An Approach to Improvise Blockchain Scalability Using Sharding and PBFT

Authors

  • Murthy D. H. R. Assistant Professor & Research Scholar School of Computer Science and Information Science Presidency University, Bangalore, India.
  • K. G. Mohan Professor, Department of Computer Science & Engineering, GITAM School Technology, Bengaluru, India
  • Jacob Augustine Professor, School of Computer Science and Information Science, Presidency University, Bangalore, India
  • G. K. Patra Chief Scientist, CSIR Fourth Paradigm Institute, Bangalore, India

Keywords:

Blockchain, IoT, Scalability, Deep Adversarial Neural Network, Shard, PBFT

Abstract

IoT is mainly used to forward the data to blockchain enabled networks to avert the tampering of sensitive data. This enhances the reliability and scalability of the IoT based services. Meanwhile, the advancement of technologies might affect the blockcahin system and hence the transaction rate per second and the scalability got reduced. In concern with this, we propose a novel Shard technology along with PBFT Blockchain. This enhances the throughput along with mitigated latency. In addition to this we have developed decentralized student database using the IPFS and estimated the transaction time and compared those results with the deployment of Sharding technique and PBFT technique. The Black Hole Optimization (BHO) algorithm improves the throughput per second and thus improvize the scalability and also reduces the tradeoff between the scalability and delay. Simulation outcomes outlined the system that deployed with the sharding and PBFT techniques improvise the scalability and reliability of the system.

Downloads

Download data is not yet available.

References

Bodkhe, U., Tanwar, S., Parekh, K., Khanpara, P., Tyagi, S., Kumar, N. and Alazab, M., 2020. Blockchain for industry 4.0: A comprehensive review. IEEE Access, 8, pp.79764-79800.

Xin, G., Han, J., Yin, T., Zhou, Y., Yang, J., Cheng, X. and Zeng, X., 2020. VPQC: A domain-specific vector processor for post-quantum cryptography based on RISC-V architecture. IEEE transactions on circuits and systems I: regular papers, 67(8), pp.2672-2684.

Opare, E.A. and Kim, K., 2020. A compendium of practices for central bank digital currencies for multinational financial infrastructures. IEEE Access, 8, pp.110810-110847.

Stecca, M., Elizondo, L.R., Soeiro, T.B., Bauer, P. and Palensky, P., 2020. A comprehensive review of the integration of battery energy storage systems into distribution networks. IEEE Open Journal of the Industrial Electronics Society, 1, pp.46-65.

Vuppalapati, C., Ilapakurti, A., Chillara, K., Kedari, S. and Mamidi, V., 2020, December. Automating tiny ml intelligent sensors devops using microsoft azure. In 2020 ieee international conference on big data (big data) (pp. 2375-2384). IEEE.

Zichichi, M., Ferretti, S. and D’angelo, G., 2020. A framework based on distributed ledger technologies for data management and services in intelligent transportation systems. IEEE Access, 8, pp.100384-100402.

Li, J., Ye, H., Li, T., Wang, W., Lou, W., Hou, Y.T., Liu, J. and Lu, R., 2020. Efficient and secure outsourcing of differentially private data publishing with multiple evaluators. IEEE Transactions on Dependable and Secure Computing, 19(1), pp.67-76.

Cui, Z., Fei, X.U.E., Zhang, S., Cai, X., Cao, Y., Zhang, W. and Chen, J., 2020. A hybrid blockchain-based identity authentication scheme for multi-WSN. IEEE Transactions on Services Computing, 13(2), pp.241-251.

Khan, M.A., Abbas, S., Rehman, A., Saeed, Y., Zeb, A., Uddin, M.I., Nasser, N. and Ali, A., 2020. A machine learning approach for blockchain-based smart home networks security. IEEE Network, 35(3), pp.223-229.

Cai, X., Geng, S., Zhang, J., Wu, D., Cui, Z., Zhang, W. and Chen, J., 2021. A sharding scheme-based many-objective optimization algorithm for enhancing security in blockchain-enabled industrial internet of things. IEEE Transactions on Industrial Informatics, 17(11), pp.7650-7658.

Chen, H. and Wang, Y., 2019. SSChain: A full sharding protocol for public blockchain without data migration overhead. Pervasive and Mobile Computing, 59, p.101055.

Feng, X., Ma, J., Miao, Y., Meng, Q., Liu, X., Jiang, Q. and Li, H., 2019. Pruneable sharding-based blockchain protocol. Peer-to-Peer Networking and Applications, 12, pp.934-950.

Asheralieva, A. and Niyato, D., 2020. Reputation-based coalition formation for secure self-organized and scalable sharding in iot blockchains with mobile-edge computing. IEEE Internet of Things Journal, 7(12), pp.11830-11850.

Liu, Y., Liu, J., Wu, Q., Yu, H., Hei, Y. and Zhou, Z., 2020. SSHC: A secure and scalable hybrid consensus protocol for sharding blockchains with a formal security framework. IEEE Transactions on Dependable and Secure Computing, 19(3), pp.2070-2088.

Du, M., Chen, Q. and Ma, X., 2020. MBFT: A new consensus algorithm for consortium blockchain. IEEE Access, 8, pp.87665-87675.

Huang, C., Wang, Z., Chen, H., Hu, Q., Zhang, Q., Wang, W. and Guan, X., 2020. Repchain: A reputation-based secure, fast, and high incentive blockchain system via sharding. IEEE Internet of Things Journal, 8(6), pp.4291-4304.

Kwak, J.Y., Yim, J., Ko, N.S. and Kim, S.M., 2020. The design of hierarchical consensus mechanism based on service-zone sharding. IEEE Transactions on Engineering Management, 67(4), pp.1387-1403.

Yun, J., Goh, Y. and Chung, J.M., 2020. DQN-based optimization framework for secure sharded blockchain systems. IEEE Internet of Things Journal, 8(2), pp.708-722.

Cai, X., Geng, S., Zhang, J., Wu, D., Cui, Z., Zhang, W. and Chen, J., 2021. A sharding scheme-based many-objective optimization algorithm for enhancing security in blockchain-enabled industrial internet of things. IEEE Transactions on Industrial Informatics, 17(11), pp.7650-7658.

Yun, J., Goh, Y. and Chung, J.M., 2020. DQN-based optimization framework for secure sharded blockchain systems. IEEE Internet of Things Journal, 8(2), pp.708-722.

Tao, Q., Cui, X., Huang, X., Leigh, A.M. and Gu, H., 2019. Food safety supervision system based on hierarchical multi-domain blockchain network. IEEE access, 7, pp.51817-51826.

Yun, J., Goh, Y. and Chung, J.M., 2020. DQN-based optimization framework for secure sharded blockchain systems. IEEE Internet of Things Journal, 8(2), pp.708-722.

Xu, Y., Shao, J., Slaats, T. and Düdder, B., 2023. MWPoW+: a strong consensus protocol for intra-shard consensus in blockchain sharding. ACM Transactions on Internet Technology.

Moran, A., Hardin, D., Rodman, D., Allen, H.F., Beall, R.J., Borowitz, D., Brunzell, C., Campbell Iii, P.W., Chesrown, S.E., Duchow, C. and Fink, R.J., 1999. Diagnosis, screening and management of cystic fibrosis related diabetes mellitus: a consensus conference report. Diabetes research and clinical practice, 45(1), pp.61-73.

Jalalzai, M.M., Feng, C., Busch, C., Richard, G.G. and Niu, J., 2021. The Hermes BFT for Blockchains. IEEE Transactions on Dependable and Secure Computing, 19(6), pp.3971-3986.

Hatamlou, A., 2013. Black hole: A new heuristic optimization approach for data clustering. Information sciences, 222, pp.175-184.

Chaudhary, D. S. . (2021). ECG Signal Analysis for Myocardial Disease Prediction by Classification with Feature Extraction Machine Learning Architectures. Research Journal of Computer Systems and Engineering, 2(1), 06:10. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/12

Tamilselvi, T. ., Lakshmi, D. ., Lavanya, R. ., & Revathi, K. . (2023). Digital Companion for Elders in Tracking Health and Intelligent Recommendation Support using Deep Learning. International Journal on Recent and Innovation Trends in Computing and Communication, 11(3), 145–152. https://doi.org/10.17762/ijritcc.v11i3.6331

Venu, S., Kotti, J., Pankajam, A., Dhabliya, D., Rao, G.N., Bansal, R., Gupta, A., Sammy, F. Secure Big Data Processing in Multihoming Networks with AI-Enabled IoT (2022) Wireless Communications and Mobile Computing, 2022, art. no. 3893875, .

Downloads

Published

27.10.2023

How to Cite

D. H. R., M. ., Mohan, K. G. ., Augustine, J. ., & Patra, G. K. . (2023). An Approach to Improvise Blockchain Scalability Using Sharding and PBFT. International Journal of Intelligent Systems and Applications in Engineering, 12(2s), 362 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3635

Issue

Vol. 12 No. 2s (2024)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.