Bitcoin is the largest cryptocurrency in the market, which uses blockchain technology to bring in features like decentralization, anonymity, and trust. However, it still struggles with broader adaptation due to long verification times and high transaction fees. As a result, it is lagging behind competitors. We need to provide faster confirmations to tackle these issues while ensuring stable earnings for the miners. However, it is challenging to increase the block sizes or decrease the average block creation time without affecting the stability and security of the network. To address this conundrum, firstly, an optimization problem is formulated where the objective is to increase the transaction count in every cycle. Based on that, a comprehensive learning framework is developed to solve the formulated problem since the issue is intractable and hard to solve in polynomial time. The proposed learning framework includes (i) implementing a viable data-driven infrastructure with a machine learning (ML) root, (ii) training learning models with efficient generalization capability, and (iii) predicting the ideal block size in every block generation cycle. Our concept uses extreme gradient boost (XGB) as its core algorithm, which analyzes nine attributes associated with the Bitcoin network. These network-allied data points assist the model in creating an adaptive block size in the blockchain. XGB, trained using the last four years of real-world data, can predict block sizes with a 63.41% accuracy. The model ensures an all-around positive change in Bitcoin with a 12.29% increase in block size, a 13.45% increase in transaction fee (USD), and a 14.88% increase in transaction approval rate and transaction count, thus addressing the long wait time and broader adaption issue.

Cryptocurrencies are the new form of trade that has revolutionized how we look into our financial institutions. Bitcoin dominates the industry with the highest market share among the hundreds of other cryptocurrencies. However, high energy consumption leading to increasing carbon emission, prioritizing high-value transactions, and long waiting times are some of the flaws preventing it from reaching its full potential. Owing to the block rewards getting halved every four years, miners and researchers are fearful that this would be the breaking point of Bitcoin’s success. This article proposes an Industry-4.0-compliant next-generation Bitcoin architecture by introducing a dynamic and sustainable block concept. Along with our modified knapsack algorithms, i.e., priority-based 0/1 knapsack and advanced-priority-based 0/1 knapsack, we can ensure a balanced transaction selection, quicker verification, higher transaction throughput, reduced carbon emission, and increased earnings for the miners. Moreover, with the addition of only one of our proposed sustainable blocks, we can cut down verification times by 50% and increase throughput by 39%. We can also reduce carbon emissions per transaction by 61.3%, which would help reduce Bitcoins’ large carbon footprint, enabling us to approach greener digital transactions.

The use of Blockchain in cryptocurrency introduces a technology that acts as an unswervingly growing ledger with the capability to keep an everlasting record of all the transactions in a secure, chronological and immutable system. It removes dependency on central financial services, essentially removing the middle-man from the transaction. Bitcoin holds the highest share in the cryptocurrency market. However, Bitcoin has three major problems which are excessive power consumption, confirmation time, and fair reward distribution. Hence, we propose a system (FaircoinBD) where we focus on reducing energy consumption by limiting participation, increasing propagation speed and fair reward distribution. We have also compared our proposed system with Bitcoin and Ether in terms of energy consumption and have reached the conclusion, that if Bitcoin and Ethereum network used our proposed model there would be a 83.33% and 80.0012% reduction of energy consumption respectively.