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Scalability of blockchain: a comprehensive review and future research direction

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Abstract

This comprehensive review paper examines the challenges faced by blockchain technology in terms of scalability and proposes potential solutions and future research directions. Scalability poses a significant hurdle for Bitcoin and Ethereum, manifesting as low throughput, extended transaction delays, and excessive energy consumption, thereby compromising efficiency. The current state of blockchain scalability is analyzed, encompassing the limitations of existing solutions such as Sharding and off-chain scaling. Various proposed remedies, including layer 2 scaling solutions, consensus mechanisms, and alternative approaches, are investigated. The paper also explores the impact of scalability on diverse blockchain applications and identifies potential future research directions by integrating data science techniques with blockchain technology. Notably, nearly 110 primary research papers from reputable scientific databases like Scopus, IEEE Explore, Science Direct, and Web of Science were reviewed, demonstrating scalability in blockchain comprising several elements. Transaction throughput and network latency emerge as the most prominent concerns. Consequently, this review offers future research avenues to address scalability challenges by leveraging data science techniques like distributed computing and parallel processing to divide and process vast datasets across multiple machines. The synergy between data science and blockchain holds promise as an optimal solution. Overall, this up-to-date understanding of blockchain scalability is invaluable to researchers, practitioners, and policy makers engaged in this domain.

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References

  1. Wood, G., et al.: Ethereum: a secure decentralised generalised transaction ledger. Ethereum Proj. Yellow Pap. 151(2014), 1–32 (2014)

    Google Scholar 

  2. Chauhan, A. et al.: Blockchain and scalability. In: 2018 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C), pp. 122–128. IEEE (2018)

  3. Ochôa, I.S. et al.: Experimental analysis of the scalability of ethereum blockchain in a private network. In: Anais do II Workshop em Blockchain: Teoria, Tecnologia e Aplicações. SBC (2019)

  4. Zmaznev, E.: Bitcoin and ethereum evolution. PhD thesis. Centria University of Applied Sciences (2017). https://www.theseus.fi/bitstream/handle/10024/141520/Thesis.pdf

  5. Buterin, V. et al.: Ethereum white paper: a next generation smart contract & decentralized application platform. First version 53 (2014)

  6. Shahbazi, Z., Byun, Y.-C.: Integration of blockchain, IoT and machine learning for multistage quality control and enhancing security in smart manufacturing. Sensors 21(4), 1467 (2021)

    Google Scholar 

  7. Guangjun, W., et al.: Privacy-preserved electronic medical record exchanging and sharing: a blockchain-based smart healthcare system. IEEE J. Biomed. Health Inform. 26(5), 1917–1927 (2021)

    Google Scholar 

  8. Sanka, A.I., Cheung, R.C.C.: A systematic review of blockchain scalability: issues, solutions, analysis and future research. J. Netw. Comput. Appl. 195, 103232 (2021)

    Google Scholar 

  9. Hafid, A., Hafid, A.S., Samih, M.: Scaling blockchains: a comprehensive survey. IEEE Access 8, 125244–125262 (2020)

    Google Scholar 

  10. Ferretti, S., D’Angelo, G.: On the ethereum blockchain structure: a complex networks theory perspective. Concurr. Comput.: Pract. Exp. 32(12), e5493 (2020)

    Google Scholar 

  11. Wang, Z., Hu, Q.: Blockchain-based federated learning: a comprehensive survey. arXiv preprint arXiv:2110.02182 (2021)

  12. Bez, M., Fornari, G., Vardanega, T.: The scalability challenge of ethereum: an initial quantitative analysis. In: 2019 IEEE International Conference on Service-Oriented System Engineering (SOSE), pp. 167–176. IEEE (2019)

  13. Jabbar, A., Dani, S.: Investigating the link between transaction and computational costs in a blockchain environment. Int. J. Prod. Res. 58(11), 3423–3436 (2020)

    Google Scholar 

  14. Rondelet, A.: Zecale: reconciling privacy and scalability on ethereum. arXiv preprint arXiv:2008.05958 (2020)

  15. Ramanan, P., Nakayama, K.: Baffle: blockchain based aggregator free federated learning. In: IEEE International Conference on Blockchain (Blockchain), pp. 72–81. IEEE (2020)

  16. Drungilas, V., et al.: Towards blockchain-based federated machine learning: smart contract for model inference. Appl. Sci. 11(3), 1010 (2021)

    Google Scholar 

  17. Harris, J.D., Waggoner, B.: Decentralized and collaborative AI on blockchain. In: 2019 IEEE International Conference on Blockchain (Blockchain), pp. 368–375. IEEE (2019)

  18. Awoke, T. et al.: Bitcoin price prediction and analysis using deep learning models. In: Communication Software and Networks: Proceedings of INDIA 2019, pp. 631–640. Springer (2020)

  19. Liu, Y., et al.: Blockchain and machine learning for communications and networking systems. IEEE Commun. Surv. Tutor. 22(2), 1392–1431 (2020)

    Google Scholar 

  20. Simpson, T. et al.: Fetch: Technical introduction. A decentralized digital world for the future economy (2018). https://fetch.ai

  21. Van Otterlo, M.: A machine learning view on profiling. In: Privacy, Due Process and the Computational Turn-Philosophers of Law Meet Philosophers of Technology, pp. 41–64. Routledge, Abingdon (2013)

  22. Hutchins, P.: Polygon Lightpaper. (2018). https://www.forbes.com/sites/forbestechcouncil/2018/10/02/creating-scalability-on-ethereum/#6eeefb575226

  23. Harm, J., Obregon, J., Stubbendick, J.: Ethereum vs. bitcoin. www.economist.com (2016)

  24. Kim, H., et al.: Blockchained on-device federated learning. IEEE Commun. Lett. 24(6), 1279–1283 (2019)

    Google Scholar 

  25. Mohammed, A.H., Abdulateef, A.A., Abdulateef, I.A.: Hyperledger, Ethereum and blockchain technology: a short overview. In: 2021 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), pp. 1–6. IEEE (2021)

  26. Tun, M.T., Nyaung, D.E., Phyu, M.P.: Performance evaluation of intrusion detection streaming transactions using apache kafka and spark streaming. In: 2019 International Conference on Advanced Information Technologies (ICAIT), pp. 25–30. IEEE (2019)

  27. Nie, JY.: Institute of Electrical and Electronics Engineers, and IEEE Computer Society. In: 2017 IEEE International Conference on Big Data: proceedings, pp. 11–14 (2017)

  28. Jani, S.: An overview of ethereum & its comparison with bitcoin. Int. J. Sci. Eng. Res. 10(8), 1–6 (2017)

    Google Scholar 

  29. Toyoda, K., et al.: Function-level bottleneck analysis of private proof-of authority ethereum blockchain. IEEE Access 8, 141611–141621 (2020)

    Google Scholar 

  30. Zhang, L. et al.: Evaluation of ethereum end-to-end transaction latency. In: 2021 11th IFIP International Conference on New Technologies, Mobility and Security (NTMS), pp. 1–5. IEEE (2021)

  31. Gencer, A.E.: On scalability of blockchain technologies. PhD thesis. Cornell University (2017). https://search.proquest.com/docview/1964277559

  32. Kanani, J. et al.: Polygon Lightpaper (2021). https://www.proquest.com/docview/1964277559

  33. Croman, K. et al.: On Scaling Decentralized Blockchains Initiative for CryptoCurrencies and Contracts (IC3). http://fc16.ifca.ai/bitcoin/papers/CDE+16.pdf

  34. Mahmood, Z., Jusas, V.: Implementation framework for a blockchainbased federated learning model for classification problems. Symmetry 13(7), 1116 (2021)

    Google Scholar 

  35. Guangsheng, Y., et al.: Survey: sharding in blockchains. IEEE Access 8, 14155–14181 (2020)

    Google Scholar 

  36. Chen, X. et al.: When machine learning meets blockchain: a decentralized, privacy-preserving and secure design. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 1178–1187. IEEE (2018)

  37. Khalil, R. et al.: Commit-chains: secure, scalable off-chain payments. In: Cryptology ePrint Archive (2018). https://eprint.iacr.org/2018/642.pdf

  38. Schäffer, M., Di Angelo, M., Salzer, G.: Performance and scalability of private Ethereum blockchains. In: Business Process Management: Blockchain and Central and Eastern Europe Forum: BPM 2019 Blockchain and CEE Forum, Vienna, Austria, September 1–6, 2019. Proceedings 17, pp. 103–118. Springer (2019)

  39. Zhou, Q., et al.: Solutions to scalability of blockchain: a survey. IEEE Access 8, 16440–16455 (2020)

    Google Scholar 

  40. Vujičić, D., Jagodić, D., Ranđić, S.: Blockchain technology, bitcoin, and Ethereum: a brief overview. In: 17th International Symposium Infoteh-Jahorina (infoteh), pp. 1–6. IEEE (2018)

  41. Khan, D., Jung, L.T., Hashmani, M.A.: Systematic literature review of challenges in blockchain scalability. Appl. Sci. 11(20), 9372 (2021)

    Google Scholar 

  42. Swathi, P., Venkatesan, M.: Scalability improvement and analysis of permissioned-blockchain. ICT Express 7(3), 283–289 (2021)

    Google Scholar 

  43. Oliva, G.A., Hassan, A.E., Jiang, Z.M.: An exploratory study of smart contracts in the Ethereum blockchain platform. Empir. Softw. Eng. 25, 1864–1904 (2020)

    Google Scholar 

  44. Benčić, F.M., Hrga, A., Žarko, I.P.: Aurora: a robust and trustless verification and synchronization algorithm for distributed ledgers. In: 2019 IEEE International Conference on Blockchain (Blockchain), pp. 332–338. IEEE (2019)

  45. Abbas, K., et al.: A blockchain and machine learning-based drug supply chain management and recommendation system for smart pharmaceutical industry. Electronics 9(5), 852 (2020)

    Google Scholar 

  46. Chen, S. et al.: A comparative testing on performance of blockchain and relational database: foundation for applying smart technology into current business systems. In: Distributed, Ambient and Pervasive Interactions: Understanding Humans: 6th International Conference, DAPI 2018, Held as Part of HCI International 2018, Las Vegas, NV, USA, July 15–20, 2018, Proceedings, Part I 6, pp. 21–34. Springer (2018)

  47. Singh, S.K., Rathore, S., Park, J.H.: Blockiotintelligence: a blockchain-enabled intelligent IoT architecture with artificial intelligence. Future Gener. Comput. Syst. 110, 721–743 (2020)

    Google Scholar 

  48. Frahat, R.T., Monowar, M.M., Buhari, S.M.: Secure and scalable trust management model for IoT P2P network. In: 2019 2nd International Conference on Computer Applications & Information Security (ICCAIS), pp. 1–6. IEEE (2019)

  49. Safana, M.A., Arafa, Y., Ma, J.: Improving the performance of the proof-of-work consensus protocol using machine learning. In: 2020 Second International Conference on Blockchain Computing and Applications (BCCA), pp. 16–21. IEEE (2020)

  50. Liu, X., Farahani, B., Firouzi, F.: Distributed ledger technology. Intelligent Internet of Things: From Device to Fog and Cloud, pp. 393–431 (2020)

  51. Dobbelaere, P., Esmaili, K.S.: Kafka versus RabbitMQ: a comparative study of two industry reference publish/subscribe implementations: industry paper. In: Proceedings of the 11th ACM International Conference on Distributed and Event-Based Systems, pp. 227–238 (2017)

  52. Borrero, J.D., Mariscal, J.: A case study of a digital data platform for the agricultural sector: a valuable decision support system for small farmers. Agriculture 12(6), 767 (2022)

    Google Scholar 

  53. General Data Protection Regulation. General data protection regulation (GDPR). In: Intersoft Consulting. Accessed in October 24(1) (2018)

  54. Estupiñán, A.: Analysis of Modern Blockchain Networks Using Graph Databases. PhD thesis. Master’s thesis, Technische Universitat Berlin (2020)

  55. Choi, W., Hong, J.W.-K.: Performance evaluation of ethereum private and testnet networks using hyperledger caliper. In: 2021 22nd Asia-Pacific Network Operations and Management Symposium (APNOMS), pp. 325–329. IEEE (2021)

  56. Dabbagh, M. et al.: Performance analysis of blockchain platforms: empirical evaluation of hyperledger fabric and ethereum. In: 2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), pp. 1–6. IEEE (2020)

  57. Iqbal, R., et al.: An experimental study of classification algorithms for crime prediction. Indian J. Sci. Technol. 6(3), 4219–4225 (2013)

    Google Scholar 

  58. Venkatesan, N.J., et al.: Analysis of real-time data with spark streaming. J. Adv. Technol. Eng. Res. 3(4), 108–116 (2017)

    Google Scholar 

  59. Mazlan, A.A., et al.: Scalability challenges in healthcare blockchain system—a systematic review. IEEE Access 8, 23663–23673 (2020)

    Google Scholar 

  60. Schäffer, M., Di Angelo, M., Salzer, G.: Performance and scalability of private Ethereum blockchains. In: Business Process Management: Blockchain and Central and Eastern Europe Forum: BPM 2019 Blockchain and CEE Forum, Vienna, Austria, September 1–6, 2019, Proceedings 17, pp. 103–118. Springer (2019)

  61. Chris, D.: Introducing Ethereum and Solidity Foundations of Cryptocurrency and Blockchain Programming for Beginners. Apress, New York (2017). https://doi.org/10.1007/978-1-4842-2535-6

    Book  Google Scholar 

  62. Lewis, A.: Blockchain explained. In: Blockchain Technol. (2015). http://www.blockchaintechnologies.com/blockchain-definition

  63. Ng, W.Y., et al.: Blockchain applications in health care for COVID-19 and beyond: a systematic review. Lancet Digit. Health 3(12), e819–e829 (2021)

    Google Scholar 

  64. Chukwu, E., Garg, L.: A systematic review of blockchain in healthcare: frameworks, prototypes, and implementations. IEEE Access 8, 21196–21214 (2020)

    Google Scholar 

  65. Xie, J., et al.: A survey on the scalability of blockchain systems. IEEE Netw. 33(5), 166–173 (2019)

    Google Scholar 

  66. Rouhani, S., Deters, R.: Performance analysis of ethereum transactions in private blockchain. In: 2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS), pp. 70–74. IEEE (2017)

  67. Memon, R.A., Li, J.P., Ahmed, J.: Simulation model for blockchain systems using queuing theory. Electronics 8(2), 234 (2019)

    Google Scholar 

  68. Memon, R.A., et al.: Cloud-based vs. blockchain-based IoT: a comparative survey and way forward. Front. Inf. Technol. Electron. Eng. 21(4), 563–586 (2020)

    Google Scholar 

  69. Memon, R.A., et al.: DualFog-IoT: additional fog layer for solving blockchain integration problem in Internet of Things. IEEE Access 7, 169073–169093 (2019)

    Google Scholar 

  70. Memon, R.A. et al.: Modeling of blockchain based systems using queuing theory simulation. In: 2018 15th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), pp. 107–111. IEEE (2018)

  71. Donawa, A., Orukari, I., Baker, C.E.: Scaling blockchains to support electronic health records for hospital systems. In: 2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), pp. 0550–0556. IEEE (2019)

  72. Gao, Z. et al.: Scalable blockchain based smart contract execution. In: 2017 IEEE 23Rd International Conference on Parallel and Distributed Systems (ICPADS), pp. 352–359. IEEE (2017)

  73. Blanchard, P. et al.: Machine learning with adversaries: Byzantine tolerant gradient descent. Adv. Neural Inf. Process. Syst. 30 (2017)

  74. Singla, K., Bose, J., Katariya, S.: Machine learning for secure device personalization using blockchain. In: 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 67–73. IEEE (2018)

  75. Mugunthan, V., Rahman, R., Kagal, L.: Blockflow: an accountable and privacy-preserving solution for federated learning. arXiv preprint arXiv:2007.03856 (2020)

  76. Li, Y., et al.: A blockchain-based decentralized federated learning framework with committee consensus. IEEE Netw. 35(1), 234–241 (2020)

    Google Scholar 

  77. Nagar, A.: Privacy-preserving blockchain based federated learning with differential data sharing. arXiv preprint arXiv:1912.04859 (2019)

  78. Chen, P., et al.: Research on scalability of blockchain technology: problems and methods. J. Comput. Res. Dev. 55(10), 2099–2110 (2018)

    Google Scholar 

  79. Bouoiyour, J., Selmi, R.: Ether: bitcoin’s competitor or ally? arXiv preprint arXiv:1707.07977 (2017). http://arxiv.org/abs/1707.07977

  80. Antwi, M.S., et al.: The case of HyperLedger Fabric as a blockchain solution for healthcare applications. Blockchain: Res. Appl. 2(1), 100012 (2021)

    Google Scholar 

  81. Saudi Computer Society. In: 2nd International Conference on Computer Applications & Information Security (ICCAIS’ 2019). Riyadh, Kingdom of Saudi Arabia (2019)

  82. Gurusamy, V., Kannan, S., Nandhini, K.: The real time big data processing framework: advantages and limitations. Int. J. Comput. Sci. Eng. 5(12), 305–312 (2017)

    Google Scholar 

  83. Roehrs, A., et al.: Analyzing the performance of a blockchain-based personal health record implementation. J. Biomed. Inform. 92, 103140 (2019)

    Google Scholar 

  84. Omar, I.A., et al.: Supply chain inventory sharing using ethereum blockchain and smart contracts. IEEE Access 10, 2345–2356 (2021)

    Google Scholar 

  85. Rubin, J.: Btcspark: scalable analysis of the bitcoin blockchain using spark. Dec 16, 1–14 (2015)

    Google Scholar 

  86. Wang, K., et al.: Securing data with blockchain and AI. IEEE Access 7, 77981–77989 (2019)

    Google Scholar 

  87. Singh, S., Hosen, A.S.M.S., Yoon, B.: Blockchain security attacks, challenges, and solutions for the future distributed iot network. IEEE Access 9, 13938–13959 (2021)

    Google Scholar 

  88. Blockchain-based security management of IoT infrastructure

  89. Zhang, Z., et al.: Recent advances in blockchain and artificial intelligence integration: feasibility analysis, research issues, applications, challenges, and future work. Secur. Commun. Netw. 2021, 1–15 (2021)

    Google Scholar 

  90. Bao, X. et al.: Flchain: a blockchain for auditable federated learning with trust and incentive. In: 2019 5th International Conference on Big Data Computing and Communications (BIGCOM), pp. 151–159. IEEE (2019)

  91. Thibault, L.T., Sarry, T., Hafid, A.S.: Blockchain scaling using rollups: a comprehensive survey. In: IEEE Access (2022)

  92. Wang, Z., Cui, B., Hou, W.: A dynamic load balancing scheme based on network Sharding in private Ethereum blockchain. In: 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC), pp. 362–367. IEEE (2022)

  93. Dhulavvagol, P.M., Bhajantri, V.H., Totad, S.G.: Blockchain ethereum clients performance analysis considering E-voting application. Procedia Comput. Sci. 167, 2506–2515 (2020)

    Google Scholar 

  94. Johnson, S., Robinson, P., Brainard, J.: Sidechains and interoperability. arXiv preprint arXiv:1903.04077 (2019)

  95. Cao, L.: Data science: a comprehensive overview. ACM Comput. Surv. (CSUR) 50(3), 1–42 (2017)

    Google Scholar 

  96. Yoo, Y.: The tables have turned: how can the information systems field contribute to technology and innovation management research? J. Assoc. Inf. Syst. 14(5), 227 (2013)

    Google Scholar 

  97. Dinh, T.T.A. et al.: Blockbench: a framework for analyzing private blockchains. In: Proceedings of the 2017 ACM International Conference on Management of Data, pp. 1085–1100 (2017)

  98. Sandner, P., Gross, J., Richter, R.: Convergence of blockchain, IoT, and AI. Front. Blockchain 3, 522600 (2020)

    Google Scholar 

  99. Kurtulmus, A.B., Daniel, K.: Trustless machine learning contracts; evaluating and exchanging machine learning models on the ethereum blockchain. arXiv preprint arXiv:1802.10185 (2018)

  100. Kim, H. et al.: On-device federated learning via blockchain and its latency analysis. arXiv preprint arXiv:1808.03949 (2018)

  101. Thein, K.M.M.: Apache kafka: next generation distributed messaging system. Int. J. Sci. Eng. Technol. Res. 3(47), 9478–9483 (2014)

    Google Scholar 

  102. Bandara, E. et al.: Mystiko—blockchain meets big data. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 3024–3032. IEEE (2018)

  103. Bandara, E., et al.: Rahasak-scalable blockchain architecture for enterprise applications. J. Syst. Archit. 116, 102061 (2021)

    Google Scholar 

  104. Rondelet, A.: Zecale: reconciling privacy and scalability on ethereum. arXiv preprint arXiv:2008.05958 (2020). http://arxiv.org/abs/2008.05958

  105. Roy, M., Singh, M., Radhakrishnan, B.: Blockchain scalability: solutions, challenges and future possibilities. In: International Conference on Signal & Data Processing, pp. 133–149. Springer (2022)

  106. Chan, W., Olmsted, A.: Ethereum transaction graph analysis. In: 2017 12th International Conference for Internet Technology and Secured Transactions (ICITST), pp. 498–500. IEEE (2017)

  107. Zheng, Z., et al.: Blockchain challenges and opportunities: a survey. Int. J. Web Grid Serv. 14(4), 352–375 (2018)

    MathSciNet  Google Scholar 

  108. Chen, F., et al.: Machine learning in/for blockchain: future and challenges. Can. J. Stat. 49(4), 1364–1382 (2021)

    MathSciNet  Google Scholar 

  109. Lee, H.-A., et al.: An architecture and management platform for blockchainbased personal health record exchange: development and usability study. J. Med. Internet Res. 22(6), e16748 (2020)

    Google Scholar 

  110. Choi, Y., et al.: Development of a mobile personal health record application designed for emergency care in Korea; integrated information from multicenter electronic medical records. Appl. Sci. 10(19), 6711 (2020)

    Google Scholar 

  111. Hussien, H.M., et al.: Blockchain technology in the healthcare industry: trends and opportunities. J. Ind. Inf. Integr. 22, 100217 (2021)

    MathSciNet  Google Scholar 

  112. Zhuang, Y., et al.: Generalizable layered blockchain architecture for health care applications: development, case studies, and evaluation. J. Med. Internet Res. 22(7), e19029 (2020)

    Google Scholar 

  113. Roehrs, A., Da Costa, C.A., da Rosa Righi, R.: OmniPHR: a distributed architecture model to integrate personal health records. J. Biomed. Inform. 71, 70–81 (2017)

    Google Scholar 

  114. Chang, R.-I., et al.: Blockchain for bounded-error-pruned content protection. ICT Express 7(3), 295–299 (2021)

    Google Scholar 

  115. Balistri, E., et al.: BlockHealth: blockchain-based secure and peer-to-peer health information sharing with data protection and right to be forgotten. ICT Express 7(3), 308–315 (2021)

    Google Scholar 

  116. Wang, Z. et al.: Kafka and its using in high-throughput and reliable message distribution. In: 2015 8th International Conference on Intelligent Networks and Intelligent Systems (ICINIS), pp. 117–120. IEEE (2015)

  117. Eyal, I. et al.: Bitcoin-NG: a scalable blockchain protocol. In: 13th USENIX symposium on networked systems design and implementation (NSDI 16), pp. 45–59 (2016)

  118. De Vries, A.: Bitcoin’s growing energy problem. Joule 2(5), 801–805 (2018)

    Google Scholar 

  119. Moser, M.: Anonymity of bitcoin transactions (2013)

  120. Clack, C.D.: Smart contract templates: legal semantics and code validation. J. Digit. Bank. 2(4), 338–352 (2018)

    Google Scholar 

  121. Fan, C. et al.: Towards a scalable DAG-based distributed ledger for smart communities. In: 2019 IEEE 5th World Forum on Internet of Things (WFIoT), pp. 177–182. IEEE (2019)

  122. Gangwani, P., et al.: Securing environmental IoT data using masked authentication messaging protocol in a DAG-based blockchain: IOTA tangle. Future Internet 13(12), 312 (2021)

    Google Scholar 

  123. Wang, Q., et al.: Sok: Dag-based blockchain systems. ACM Comput. Surv. 55(12), 1–38 (2023)

    Google Scholar 

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Acknowledgements

The work is supported financially by the Ministry of Higher Education Malaysia via Fundamental Research Grant Scheme (FRGS/1/2019/ICT05/UM/01/1).

Funding

“This research was funded by the Ministry of Higher Education Malaysia via Fundamental Research Grant Scheme (FRGS/1/2019/ICT05/UM/01/1).

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Authors and Affiliations

  1. Department of Computer System & Technology, Universiti Malaya, Malaysia, Kaula Lampur, 50603, Malaysia

    Iqra Sadia Rao & M. L. Mat Kiah

  2. Department of Computer Science, Bahauddin Zakariya University, Multan, Pakistan

    M. Muzaffar Hameed

  3. Department of Electronics Engineering, University of Sindh, Jamshoro, Pakistan

    Zain Anwer Memon

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Rao: Drafted the manuscript, including the introduction, methods, results, and discussion sections. Also, reviewed and revised the paper. M.K.: Provided supervision and guidance throughout the research process, and the content and paper were thoroughly discussed with her. MMH: Reviewed the paper and provided guidance in the results and discussion chapters. ZAM: Prepared the initial draft, worked on the journal's template, and created figures and tables.

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Rao, I.S., Kiah, M.L.M., Hameed, M.M. et al. Scalability of blockchain: a comprehensive review and future research direction. Cluster Comput 27, 5547–5570 (2024). https://doi.org/10.1007/s10586-023-04257-7

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