Christian P. Nielsen, Elias Ribeiro da Silva, Fei Yu
Digital Twins and Blockchain are key elements that when connected allow continuous data acquisition in the factory. As the connection between digital twins and blockchain is rather under-explored, the key contribution of this paper is the conceptual development of a digital twin prototype connected with an Ethereum-based blockchain. The outcome of the paper provides a concept to ensure the unique tokens represent the physical assets without being tampered with by applying digital twin technology. The paper includes a case study focused on Matrix-Structured Manufacturing Systems for Small and Medium-sized Enterprises.
M. Sivaram, E. Laxmi Lydia, Irina V. Pustokhina, Denis A. Pustokhin · 7 authors
The booming applications of bitcoin Blockchain technologies made investors concerned about the return and risk of financial products. So, the return rate of bitcoin must be foreseen in prior. This research article devises an effective return rate prediction technique for Blockchain financial products based on Optimal Least Square Support Vector Machine (OLS-SVM) model. The parameter optimization of the LS-SVM model was performed using hybridization of Grey Wolf Optimization (GWO) with Differential Evolution (DE), called optimal GWO (OGWO) algorithm. The hybridization process is performed to eliminate the local optima problem of GWO and enhance the diversity of the population. To verify the goodness of the proposed model, the Ethereum (ETH) return rate was chosen as the target and experimental analysis was performed on it to verify the predictive results on the time series. The experimental outcome was analyzed in terms of two performance measures namely Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). The obtained simulation outcome infers that the OLS-SVM model yielded better predictive outcome of the return rate of financial products.
Marc Eisenstadt, Manoharan Ramachandran, Niaz Chowdhury, Allan Third · 5 authors
Goal: As the Coronavirus Pandemic of 2019/2020 unfolds, a COVID-19 ‘Immunity Passport’ has been mooted as a way to enable individuals to return back to work. While the quality of antibody testing, the avail- the ability of vaccines, and the likelihood of even attaining COVID-19 immunity continue to be researched, we address the issues involved in providing tamper-proof and privacy-preserving certification for test results and vaccinations. Methods: We developed a prototype mobile phone app and requisite decentralized server architecture that facilitates instant verification of tamper-proof test results. Personally identifiable information is only stored at the user’s discretion, and the app allows the end-user selectively to present only the specific test result with no other personal information revealed. The architecture, designed for scalability, relies upon (a) the 2019 World Wide Web Consortium standard called ‘Verifiable Credentials’, (b) Tim Berners-Lee’s decentralized personal data platform ‘Solid’, and (c) a Consortium Ethereum-based blockchain. Results: Our mobile phone app and decentralized server architecture enable the mixture of verifiability and privacy in a manner derived from public/private key pairs and digital signatures, generalized to avoid restrictive ownership of sensitive digital keys and/or data. Benchmark performance tests show it to scale linearly in the worst case, as significant processing is done locally on each app. For the test certificate Holder, Issuer (e.g. healthcare staff, pharmacy) and Verifier (e.g. employer), it is ‘just another app’ which takes only minutes to use. Conclusions: The app and decentralized server architecture offer a prototype proof of concept that is readily scalable, applicable generically, and in effect ‘waiting in the wings’ for the biological issues, plus key ethical issues raised in the discussion section, to be resolved.
Maha Kadadha, Rabeb Mizouni, Shakti Singh, Hadi Otrok · 5 authors
In this paper, a fully distributed auction-blockchain-based crowdsourcing framework is proposed-ABCrowd. In a typical crowdsourcing framework, independent workers compete to be allocated requesters' tasks. These workers advertise their costs to the centralized platform, which then decides the final allocation of tasks. While performing the allocation, centralized platforms face two main challenges: 1) how to ensure trusted execution for the allocation of tasks, and 2) how to motivate workers to declare their truthful costs. To address these challenges, ABCrowd proposes to run the crowdsourcing platform entirely on Ethereum Blockchain while incorporating auctions. Blockchain and smart contracts guarantee trusted execution for the allocation through autonomous and transparent on-Chain execution. ABCrowd uses the Repeated-Single-Minded Bidder (R-SMB) auction mechanism, which motivates workers to bid truthfully before allocating them and calculating their payments. R-SMB is an approximation of the optimized off-Chain Vickrey-Clarke-Groves (VCG) mechanism in terms of maximized profit. It entails repeating the Single-Minded Bidder (SMB) auction mechanism to meet the allocation requirement of crowdsourcing applications. ABCrowd is implemented and evaluated using Solidity on a private Ethereum Blockchain, where a real publicly available dataset is used. The proposed on-Chain R-SMB auction mechanism is compared to the off-Chain VCG mechanism, where the results show that R-SMB provides similar performance to VCG in terms of the average number of allocated tasks. Furthermore, R-SMB outperforms VCG in workers' travelled distance and requesters' costs, at a low execution cost.
The rise of blockchain technology has injected new vitality into the energy market. At present, blockchain projects for distributed photovoltaic power generation and carbon trading are rapidly emerging, but due to the barrier of two markets and the independent operation of such projects in two chains, the two market values cannot be two-way circulation, which is not conducive to the expansion of power trading business and the scale of carbon market. This paper proposed a joint operation mechanism of cross-chain trading, combined distributed photovoltaic power generation market and the carbon market by the blockchain technology. The novelty is to construct two chains which includes the mainchain and sidechain that enables the two markets to share data and circulate value, and to design a two-way anchoring method that achieves equating between carbon trading and electricity trading by cryptocurrency. The simulation studies took the improved IEEE 33 system as an example to simulate transactions on the Ethereum platform, and technically verify the feasibility of cross-chain transactions in these two markets, and use Ether as the settlement currency.
Ahed Abugabah, Nishara Nizamuddin, Ahmad Ali AlZubi
The healthcare sector is one of the most rapidly growing sectors globally. With the ever-growing technology, patient care, regulatory compliance, and digital transformation, there is an increased need for healthcare sectors to collaborate with all stakeholders - both within the healthcare ecosystem and in concurring industries. In recent times, telemedicine has proven to provide high quality, affordable, and predominantly adapted healthcare services. However, telemedicine suffers from several risks in implementation, such as data breach, restricted access across medical fraternity, incorrect diagnosis and prescription, fraud, and abuse. In this work, introduce blockchain-based framework that would unlock the future of the healthcare sector and improved services. Our proposed solution utilizing Ethereum smart contracts to develop a transparent, tamper-proof telemedicine healthcare framework, and ensure the integrity of sensitive patient data eliminating a central administrator. Moreover, the smart contract regulates the interaction between all the parties involved in the network and keeps the patient meticulously informed about the transactions in the network.
Bitcoinin julkaisu merkitsi samalla lohkoketjusovellusten syntymää. Johtuen muun muassa julkisen todennettavuuden tarpeesta, lohkoketjussa säilytettävä tieto on tyypillisesti läpinäkyvää, joka voi olla ongelmallista yksityisyyden kannalta. Yksityisyyden parantamiseksi on kehitetty menetelmiä, jotka hämärtävät lohkoketjun tietojen läpinäkyvyyttä säilyttäen niiden eheyden ja todennettavuuden. Lupaava kryptografinen menetelmä tähän tarkoitukseen on nollatietotodistus, joka mahdollistaa väitteen todistamisen siten, että ainoa todistuksessa paljastuva tieto on väitteen totuusarvo. \n \nNollatietotodistuksiin perehtyminen aloitetaan niiden teoreettisesta perustasta. Lohkoketjujen kannalta tärkeitä vaatimuksia nollatietotodistuksille ovat ei-interaktiivisuus ja ytimekkyys, ja nämä ehdot täyttäviä todistusrakenteita kutsutaan yleisesti nimellä zk-SNARK. Fiat-Shamir muunnos on käyttökelpoinen menetelmä ei-interaktiivisen nollatietotodistuksen muodostamiseen tapauksissa, joissa ytimekkyys ei ole tärkeää. Nollatietotodistusten käyttöä tutkitaan erityisesti yksityisyyden ja käyttökelpoisuuden kannalta kolmessa lohkoketjusovelluksessa, jotka ovat Zcash, Ethereum ja Monero.
Smart contract has greatly improved the services and capabilities of blockchain, but it has become the weakest link of blockchain security because of its code nature. Therefore, efficient vulnerability detection of smart contract is the key to ensure the security of blockchain system. Oriented to Ethereum smart contract, the study solves the problems of redundant input and low coverage in the smart contract fuzz. In this paper, a taint analysis method based on EVM is proposed to reduce the invalid input, a dangerous operation database is designed to identify the dangerous input, and genetic algorithm is used to optimize the code coverage of the input, which construct the fuzzing framework for smart contract together. Finally, by comparing Oyente and ContractFuzzer, the performance and efficiency of the framework are proved.
The application of machine learning algorithms in predicting cryptocurrency prices has gained significant attention in recent years. Researchers have explored various approaches such as recurrent neural networks, deep learning neural networks, Bayesian regression, k-nearest neighbor, support vector machine, and other algorithms to forecast the prices of cryptocurrencies like Bitcoin, Ethereum, Dogecoin and Litecoin. This paper will draw on established literature on price prediction using machine learning, including studies on NFT sales predictability, NFT sale price fluctuations prediction, gold price prediction, and silver price forecasting. The research paper has focused on utilizing high-dimensional features, time-series analysis, as well as the comparison of different statistical models and machine learning algorithms. Additionally, the prediction models have incorporated factors such as market liquidity, exchange market dynamics. While the literature acknowledges the potential of machine learning in cryptocurrency price prediction, gold, silver and NFT’s there is a recognized gap in the application of these techniques across a broader range of cryptocurrencies. The proposed methodology will integrate various machine learning models and statistical methods to predict the prices of cryptocurrencies, gold, silver, and NFTs, taking into account factors such as market trends, trade networks and visual features. Furthermore, the studies emphasize the importance of feature engineering, sample dimension engineering, and the use of various machine learning techniques to enhance the accuracy and stability of cryptocurrency price predictions. As the cryptocurrency market continues to expand, there is a need for further research to develop robust machine learning models that can effectively forecast the prices of diverse cryptocurrencies, contributing to the advancement of this field.
Automated digital contact tracing is effective and efficient, and one of the non-pharmaceutical complementary approaches to mitigate and manage epidemics like Coronavirus disease 2019 (COVID-19). Despite the advantages of digital contact tracing, it is not widely used in the western world, including the US and Europe, due to strict privacy regulations and patient rights. We categorized the current approaches for contact tracing, namely: mobile service-provider-application, mobile network operators' call detail, citizen-application, and IoT-based. Current measures for infection control and tracing do not include animals and moving objects like cars despite evidence that these moving objects can be infection carriers. In this article, we designed and presented a novel privacy anonymous IoT model. We presented an RFID proof-of-concept for this model. Our model leverages blockchain's trust-oriented decentralization for on-chain data logging and retrieval. Our model solution will allow moving objects to receive or send notifications when they are close to a flagged, probable, or confirmed diseased case, or flagged place or object. We implemented and presented three prototype blockchain smart contracts for our model. We then simulated contract deployments and execution of functions. We presented the cost differentials. Our simulation results show less than one-second deployment and call time for smart contracts, though, in real life, it can be up to 25 seconds on Ethereum public blockchain. Our simulation results also show that it costs an average of $1.95 to deploy our prototype smart contracts, and an average of $0.34 to call our functions. Our model will make it easy to identify clusters of infection contacts and help deliver a notification for mass isolation while preserving individual privacy. Furthermore, it can be used to understand better human connectivity, model similar other infection spread network, and develop public policies to control the spread of COVID-19 while preparing for future epidemics.
Cryptocurrency can be defined as a digital asset and a virtual element designed to be an alternative exchange tool for cash in terms of how it works, securing transactions using encryption (cryptography).Looking across the world, there are Bitcoin, Ethereum, Bitcoin Cash, Ripple, Litecoin, Cardano, Nem, Iota, Stellar, Dash and many more cryptocurrencies.The most famous of the cryptocurrencies today is Bitcoin, which is the most preferred in terms of transaction volume and constitutes approximately 50% of the cryptocurrency volume.Bitcoin, created by a person or community named Satoshi Nakatomo in 2008 and the first transfer in 2009, is a digitally created cryptocurrency.The aim of this study is to investigate the relationship between Bitcoin, a cryptocurrency, and gold ounce prices and dollar index.In the study, 2012-2019 was determined as the term and the monthly data were examined.ARDL Boundary test approach was used as a method to determine the cointegration relationship between the examined variables.As a result of the study, a long-term co-integrated relationship between Bitcoin's gold and foreign exchange price was determined.With this result, a 1% increase in the gold ounce price will increase Bitcoin prices by about 15% in the long run; The 1-unit increase in the USD index indicates that it will increase Bitcoin prices by about 0.28%.However, it was concluded that there was no co-integrated relationship between the variables in the short term.
Over five thousand digital currencies have been issued by private sector actors since the release of the Bitcoin digital currency in 2009. Private sector issuance of distributed ledger technology (DLT)-based digital currencies such as Bitcoin, Ethereum and other altcoins threaten the stability of financial market infrastructures and preservation of monetary policy. Consequently, many central banks and monetary authorities have begun research and experimentation on central bank-issued digital currencies (CBDCs) to mitigate this threat. In this paper, we present a comprehensive survey of publicly available DLT-based CBDC experiments with completed proof-of-concept prototypes from across the world to enable an understanding of the motivations and best practice approaches for undertaking CBDC experiments. We provide a classification and generic framework for CBDCs and highlight existing DLT platform limitations and use cases in the financial services industry. Overall, our paper organizes in one place, all the relevant, publicly available DLT-based CBDC experiments with completed proof-of-concept prototypes to serve as a reference point for central banks, monetary authorities and researchers desiring to undertake research on DLT-based CBDCs. Ultimately, we present a survey on the technical feasibility and challenges of leveraging DLT to issue the selected CBDC experiments surveyed in this paper.
This paper presents a novel blockchain-based energy trading architecture for electric vehicles (EVs) within smart cities. By allowing local renewable energy providers to supply public charging stations, EV drivers can gain access to affordable energy and optimally plan for their charging operations. For this purpose, we present a smart-contract based trading platform that runs on top of a private Ethereum network. Contrary to existing solutions, we rely on the legacy billing and metering of the existing utility company in order to avoid making major changes to the existing infrastructure. The trading logic, including the auction mechanism, used to exchange energy can be defined in a smart-contract and applied within the platform. We conduct extensive experiments to evaluate the performance of some existing auction mechanisms and the underlying private Ethereum network in supporting the corresponding energy trading transaction load. We develop a virtualization-based simulator for Ethereum and measure both the transaction throughput and latency under different network and workload scenarios. The obtained results have shown that the current Ethereum implementation can support charging requests from EVs during peak hours in very crowded cities, such as Singapore.
Sarada Prasad Gochhayat, Sachin Shetty, Ravi Mukkamala, Peter Foytik · 6 authors
Blockchain promises to provide a distributed and decentralized means of trust among untrusted users. However, in recent years, a shift from decentrality to centrality has been observed in the most accepted Blockchain system, i.e., Bitcoin. This shift has motivated researchers to identify the cause of decentrality, quantify decentrality and analyze the impact of decentrality. In this work, we take a holistic approach to identify and quantify decentrality in Blockchain based systems. First, we identify the emergence of centrality in three layers of Blockchain based systems, namely governance layer, network layer and storage layer. Then, we quantify decentrality in these layers using various metrics. At the governance layer, we measure decentrality in terms of fairness, entropy, Gini coefficient, Kullback-Leibler divergence, etc. Similarly, in the network layer, we measure decentrality by using degree centrality, betweenness centrality and closeness centrality. At the storage layer, we apply a distribution index to define centrality. Subsequently, we evaluate the decentrality in Bitcoin and Ethereum networks and discuss our observations. We noticed that, with time, both Bitcoin and Ethereum networks tend to behave like centralized systems where a few nodes govern the whole network.
Cryptocurrency is a recent and popular topic that attracts the interest of investors and fund managers. Beyond the market discipline, researchers question the interaction between cryptocurrencies and macroeconomic variables. This study focuses on how the changes in gold and oil prices affect the daily price movements of various cryptocurrencies. The daily database used in this study includes the prices of the cryptocurrencies such as Bitcoin, Tether, Ethereum, Litecon and EOS for the period of August 1, 2017 and April 3, 2019. Initially, the stationarity of the time series is tested by The existence of the cointegration relationship among the series is tested by The presence of causality relationships among the series is investigated with the Dolado and Ltkepohl (1996) causality test. The empirical results support that there exists a cointegration relationship only in between Tether and gold and oil prices.
Since their introduction, Online Social Networks (OSNs) have transformed the way people interact with each other. Lately, a new trend is rising in the development of OSNs, fueled by an increasing interest of the blockchain technology and the benefits it can bring to the world of OSNs. Blockchain Online Social Media (BOSMs) are Social Media applications that are supported by the blockchain technology. Thanks to a blockchain, BOSMs either try to enforce the privacy of the users or try to redistribute with their users the economic wealth generated by the platform through a rewarding system. There are countless BOSMs available which incorporate a rewarding system. Among them, Steemit can be considered the most well-known platform exceeding 1 million registered users. Steemit is supported by the blockchain Steem, which is a blockchain that natively supports the development of social applications by the usage of transactions that model social activity. Even if other important blockchains, such as Ethereum has been widely analysed, at the best of our knowledge, no study exists concerning the topology of the transactions graph of Steem. The main goal of this paper is to study the structure of the Steem transaction graph to understand its characteristics and unveil crucial knowledge concerning their users. More in detail, we build the Interactions Graph and, after its study, we evaluate three subgraphs that capture its social and monetary aspects. The degree distributions of the graphs follow a power-law. Additionally, we detect a substantial number of bots that offer paid services on the platform among the most active users. Lastly, the investigation of the four analysed graphs through a bow-tie structure, suggesting that half of the users have a passive social behaviour and that 80% of the users tend to accrue economic value
The exploitation of smart-contract vulnerabilities can have catastrophic consequences such as the loss of millions of pounds worth of crypto assets. Formal verification can be a useful tool in identifying vulnerabilities and proving that they have been fixed. In this paper, we present a formalisation of Solidity and the Ethereum blockchain using the Solid language and its blockchain; a Solid program is obtained by explicating/desugaring a Solidity program. We make some abstractions that over-approximate the way in which Solidity/Ethereum behave. Based on this formalisation, we create Solidifier: a bounded model checker for Solidity. It translates Solid into Boogie, an intermediate verification language, that is later verified using Corral, a bounded model checker for Boogie. Unlike much of the work in this area, we do not try to find specific behavioural/code patterns that might lead to vulnerabilities. Instead, we provide a tool to find errors/bad states, i.e. program states that do not conform with the intent of the developer. Such a bad state, be it a vulnerability or not, might be reached through the execution of specific known code patterns or through behaviours that have not been anticipated.
Tingting Song, Bo Cui, Ru Li, Jing Liu · 5 authors
Named Data Networking (NDN) is a new clean-slate architecture for the future Internet. Efficient content retrieval is the original intention of NDN design. The content retrieval process driven by content consumers in NDN includes the following challenges, consumers do not know whether the content exists and whether the content producer is reliable. Invalid interest packets could cause the occupation of limited network resources and DoS attack problem. To ensure the authenticity and integrity of the data packets, consumers need to pre-configure the trust schema, which is centralized and prone to the single point of failure problem. Blockchain has widespread attention to build trust in a distributed way, and Ethereum is a programmable blockchain, a decentralized smart contract platform. To lighten the burden of consumers, we proposed a Smart Contract-based Trusted Content Retrieval Mechanism (SCTCRM) for NDN in this paper. The mechanism contains a trustworthy information base for content and producers based on smart contracts, and provides content retrieval and name resolution services for content consumers. The purpose of this mechanism is to improve the efficiency and security of content retrieval process. We described the framework and the workflow of SCTCRM, and used Colored Petri Nets to create a formal mathematical model and analyze the security of the mechanism. Finally, the cost of storage and Gas in smart contracts are evaluated through the prototype deployment. From the results, we can see that the proposed mechanism is security and practicality.
Mohd Sabri Ismail, Saiful Izzuan Hussain, Mohd Salmi Md Noorani
This study explores persistent homology to detect early warning signals of the 2017 and 2019 major financial crashes in Bitcoin. Sliding window is used to obtain point cloud datasets from a multidimensional time series (Bitcoin, Ethereum, Litecoin and Ripple). We apply persistent homology to quantify transient loops that appear in multiscale topological spaces, which associated on each point cloud dataset and encode the quantified information in a persistence landscape. Temporal changes in persistence landscapes are measured via their L1-norms. Consequently, a new representative is attained, called L1-norms time series. The L1-norms is associated with indicators: autocorrelation function at lag 1, variance and mean power spectrum at low frequencies to detect the signals. By using Kendall's tau correlation and significance test, significant rising trend events that occur before major financial crashes in Bitcoin are defined as the signals. A threshold is determined to scan entire data and record all the significant rising trend events. Lastly, we compare L1-norms with residuals time series, which is another representative obtained from de-trending approach. Our result portrays that autocorrelation function at lag 1 and variance of the L1-norms successfully detect early warning signals before the 2017 and 2019 major financial crashes. However, variance of the L1-norms is better since it able to signal another 2018 major financial crash. For the residuals, no early warning signals are detected. Hence, persistent homology provides a better representative than de-trending approach. Overall, persistent homology is a promising method to detect early warning signals of major financial crashes in Bitcoin.