Faculty Publications
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Item Unconfirmed Transactions in Cryptocurrency: Reasons, Statistics, and Mitigation(Institute of Electrical and Electronics Engineers Inc., 2022) Kallurkar, H.S.; Chandavarkar, B.R.Blockchain has emerged to be a pioneer fundamental technology for distributed applications. Not only it is limited to financial sector, but it also has extended in the fields of health & medicare, managing logistics of goods through effective supply chain management etc. Although there are numerous applications of blockchain, cryptocurrencies remains at the top, in terms of popularity and cryptographic security it provides in maintenance of digital assets. Miner(s) in a cryptocurrency is/are an individual/group of individuals who benefit after per-forming Proof-of-Work for validating a transaction. The top two cryptocurrencies according to market cap value are Bitcoin and Ether. Millions of transactions happen on their blockchain on a daily basis, but not all of them result in success. Some are also marked as failed/unconfirmed, even if they are less compared to the confirmed transactions. Some of the reasons for this behavior could be too many transactions present in mempool of miners or insufficient fees is provided as the incentive to the miners of the network. Though the number of transactions that go unconfirmed per day is very small compared to the ones getting confirmed, still the area of failed cryptocurrency transactions remain unexplored. This paper focuses on statistics of failed cryptocurrency transactions, some primary reasons of failure in a cryptocurrency transaction. Furthermore, it also presents existing approaches to minimize the failure of transactions. © 2022 IEEE.Item Transaction fee forecasting in post EIP-1559 Ethereum using 1-D Convolutional Neural Network(Institute of Electrical and Electronics Engineers Inc., 2023) Kallurkar, H.S.; Chandavarkar, B.R.Cryptocurrencies have established their identity as a healthy alternative to the maintenance of digital assets. Their applications include low-cost money transfers and yield farming. Ethereum is a blockchain that provides the functionality of doing more than a transaction regarding cryptocurrency. Ether is the default cryptocurrency of Ethereum, which is issued to the miners after the successful completion of the consensus mechanism to avoid fraudulent miners gaining profits. Transactions in Ether require that the user should include what is called a 'fee' besides the amount that is sent by the user. The EIP-1559 (Ethereum Improvement Proposals) upgrade to the Ethereum protocol has substantially changed how the transaction fee is calculated. Since this transaction data can be considered time-series data, many prior approaches have been proposed to forecast such a transaction fee using suitable methods effectively. One-dimensional Convolutional Neural Networks have recently been successfully applied to time-series forecasting problems, showing promising results. This paper proposes a univariate 1-D CNN for an effective forecast of transaction fees in the new Ethereum protocol. Furthermore, this paper also compares the proposed method with existing standard approaches, and the results show the superior performance of simple 1-dimensional convolutional neural networks over existing hybrid models. © 2023 IEEE.Item A Hybrid CNN–LSTM Model for Transaction Fee Forecasting in Post EIP-1559 Ethereum(Springer, 2024) Kallurkar, H.S.; Chandavarkar, B.R.Over the past decade, cryptocurrencies have experienced a significant surge in popularity. Several factors have contributed to their rise. First, the decentralized nature of cryptocurrencies, enabled by blockchain technology, has appealed to individuals seeking financial autonomy and freedom from traditional banking systems. Additionally, the potential for substantial financial gains, as demonstrated by the surge in the value of Bitcoin and Ethereum. Cryptocurrency transactions require the sender to include a transaction fee before initiating. Concerning the Ethereum protocol, the transaction fee calculation before the London upgrade, i.e., Ethereum Improvement Proposals (EIP-1559), led to delayed transaction confirmation and increased congestion in the Ethereum network. Ever since this upgrade, the transaction fee has become dynamic and user-friendly such that transactions get confirmed within a reasonable time. For such a scenario, the need of the hour for an effective forecasting technique can prove critical from the user’s point of view. After the EIP-1559 upgrade, there is a lack of literature that efficiently utilizes cryptocurrency transaction data’s time-series nature. To solve these issues, this paper proposes a hybrid deep learning model to predict total transaction fees in post EIP-1559 Ethereum precisely. The proposed convolutional neural network (CNN)-long short term memory (LSTM) leverages the advantages of convolutional layers and is followed by effective learning of time-series dependencies between the data by LSTM layers. The experimentation and comparison with state-of-the-art suggest significant improvement when CNN–LSTM is leveraged for this type of forecasting. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.