Securing smart grids relies in part on the reliable integration of blockchain technologies for the automation of energy transactions. However, the presence of vulnerabilities in smart contracts poses a direct threat to the integrity and resilience of these critical systems. This work presents a unique and structured dataset of real-world vulnerabilities observed in smart contracts, intended for cybersecurity research applied to smart energy infrastructures.
Digital technology's pervasive impact is often limited by data silos within institutions. Enabling data sharing via Commonly Accessible Platforms (CAPs) across sectors like healthcare and education promises greater value. However, trust hinges on privacy, security, immutability, and cost-effectiveness. While Blockchain Technology (BCT) offers immutability, a research-practice gap exists due to operational challenges. This paper introduces Vedic Recordchain, a novel multi-hash platform offering an alternative to BCT for CAPs, addressing these challenges.
The incorporation of Internet of Things (IoT) technology with agriculture has transformed several farming practices, bringing unparalleled simplicity and efficiency. This article explores the robust integration of IoT and blockchain technology(BIoT) in agricultural operations, offering insight into the resulting BIoT system’s design. This study investigates the potential benefits of merging the IoT and blockchain technologies in agriculture. A system for tracking plant growth using sensors and blockchain-integrated IoT has been developed and analyzed.
This dataset contains results from a comparative simulation of blockchain-enabled trade systems and traditional trade mechanisms across five global trade hubs: the USA, China, Germany, Japan, and India. The dataset includes information on 10,000 simulated transactions, focusing on key metrics such as processing time, transaction costs, failure rates, and resilience to risk events.
Our dataset comes from the paper called "XBlock-ETH: Extracting and exploring blockchain data from Ethereum", the datasets are the on-chain data obtained by running all nodes of Ethereum. For the purpose of the experiment, we only selected block transactions from 0-2,000,000 blocks. These datasets are sufficient to support the experiments. You can get more details and analysis from the paper called "XBlock-ETH: Extracting and Exploring Blockchain Data from Ethereum". The citation of the paper as follows: P. Zheng, Z. Zheng, J. Wu, and H.-N.
The COronaVIrus Disease of 2019 (COVID19) pandemic poses a significant global challenge, with millions
affected and millions of lives lost. This study introduces a privacy conscious approach for early detection of COVID19,
employing breathing sounds and chest X-ray images. Leveraging Blockchain and optimized neural networks, proposed
method ensures data security and accuracy. The chest X-ray images undergo preprocessing, segmentation and feature
The e-commerce market heavily relies on e-coupons, and their digital nature presents challenges in establishing a secure e-coupon infrastructure, which incurs maintenance costs. To address this, we explore using public blockchains for the e-coupon system, providing a highly reliable decentralized infrastructure with no maintenance costs. Storing coupon information on a blockchain ensures tamper resistance and protection against double redemption. However, using public blockchains shifts gas cost responsibility to users, potentially impacting user experience if not managed carefully.
Calculations to compute the expected Bitcoin transaction efficiency (measured in transactions per block) using CRYSTALS-Dilithium vs using Elliptic Curve Digital Signature Algorithm (ECDSA).
Change address identification is one of the difficulties in bitcoin address clustering as an emerging social computing problem. Most of the current related research only applies to certain specific types of transactions and faces the problems of low recognition rate and high false positive rate. We innovatively propose a clustering method based on multi-conditional recognition of one-time change addresses and conduct experiments with on-chain bitcoin transaction data. The results show that the proposed method identifies at least 12.3\% more one-time change addresses than other heuristics.
The dataset contains development archives of more or less interesting conversations, announcements, discussions, presentations and so on regarding consensus changes in Bitcoin.
