This paper investigates the potential of integrating supply chain management with blockchain technology, specifically by implementing smart contracts on the Ethereum network using Solidity. The paper explores supply chain management concepts, blockchain, distributed ledger technology, and smart contracts in the context of their integration into supply chains to increase traceability, transparency, and accountability with faster processing times. After investigating these technologies’ applications and potential use cases, a framework for smart contract implementation for supply chain management is constructed. Potential data models and functions of a smart contract implementation improving supply chain management processes are discussed. After constructing a framework, the effects of the proposed system on supply chain processes are explained. The proposed framework increases the reliability of the supply chain history due to the usage of DLT (distributed ledger technology). It utilizes smart contracts to increase the manageability and traceability of the supply chain. The proposed framework also eliminates the SPoF (Single Point of Failure) vulnerabilities and external alteration of the transactional data. However, due to the ever-changing and variable nature of the supply chains, the proposed architecture might not be a one-size-fits-all solution, and tailor-made solutions might be necessary for different supply chain management implementations.
TrenchX stands as a groundbreaking initiative at the intersection of e-commerce and blockchain technology, dedicated to empowering artists, entrepreneurs, and users in the digital marketplace. Leveraging the decentralized and transparent nature of blockchain, TrenchX offers a user-friendly platform for creating, managing, and trading Non-Fungible Tokens (NFTs). These NFTs, powered by smart contracts, serve as unique digital assets representing ownership of various forms of digital content, including artworks, collectibles, and more. By seamlessly bridging traditional e-commerce with blockchain innovation, TrenchX provides a secure and transparent environment for individual creators and businesses to thrive alongside their conventional counterparts. The project's objectives are multifaceted, aiming to democratize access to the digital marketplace, ensure transparent and fair transactions, and foster a vibrant, community-driven ecosystem of creativity and entrepreneurship. Through a comprehensive exploration of its technical architecture, implementation strategies, and potential impacts, this research paper endeavors to shed light on the transformative potential of TrenchX in revolutionizing e-commerce and empowering creators in the digital age. Keywords: Blockchain Technology, Decentralized Platform, E-commerce, Non-Fungible Tokens (NFT) Marketplace, Ethereum, Security and Privacy, Transparent Exchanges.
Fozia Zeeshan, Narayan Nepal, Mohammad Norouzifard
In the fast-evolving cryptocurrency market, accurately predicting Ethereum prices is crucial for investors, traders, and financial analysts. Traditional machine learning (ML) models often struggle to capture the market's complex dynamics due to their inability to consider all influencing factors. This study introduces an advanced ensemble machine learning approach to enhance Ethereum price prediction accuracy. By combining the strengths of Bi-directional Long Short-Term Memory (Bi-LSTM) and Convolutional Neural Network (CNN) models, our ensemble averaging method compensates for individual model weaknesses, improving forecast reliability and precision. Results show that our ensemble model offers significant advantages, particularly in terms of generalizability and resistance to overfitting with LSTM and CNN models and this technique is offering a more effective tool for navigating cryptocurrency market complexities. This research highlights the importance of ensemble learning in financial forecasting and provides a practical framework for developing superior predictive models. “Moreover, This study explores an advanced ensemble machine learning approach to enhance Ethereum price predictions, combining the strengths of Bi-directional Long Short-Term Memory (Bi-LSTM) and Convolutional Neural Network (CNN) models. While Bi-LSTM individually exhibits slightly higher performance in our tests, the ensemble method demonstrates enhanced stability and reliability, making it a valuable tool for navigating the unpredictable dynamics of the cryptocurrency market. We found that Bi-LSTM is good on its own, but the balanced approach of the ensemble model is far better, especially when it comes to generalizability and overfitting resistance. Insights into creating flexible and trustworthy prediction models are provided by this study, which highlights the possibilities of ensemble learning in financial forecasting.
Maximal Extractable Value (MEV) drives the prosperity of the blockchain ecosystem. By strategically including, excluding, or reordering transactions within blocks, block producers can extract additional value, which in turn incentivizes them to keep the decentralization of the whole blockchain platform. Before September 2022, around $675M was extracted in terms of MEV in Ethereum. Despite its importance, current work on identifying MEV activities suffers from two limitations. On the one hand, current methods heavily rely on clumsy heuristic rule-based patterns, leading to numerous false negatives or positives. On the other hand, the observations and conclusions are drawn from the early stage of Ethereum, which cannot be used as effective guiding principles after The Merge. To address these challenges, in this work, we innovatively proposed a profitability identification algorithm. Based on this, we designed two robust algorithms to identify MEV activities on our collected largest-ever dataset. Based on the identified results, we have characterized the overall landscape of the Ethereum MEV ecosystem, the impact the private transaction architectures bring in, and the adoption of back-running mechanisms. Our research sheds light on future MEV-related work.
Юлія Кривенко, Вячеслав Бізянов, Олександр Секретар
Стаття присвячена дослідженню інноваційних інструментів фінансового ринку в системі міжнародних розрахунків. Зокрема, досліджувалися переваги використання інноваційних фінансових інструментів порівняно з традиційними методами. Крім того, у статті аналізуються ризики та виклики, пов’язані з впровадженням інноваційних інструментів. У статті розглянуто такі інноваційні інструменти фінансового ринку, як блокчейн і криптовалюти (Bitcoin і Ethereum), фінтех-платформи (PayPal, TransferWise (зараз Wise) та Revolut), цифрові валюти центральних банків (CBDC), смарт-контракти, АРІ та відкриті банківські рішення, Big Data, аналітика та штучний інтелект (Al) і машинне навчання та роботизована автоматизація процесів (RPA). Проаналізовано використання інноваційних інструментів фінансового ринку в світі.
This paper presents a theoretical extension of the DeTEcT framework proposed by Sadykhov et al., DeTEcT, where a formal analysis framework was introduced for modelling wealth distribution in token economies. DeTEcT is a framework for analysing economic activity, simulating macroeconomic scenarios, and algorithmically setting policies in token economies. This paper proposes four ways of parametrizing the framework, where dynamic vs static parametrization is considered along with the probabilistic vs non-probabilistic. Using these parametrization techniques, we demonstrate that by adding restrictions to the framework it is possible to derive the existing wealth distribution models from DeTEcT. In addition to exploring parametrization techniques, this paper studies how money supply in DeTEcT framework can be transformed to become dynamic, and how this change will affect the dynamics of wealth distribution. The motivation for studying dynamic money supply is that it enables DeTEcT to be applied to modelling token economies without maximum supply (i.e., Ethereum), and it adds constraints to the framework in the form of symmetries.
Despite their recent inception, cryptocurrencies have become globally recognized for their dispersal, diversity, and high market capitalization. This volatility developed into a challenge for investors looking to predict price movements. Thus, it has become an attractive investment opportunity. To increase prediction accuracy, researchers integrate machine learning algorithms with technical indicators. In this review, a systematic comparison has been employed to identify efficient algorithms, and researchers have employed statistical measures to make short- and long-term forecasts of decentralized money prices. Moreover, the paper highlights the results of researchers based on machine learning and deep learning methodologies on multiple types of cryptocurrencies like Bitcoin, Ethereum, Monero, etc. Lastly, the work emphasizes the limitations, gaps, and challenges facing researchers to take advantage of existing literature for future works.
Today, scientific research is increasingly becoming data-centric and compute-intensive, relying on data and models across distributed sources. However, challenges still exist in the traditional cooperation mode, given the high storage and computing costs, geolocation barriers, and local confidentiality regulations. The Jupyter environment has recently emerged and evolved into a vital virtual research environment for scientific computing, which researchers can use to scale computational analyses up to larger datasets and high-performance computing resources. Nevertheless, existing approaches lack robust support of a decentralized cooperation mode to unlock the full potential of decentralized collaborative scientific research, e.g., seamlessly secure data sharing. In this work, we change the basic structure and legacy norms of current research environments via the seamless integration of Jupyter with Ethereum blockchain capabilities. As such, it creates a Decentralized Virtual Research Environment (D-VRE) from private computational notebooks to a decentralized collaborative research ecosystem. We propose a novel architecture for the D-VRE and prototype some essential D-VRE elements for enabling secure data sharing with decentralized identity, user-centric agreement-making, membership, and research asset management. To validate our method, we conduct an experimental study to test all functionalities of D-VRE smart contracts and their gas consumption. In addition, we deploy the D-VRE prototype on a test net of the Ethereum blockchain for demonstration. The feedback from the studies showcases the current prototype's usability, ease of use, and potential, and suggests further improvements.
Yago de R. dos Santos, Guilherme Nunes Nasseh Barbosa, Lúcio Henrik A. Reis, Nicollas R. de Oliveira · 7 authors
A expansão da Saúde Digital traz desafios crescentes de privacidade e segurança de dados, especialmente devido à coleta de dados por parte de provedores de serviço e terceiros. A abordagem descentralizada da Identidade Auto Soberana surge como solução, oferecendo controle direto aos usuários sobre seus dados. Este artigo estende a ferramenta SmartMed, investigando o uso das plataformas de blockchain Ethereum e Besu para controle de acesso a dados médicos. A proposta integra contratos inteligentes para controlar o acesso e manter registros de atividades, destacando-se pela análise detalhada do desempenho nas duas plataformas com protocolos de consenso distintos. Os resultados revelam a superioridade da plataforma Besu em relação à Ethereum, indicando um custo computacional inferior por transação. Esta proposta inova ao propor um sistema baseado em contratos inteligentes para garantir a autenticidade dos dados médicos, complementado pelo uso do Keycloak na gestão de acesso aos sistemas de saúde.
Gislainy Crisostomo Velasco, Noelí Antonia Pimentel Vaz, Sérgio T. Carvalho
The development of smart contracts presents significant challenges compared to traditional software development, such as the immutability of the blockchain and the limitation of program size. These challenges can lead to human errors and the existence of vulnerabilities that may be exploited by malicious individuals, resulting in substantial financial losses. Contract developers face language and infrastructure constraints and insufficient information on interface patterns and implementation specifications. Existing proposals are often challenging to understand, with complex formal verifications requiring expertise in this approach. This article proposes using Model-Driven Engineering (MDE), employing a metamodel for contract development and code generation for the corresponding platform. The metamodel proposed in this study referred to as the High-Level Metamodel for Smart Contract (HLM-SC), is an abstraction applicable to contract development in various contexts. HLM-SC consists of a set of metaclasses allowing the declaration of essential elements for constructing a contract on the Ethereum Virtual Machine (EVM). A graphical tool has been developed to facilitate contract modeling following HLM-SC specifications. Additionally, the model generated from the tool is transformed into Solidity code. This approach aims to overcome developers’ limitations, offering a more understandable and efficient approach to building smart contracts on the blockchain.
Johnnatan Messias Peixoto Afonso, Krzysztof Gogol, Maria Inês Silva, Benjamin Livshits
This paper examines inscription-related transactions on Ethereum and major EVM-compatible rollups, assessing their impact on scalability during transaction surges. Our results show that, on certain days, inscriptions accounted for nearly 90% of transactions on Arbitrum and ZKsync Era, while 53% on Ethereum, with 99% of these inscriptions involving meme coin minting. Furthermore, we show that ZKsync and Arbitrum saw lower median gas fees during these surges. ZKsync Era, a ZK-rollup, showed a greater fee reduction than the optimistic rollups studied -- Arbitrum, Base, and Optimism.
O presente artigo emprega a abordagem Kitchenham para realizar um mapeamento sistemático das técnicas de escalonamento presentes na blockchain Ethereum. O estudo focou em analisar as vantagens e desvantagens de sete das soluções mais populares, incluindo: sharding, state channel, sidechains, plasma, validium, rollup zk e otimista. Os resultados indicam que as técnicas mapeadas oferecem benefícios, como aumento da capacidade de transações e redução dos custos. No entanto, também apresentam limitações e riscos que afetam a segurança da rede.
Abstract: The advancement of Web3 and blockchain is happening rapidly in various fields, including healthcare, social services, and electronic voting. Blockchain technology is being used by crowdfunding platforms to combine its benefits of speed and low fees with traditional finance, creating a new way of raising money. By integrating Reacts user-friendly interface, Solidity smart contracts, Meta-Mask wallet integration and Hardhat development and testing capabilities, it forms a versatile and secure platform. This project aims to find out how much money is missing in the current crowdfunding market and offer them smart contracts and tools that use Ethereum to create their own application businesses. Hybrid Model Platform is a ThirdWeb tool that allows for easy and flexible scaling and visibility. this new crowdfunding app is a major leap in fundraising, fulfilling numerous customer requirements and poised to revolutionize crowdfunding with its fresh and inventive strategy.
Nehal N. AlMadany, Omar Hujran, Ghazi Al‐Naymat, Aktham Maghyereh
The emergence of cryptocurrencies has generated enthusiasm and concern in the modern global economy. However, their high volatility, erratic price fluctuations, and tendency to exhibit price bubbles have made investors cautious about investing in them. Consequently, it is essential to develop methods and models to forecast cryptocurrency returns to benefit investors, traders, and the scientific community. Despite the considerable volume of research on Bitcoin price forecasting, other cryptocurrencies have received little attention in academic literature. Additionally, the current body of literature on predicting cryptocurrency prices or returns emphasizes the use of in-sample methodologies. However, this method is susceptible to overfitting. To address these gaps in the literature, this study employs autoregressive moving average (ARMA), generalized autoregressive conditional heteroskedasticity (GARCH), exponential generalized autoregressive conditional heteroskedasticity (EGARCH), and long short-term memory (LSTM) deep learning neural networks to forecast returns for the ten most actively traded digital currencies: Bitcoin, Ethereum, Ripple, Chainlink, Litecoin, Cardano, Ethereum Classic, Bitcoin Cash, Tether, and Binance Coin. To assess the accuracy of the two models, this study utilizes an out-of-sample method with data gathered sequentially from November 9, 2017, to September 18, 2022. The results indicate that all models exhibit high accuracy, as evidenced by their low root mean square error (RMSE), mean absolute error (MAE), and mean squared error (MSE) values. Meanwhile, the hybrid EGARCH-LSTM or GARCH-LSTM models demonstrate slightly better accuracy compared with the other models. The findings are valuable for investors, traders, and researchers involved in cryptocurrency forecasting.
Gabriel Felipe Nunes do Nascimento, Gabrielli Caroline Moraes Curtarelli, Ihgor Jean Rego
Este artigo propõe uma análise dos desafios jurídicos emergentes no cenário das criptomoedas, notadamente Bitcoin e Ethereum, exigindo uma abordagem legislativa cuidadosa. O objetivo geral é examinar os entraves legais nesse contexto, destacando quatro desafios prementes. A metodologia escolhida é a analítica, visando desmembrar e analisar minuciosamente os elementos constitutivos dos desafios jurídicos das criptomoedas. Um desafio crucial envolve a busca por regulamentações que conciliem a inovação tecnológica com a segurança pública, especialmente contra atividades criminosas. A identificação dos participantes nas transações e a imposição de medidas rigorosas de conformidade por parte das exchanges são considerações essenciais. A tributação de transações criptográficas, dada a sua natureza descentralizada, emerge como um desafio adicional. As autoridades fiscais enfrentam a complexidade de garantir a declaração e tributação adequada dessas transações, demandando abordagens inovadoras. A proteção dos investidores constitui outro desafio significativo, dada a volatilidade inerente das criptomoedas. A formulação de regulamentações que garantam transparência, responsabilidade e segurança dos investidores, com foco nas ofertas iniciais de moedas (ICOs), é uma necessidade imperativa. Por fim, a regulação das criptomoedas transcende fronteiras, demandando esforços de cooperação internacional. A harmonização das regulamentações globalmente é vital para enfrentar desafios de jurisdição e assegurar uma abordagem coesa em escala mundial. Os resultados esperados compreendem uma regulamentação equilibrada, que promova a inovação tecnológica, proteja os investidores, assegure a tributação adequada e promova a cooperação global. A metodologia analítica proporciona uma estrutura sólida para desvendar esses desafios complexos, orientando a formulação de regulamentações adaptáveis e eficazes.
The role played by email communication in our lives nowadays has been such a tremendous one especially when it comes to fast exchange of information. Nevertheless, this convenience is marred by the omnipresent threat of email spam that not only disrupts channels of communication but also present serious security and privacy concerns. Traditional models of spam detection which are based on rules or heuristics tend to fail because they do not adapt quickly enough to the new techniques employed by spammers. In response to these challenges, this paper proposes an inventive solution to the problem—integration of blockchain technology into the process of detecting email spams.Email spam is often defined as an unwanted and usually malicious form of correspondence, thus it has continued being a notable cyber security worry. The conventional mechanisms for discovering them are prone to false positives and negatives at times. Additionally, such systems have centralized data which can be interfered with and accessed without permission. Weighing up the limitations inherent in existing methods, this research examines how blockchain may change email spam detection. Keywords— Blockchain technology, ethereum, Spam, email
The rise in the cost of essentials affects every nation around the world, but it has become a major concern for developing nations. It is getting increasingly difficult to keep up with rising prices for everyday items in these countries, where the majority of the population is from the middle class or lower middle class. Inflation, pandemics, wars, and other important variables all contribute to price increases. There may be another significant factor at play, which is supply-chain corruption. The supply chain's unreliable, chaotic, and opaque nature is to blame for this corruption. We are concentrating on the agri-food supply chain in our study. Because many of the current agri-food supply chains are intricate and challenging to monitor, dishonest parties can exploit the situation. Therefore, we suggested a thorough blockchain-based agri-food supply chain to identify the source of price increases. The private Ethereum blockchain was used in the suggested system. Since the private Ethereum blockchain is more efficient, safe, and fast, it was chosen. Smart contracts were created to describe the system and its underlying rules and laws. Furthermore, in order to showcase the usefulness of our smart contracts, we exhibited a sample decentralized application to support our hypothesis. We also gave the system a complete security and vulnerability assessment to make sure it is operating properly and is protected from threats and attacks. Due to the use of blockchain, the system is immutable, transparent, and simple to track and monitor. The proposed system has demonstrated greater transparency, traceability, reliability, speed, security, and cost-efficiency compared to conventional systems. It effectively traces the origin of corruption in the supply chain, providing a more straightforward means to tackle concerns related to price hikes.
In multi-stakeholder systems, such as healthcare, the Internet of Things, and supply chain management, there is frequent data generation, exchange, and sharing. As a result, data owners often desire control over their data and maintain privacy, while data consumers require methods to ascertain the origins and creators of the data. These conflicts of interest require developing data governance systems that guarantee data provenance, privacy protection, consent management, and selective disclosure. This research proposed a decentralized data governance system utilizing blockchain technology, proxy re-encryption (PRE), and Boneh, Boyen, and Shacham (BBS) signatures to address these challenges. The proposed system enables data owners to control, selectively share, and track their data through privacy-enhancing, consent management, and selective disclosure mechanisms while also allowing data consumers to understand the lineage of the data through a blockchain-based provenance mechanism. As a case study, the research examined and evaluated electronic prescriptions involving sensitive data and multiple stakeholders, including patients as data owners and doctors and pharmacists as data consumers. The research was structured as a collection of published articles organized in the following sequence: problem formulation and developing smart contracts, implementing privacy and consent management through PRE, and applying BBS signatures for selective data sharing. The proof-of-concept implementation and evaluations, conducted using CosmWasm, Hyperledger Besu, Ethereum, pyUmbral PRE, and BBS signatures, demonstrate that the proposed decentralized system is platform-agnostic, scalable, and capable of providing a higher level of transparency, privacy, and trust with minimal overhead.
Recognizing the necessity to preserve the integrity and good name of degrees awarded by Yemeni universities. It should provide an efficient way of verifying certificates. The effectiveness of the conventional method of confirming the validity of certificates in reducing fraud has not been very strong. Therefore, it is necessary to stop this kind of fraud by using blockchain technology, which has several benefits, such as encryption, sharing of data, and the capacity to store information as permanent data that cannot be altered. Low latency and low cost will be available for the issuance, sharing, and verification of these certifications in universities if the suggested system is implemented. The paper presents the proposed framework, which uses smart contracts in the Ethereum blockchain and a distributed peer-to-peer network (filebase) for certification verification by a certificate’s hash immediately without waiting for the facility’s response. It also contains an estimate of the average cost of publishing a certificate. It also frees students from having to constantly carry paper copies of their documents by enabling them to access them via a certificate's hash. Furthermore, there is no extra cost for the verification process, and it does not require an Ethereum network account.
Diabetes poses a global health challenge, demanding continuous monitoring and expert care for effective management. Conventional monitoring methods lack real-time insights and secure data-sharing capabilities, necessitating innovative solutions that leverage emerging technologies. Existing centralized monitoring systems often entail risks such as data breaches and single points of failure, emphasizing the necessity for a secure, decentralized approach that integrates the Internet of Things (IoT), blockchain, and machine learning for efficient and secure diabetes management. This paper introduces a decentralized, blockchain-based framework for remote diabetes monitoring, IoT sensors, machine learning models, and decentralized applications (DApps). The proposed framework comprises five layers: the IoT Sensor Layer, which collects real-time health data from patients; the Blockchain Layer, leveraging smart contracts on the Ethereum blockchain for secure data sharing and transactions; the machine learning Layer, analyzing patient data to detect diabetes; and the DApps Layer, facilitating interactions between patients, doctors, and hospitals. For intelligent decision-making regarding diabetes based on data collected from different sensors, nine machine learning algorithms, including logistic regression, K-nearest neighbors (KNN), support vector machine (SVM), Decision Tree, Random Forest, AdaBoost, stochastic gradient boosting (SGD), and Naive Bayes, were trained and tested on the PIMA dataset. Based on the performance evaluation parameters such as accuracy, recall, F1-score, and the area under the curve (AUC), it was found that the AdaBoost model achieved the highest predictive accuracy of 92.64%, followed by the Decision Tree with an accuracy of 92.21% in diabetes classification.
Maykon Valério da Silva, Aldri Santos, Luiz A. Rodrigues
Com a expansão da Internet das Coisas, muitos dispositivos estão sendo conectados, coletando e transmitindo dados. O Blockchain é uma solução segura para registro distribuído, mas seu alto custo computacional e energético é restritivo para equipamentos com recursos limitados. Neste estudo, o Blockbench foi aprimorado para avaliar o desempenho de redes Ethereum privadas com a inclusão de um dispositivo Raspberry Pi. Foram comparados cenários com e sem o dispositivo, bem como os protocolos de consenso Ethash e Clique. Os resultados mostram que o Raspberry Pi não pode ser um minerador na rede Ethash, apenas um nó leve, e destacam a superioridade do consenso Clique em termos de latência, vazão e consumo de recursos computacionais.
Alexandre Fontinele, Josué N. Campos, I. R. de Oliveira, Glauber Dias Gonçalves · 7 authors
O suporte a contratos inteligentes na Blockchain Ethereum propiciou a emergência de um novo ecossistema de finanças descentralizado e automatizado, denominado DeFi. Esse ambiente é altamente competitivo e seus protocolos vem sendo explorados em busca de vulnerabilidades que oferecem ganhos econômicos a usuários estratégicos. Recentemente, a fila de transações pendentes do Ethereum tornou-se alvo de especulações financeiras. Na esperança de obter algum lucro, atacantes monitoram continuamente a fila e tentam antecipar transações de outros usuários, inserindo estrategicamente suas transações antes e após a transação da potencial vítima, o que se tornou conhecido como ataque sanduíche. Neste artigo, avaliamos suspeitas de ataques sanduíche na Blockchain Ethereum durante o ano de 2023, atualizando os conhecimentos sobre esse ataque. Nossas análises baseadas em 113.774 dos 2.599.105 blocos processados demonstram a ocorrência de 1.553.362 especulações de ataques, com um lucro de em média USD 3.202,82 para os atacantes, fornecendo fortes evidências que ataques sanduíche continuam ocorrendo no ecossistema DeFi.
With the application of robotics in security monitoring, medical care, image analysis, and other high-privacy fields, vision sensor data in robotic operating systems (ROS) faces the challenge of enhancing secure storage and transmission. Recently, it has been proposed that the distributed advantages of blockchain be taken advantage of to improve the security of data in ROS. Still, it has limitations such as high latency and large resource consumption. To address these issues, this paper introduces PrivShieldROS, an extended robotic operating system developed by InterPlanetary File System (IPFS), blockchain, and HybridABEnc to enhance the confidentiality and security of vision sensor data in ROS. The system takes advantage of the decentralized nature of IPFS to enhance data availability and robustness while combining HybridABEnc for fine-grained access control. In addition, it ensures the security and confidentiality of the data distribution mechanism by using blockchain technology to store data content identifiers (CID) persistently. Finally, the effectiveness of this system is verified by three experiments. Compared with the state-of-the-art blockchain-extended ROS, PrivShieldROS shows improvements in key metrics. This paper has been partly submitted to IROS 2024.