Comprehensive Analysis of Blockchain Algorithms
DOI:
https://doi.org/10.4108/eetiot.4549Keywords:
blockchain, consensus algorithm, blockchain securityAbstract
INTRODUCTION: Blockchain technology has gained significant attention across various sectors as a distributed ledger solution. To comprehend its applicability and potential, a comprehensive understanding of blockchain's essential elements, functional traits, and architectural design is imperative. Consensus algorithms play a critical role in ensuring the proper operation and security of blockchain networks. Consensus algorithms play a vital role in maintaining the proper operation of a blockchain network, and their selection is crucial for optimal performance and security.
OBJECTIVES: The objective of this research is to analyse and compare various consensus algorithms based on their performance and efficiency in mining blocks.
METHODS: To achieve this, an experimental model was developed to measure the number of mined blocks over time for different consensus algorithms.
RESULTS: The results provide valuable insights into the effectiveness and scalability of these algorithms. The findings of this study contribute to the understanding of consensus algorithm selection and its impact on the overall performance of blockchain systems.
CONCLUSION: The findings of this study contribute to the understanding of consensus algorithm selection and its impact on the overall performance of blockchain systems. By enhancing our knowledge of consensus algorithms, this research aims to facilitate the development of more secure and efficient blockchain applications.
Downloads
References
Panda S.K., Dash S.P., Jena A.K. (2021) Optimization of Block Query Response Using Evolutionary Algorithm. In: Bhateja V., Satapathy S.C., Travieso-González C.M., Aradhya V.N.M. (eds) Data Engineering and Intelligent Computing. Advances in Intelligent Systems and Computing, vol 1. Springer, Singapore. https://doi.org/10.1007/978-981-16-0171-2_54 DOI: https://doi.org/10.1007/978-981-16-0171-2_54
Nanda, S.K., Panda, S.K., Das, M., Satapathy, S.C. (2022). Automating Vehicle Insurance Process Using Smart Contract and Ethereum. In: Chakravarthy, V.V.S.S.S., Flores-Fuentes, W., Bhateja, V., Biswal, B. (eds) Advances in Micro-Electronics, Embedded Systems and IoT. Lecture Notes in Electrical Engineering, vol 838. Springer, Singapore. https://doi.org/10.1007/978-981-16-8550-7_23. DOI: https://doi.org/10.1007/978-981-16-8550-7_23
Varaprasada Rao, K., Panda, S.K. (2023). Secure Electronic Voting (E-voting) System Based on Blockchain on Various Platforms. In: Satapathy, S.C., Lin, J.CW., Wee, L.K., Bhateja, V., Rajesh, T.M. (eds) Computer Communication, Networking and IoT. Lecture Notes in Networks and Systems, vol 459. Springer, Singapore. https://doi.org/10.1007/978-981-19-1976-3_18 DOI: https://doi.org/10.1007/978-981-19-1976-3_18
Varaprasada Rao, K., Panda, S.K. (2023). A Design Model of Copyright Protection System Based on Distributed Ledger Technology. In: Satapathy, S.C., Lin, J.CW., Wee, L.K., Bhateja, V., Rajesh, T.M. (eds) Computer Communication, Networking and IoT. Lecture Notes in Networks and Systems, vol 459. Springer, Singapore. https://doi.org/10.1007/978-981-19-1976-3_17 DOI: https://doi.org/10.1007/978-981-19-1976-3_17
Panda SK, Mohammad GB, Nandan Mohanty S, Sahoo S. Smart contract-based land registry system to reduce frauds and time delay. Security and Privacy. 2021; e172. https://doi.org/10.1002/spy2.172.[23] Panda, S.K., Satapathy, S.C. Drug traceability and transparency in medical supply chain using blockchain for easing the process and creating trust between stakeholders and consumers. Pers Ubiquit Comput (2021). https://doi.org/10.1007/s00779-021-01588-3 DOI: https://doi.org/10.1007/s00779-021-01588-3
Panda, S.K., Sathya, A.R., Das, S. (2023). Bitcoin: Beginning of the Cryptocurrency Era. In: Panda, S.K., Mishra, V., Dash, S.P., Pani, A.K. (eds) Recent Advances in Blockchain Technology. Intelligent Systems Reference Library, vol 237. Springer, Cham. https://doi.org/10.1007/978-3-031-22835-3_2 DOI: https://doi.org/10.1007/978-3-031-22835-3_2
Murala, D.K., Panda, S.K., Sahoo, S.K. (2023). Securing Electronic Health Record System in Cloud Environment Using Blockchain Technology. In: Panda, S.K., Mishra, V., Dash, S.P., Pani, A.K. (eds) Recent Advances in Blockchain Technology. Intelligent Systems Reference Library, vol 237. Springer, Cham. https://doi.org/10.1007/978-3-031-22835-3_4 DOI: https://doi.org/10.1007/978-3-031-22835-3_4
Rao, K.V., Murala, D.K., Panda, S.K. (2023). Blockchain: A Study of New Business Model. In: Panda, S.K., Mishra, V., Dash, S.P., Pani, A.K. (eds) Recent Advances in Blockchain Technology. Intelligent Systems Reference Library, vol 237. Springer, Cham. https://doi.org/10.1007/978-3-031-22835-3_9 DOI: https://doi.org/10.1007/978-3-031-22835-3_9
Nanda, S.K., Panda, S.K., Das, M., Satapathy, S.C. (2023). Decentralization of Car Insurance System Using Machine Learning and Distributed Ledger Technology. In: Bhateja, V., Yang, XS., Chun-Wei Lin, J., Das, R. (eds) Intelligent Data Engineering and Analytics. FICTA 2022. Smart Innovation, Systems and Technologies, vol 327. Springer, Singapore. https://doi.org/10.1007/978-981-19-7524-0_52 DOI: https://doi.org/10.1007/978-981-19-7524-0_52
Agarwal N., Jain A., Gupta A., Tayal D.K. (2022) Applying XGBoost Machine Learning Model to Succor Astronomers Detect Exoplanets in Distant Galaxies. In: Dev A., Agrawal S.S., Sharma A. (eds) Artificial Intelligence and Speech Technology. AIST 2021. Communications in Computer and Information Science, vol 1546. Springer, Cham. https://doi.org/10.1007/978-3-030-95711-7_33. DOI: https://doi.org/10.1007/978-3-030-95711-7_33
Agarwal, N., Srivastava, R., Srivastava, P., Sandhu, J., Singh, Pratap P. Multiclass Classification of Different Glass Types using Random Forest Classifier. 6th International Conference on Intelligent Computing and Control Systems (ICICCS), 2022. p. 1682-1689. DOI: https://doi.org/10.1109/ICICCS53718.2022.9788326
Agarwal, N., Singh, V., Singh, P. Semi-Supervised Learning with GANs for Melanoma Detection. 6th International Conference on Intelligent Computing and Control Systems (ICICCS), 2022. p. 141-147. DOI: https://doi.org/10.1109/ICICCS53718.2022.9787990
Tayal, D.K., Agarwal, N., Jha, A., Deepakshi, Abrol, V. To Predict the Fire Outbreak in Australia using Historical Database. 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), 2022. p. 1-7. DOI: https://doi.org/10.1109/ICRITO56286.2022.9964603
Agarwal, N., Tayal, D.K. FFT based ensembled model to predict ranks of higher educational institutions. Multimed Tools Appl 81, 2022. DOI: https://doi.org/10.1007/s11042-022-13180-9
Ghosh, H., Tusher, M.A., Rahat, I.S., Khasim, S., Mohanty, S.N. (2023). Water Quality Assessment Through Predictive Machine Learning. In: Intelligent Computing and Networking. IC-ICN 2023. Lecture Notes in Networks and Systems, vol 699. Springer, Singapore. https://doi.org/10.1007/978-981-99-3177-4_6 DOI: https://doi.org/10.1007/978-981-99-3177-4_6
Alenezi, F.; Armghan, A.; Mohanty, S.N.; Jhaveri, R.H.; Tiwari, P. Block-Greedy and CNN Based Underwater Image Dehazing for Novel Depth Estimation and Optimal Ambient Light. Water 2021, 13, 3470. https://doi.org/10.3390/w13233470 DOI: https://doi.org/10.3390/w13233470
G. P. Rout and S. N. Mohanty, "A Hybrid Approach for Network Intrusion Detection," 2015 Fifth International Conference on Communication Systems and Network Technologies, Gwalior, India, 2015, pp. 614-617, doi: 10.1109/CSNT.2015.76. DOI: https://doi.org/10.1109/CSNT.2015.76
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 EAI Endorsed Transactions on Internet of Things
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 3.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.
