Blockchain Papers

R. Murugesan, V. Shanmugaraja, A. Vadivel

No abstract is available for this record.

Olayemi Mikail Olaniyi, Abraham Ayegba Alfa, Buhari Ugbede Umar

Blockchain technology has gained lots of traction in the past five years due to the innovations introduced in digital currency, the Bitcoin. This technology is powered by distributed ledger technology, which is a distributed database system. It is often renowned for decentralization, anti-attack, and unfalsified attributes making it a top choice in several non-monetary applications. In fact, the problem of privacy and security of the Internet of Things has been undertaken aggressively with Blockchain. Several problems have been identified with blockchain technology such as large delays and lack of support for real-time transaction processing, authorization, node verification, and consensus mechanisms. This article intends to provide a comprehensive survey on the recent advances and solutions to the problems of blockchain technology by leveraging the artificial intelligence approaches. The outcomes of this study will provide valuable information and guidance on the design of Blockchain-based systems to support time-sensitive and real-time specific applications and processes.

Michael Pacheco, Gustavo A. Oliva, Gopi Krishnan Rajbahadur, Ahmed E. Hassan

The Ethereum platform allows developers to implement and deploy applications called Dapps onto the blockchain for public use through the use of smart contracts. To execute code within a smart contract, a paid transaction must be issued towards one of the functions that are exposed in the interface of a contract. However, such a transaction is only processed once one of the miners in the peer-to-peer network selects it, adds it to a block, and appends that block to the blockchain This creates a delay between transaction submission and code execution. It is crucial for Dapp developers to be able to precisely estimate when transactions will be processed, since this allows them to define and provide a certain Quality of Service (QoS) level (e.g., 95% of the transactions processed within 1 minute). However, the impact that different factors have on these times have not yet been studied. Processing time estimation services are used by Dapp developers to achieve predefined QoS. Yet, these services offer minimal insights into what factors impact processing times. Considering the vast amount of data that surrounds the Ethereum blockchain, changes in processing times are hard for Dapp developers to predict, making it difficult to maintain said QoS. In our study, we build random forest models to understand the factors that are associated with transaction processing times. We engineer several features that capture blockchain internal factors, as well as gas pricing behaviors of transaction issuers. By interpreting our models, we conclude that features surrounding gas pricing behaviors are very strongly associated with transaction processing times. Based on our empirical results, we provide Dapp developers with concrete insights that can help them provide and maintain high levels of QoS.

Wenjun Xia, Xiaohong Chen, Chao Song

At present, water resource information management in China is mainly a centralized model, and there exist some problems such as high cost, low efficiency, and data storage insecurity. Blockchain technology provides a good solution which can create an efficient trust mechanism among the links in the process of water resource utilization. It guarantees the security of the data, avoiding the sudden collapse of the central institutions caused by some normal operations of the entire system. Based on a decentralization blockchain, we propose a decentralized water resource information management system for the whole process of “supply-use-consumption-discharge,” which improves the traditional water data storage. Specifically, the monitoring and business data are encrypted by the blockchain and are transmitted using a peer-to-peer network. Moreover, the centralized management mode is changed and part of the management work is dispersed to each node. Thus, decisions and measures can be made and implemented quickly after discovering problems to improve the efficiency of information transmission and management. In addition, two typical blockchain-based application scenarios for water resource management are designed. A blockchain-based approach makes issuing and monitoring water abstraction permits more convenient and obtaining license information more secure and verifiable. A reliable mechanism for tracing water quality ensures the accuracy and reliability of water quality information, enables the detection of locations with inadequate water quality, and clarifies people’s responsibility, thus guaranteeing the water safety of the residents.

Nicholas Pinelli, Remo Pareschi

Social media and financial markets are ecosystems bound to interact and overlap more and more. For example, cryptocurrencies, Bitcoin in the first place, have long been among the hottest topics on social media. Here we illustrate a methodology for correlating Bitcoin price trends and social media that operates on the aggregation of news from multiple social networks as typically occurs on dedicated channels in sites such as Reddit. For this purpose, we define general laws to map the financial fluctuations of Bitcoin in a space defined on three dimensions: content volume, sentiment and time. These laws provide the foundation upon which to build effective methodologies for social media-based prediction of Bitcoin performance.

Jayasri Angara, Ravi Shankar Saripalle

Abstract Tracking unseen water in products (Embedded Virtual Water) has generated great interest in the scientific community. This water transfers between geographies via suppliers, manufacturers, distributors, retailers and customers in multiple phases. However, the Virtual Water Trading System lacks proper accounting standards, established protocols and processes in the context of product manufacturing. Therefore, there is a need to establish a technology platform to handle the complex virtual water international trade. Such a platform should uphold transparency and create ‘water consciousness’ and awareness among companies and consumers. The concept of a virtual water currency and blockchain technology platform together can manage these processes. Blockchain helps in setting up secure, verifiable, scalable and traceable systems. Blockchain manages the audit and contract management processes with ease. Virtual water currency is critical to advocate sustainability. The objective of this paper is to establish the key linkages between virtual water and usage of blockchain. A systematic literature survey was conducted on 16 journal repositories (153 journal papers) of IWA Publishing to establish virtual water linkages and five journal databases (IEEE Xplore, Sciencedirect, ACM Digital Library, Springer Link and Wiley Online Library covering 5026 journal papers) for blockchain and water management linkages. This study proposes to introduce virtual water currency and set up an International Virtual Water Trading System using blockchain. The proposed platform seamlessly integrates the quality, cost and sustainability of industrial products and their sub-components.

Giulio Caldarelli

Whereas the use of distributed ledger technologies has previously been limited to cryptocurrencies, other sectors—such as healthcare, supply chain, and finance—can now benefit from them because of bitcoin scripts and smart contracts. However, these applications rely on oracles to fetch data from the real world, which cannot reproduce the trustless environment provided by blockchain networks. Despite their crucial role, academic research on blockchain oracles is still in its infancy, with few contributions and a heterogeneous approach. This study undertakes a bibliometric analysis by highlighting institutions and authors that are actively contributing to the oracle literature. Investigating blockchain oracle research state of the art, research themes, research directions, and converging studies will also be highlighted to discuss, on the one hand, current advancements in the field and, on the other hand, areas that require more investigation. The results also show that although worldwide collaboration is still lacking, various authors and institutions have been working in similar directions.

Chuansheng Wang, Xuecheng Tan, Cuiyou Yao, Feng Gu · 6 authors

Single point of failure and node attack tend to cause instability in the centralized Internet of Things (IoT). Combined with blockchain technology, the deficiency of traditional IoT architecture can be effectively alleviated. However, the existing blockchain consensus mechanism still has the problems of forks and wasting of computing power. Therefore, this paper proposes a new framework based on a two-stage credit calculation to handle these problems. Notably, the nodes are selected through the model, and these nodes will compete on the chain according to the behavior of participating in the creation of the block. Then, a comparative simulation with the existing consensus mechanism proof of work (PoW) is presented. The results show that the proposed framework can quickly eliminate malicious nodes, maintain the overall security of the blockchain and reduce consensus delay.

Enrico Tedeschi, Tor-Arne S. Nordmo, Dag Johansen, Håvard D. Johansen

The transaction-rate bottleneck built into popular proof-of-work (PoW)-based cryptocurrencies, like Bitcoin and Ethereum, leads to fee markets where transactions are included according to a first-price auction for block space. Many attempts have been made to adjust and predict the fee volatility, but even well-formed transactions sometimes experience unexpected delays and evictions unless a substantial fee is offered. In this article, we propose a novel transaction inclusion model that describes the mechanisms and patterns governing miners decisions to include individual transactions in the Bitcoin system. Using this model we devise a Machine Learning (ML) approach to predict transaction inclusion. We evaluate our predictions method using historical observations of the Bitcoin network from a five month period that includes more than 30 million transactions and 120 million entries. We find that our Machine Learning (ML) model can predict fee volatility with an accuracy of up to 91%. Our findings enable Bitcoin users to improve their fee expenses and the approval time for their transactions.

Song Tang, Zhiqiang Wang, Jian Jiang, Suli Ge · 5 authors

With the continuous development of blockchain technology, the application scenarios of alliance blockchain are also increasing. The consensus algorithm can achieve distributed consensus among nodes in the network. At present, the practical byzantine fault tolerance algorithm (PBFT) consensus algorithm commonly used in alliance blockchain requires all nodes in the network to participate in the consensus process. Experiments show that when the number of consensus nodes in the system exceeds 100, the bandwidth consumption and consensus delay will greatly increase, resulting in the inability of PBFT to be applied. In scenes with many nodes. How to improve the performance of alliance blockchains safely and efficiently has become an urgent problem to be solved at present. For the PBFT commonly used in alliance blockchains, there are some problems, such as large communication overhead, simple selection of master nodes, and inability to expand and exit nodes dynamically in the network. This paper proposes an improved algorithm tPBFT (trust-based practical Byzantine algorithm), which is suitable for high-frequency trading scenarios of consortium chains. By introducing a trust equity scoring mechanism between nodes in the network, the list of consensus nodes can be dynamically adjusted. tPBFT simplifies the pre-prepare stage of the PBFT consensus process, and realizes the verification of the hash transaction list in the reply stage, thereby reducing the interaction overhead between network nodes. Theoretical analysis and experiments show that when the number of nodes in the network is greater than 30, with the further increase of the number of nodes, the improved tPBFT algorithm has a relatively large performance in terms of node communication overhead, consensus efficiency and scalability outperforms the PBFT algorithm.

Eugenio Cortesi, Francesco Bruschi, Stefano Secci, Sami Taktak

No abstract is available for this record.

Shengwen Ding, Chenhui Hu

Machine learning (ML) has been pervasively researched nowadays and it has been applied in many aspects of real life. Nevertheless, issues of model and data still accompany the development of ML. For instance, training of traditional ML models is limited to the access of data sets, which are generally proprietary; published ML models may soon be out of date without an update of new data and continuous training; malicious data contributors may upload wrongly labeled data that leads to undesirable training results; and the abuse of private data and data leakage also exit. With the utilization of blockchain, an emerging and swiftly developing technology, these problems can be efficiently solved. In this paper, we survey the convergence of collaborative ML and blockchain. Different ways of the combination of these two technologies are investigated and their fields of application are examined. Discussion on the limitations of current research and their future directions are also included.

Pragya Mishra, Shubham Bharadwaj

Activation functions are critical components of neural networks, helping the model learn highly-intricate dependencies, trends, and patterns. Non-linear activation functions allow the model to behave as a functional approximator, learning complex decision boundaries and multi-dimensional patterns in the data. Activation functions can be combined with one another to learn better representations with the objective of improving gradient flow, performance metrics reducing training time and computational cost. Recent work on oscillatory activation functions\cite{noel2021growing}\cite{noel2021biologically} showcased their ability to perform competitively on image classification tasks using a compact architecture. Our work proposes the utilization of these oscillatory activation functions for predicting the volume-weighted average of Bitcoin on the G-Research Cryptocurrency Dataset. We utilize a popular LSTM architecture for this task achieving competitive results when compared to popular activation functions formally used.

Sutiwat Simtharakao

Bitcoin is a high-risk asset with a potentially high return. Predicting Bitcoin candlestick, i.e., open, high, low, and close (OHLC) prices, can help investors make trading decisions. The objective of this study is to develop a neural network model to predict the candlestick prices of Bitcoin for the next period. Additionally, this study investigates methods to enhance the model's forecasting performance by feature transformations, specifically data normalization. This study employs two neural network algorithms, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), to forecast daily Bitcoin OHLC prices. To enhance the model's performance, we compare sliding window normalization with whole set normalization techniques. The normalization techniques investigated for both whole set and sliding window data include z-score normalization, min-max normalization, and relative change normalization. Furthermore, this study compares two candlestick prediction methods, namely using OHLC prices and using candle wick (CULR) to predict OHLC prices. The models use historical OHLC prices over several days to predict the next day's OHLC prices. The results indicate that the best-performing model is the OHLC method using GRU algorithm with sliding window z-score normalization, which achieves an MAPE of 1.95% and an RMSE of 767.71. Moreover, the sliding window normalization generally outperforms the whole set normalization for both LSTM and GRU models in terms of RMSE and MAPE. Regarding the candlestick prediction methods, there was no significant difference in their performance in terms of accuracy and forecasting error. However, our results suggest that the OHLC method performs slightly better than the CULR method.

Muhammad Hussain Mughal, Zaffar Ahmed Shaikh, Khurshed Ali, Safdar Ali · 5 authors

The streamflow data acquisition with various techniques and dispersing of River’s locations demand improvement of reliability and frequency aspects. One of the reliability measurement characteristics is data ownership. The authority sharing data authorizes its quality and is also responsible for the wrong decision triggered by incorrect data. The consensus-based crowdsourcing data contribute to aggregated streamflow records’ generation. The aggregated streamflow records are stored on a streamflow ledger, a Hyperledger fabric-based ledger for rivers’ streamflow data. In contrast, InterPlanery File System(IPFS) distributed file storage system is helpful for policy documents and distribution scheme storage. Blockchain-based techniques for improvement of data-intensive decision support systems. The distributed river streamflow data measured and shared by distributed gauging officials and stored on the blockchain-based distributed storage system contributes to scalability, transparency, availability, and accessibility of shareable data. All stakeholders require quality data with trust and provenance management through a persistent, linked, and immutable copy of data. This technique resolves the conflict or disagreement on streamflow optimization and flood mitigation decisions. In a nutshell, the blockchain technology with IPFS for off-chain large files storage would contribute twofold to irrigation systems and flood mitigation domains. On one side, the streamflow data aggregation has consensus from streamflow assessment for software agents and stakeholders. Secondly, the persistent data copy is shared among distributed stakeholders using IPFS-based content addressed file-sharing protocol for a common operating picture to improve effective collaboration and coordination among managers of irrigation systems and flood mitigation activities.

Marco Di Gennaro, Lorenzo Italiano, Giovanni Meroni, Giovanni Quattrocchi

Thanks to built-in immutability and persistence, the blockchain is often seen as a promising technology to certify information. However, when the information does not originate from the blockchain itself, its correctness cannot be taken for granted. To address this limitation, blockchain oracles -- services that validate external information before storing it in a blockchain -- were introduced. In particular, when the validation cannot be automated, oracles rely on humans that collaboratively cross-check external information. In this paper, we present DeepThought, a distributed human-based oracle that combines voting and reputation schemes. An empirical evaluation compares DeepThought with a state-of-the-art solution and shows that our approach achieves greater resistance to voters corruptions in different configurations.

Panpan Li, Shengbo Gong, Shaocong Xu, Jiajun Zhou · 6 authors

Blockchain finance has become a part of the world financial system, most typically manifested in the attention to the price of Bitcoin. However, a great deal of work is still limited to using technical indicators to capture Bitcoin price fluctuation, with little consideration of historical relationships and interactions between related cryptocurrencies. In this work, we propose a generic Cross-Cryptocurrency Relationship Mining module, named C2RM, which can effectively capture the synchronous and asynchronous impact factors between Bitcoin and related Altcoins. Specifically, we utilize the Dynamic Time Warping algorithm to extract the lead-lag relationship, yielding Lead-lag Variance Kernel, which will be used for aggregating the information of Altcoins to form relational impact factors. Comprehensive experimental results demonstrate that our C2RM can help existing price prediction methods achieve significant performance improvement, suggesting the effectiveness of Cross-Cryptocurrency interactions on benefitting Bitcoin price prediction.

Patrick Ocheja, Friday Joseph Agbo, Solomon Sunday Oyelere, Brendan Flanagan · 5 authors

The advent of blockchain technology over the last decade has led to the development of multiple use-cases of decentralization in various fields including education. This paper presents a unique bibliometric and qualitative analysis of the blockchain in education with novel contributions on temporal development, emerging themes and practical case studies on adoption and integration with existing educational technologies. We focus on identifying the major actors in the space, demographic participation and adoption, current hot topics, grey areas, and potential areas for innovation. Our analysis shows that while the blockchain has been around for about 13 years, blockchain in education only became prominent 5 years ago. This research also reveals that most of the efforts have been focused on reporting and verifying academic certificates and transcripts: only very few research focused on reporting and connecting in-depth academic records such as learning behaviour logs, learning contents and assessment data. This calls for concern as current education blockchain systems do not consider interoperability at the blockchain level and the heterogeneous nature in which institutes create and consume academic data. Finally, we present discussions on the implications of our findings, potential solutions and aspects of education blockchain research that can help to improve educational outcomes for various stakeholders.

Otabek Sattarov, Jaeyoung Choi

Aspiring to achieve an accurate Bitcoin price prediction based on people's opinions on Twitter usually requires millions of tweets, using different text mining techniques (preprocessing, tokenization, stemming, stop word removal), and developing a machine learning model to perform the prediction. These attempts lead to the employment of a significant amount of computer power, central processing unit (CPU) utilization, random-access memory (RAM) usage, and time. To address this issue, in this paper, we consider a classification of tweet attributes that effects on price changes and computer resource usage levels while obtaining an accurate price prediction. To classify tweet attributes having a high effect on price movement, we collect all Bitcoin-related tweets posted in a certain period and divide them into four categories based on the following tweet attributes: $(i)$ the number of followers of the tweet poster, $(ii)$ the number of comments on the tweet, $(iii)$ the number of likes, and $(iv)$ the number of retweets. We separately train and test by using the Q-learning model with the above four categorized sets of tweets and find the best accurate prediction among them. Especially, we design several reward functions to improve the prediction accuracy of the Q-leaning. We compare our approach with a classic approach where all Bitcoin-related tweets are used as input data for the model, by analyzing the CPU workloads, RAM usage, memory, time, and prediction accuracy. The results show that tweets posted by users with the most followers have the most influence on a future price, and their utilization leads to spending 80\% less time, 88.8\% less CPU consumption, and 12.5\% more accurate predictions compared with the classic approach.

Ningyu He, Weihang Su, Zhou Yu, Xinyu Liu · 10 authors

The continuing expansion of the blockchain ecosystems has attracted much attention from the research community. However, although a large number of research studies have been proposed to understand the diverse characteristics of individual blockchain systems (e.g., Bitcoin or Ethereum), little is known at a comprehensive level on the evolution of blockchain ecosystems at scale, longitudinally, and across multiple blockchains. We argue that understanding the dynamics of blockchain ecosystems could provide unique insights that cannot be achieved through studying a single static snapshot or a single blockchain network alone. Based on billions of transaction records collected from three representative and popular blockchain systems (Bitcoin, Ethereum and EOSIO) over 10 years, we conduct the first study on the evolution of multiple blockchain ecosystems from different perspectives. Our exploration suggests that, although the overall blockchain ecosystem shows promising growth over the last decade, a number of worrying outliers exist that have disrupted its evolution.

Fátima Leal, Bruno Veloso, Benedita Malheiro, Juan C. Burguillo · 6 authors

Explainable recommendations enable users to understand why certain items are suggested and, ultimately, nurture system transparency, trustworthiness, and confidence. Large crowdsourcing recommendation systems ought to crucially promote authenticity and transparency of recommendations. To address such challenge, this paper proposes the use of stream-based explainable recommendations via blockchain profiling. Our contribution relies on chained historical data to improve the quality and transparency of online collaborative recommendation filters – Memory-based and Model-based – using, as use cases, data streamed from two large tourism crowdsourcing platforms, namely Expedia and TripAdvisor. Building historical trust-based models of raters, our method is implemented as an external module and integrated with the collaborative filter through a post-recommendation component. The inter-user trust profiling history, traceability and authenticity are ensured by blockchain, since these profiles are stored as a smart contract in a private Ethereum network. Our empirical evaluation with HotelExpedia and Tripadvisor has consistently shown the positive impact of blockchain-based profiling on the quality (measured as recall) and transparency (determined via explanations) of recommendations.

K. Sri Lakshmi Sruthi, D. Ratnagiri, Rudru Jyothika, Salunkhe Sneha · 6 authors

Bitcoin is one of the most popular and valuable cryptocurrencies in the current financial market, attracting traders for investment and thereby opening new research opportunities for researchers. Countless research works have been performed on Bitcoin price prediction with different machine learning prediction algorithms. For the project: relevant features are taken from the dataset having strong correlation with Bitcoin prices and random data chunks are then selected to train and test the model. The random data which has been selected for model training, may cause unfitting outcomes thus reducing the price prediction accuracy. Here, a proper method to train a prediction model is being scrutinised. The proposed methodology is then applied to train a simple Long Short-Term Memory (LSTM) model to predict the bitcoin price for the upcoming 30 days. When the LSTM model is trained with a suitable data chunk, thus identified, sustainable results are found for the prediction. In the end of this project, the work culminates with future improvements. Bitcoin is a kind of Cryptocurrency and now is one of type of investment on the stock market. Stock markets are influenced by many risks of factor. And bitcoin is one kind of cryptocurrency that keep rising in recent few years, and sometimes sudden fall without knowing influence behind it on the stock market. Because it's fluctuations, there's a need and automation tool to predict bitcoin on the stock market. This research study learns how to create model prediction bitcoin stock market prediction using LSTM, LSTM (Long Short-Term Memory) is another type of module provided for RNN later developed and popularized by many researchers, like RNN, the LSTM also consists of modules with recurrent consistency. The Method that we apply on this project, also technique and tools to predict Bitcoin on stock market yahoo finance can predict the result above $ 12600 USD for next days after prediction, in the last section we make conclusions and discuss future works.

Giuseppe Antonio Pierro, Henrique Rocha, Sté́phane Ducasse, Michele Marchesi · 5 authors

No abstract is available for this record.

Jeyakumar Samantha Tharani, E.Y.A. Charles, Zhé Hóu, Marimuthu Palaniswami · 5 authors

Blockchain is a digital technology built on three pillars: decentralization, transparency and immutability. Bitcoin and Ethereum are two prevalent Blockchain platforms, where the participants are globally connected in a peer-to-peer manner and anonymously perform trade electronically. The vast number of decentralized transactions and the pseudo-anonymity of participants open the door for scams, cyber frauds, hacks, money laundering and fraudulent transactions. It is challenging to detect such fraudulent activities using traditional auditing techniques, since they need more processing power, time and memory for complex queries to join combinations of tables. This paper proposes several algorithms to extract the transaction- related features from the Bitcoin and Ethereum networks and to represent the features as graphs. Moreover, the paper discusses how visualisation of graphs can reflect the anomalies and patterns of fraudulent activities.