaipur, India, December 12-15, 2021, Proceedings 22, pages 559-583. Springer. DOI: 10.1007/978-3-030-92518-5_25.
Castro, M., Liskov, B., et al. (1999). Practical byzantine fault tolerance. In OsDI, volume 99, pages 173-186. DOI: 10.5555/296806.296824.
De Angelis, S., Aniello, L., Baldoni, R., Lombardi, F., Margheri, A., Sassone, V., et al. (2018). Pbft vs proof-of-authority: Applying the cap theorem to permissioned blockchain. In CEUR workshop proceedings, volume 2058. CEUR-WS. DOI: 10.5281/zenodo.1169272.
de Morais, A. M., Lins, F. A. A., and Rosa, N. S. (2023a). Integration and evaluation of blockchain consensus algorithms for iot environments. In Barolli, L., editor, Advanced Information Networking and Applications, pages 1-13, Cham. Springer International Publishing. DOI: 10.1007/978-3-031-28451-9_1.
de Morais, A. M., Lins, F. A. A., and Rosa, N. S. (2023b). Survey on integration of consensus mechanisms in iot-based blockchains. JUCS - Journal of Universal Computer Science, 29(10):1139-1160. DOI: 10.3897/jucs.94929.
de Morais, A. M., Lins, F. A. A., and Rosa, N. S. (2024). Selecting blockchain consensus algorithms integrations for iot-based enviroments. In Proceedings of the 13th Latin-American Symposium on Dependable and Secure Computing, pages 116-125. DOI: 10.1145/3697090.3697100.
Digitale, J. C., Martin, J. N., and Glymour, M. M. (2022). Tutorial on directed acyclic graphs. Journal of Clinical Epidemiology, 142:264-267. DOI: 10.1016/j.jclinepi.2021.08.001.
Fu, W., Wei, X., and Tong, S. (2021). An improved blockchain consensus algorithm based on raft. Arabian Journal for Science and Engineering, 46(9):8137-8149. DOI: 10.1007/s13369-021-05427-8.
Gaži, P., Kiayias, A., and Zindros, D. (2019). Proof-of-stake sidechains. In 2019 IEEE Symposium on Security and Privacy (SP), pages 139-156. IEEE. Available at:[link].
Holland, J. H. (1975). Adaptation in natural and artificial systems. The University of Michigan Press. DOI: 10.7551/mitpress/1090.001.0001.
Howard, H. and Mortier, R. (2020). Paxos vs raft: Have we reached consensus on distributed consensus? In Proceedings of the 7th Workshop on Principles and Practice of Consistency for Distributed Data, pages 1-9. DOI: 10.48550/arXiv.2004.05074.
Isaacs, K. E. and Gamblin, T. (2018). Preserving command line workflow for a package management system using ascii dag visualization. IEEE transactions on visualization and computer graphics, 25(9):2804-2820. DOI: 10.1109/tvcg.2018.2859974.
Jain, R. (1991). The art of computer systems performance analysis: techniques for experimental design, measurement, simulation, and modeling, volume 1. Wiley New York. Book.
Jakobović, D. and Golub, M. (1999). Adaptive genetic algorithm. Journal of computing and information technology, 7(3):229-235. Avaialble at:[link].
Javed, M. U., Rehman, M., Javaid, N., Aldegheishem, A., Alrajeh, N., and Tahir, M. (2020). Blockchain-based secure data storage for distributed vehicular networks. Applied Sciences, 10(6):2011. DOI: 10.3390/app10062011.
Kamran, M., Khan, H. U., Nisar, W., Farooq, M., and Rehman, S.-U. (2020). Blockchain and internet of things: A bibliometric study. Computers & Electrical Engineering, 81:106525. DOI: 10.1016/j.compeleceng.2019.106525.
Karantias, K., Kiayias, A., and Zindros, D. (2020). Proof-of-burn. In Financial Cryptography and Data Security: 24th International Conference, FC 2020, Kota Kinabalu, Malaysia, February 10-14, 2020 Revised Selected Papers 24, pages 523-540. Springer. DOI: 10.1007/978-3-030-51280-4_28.
Khan, A. A., Laghari, A. A., Rashid, M., Li, H., Javed, A. R., and Gadekallu, T. R. (2023). Artificial intelligence and blockchain technology for secure smart grid and power distribution automation: A state-of-the-art review. Sustainable Energy Technologies and Assessments, 57:103282. DOI: 10.1016/j.seta.2023.103282.
Khan, M., den Hartog, F., and Hu, J. (2022). A survey and ontology of blockchain consensus algorithms for resource-constrained iot systems. Sensors, 22(21):8188. DOI: 10.3390/s22218188.
Kiayias, A., Russell, A., David, B., and Oliynykov, R. (2017). Ouroboros: A provably secure proof-of-stake blockchain protocol. In Annual international cryptology conference, pages 357-388. Springer. DOI: 10.1007/978-3-319-63688-7_12.
Larimer, D. (2014). Delegated proof-of-stake (dpos). Bitshare whitepaper, 81:85. Available at:[link].
Lee, Y., Rathore, S., Park, J. H., and Park, J. H. (2020). A blockchain-based smart home gateway architecture for preventing data forgery. Human-centric Computing and Information Sciences, 10(1):9. DOI: 10.1186/s13673-020-0214-5.
Lei, M., Xu, L., Liu, T., Liu, S., and Sun, C. (2022). Integration of privacy protection and blockchain-based food safety traceability: Potential and challenges. Foods, 11(15):2262. DOI: 10.3390/foods11152262.
Liao, Z. and Cheng, S. (2023). Rvc: A reputation and voting based blockchain consensus mechanism for edge computing-enabled iot systems. Journal of Network and Computer Applications, 209:103510. DOI: 10.1016/j.jnca.2022.103510.
Lobo, F., Lima, C. F., and Michalewicz, Z. (2007). Parameter setting in evolutionary algorithms, volume 54. Springer Science & Business Media. DOI: 10.1007/978-3-540-69432-8.
Mäkinen, V., Tomescu, A. I., Kuosmanen, A., Paavilainen, T., Gagie, T., and Chikhi, R. (2019). Sparse dynamic programming on dags with small width. ACM Transactions on Algorithms (TALG), 15(2):1-21. DOI: 10.1145/3301312.
Meshcheryakov, Y., Melman, A., Evsutin, O., Morozov, V., and Koucheryavy, Y. (2021). On performance of pbft blockchain consensus algorithm for iot-applications with constrained devices. IEEE Access, 9:80559-80570. DOI: 10.1109/access.2021.3085405.
Milutinovic, M., He, W., Wu, H., and Kanwal, M. (2016). Proof of luck: An efficient blockchain consensus protocol. In proceedings of the 1st Workshop on System Software for Trusted Execution, pages 1-6. DOI: 10.1145/3007788.3007790.
Monrat, A. A., Schelén, O., and Andersson, K. (2019). A survey of blockchain from the perspectives of applications, challenges, and opportunities. IEEE Access, 7:117134-117151. DOI: 10.1109/access.2019.2936094.
Nakamoto, S. (2008). A peer-to-peer electronic cash system. Bitcoin, 4. Available at:[link].
Ongaro, D. and Ousterhout, J. (2015). The raft consensus algorithm. Lecture Notes CS, 190:2022. Available at:[link].
P4Titan (2014). A peer-to-peer crypto-currency with proof-of-burn “mining without powerful hardware”. Available at:[link] acesso em 22/03/2024.
Paul, R., Ghosh, N., Sau, S., Chakrabarti, A., and Mohapatra, P. (2021). Blockchain based secure smart city architecture using low resource iots. Computer Networks, 196:108234. DOI: 10.1016/j.comnet.2021.108234.
Rasolroveicy, M. and Fokaefs, M. (2020). Dynamic reconfiguration of consensus protocol for iot data registry on blockchain. In Proceedings of the 30th Annual International Conference on Computer Science and Software Engineering, pages 227-236. Avaialble at:[link].
Salimitari, M., Chatterjee, M., and Fallah, Y. P. (2020). A survey on consensus methods in blockchain for resource-constrained iot networks. Internet of Things, 11:100212. DOI: 10.36227/techrxiv.12152142.v1.
Silvano, W. F. and Marcelino, R. (2020). Iota tangle: A cryptocurrency to communicate internet-of-things data. Future generation computer systems, 112:307-319. DOI: 10.1016/j.future.2020.05.047.
Son, B., Lee, J., and Jang, H. (2020). A scalable iot protocol via an efficient dag-based distributed ledger consensus. Sustainability, 12(4):1529. DOI: 10.3390/su12041529.
Tapwal, R., Deb, P. K., Misra, S., and Pal, S. K. (2021). Amaurotic-entity-based consensus selection in blockchain-enabled industrial iot. IEEE Internet of Things Journal, 9(14):11648-11655. DOI: 10.1109/jiot.2021.3131501.
Ugochukwu, N. A., Goyal, S., Rajawat, A. S., Islam, S. M., He, J., and Aslam, M. (2022). An innovative blockchain-based secured logistics management architecture: utilizing an rsa asymmetric encryption method. Mathematics, 10(24):4670. DOI: 10.3390/math10244670.
Van Meerten, M., Ozkan, B. K., and Panichella, A. (2023). Evolutionary approach for concurrency testing of ripple blockchain consensus algorithm. In 2023 IEEE/ACM 45th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP), pages 36-47. IEEE. DOI: 10.1109/icse-seip58684.2023.00009.
Vose, M. D. (1999). The simple genetic algorithm: foundations and theory. MIT press. Book.
Wang, Q., Li, R., Wang, Q., Chen, S., and Xiang, Y. (2022). Exploring unfairness on proof of authority: Order manipulation attacks and remedies. In Proceedings of the 2022 ACM on Asia Conference on Computer and Communications Security, pages 123-137. ACM. DOI: 10.48550/arxiv.2203.03008.
Wang, Q., Yu, J., Chen, S., and Xiang, Y. (2023). Sok: Dag-based blockchain systems. ACM Computing Surveys, 55(12):1-38. DOI: 10.1145/3576899.
Wang, T., Wang, Q., Shen, Z., Jia, Z., and Shao, Z. (2021). Understanding characteristics and system implications of dag-based blockchain in iot environments. IEEE Internet of Things Journal, 9(16):14478-14489. DOI: 10.1109/jiot.2021.3108527.
Wu, M., Wang, K., Cai, X., Guo, S., Guo, M., and Rong, C. (2019). A comprehensive survey of blockchain: From theory to iot applications and beyond. IEEE Internet of Things Journal, 6(5):8114-8154. DOI: 10.1109/jiot.2019.2922538.
Xiao, B., Jin, C., Li, Z., Zhu, B., Li, X., and Wang, D. (2021). Proof of importance: A consensus algorithm for importance based on dynamic authorization. In IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, pages 510-513. DOI: 10.1145/3498851.3499007.
Yang, C., Wang, T., and Wang, K. (2020). A consensus mechanism based on an improved genetic algorithm. Open Access Library Journal, 7(9):1-6. DOI: 10.4236/oalib.1106713.
Zaabar, B., Cheikhrouhou, O., Jamil, F., Ammi, M., and Abid, M. (2021). Healthblock: A secure blockchain-based healthcare data management system. Computer Networks, 200:108500. DOI: 10.1016/j.comnet.2021.108500.
Zhipeng, F. (2019). Research on blockchain hybrid consensus algorithm based on internet of things. Int. J. Internet Things Big Data. DOI: 10.21742/ijitbd.2019.4.1.05.
Zubaydi, H. D., Varga, P., and Molnár, S. (2023). Leveraging blockchain technology for ensuring security and privacy aspects in internet of things: a systematic literature review. Sensors, 23(2):788. DOI: 10.3390/s23020788.
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