Faculty Publications
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Item Adoption of Blockchain Technology in Land Registry Systems(Institute of Electrical and Electronics Engineers Inc., 2023) Sah, C.K.; Chandavarkar, B.R.The adoption of blockchain technology in land registry systems has gained significant attention in recent years due to its potential to increase efficiency, transparency, and security. This paper provides an overview of the current state of land registry systems and the challenges they face, in-cluding issues related to fraud, corruption, and a lack of trust. Then it discusses how blockchain technology can address these challenges by providing a decentralized, tamper-proof, and transparent platform for recording land transactions. The paper also examines some of the existing blockchain-based land registry systems and their features, using approaches and methodologies, including the use of smart contracts and digital signatures. Finally, the paper discusses the potential benefits and limitations of adopting blockchain technology in land registry systems and provides recommendations for policymakers and stakeholders interested in implementing these systems. © 2023 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 BlockFIR: Blockchain based First Information Report System(Institute of Electrical and Electronics Engineers Inc., 2023) Kamble, S.; Chandavarkar, B.R.India is experiencing a sharp rise in criminal activity. This is a serious problem, as many of these crimes go unreported. Although there is an online platform for the police to store First Information Reports (FIR) and Non-Cognizable Reports (NCR), most FIRs are still written by hand. This is inefficient and can lead to errors. Additionally, the complainant must typically be at the police station to report a cognizable offense. This can be inconvenient and time-consuming, especially for victims who live in rural areas. In 2009, the Crime and Criminal Tracking Network and Systems (CCTNS) were launched as an efficient e-governance system. This system has helped to improve the reporting of crimes, but it is still a centralized system. This means that it is vulnerable to cyberattacks and can be easily shut down by a single point of failure. Therefore, a fully decentralized system is required to ensure no single point of failure and that complaints are handled safely and securely to prevent unauthorized access. This paper proposes a blockchain-based solution called BlockFIR to manage complaints against cognizable and non-cognizable offenses. Using this system, complaints can be registered by users. The police stations will be able to see complaints registered in their jurisdiction, register FIRs/NCRs accordingly, and take action on them. Through a prototype implementation using Go-Ethereum (Geth), smart contracts, and Django web server, we demonstrate the practical use of BlockFIR. We show that our system can be easily used by users, police personnel, and Higher Authorities to improve the current systems in India. © 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.