Logo PTI Logo FedCSIS

Proceedings of the 19th Conference on Computer Science and Intelligence Systems (FedCSIS)

Annals of Computer Science and Information Systems, Volume 39

Efficiency and Reliability of Avalanche Consensus Protocol in Vehicular Communication Networks

, ,

DOI: http://dx.doi.org/10.15439/2024F3909

Citation: Proceedings of the 19th Conference on Computer Science and Intelligence Systems (FedCSIS), M. Bolanowski, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 39, pages 695–700 ()

Full text

Abstract. n vehicular communication networks, security issues such as privacy preservation, secure authentication, and threats from insiders and compromised authorities pose significant challenges due to the centralized nature of existing systems. Addressing these concerns, we propose a blockchainbased system that decentralizes control and enhances security using the Avalanche consensus protocol, known for its innovation and scalability. Our proposed system achieves a substantial throughput, with PBFT registering 12.8 transactions per second (TPS) and Avalanche demonstrating an impressive 1007 TPS for 100 validators. In terms of delay, PBFT experiences 6.61 seconds, whereas Avalanche achieves a remarkably low delay of just 1 millisecond, both with 100 validators. These findings underscore the superiority of our proposed system, offering heightened security, privacy, and transaction throughput essential for future vehicular communication systems.

References

  1. Z. Ullah, M. Naeem, A. Coronato, P. Ribino, and G. De Pietro, “Blockchain applications in sustainable smart cities,” Sustainable Cities and Society, p. 104697, 2023.
  2. G. Paragliola, A. Coronato, M. Naeem, and G. De Pietro, “A reinforcement learning-based approach for the risk management of e-health environments: A case study,” in 2018 14th international conference on signal-image technology & internet-based systems (SITIS). IEEE, 2018, pp. 711–716.
  3. A. Coronato, G. de Pietro, and G. Paragliola, “A monitoring system enhanced by means of situation-awareness for cognitive impaired people,” in BodyNets ’13: Proceedings of the 8th International Conference on Body Area Networks. Brussels, BEL: ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), Sep. 2013, pp. 124–127.
  4. M. Jamal, Z. Ullah, M. Naeem, M. Abbas, and A. Coronato, “A hybrid multi-agent reinforcement learning approach for spectrum sharing in vehicular networks,” Future Internet, vol. 16, no. 5, p. 152, 2024.
  5. S. Nakamoto, “Bitcoin: A peer-to-peer electronic cash system,” 2008.
  6. S. Yan, “Analysis on blockchain consensus mechanism based on proof of work and proof of stake,” in 2022 International Conference on Data Analytics, Computing and Artificial Intelligence (ICDACAI), 2022, pp. 464–467.
  7. S. S. Mahdi, Z. Ullah, G. Battineni, M. G. Babar, and U. Daood, “The telehealth chain: a framework for secure and transparent telemedicine transactions on the blockchain,” Irish Journal of Medical Science (1971-), pp. 1–9, 2024.
  8. V. Buterin et al., “A next-generation smart contract and decentralized application platform,” white paper, vol. 3, no. 37, pp. 2–1, 2014.
  9. M. Naeem, S. Bashir, M. U. Khan, and A. A. Syed, “Performance comparison of scheduling algorithms for mu-mimo systems,” in 2016 13th International Bhurban Conference on Applied Sciences and Technology (IBCAST), 2016, pp. 601–606.
  10. S. Islam, M. J. Islam, M. Hossain, S. Noor, K.-S. Kwak, and S. M. R. Islam, “A survey on consensus algorithms in blockchain-based applications: Architecture, taxonomy, and operational issues,” IEEE Access, vol. 11, pp. 39 066–39 082, 2023.
  11. M. Jamal, Z. Ullah, and M. Abbas, “Self-adapted resource allocation in v2x communication,” in Workshop Proceedings of the 19th International Conference on Intelligent Environments (IE2023), vol. 32. IOS Press, 2023, p. 104.
  12. Z. Lu, Q. Wang, G. Qu, H. Zhang, and Z. Liu, “A blockchain-based privacy-preserving authentication scheme for vanets,” IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 27, no. 12, pp. 2792–2801, 2019.
  13. M. Naeem, G. Paragiola, A. Coronato, and G. De Pietro, “A cnn based monitoring system to minimize medication errors during treatment process at home,” in Proceedings of the 3rd International Conference on Applications of Intelligent Systems, 2020, pp. 1–5.
  14. C. Wang, J. Shen, J.-F. Lai, and J. Liu, “B-tsca: Blockchain assisted trustworthiness scalable computation for v2i authentication in vanets,” IEEE Transactions on Emerging Topics in Computing, vol. 9, no. 3, pp. 1386–1396, 2021.
  15. D. Gabay, K. Akkaya, and M. Cebe, “Privacy-preserving authentication scheme for connected electric vehicles using blockchain and zero knowledge proofs,” IEEE Transactions on Vehicular Technology, vol. 69, no. 6, pp. 5760–5772, 2020.
  16. Q. Feng, D. He, S. Zeadally, and K. Liang, “Bpas: Blockchain-assisted privacy-preserving authentication system for vehicular ad hoc networks,” IEEE Transactions on Industrial Informatics, vol. 16, no. 6, pp. 4146–4155, 2020.
  17. C. Lin, D. He, X. Huang, N. Kumar, and K.-K. R. Choo, “Bcppa: A blockchain-based conditional privacy-preserving authentication protocol for vehicular ad hoc networks,” IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 12, pp. 7408–7420, 2021.
  18. A. Alharthi, Q. Ni, and R. Jiang, “A privacy-preservation framework based on biometrics blockchain (bbc) to prevent attacks in vanet,” IEEE Access, vol. 9, pp. 87 299–87 309, 2021.
  19. G. Paragliola and M. Naeem, “Risk management for nuclear medical department using reinforcement learning algorithms,” Journal of Reliable Intelligent Environments, vol. 5, pp. 105–113, 2019.
  20. D. Zheng, C. Jing, R. Guo, S. Gao, and L. Wang, “A traceable blockchain-based access authentication system with privacy preservation in vanets,” IEEE Access, vol. 7, pp. 117 716–117 726, 2019.
  21. A. R. Javed, M. M. Hassan, F. Shahzad, W. Ahmed, S. Singh, T. Baker, and T. R. Gadekallu, “Integration of blockchain technology and federated learning in vehicular (iot) networks: A comprehensive survey,” Sensors, vol. 22, no. 12, p. 4394, Jun 2022.
  22. M. Dibaei, X. Zheng, Y. Xia, and X. Xu, “Investigating the prospect of leveraging blockchain and machine learning to secure vehicular networks: A survey,” IEEE Transactions on Intelligent Transportation Systems, vol. PP, no. 99, pp. 1–18, August 2021.
  23. T. Rocket, M. Yin, K. Sekniqi, R. van Renesse, and E. G. Sirer, “Avalanche: A novel metastable consensus protocol family for cryptocurrencies,” https://www.avalabs.org/whitepapers, 2020, accessed: 2024-07-06.
  24. G. Bigini, V. Freschi, and E. Lattanzi, “Blockchain in the iot: Architectures and implementation,” Future Internet, vol. 12, no. 12, p. 208, 2020, submission received: 3 November 2020 / Revised: 20 November 2020 / Accepted: 23 November 2020 / Published: 25 November 2020. [Online]. Available: https://www.mdpi.com/1999-5903/12/12/208
  25. J. Noh, S. Jeon, and S. Cho, “Distributed blockchain-based message authentication scheme for connected vehicles,” Electronics, vol. 9, no. 1, p. 74, 2020.
  26. U. Hernandez-Jayo, A. S. K. Mammu, and I. De-la Iglesia, “Reliable communication in cooperative ad hoc networks,” Contemporary Issues in Wireless Communications, 2014.
  27. D. Manivannan, S. S. Moni, and S. Zeadally, “Secure authentication and privacy-preserving techniques in vehicular ad-hoc networks (vanets),” Vehicular Communications, vol. 25, p. 100247, 2020.
  28. J. Meijers, P. Michalopoulos, S. Motepalli, G. Zhang, S. Zhang, A. Veneris, and H.-A. Jacobsen, “Blockchain for v2x: Applications and architectures,” IEEE Open Journal of Vehicular Technology, vol. 3, pp. 193–209, 2022.