A Intelligent Hybrid Bio-Inspired (Raft Consensus - Foraging Hum-mingbird) for Distributed Storage System of Academic Information
Keywords:
Centralized blockchain, Decentralized data, Hummingbird algorithm Raft consensus, Multiple nodesAbstract
The giant bulk of records saved mostly on block chain is completed in a centralized way on a unmarried server. This holds true for the splendid majority of statistics. As a result, facts accessibility will be sincerely not possible in the occasion which the server crashes. To cope with the problem of a centralised blockchain backup mechanism, we applied a machine for data storage that is decentralized rather than centralized and runs on the basis of allotted rather than centralized computing in educational institutions. The fact that the facts can be kept on many servers assures that there can be no statistics loss even if one of the servers becomes unreachable. The Raft consensus-building technique is employed. This approach is a decentralized facts consensus technique in blockchain that distributes facts across multiple nodes whilst keeping educational material saved in establishments. furthermore, the hybrid of the Raft consensus technique and the Foraging Hummingbird set of rules has executed mechanism assures that no server failures will arise at any moment in time.
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D. Berdik, S. Otoum, N. Schmidt, D. Porter, and Y. Jararweh, “A Survey on Blockchain for Infor-mation Systems Management and Security,” Inf. Process. Manag., vol. 58, no. 1, p. 102397, 2021, doi: 10.1016/j.ipm.2020.102397.
A. A. Monrat, O. Schelén, and K. Andersson, “A survey of blockchain from the perspectives of ap-plications, challenges, and opportunities,” IEEE Access, vol. 7, pp. 117134–117151, 2019, doi: 10.1109/ACCESS.2019.2936094.
V. J. Morkunas, J. Paschen, and E. Boon, “How blockchain technologies impact your business model,” Bus. Horiz., vol. 62, no. 3, pp. 295–306, 2019, doi: 10.1016/j.bushor.2019.01.009.
W. Fu, X. Wei, and S. Tong, “An Improved Block-chain Consensus Algorithm Based on Raft,” Arab. J. Sci. Eng., vol. 46, no. 9, pp. 8137–8149, 2021, doi: 10.1007/s13369-021-05427-8.
P Gite, A Shrivastava, KM Krishna, GH Kusumadev, Under water motion tracking and monitoring using wireless sensor network and Machine learning, Ma-terials Today: Proceedings, Volume 80, Part 3, 2023, Pages 3511-3516
Anurag Shrivastava, Midhun Chakkaravathy, Mohd Asif Shah, A Novel Approach Using Learn-ing Algorithm for Parkinson’s Disease Detection with Handwritten Sketches’, Cybernetics and Sys-tems, Taylor & Francis
Mukesh Patidar, Anurag Shrivastava, Shahajan Miah, Yogendra Kumar, Arun Kumar Sivaraman, An energy efficient high-speed quantum-dot based full adder design and parity gate for nano application, Materials Today: Proceedings, Volume 62, Part 7, 2022, Pages 4880-4890
S. Pahlajani, A. Kshirsagar, and V. Pachghare, “Survey on Private Blockchain Consensus Algo-rithms,” Proc. 1st Int. Conf. Innov. Inf. Commun. Technol. ICIICT 2019, no. July, pp. 1– 6, 2019, doi: 10.1109/ICIICT1.2019.8741353.
J. S. Ahn, W. H. Kang, K. Ren, G. Zhang, and S. Ben-Romdhane, “Designing an efficient replicated log store with consensus protocol,” 11th USENIX Work. Hot Top. Cloud Comput. HotCloud 2019, co-located with USENIX ATC 2019, 2019.
D. Huang, X. Ma, and S. Zhang, “Performance Analysis of the Raft Consensus Algorithm for Pri-vate Blockchains,” IEEE Trans. Syst. Man, Cybern. Syst., vol. 50, no. 1, pp. 172–181, 2020, doi: 10.1109/TSMC.2019.2895471.
H. Howard and R. Mortier, “Paxos vs Raft: Have we reached consensus on distributed consensus?,” Proc. 7th Work. Princ. Pract. Consistency Distrib. Data, PaPoC 2020, pp. 8–10, 2020, doi: 10.1145/3380787.3393681.
E. Sakic, P. Vizarreta, and W. Kellerer, “SEER: Per-formance-Aware Leader Election in SingleLeader Consensus,” 2021, [Online]. Available: http://arxiv.org/abs/2104.01355.
D. Huang et al., “TiDB: a Raft-based HTAP data-base,” Proc. VLDB Endow., vol. 13, no. 12, pp. 3072–3084, 2020.
S. Tian, Y. Liu, Y. Zhang, and Y. Zhao, “A Byzan-tine Fault-Tolerant Raft Algorithm Combined with Schnorr Signature,” Proc. 2021 15th Int. Conf. Ubiquitous Inf. Manag. Commun. IMCOM 2021, pp. 1–5, 2021, doi: 10.1109/IMCOM51814.2021.9377376.
Anurag Shrivastava, S. J. Suji Prasad, Ajay Reddy Yeruva, P. Mani, Pooja Nagpal & Abhay Chatur-vedi (2023): IoT Based RFID Attendance Monitor-ing System of Students using Arduino ESP8266 & Adafruit.io on Defined Area, Cybernetics and Sys-tems, DOI: 10.1080/01969722.2023.2166243
P. William, A. Shrivastava, H. Chauhan, P. Nagpal, V. K. T. N and P. Singh, "Framework for Intelligent Smart City Deployment via Artificial Intelligence Software Networking," 2022 3rd International Con-ference on Intelligent Engineering and Manage-ment (ICIEM), 2022, pp. 455-460, doi: 10.1109/ICIEM54221.2022.9853119.
D. Tan, J. Hu, and J. Wang, “VBBFT-Raft: An Un-derstandable Blockchain Consensus Protocol with High Performance,” Proc. IEEE 7th Int. Conf. Comput. Sci. Netw. Technol. ICCSNT 2019, pp. 111–115, 2019, doi: 10.1109/ICCSNT47585.2019.8962479.
L. E. Wang, Y. Bai, Q. Jiang, V. C. M. Leung, W. Cai, and X. Li, “Beh-Raft-Chain: A BehaviorBased Fast Blockchain Protocol for Complex Networks,” IEEE Trans. Netw. Sci. Eng., vol. 8, no. 2, pp. 1154–1166, 2021, doi: 10.1109/TNSE.2020.2984490.
D. Kraft, “Difficulty control for blockchain-based consensus systems,” Peer-to-Peer Netw. Appl., vol. 9, no. 2, pp. 397–413, 2016, doi: 10.1007/s12083-015-0347-x.
C. Cachin and M. Vukolić, “Blockchain consensus protocols in the wild,” Leibniz Int. Proc. Informat-ics, LIPIcs, vol. 91, 2017, doi: 10.4230/LIPIcs.DISC.2017.1
Babu, D. R. ., & Sathyanarayana, B. . (2023). De-sign and Implementation of Technical Analysis Based LSTM Model for Stock Price Prediction. In-ternational Journal on Recent and Innovation Trends in Computing and Communication, 11(4s), 01–07. https://doi.org/10.17762/ijritcc.v11i4s.6301
Kwame Boateng, Machine Learning in Cybersecu-rity: Intrusion Detection and Threat Analysis , Ma-chine Learning Applications Conference Proceed-ings, Vol 3 2023.
Anupong, W., Azhagumurugan, R., Sahay, K. B., Dhabliya, D., Kumar, R., & Vijendra Babu, D. (2022). Towards a high precision in AMI-based smart meters and new technologies in the smart grid. Sustainable Computing: Informatics and Sys-tems, 35 doi:10.1016/j.suscom.2022.100690
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