An Extremely Scalable and Versatile Learning-Oriented Blockchain Performance Optimisation Platform

• Published in: 2024 Global Conference on Communications and Information Technologies (GCCIT) pp. 1 - 7
• Main Authors: Akkur, Malatesh, Maral, Vikas B., Sunori, S.K, Hemelatha, S., Chandna, Manisha, B, Umamaheswari
• Format: Conference Proceeding
• Language: English
• Published: IEEE 25.10.2024
• Subjects: and IPFS; Blockchain Performance Optimisation; Blockchains; Distributed PoS; Internet of Things; InterPlanetary File System; Machine Learning Model; Performance evaluation; Proof of stake; Scalability; Security; Sharding; Testing; Throughput
• DOI: 10.1109/GCCIT63234.2024.10862233

Blockchain technology has recently been the subject of investigation as a potential solution to security issues plaguing IoT networks. However, when dealing with a large number of IoT gadgets and the massive amounts of data produced by these networks, the inherent scalability difficulties of blockchain-based systems become obvious. Nevertheless, there are areas where the permissioned Blockchain falls short, and these include throughput and scalability. Incorporating data science methodologies, this study proposes a way to address permissioned Blockchain's scalability problem. With a variable number of transactions, the scalability study of the suggested solution is done in the hyperledger fabric architecture, which improves scalability. Because of this, we address these issues in this research by using a lightweight consensus approach. We provide a system for handling IoT data that is scalable and built on blockchain technology. It can handle data from a large number of gadgets. To guarantee improved efficiency and effectiveness in resource-constrained IoT networks, this system employs the Delegated Proof of Stake (DPoS) consensus process. DPoS mitigates the performance and effectiveness deterioration in blockchain-based IoT networks by using a certain amount of elected delegates to verify and approve transactions. It is a lightweight consensus mechanism. In this article, we used Docker, the XGBoost mapping model, and the Interplanetary File System (IPFS) to assess the network's performance in relation to latency, resource consumption, and throughput. The four components of our distributed storage study were latency, throughput, resource consumption, and the duration and speed of file uploads. We found that our framework has a low latency of less than 0.976 ms in our empirical results. One state-of-the-art consensus method, Proof of Stake (PoS), is outperformed by the suggested method. We also show that the suggested method works well for Internet of Things (IoT) uses that call for minimal latency or efficient use of resources.