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Journal of Information and Optimization Sciences cover
Open Access ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

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Open Access Review Article

Blockchain consensus challenges and an efficient novel consensus mechanism

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* Corresponding author · click or hover a name for details

pp. 863–872Vol. 45Issue 4May 2024DOI: 10.47974/JIOS-1611XML
Published Online:
08 Jun 2024
Article type:
Review Article
Language:
EN
Article no.:
JIOS-1611
Pages:
863–872

Abstract

The blockchain technology has gain great popularity in recent years. As its adoption for different domains is increasing the consensus mechanism, a core part of blockchain technology is also explored to make it more efficient for increasing applicability of blockchain for decentralised applications. Initially most of the application, in which financial domain is dominating, were developed using public blockchains but as different domains are explored the private and consortium blockchains are also used. After studying the challenges faced by the decentralised applications, we have proposed a voting based consensus for consortium blockchain which is efficient and gives high performance. The proposed consensus mechanism is analyzed to capture its performance in terms of throughput and latency.

Keywords

Subject Classifications

68xx

References

[1] Singh, Arjun, et al. “Blockchain enabled security mechanism for preventing data forgery in IoT-based smart homes.” Journal of Discrete Mathematical Sciences and Cryptography, vol. 26, no. 5, pp. 1437–1446 (2023), doi: 10.47974/JDMSC-1769.
[2]] Singh, Arjun, Chauhan, Surbhi, Dargar, Shashi Kant, Tharewal, Sumegh, Gutte, Vitthal Sadashiv, Tiwari, Pradeep Kumar & Gupta, Sonam. Blockchain enabled security mechanism for preventing data forgery in IoT-based smart homes, Journal of Discrete Mathematical Sciences and Cryptography, 26:5, 1437–1446 ((2023)), DOI: 10.47974/JDMSC-1769.
[3]] Ajitha, P., et al. “An IoT-based integrated health monitoring of bulk milk cooler.” Journal of Discrete Mathematical Sciences and Cryptography, vol. 26, no. 3, (2023) pp. 851-860, doi: 10.47974/JDMSC-1764. 
[4]] Sharma, Raj Kumar, and Manisha Jailia. “Machine learning and IoT-based garbage detection system for smart cities.” Journal of Information and Optimization Sciences, vol. 44, no. 3, (2023) pp. 393–406, doi: 10.47974/JIOS-1349.
[5]] Hassan, S. I., et al. “A Systematic Review on Monitoring and Advanced Control Strategies in Smart Agriculture.” IEEE Access, vol. 9,  pp. 32517-32548 (2021), doi: 10.1109/ACCESS.2021.3057865.
[6] Zhao, J. W., et al. “Ground-Level Mapping and Navigating for Agriculture Based on IoT and Computer Vision.” IEEE Access, vol. 8, pp. 221975-221985 (2020), doi: 10.1109/ACCESS.2020.3043662.
[7]] Yang, X.-B., et al. “Hybrid deep learning predictor for smart agriculture sensing based on empirical mode decomposition and gated recurrent unit group model.” Sensors, vol. 20, no. 5, pp. 1334 (2020), doi: 10.3390/s20051334.
[8]] González-González, M. G., et al. “CitrusYield: A dashboard for mapping yield and fruit quality of citrus in precision agriculture.” Agronomy, vol. 10, pp. 128 (2020).
[9] Muñoz, M., et al. “An IoT architecture for water resource management in agroindustrial environments: A case study in Almería (Spain).” Sensors, vol. 20, pp. 596 (2020).
[10]] Poonia, Ramesh C., et al., eds. Smart Farming Technologies for Sustainable Agricultural Development. IGI Global (2018).
[11] Dadheech, Pankaj, et al. “A neural network-based approach for pest detection and control in modern agriculture using internet of things.” Smart agricultural services using deep learning, big data, and IoT. IGI Global, 1-31 (2021).
[12] Ayaz, M., et al. “Internet-of-Things (IoT)-Based Smart Agriculture: Toward Making the Fields Talk.” IEEE Access, vol. 7, pp. 129551-129583 (2019), doi: 10.1109/ACCESS.2019.2932609.
[13] Li, H., et al. “Application of Multi-Sensor Image Fusion of Internet of Things in Image Processing.” IEEE Access, vol. 6, pp. 50776-50787 (2018), doi: 10.1109/ACCESS.2018.2868227.
[14] Vaibhav Nivrutti Patil and Vijay H. Kalmani, “Enhancing security and ensuring secure performance: A performance evaluation of consensus algorithms in a distributed healthcare blockchain system”, Journal of Statistics & Management Systems, Vol. 26 (2023).
[15] L. Lamport, R. Shostak, and M. Pease, “The byzantine generals problem,” ACM Transactions on Programming Languages and Systems (TOPLAS), vol. 4, no. 3, pp. 382–401 (1982).
[16] “Hyperledger Caliper”, Hyperledger Foundation, available at https://github.com/hyperledger/caliper accessed on Nov (2022).
[17] “Hyperledger Besu for private networks”, Hyperledger Foundation, available at https://besu.hyperledger.org/en/stable/private-networks/ accessed on Nov (2022).
[18] “Hyperledger Blockchain Performance Metrics”, White paper, Hyperledger Foundation, available at hyperledger.org.
[19] A. Gervais, G. O. Karame, K. Wu¨st, et al. “On the security and performance of proof of work blockchains,” in Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. ACM, March (2016).

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