Simulation and Evaluation of Distributed consensus Network for Multi-Agent Systems for Sybil Attacks
DOI:
https://doi.org/10.31185/wjcms.215Abstract
Distributed average consensus represents the amount of computing of inputs held by multiple agents that communicate through peer-to-peer networks. Collaboration among operators is essential for any distributed standard consensus protocol as every specialist needs to contribute to different operators, typically the adjacent (neighbouring) operators. Internet-of-Things (IoT) implementation is challenging because of its heterogeneous, massively distributed nature. The challenges of this challenge can be addressed with blockchain-based platforms and technologies. Testing and evaluation platforms are required for Blockchain deployments in IoT. A realistic and configurable network environment is presented in this paper to evaluate consensus algorithms. Many blockchain
evaluation platforms do not provide a configurable and realistic network environment or are tied to a specific consensus protocol. With our simulator, practitioners can evaluate how consensus algorithms affect network events in congested or contested scenarios to determine the best consensus algorithm. It is proposed to achieve this task by generalizing consensus methods. The Blockchain simulator employs Discrete event network simulations for increased fidelity and scalability. In addition to evaluating the time, state block rate (%), estimation error, average throughput, and simulation time, we evaluate the performance of the proposed techniques based on the number of peer nodes. A comparison of the average transaction delivery rate with a traditional protocol is shown. The proposed protocol has a higher throughput average than the traditional one.
References
G. Angelo, S. Ferretti, and M. Marzolla, “A blockchain-based flight data recorder for cloud accountability,” Proceedings of the 1st Workshop on Cryptocurrencies and Blockchains for Distributed Systems, pp. 93–98, 2018.
M. Zichichi, M. Contu, S. Ferretti, and G. D. Angelo, “LikeStarter: a Smart-contract based Social DAO for Crowdfunding,” IEEE INFOCOM 2019-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 313–318, 2019.
P. Rani, S. Verma, S. P. Yadav, B. K. Rai, M. S. Naruka, and D. Kumar, “Simulation of the Lightweight Blockchain Technique Based on Privacy and Security for Healthcare Data for the Cloud System,” Int. J. E-Health Med. Commun. IJEHMC, vol. 13, no. 4, pp. 1–15, 2022.
Y. Zheng, J. Ma, and L. Wang, “Consensus of Hybrid Multi-Agent Systems,” IEEE Trans. Neural Netw. Learn. Syst, vol. 29, no. 4, pp. 1359–1365, 2018.
X. Dong and G. Hu, “Time-Varying Output Formation for Linear Multiagent Systems via Dynamic Output Feedback Control,” IEEE Trans. Control Netw. Syst, vol. 4, no. 2, pp. 236–245, 2017.
D. Zhang and G. Feng, “A New Switched System Approach to Leader-Follower Consensus of Heterogeneous Linear Multiagent Systems With DoS Attack,” IEEE Trans. Syst. Man Cybern. Syst, vol. 51, no. 2, pp. 1258–1266, 2021.
Y. Shang, “Resilient Consensus for Robust Multiplex Networks with Asymmetric Confidence Intervals,” IEEE Trans. Netw. Sci. Eng, vol. 8, no. 1, pp. 65–74, 2021.
P. Rani, “Federated Learning-Based Misbehaviour Detection for the 5G-Enabled Internet of Vehicles,” IEEE Trans. Consum. Electron, pp. 1–1, 2023.
D. Wang, N. Zheng, M. Xu, Y. Wu, Q. Hu, and G. Wang, “Resilient privacy-preserving average consensus for multi-agent systems under attacks,” 2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV), pp. 1399–1405, 2020.
G. Wen, Y. Lv, J. Zhou, and J. Fu, “Sufficient and necessary condition for resilient consensus under time-varying topologies,” 2020 7th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS), pp. 84–89, 2020.
Y. Shang, “Resilient consensus of switched multi-agent systems,” Syst. Control Lett, vol. 122, pp. 12–18, 2018.
S. Gil, S. Kumar, M. Mazumder, D. Katabi, and D. Rus, “Guaranteeing spoof-resilient multi-robot networks,” Auton. Robots, vol. 41, no. 6, pp. 1383–1400, 2017.
P. Rani and R. Sharma, “Intelligent transportation system for internet of vehicles based vehicular networks for smart cities,” Comput. Electr. Eng, vol. 105, pp. 108543–108543, 2023.
B. Kailkhura, S. Brahma, and P. K. Varshney, “Data Falsification Attacks on Consensus-Based Detection Systems,” IEEE Trans. Signal Inf. Process. Netw, vol. 3, no. 1, pp. 145–158, 2017.
Y. Shang, “Consensus of Hybrid Multi-Agent Systems With Malicious Nodes,” IEEE Trans. Circuits Syst. II Express Briefs, vol. 67, no. 4, pp. 685–689, 2020.
P. Rani, N. Hussain, R. A. H. Khan, Y. Sharma, and P. K. Shukla, “Vehicular Intelligence System: Time-Based Vehicle Next Location Prediction in Software-Defined Internet of Vehicles (SDN-IOV) for the Smart Cities,” in Intelligence of Things: AI-IoT Based Critical-Applications and Innovations (F. Al-Turjman, A. Nayyar, A. Devi, , and P. K. Shukla, eds.), pp. 35–54, Springer International Publishing, 2021.
Baili and JARBOUI , Wasit Journal of Computer and Mathematics Science, Vol. 2 No. 4 (2023) p. 13-26
A. E. Gencer, S. Basu, I. Eyal, R. V. Renesse, and E. G. Sirer, “Decentralization in bitcoin and ethereum networks,” in Financial Cryptography and Data Security: 22nd International Conference, pp. 439–457, Springer, 2018.
P. Maymounkov, D. Mazières, and Kademlia, “A Peer-to-Peer Information System Based on the XOR Metric,” in Peer-to-Peer Systems,” Lecture Notes in Computer Science, vol. 2429, pp. 53–65, 2002.
Y. Xiao, N. Zhang, W. Lou, and Y. T. Hou, “A Survey of Distributed Consensus Protocols for Blockchain Networks,” IEEE Commun. Surv. Tutor, vol. 22, no. 2, pp. 1432–1465, 2020.
B. Bhola, “Quality-enabled decentralized dynamic IoT platform with scalable resources integration,” IET Commun, 2022.
N. Kumar, P. Rani, V. Kumar, S. V. Athawale, and D. Koundal, “THWSN: Enhanced energy-efficient clustering approach for three-tier
heterogeneous wireless sensor networks,” IEEE Sens. J, vol. 22, no. 20, 2022.
M. Faheem, G. Fizza, M. W. Ashraf, R. A. Butt, M. A. Ngadi, and V. C. Gungor, “Big Data acquired by Internet of Things-enabled industrial multichannel wireless sensors networks for active monitoring and control in the smart grid Industry 4.0,” Data Brief, vol. 35, pp. 106854–106854, 2021.
G. Wood, “Polkadot: Vision for a heterogeneous multi-chain framework,” White Pap, vol. 21, no. 2327, pp. 4662–4662, 2016.
E. Buchman Tendermint: Byzantine fault tolerance in the age of blockchains, 2016.
J. Dilley, A. Poelstra, J. Wilkins, M. Piekarska, B. Gorlick, and M. Friedenbach, “Strong federations: An interoperable blockchain solution to centralized third-party risks,” ArXiv Prepr, 2016.
S. Thomas and E. Schwartz A protocol for interledger payments, 2015.
C. M. Li, T. Hwang, and N. Y. Lee, “Threshold-multisignature schemes where suspected forgery implies traceability of adversarial shareholders,” in Advances in Cryptology-EUROCRYPT’94: Workshop on the Theory and Application of Cryptographic Techniques, vol. 13, pp. 194–204, Springer, 1994.
A. Kiayias, N. Lamprou, and A. P. Stouka, “Proofs of Proofs of Work with Sublinear Complexity,” in Financial Cryptography and Data Security (J. Clark, S. Meiklejohn, P. Y. A. Ryan, D. Wallach, M. Brenner, , and K. Rohloff, eds.), vol. 9604, pp. 61–78, Springer, 2016.
A. Kiayias, A. Miller, and D. Zindros, “Non-interactive proofs of proof-of-work,” in Financial Cryptography and Data Security: 24th International Conference, vol. 2020, pp. 505–522, Springer, 2020.
S. Nakamoto, “Bitcoin: A peer-to-peer electronic cash system,” Decentralized Bus. Rev, 2008.
V. Buterin and E. White 2015.
S. Zhang and J. H. Lee, “Analysis of the main consensus protocols of blockchain,” ICT Express, vol. 6, no. 2, pp. 93–97, 2020.
S. Bouraga, “A taxonomy of blockchain consensus protocols: A survey and classification framework,” Expert Syst. Appl, vol. 168, pp. 114384– 114384, 2021.
S. Küfeoglu and M. Özkuran, “Bitcoin mining: A global review of energy and power demand,” ˘ ” Energy Res. Soc. Sci, vol. 58, pp. 101273–101273, 2019.
N. Stifter, A. Judmayer, and E. Weippl, “Revisiting practical byzantine fault tolerance through blockchain technologies,” Secur. Qual. Cyber-Phys. Syst. Eng. Forewords Robert M Lee Tom Gilb, pp. 471–495, 2019.
S. J. Lukasik, “Protecting users of the cyber commons,” Commun. ACM, vol. 54, no. 9, pp. 54–61, 2011.
A. V. Uzunov, E. B. Fernandez, and K. Falkner, “Securing distributed systems using patterns: A survey,” Comput. Secur, vol. 31, no. 5, pp. 681– 703, 2012.
M. Conti, E. S. Kumar, C. Lal, and S. Ruj, “A survey on security and privacy issues of bitcoin,” IEEE Commun. Surv. Tutor, vol. 20, no. 4,
pp. 3416–3452, 2018.
N. Hussain, P. Rani, H. Chouhan, and U. S. Gaur, “Cyber Security and Privacy of Connected and Automated Vehicles (CAVs)-Based Federated Learning: Challenges, Opportunities, and Open Issues,” in EAI/Springer Innovations in Communication and Computing (F. L. for IoT Applications, S. P. Yadav, B. S. Bhati, D. P. Mahato, , and S. Kumar, eds.), pp. 169–183, Springer International Publishing, 2022.
P. Lapsley, “Phreaking out ma bell,” IEEE Spectr, vol. 50, no. 2, pp. 30–35, 2013.
J. R. Douceur, “The sybil attack,” International workshop on peer-to-peer systems, pp. 251–260, 2002.
M. Rosenfeld, “Analysis of hashrate-based double spending,” ArXiv Prepr, 2014.
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