Search

Cryptocurrency has become a pivotal player in global financial transactions due to its decentralized mining process, which eliminates the need for intermediaries. However, the operation of cryptocurrency mining devices (CMDs) requires significant electrical power, leading to an increase in network-connected cryptocurrency mining loads (CMLs) that pose challenges for power grid operators. This paper evaluates the integration of CMLs into power grids, focusing on distribution network performance. A technology-accepted model for assessing cryptocurrency mining performance is presented, along with an analysis of the impacts of high CML penetration on power grid planning and operation. Findings reveal that high CML penetration can complicate load forecasting, congest transmission lines, and increase the coincident peak load in distribution networks. A case study of Iran’s power network indicates that CMLs account for approximately 2.8% of the nation’s electricity consumption. Furthermore, if 5% of residential customers in Tehran install CMDs, the average coincident peak demand for this group rises by 26.81%. Additionally, the average power factor of a sample CMD is about 0.99, while total current distortion and third harmonic current exceed permissible limits, indicating potential power quality issues. This study highlights the challenges posed by CMLs and proposes strategies for their sustainable integration into power grids.

The main aim of the proposed system is to facilitate secure and protected land registry in the domain of agriculture Industry 5.0. Considering the outlook of issues associated with it, we considered the blockchain and AI-based technology to fulfill the purpose of secure land registry. Establishing and confirming land ownership is essential for the land registry system in ensuring the protection of ownership rights, particularly crucial in the contexts of agriculture and Industry 5.0. In these sectors, land serves as a crucial resource for sustainable development and industrial innovation. Most of the existing works rely on legacy and centralized system to store land records; which result in high incidences of forgery and fraud. Therefore, maintaining a robust land registry system is essential to fostering economic investments, promoting green practices, and facilitating equitable access to land resources in agriculture and Industry 5.0 ecosystem. We proposed an AI and blockchain-enabled land registry system for agriculture and industry 5.0 that offers a more reliable, transparent, and efficient solution to the challenges of lack of transparency, data tampering, and inefficiency, which can result in disputes, fraudulent claims, and a lack of trust during the land registry. AI models, such as logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGB), and light gradient boosting machine (LGBM), are employed to classify the fraud and non-fraud land data. Only the non-fraud land data is forwarded into the blockchain network, thereby reducing the computational overhead of the proposed land registry system. In the blockchain network, we designed various smart contracts that validate the land data with unparalleled efficiency and security. Further, the slither solidity source analyzer tool is used for smart contract vulnerability assessment. After the assessment, the smart contract is deployed using the Sepolia test network. The non-fraudulent land data is redirected to the interplanetary file system (IPFS) that stores the original data and forwards the associated hash into the blockchain’s immutable ledger. The entire proposed system is evaluated with different performance parameters, such as AI statistical measures including accuracy, ROC, log-loss score, blockchain scalability comparison, gas cost utilization, and bandwidth utilization. Furthermore, the vulnerability assessment of the smart contract is analyzed using Slither to highlight the working of proposed system without any vulnerabilities. The proposed blockchain and AI-based land registry system ensure a secure and intelligent pipeline to combat against land forgery activities.

Decentralized cryptocurrencies like Bitcoin are digital assets with a price volatility nature, that allow for blockchain-based, peer-to-peer monetary transactions. Due to the price volatility problem with decentralized cryptocurrencies, research into the underlying pricing mechanism is required. Additionally, the behavior of Bitcoin prices is non-stationary, meaning that the statistical distribution of data varies over time. The proposed framework demonstrates the use of sophisticated machine learning models in predicting the short and medium-term trends and actual values of Bitcoin prices. This research goes beyond previous work that has only looked at machine learning-based categorization for a single day by instead using such models to forecast price changes seven, thirty, and ninety days into the future. The generated models are useful and work admirably, with the classification models reaching a maximum accuracy enhancement up to 31.48% for a 90-day prediction and a 11.76% F1-score for a forecast extending to the thirtieth day. In the case of regression the margin of error shifts from the one-time horizon for price projections to the next. A significant notable downfall occurs in different error metrics. These findings suggest that the models given here outperform those already found in the literature.

Blockchain has been a vibrant technology in the past decade, with a wide variety of applications across different industrial sectors. The concept of blockchain has been widely recognized as an enabler for cryptocurrency-based decentralized payments, with two major decentralized payment systems such as Bitcoin and Ethereum. However, the global acceptance of blockchain as a cryptocurrency sums up significant challenges that hinder the fast adaptation of cryptocurrency as a payment service enabler. In this survey, we explore the advantages of blockchain and its technical capabilities beyond cryptocurrency. We focus on the technical potential to ensure trust, data governance, and automation of the financial application domain utilizing the fundamental security features of blockchain, including consensus, digital signatures, and transparency. The significant subcomponents of trust, data management, and automation in banking and financial systems are also identified and discussed, including how blockchain and smart contracts can achieve the anticipated features of each subcomponent through their technical capabilities. In addition, we shed light on the position of blockchain-based applications in key application sectors of the banking and financing domain with a mapping of technical features with the application domains. Thereafter, the applicability of blockchain-based applications is evaluated with relevant regulatory definitions. Finally, we discuss open research challenges and potential future works with the blockchain in the domain of financial systems.

Despite their growing popularity in recent research, most hybrid models that harness the strengths of both classical time-series analysis and deep learning models have been explored within the univariate forecasting context. In the econometric domain, where exogenous factors play a crucial role; there is a pressing need for more studies focusing on multivariate forecasting. This paper introduces a novel hybrid model, HyBiLSTM. It integrates an ARIMAX GARCHX model for initial forecasting, followed by a second forecasting phase that addresses the residuals using a bidirectional long short-term memory model optimized through grey wolf optimization algorithm. The final forecast is a composite derived from both models. Three quantitative metrics (mean absolute error, root mean square error, and mean absolute percentage error) assessed the model performance using data that spanned social and economic variables from July 1, 2019, to December 31, 2022. The results revealed several key findings: 1) The addition of exogenous factors improved the performance of the ARIMA and GARCH models. 2) The BiLSTM variant outperformed other LSTM variants when combined with the ARIMAX GARCHX model. 3) An analysis using Shapley additive explanations indicated that bitcoin price was influenced by stock prices, Twitter volume, gold prices, and the Twitter sentiment index. 4) The presence of a structural break had a significant effect on the model’s forecasting accuracy. Beyond expanding the academic literature on hybrid models within a multivariate context, this offers valuable practical insights for investors. Specifically, it analyzes various factors that could serve as early indicators of bitcoin price fluctuations.

Bitcoin is known for its high volatility, which makes it challenging to accurately predict future prices. In this study, we aim to forecast Bitcoin prices for a month by incorporating exogenous variables, specifically the interest rate and recession probability. Our primary objective is to explore whether these variables have a positive impact on the prediction of Bitcoin prices. We used two popular time series forecasting models: Long Short-Term Memory (LSTM) and Facebook Prophet. Our approach involves exploring the impact of these exogenous variables on the performance of the models and comparing their results through plots and cross-validation. We trained the models using historical Bitcoin price data along with exogenous variables and evaluated their performance on a test dataset. Our results indicate that LSTM outperforms Facebook Prophet in terms of Bitcoin price prediction accuracy. This is because, while Facebook Prophet is optimized for statistical forecasting modeling, LSTM has the capability to learn intricate patterns and relationships given the right architecture with sufficient neurons. Importantly, we demonstrate that incorporating interest rates and recession probabilities significantly enhances the predictive capability of our models. Our findings suggest that changes in interest rates and recession probabilities have an impact on Bitcoin prices, and our models perform better when equipped with this valuable information.

In this paper, we mainly investigate three variables from the price volatility point of view: Brent crude oil, S&P500 and Bitcoin (BTCUSD), aiming to underline the impact of price volatility. Brent crude oil accounts for two-thirds of the oil market. Its price volatility has a significant impact on environmental, transportation, mobility, economic and social aspects that affect sustainability. This paper conducts an extensive examination of the forecasting capabilities of various GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models, identifying the most suitable GARCH model for estimating Value at Risk (VaR) for Brent crude oil price. The assessment of VaR for different GARCH models is carried out using Kupiec’s Probability of Failure (POF) test and Christoffersen’s test. This study leverages Brent crude oil data spanning from 2019 to 2023. Additionally, to prove the robustness of the GARCH models, we further consider the West Texas Intermediate (WTI) and Dubai oil prices that are the dominant in the U.S and Asian market. The investigation identifies the TGARCH(1,1) Skewed Student model as the optimal choice among 9 models considered for VaR estimation. The results show that TGARCH Skewed Student model surpasses the other models in the study, proving its superiority in forecasting Brent crude oil price volatility and facilitating VaR estimation. A VaR of 0.044 with a 95% confidence level means that there is a 95% chance that the portfolio will not lose more than 4.4% of its value. By incorporating skewness in addition to volatility asymmetry, the Skewed GARCH-type models provide a more realistic representation of the underlying return distribution. Furthermore, based on some information criteria, the most appropriate GARCH-type model for WTI crude oil is EGARCH(1,1) Skewed Student, with a VaR coverage of 0.39. The most appropriate GARCH-type model for Dubai crude oil is TGARCH(1,1) Skewed Student, with a VaR coverage of 0.17. Both WTI oil and Dubai crude oil have a coverage that exceeds 5%, implying a more conservative approach to estimating potential losses. Furthermore, the unidirectional causalities BTCUSD $\to $ BRENT and BTCUSD $\to \text{S}$ &P500 are identified. The results of the current research have practical implications for both importing and exporting countries, policy makers and investors. For companies in the oil sector, VaR informs operational decisions, such as production levels, capital expenditure and inventory management, by providing insights into market risk. Moreover, understanding the risks associated with oil aids in long-term strategic planning.

In the Bitcoin trading landscape, predicting price movements is paramount. Our study focuses on identifying the key factors influencing these price fluctuations. Utilizing the Pearson correlation method, we extract essential data points from a comprehensive set of 14 data features. We consider historical Bitcoin prices, representing past market behavior; trading volumes, which highlight the level of trading activity; network metrics that provide insights into Bitcoin’s blockchain operations; and social indicators: analyzed sentiments from Twitter, tracked Bitcoin-related search trends on Google and on Twitter. These social indicators give us a more nuanced understanding of the digital community’s sentiment and interest levels. With this curated data, we forge ahead in developing a predictive model using Deep Q-Network (DQN). A defining aspect of our model is its innovative reward function, tailored for enhancing predicting Bitcoin price direction, distinguished by its multi-faceted reward function. This function is a blend of several critical factors: it rewards prediction accuracy, incorporates confidence scaling, applies an escalating penalty for consecutive incorrect predictions, and includes a time-based discounting to prioritize recent market trends. This composite approach ensures that the model’s performance is not only precise in its immediate predictions but also adaptable and responsive to the evolving patterns of the cryptocurrency market. Notably, in our tests, our model achieved an impressive F1-score of 95%, offering substantial promise for traders and investors.

The rapid growth of the cryptocurrency market has led to an increasing interest in the subject. Cryptocurrency is now recognized as an asset, and laws and financial regulations have begun to emerge for supporting its practical use. As a result, it has become essential to perform data mining and attain knowledge from text data related to cryptocurrency. Previous studies have focused on analyzing data from a single source such as Twitter. However, there are unique insights to be gained from data across multiple platforms. In the present study, we utilized data mining techniques to extract insights from LexisNexis, Web of Science, and Reddit, representing the media, academia, and general public, respectively. Among unsupervised learning technologies, topic modeling was employed for the analysis. Topic modeling is a methodology that uncovers hidden meanings within the collected data. Among the diverse topic modeling techniques available, bidirectional encoder representations from transformers topic was chosen for the analysis. BERTopic considered to be state-of-the-art in the field of topic modeling. Dynamic topic modeling was employed to track changes in themes over time. Our experimental results reveal a tendency in the news to cover major events related to cryptocurrencies, such as regulatory developments and market trends. Academic papers, on the other hand, tend to focus on the technology behind cryptocurrencies and related research. Finally, social media conversations center more around information delivery from an investor’s psychological perspective, such as market sentiment and investment strategies.

Bitcoin uses an unspent transaction output (UTXO) model for coin circulation, which is similar to the banknotes. The transaction history is publicly available and allows to trace cryptocurrency flows. Different users merge transactions into a single bigger one to tangle flows. The merged transaction is called a shared send mixer (SSM). One can try to find the original subtransactions–solve an untangling problem. Based on the number of untanglings and their size, one extracts additional information about coin circulation. Theoretical analysis of the untangling problem is known from the literature. The paper aims to collect statistics of the SSM usage by transaction type for Bitcoin blockchain. We propose an algorithm to solve the problem, prove its correctness, and provide a source code. We applied the algorithm to the Bitcoin historical data. 15% transactions are SSM, and 90% of them allow unique untangling. The future work is an algorithm application to other UTXO systems and the results adaptation to an address grouping.

Blockchain is a Distributed Ledger Technology (DLT) that allows users to exchange values directly without a need for trusted third parties. Bitcoin is one of the most popular digital cryptocurrencies that is based on blockchain technology. However, it faces scalability problems including transaction throughput, latency, and starvation. Bitcoin transaction throughput is very low compared to traditional payment methods. Additionally, many Bitcoin transactions suffer from delays and starvation as miners prefer transactions with higher fees. More of the current research focuses on how to enhance Bitcoin scalability by improving the performance of consensus techniques, dividing the network into smaller ones with different parts of the blockchain, or completely changing the blockchain data structure. Unfortunately, engaging in this problem usually affects either decentralization or security; this is called the blockchain trilemma. This paper proposes the Cooperative Mining System (CMS), which depends on enhanced proof of work consensus algorithm. This proposed system increases transaction throughput and eliminates transaction latency and starvation without affecting decentralization and security. In CMS for each epoch, miners cooperated to create one super-block that contains more transactions than the traditional Bitcoin block. Whereas miners create their traditional blocks simultaneously, broadcast them, wait to receive other miners’ blocks, and lastly create a super-block that contains all the transactions of the gathered blocks. The Simulation results of the CMS and Bitcoin system using different case scenarios show a significant improvement in CMS compared to the current Bitcoin system. The CMS greatly increases the transaction throughput and eliminates transaction latency and starvation.

Cryptocurrencies have carved out a significant presence in financial transactions during the past few years. Cryptocurrency market performs similarly to other financial markets with considerable nonlinearity and volatility and its prediction is a growing research area. It is challenging to capture the inherent uncertainties connected with cryptocurrency using the currently used conventional methodologies. The popularity of random vector functional link networks (RVFLN) is attributed to its simple structural layout, quick rate of learning, and enhanced generalization ability. It computes the output layer weights using non-iterative techniques like least square methods or iterative techniques like gradient methods, and assigns hidden neuron parameters at random. Random initialization of non-optimal hidden neuron settings, however, degrades the performance. Population-based metaheuristics are a superior option to random initialization for determining the ideal parameters and avoiding the problem of local optima stagnation. In the current article, an elitist artificial electric field algorithm (eAEFA) for training RVFLN is proposed. Here, eAEFA is utilized to create an ideal RVFLN by determining the weights and biases of the hidden layer connections. The elitism method is used by AEFA to maximize its strength. Here, the most suitable entities are directly inserted to create the population of the following generation. By predicting the closing values of six widely used cryptocurrencies, including Bitcoin, Litecoin, Ethereum, ZEC, XLM, and Ripple, one may determine how well the eAEFA+RVFLN model is performing. For comparison study, models including ARIMA, multi-layer perceptron (MLP), basic RVFLN, support vector regression (SVR), LSTM, GA trained RVFLN, and AEFA trained RVFLN are also constructed concurrently. In terms of performance and statistical significance testing, the suggested eAEFA+RVFLN findings outperform the comparator models. On an average, it achieves a MAPE (mean absolute percentage of error) value of 0.0573, R2 (coefficient of determination) of 0.9589, POCID (prediction of change in direction) of 0.9676, RMSE (root mean squared error) of 0.0685, MAE (mean absolute error) of 0.0727 and an average rank of 1.346; as a result, it is possible to recommend it as a useful financial forecasting tool.

Before the advent of alternative blockchains such as Ethereum, the future of decentralization was all in the hands of Bitcoin. Together with Nakamoto itself, early developers were trying to leverage Bitcoin’s potential to decentralize traditionally centralized applications. However, because Bitcoin was a decentralized machine, the available non-trustless oracles were considered unsuitable. Therefore, strategies had to be elaborated to solve the so-called “oracle problem” in the newborn scenario. By interviewing early developers and crawling early forums and repositories, this paper aims to retrace and reconstruct the chain of events and contributions that gave birth to oracles on Bitcoin. The evolution of early protocols, along with the difficulties encountered in their development, are also outlined. Analyzing technical and social barriers to building oracles on Bitcoin, the transition to Ethereum will also be discussed.

Cryptocurrencies have recently attracted considerable attention, resulting in research mainly on deep learning-based price prediction models to maximize profit. Two research approaches have been adopted. Studies adopting the first approach directly predict the future cryptocurrency price. Long short-term memory (LSTM) and gated recurrent unit (GRU), which show high performance in time-series data, are mainly used for this approach. Further, studies adopting the second approach recommend actions to investors to maximize profits, such as “Sell”, “Buy”, and “Wait.” In this approach, classification models are used and results are derived based on probabilities. However, these action recommendation models do not consider the quality of the result. For example, it is risky to accept the result when the probability that the result of the action recommendation model for two classes is the correct answer is approximately 51%. To solve this problem, we recommend a method for adjusting the result of the action recommendation model based on Twitter sentiment analysis. The experimental results show that the proposed adjustment method improves the performance by approximately 3% compared to the conventional methods and are statistically validated.

The Dark Web, primarily Tor, has evolved to protect user privacy and freedom of speech through anonymous routing. However, Tor also facilitates cybercriminal actors who utilize it for illicit activities. Quantifying the size and nature of such activity is challenging, as Tor complicates indexing by design. This paper proposes a methodology to estimate both size and nature of illicit commercial activity on the Dark Web. We demonstrate this based on crawling Tor for single-vendor Dark Web Shops, i.e., niche storefronts operated by single cybercriminal actors or small groups. Based on data collected from Tor, we show that just in 2021, Dark Web Shops generated at least 113 million USD in revenue. Sexual abuse is the top illicit revenue category, followed by financial crime at a great distance. We also compare Dark Web Shops’ activity with a large Dark Web Marketplace, showing that these are parallel economies. Our methodology contributes towards automated analysis of illicit activity in Tor. Furthermore our analysis sheds light on the evolving Dark Web Shop ecosystem and provides insights into evidence-based policymaking regarding criminal Dark Web activity.

Blindcoin applies a Bitcoin-compatible mixing protocol with a blind signature scheme to improve the design of the popular Mixcoin. Given the openness of Bitcoin and the decentralization of the P2P network, it is imperative to formally analyze whether the malicious can break the security goals of the Blindcoin protocol. This work proposes a symbolic model for Blindcoin and conducts comprehensive formal verification. Fine-grained security goals of Blindcoin are formalized and subsequently encoded as model lemmas. However, it is challenging to verify the Blindcoin in a formal and automatic way. To tackle the challenges, we propose a tool-friendly symbolic model that can capture the semantics of multi-layers of Bitcoin and the features of Blindcoin. Our formal verification covers real-world security scenarios and discovers the Blindcoin vulnerabilities without human interaction. Furthermore, we offer several suggestions to fix the detected Blindcoin vulnerabilities and discuss the generalization of the proposed model.

In the last few years, the technological future becoming apparent by the introduction of smart contracts into mainstream technology, specifically in the development of Web3 and the metaverse. Smart contracts will play a vital role in the decentralization and autonomy of the day-to-day tasks that must be completed. Several literature reviews, considered secondary sources, highlight the current state of testing methods for smart contracts made for Blockchain applications. In this paper, we present the results from a systematic mapping study to give structure to the information found from primary sources. Systematic mapping is a well-known method to identify and categorize research papers in a field with an increasing amount of literature. For this systematic mapping, we searched for studies between 2017 and present-day (March 2022) and were able to find 303 results, from which 47 were selected, by specific inclusion and exclusion criteria, to be relevant to this study. A concept map was created from the information gathered from primary sources to the attributes such as research type, contribution type, blockchain network, smart contract language, development process, testing methods, and testing environment. We also categorized the trends and demographics found in the selected papers based on publication year, author’s country, and more. The results of this systematic mapping showed that this field is very new and quickly increasing with new research. The researchers that are interested in this field could use the results found to create opportunities for their future work.

Cryptocurrency has recently attracted substantial interest from investors due to its underlying philosophy of decentralization and transparency. Considering cryptocurrency’s volatility and unique characteristics, accurate price prediction is essential for developing successful investment strategies. To this end, the authors of this work propose a novel framework that predicts the price of Bitcoin (BTC), a dominant cryptocurrency. For stable prediction performance in unseen price range, the change point detection technique is employed. In particular, it is used to segment time-series data so that normalization can be separately conducted based on segmentation. In addition, on-chain data, the unique records listed on the blockchain that are inherent in cryptocurrencies, are collected and utilized as input variables to predict prices. Furthermore, this work proposes self-attention-based multiple long short-term memory (SAM-LSTM), which consists of multiple LSTM modules for on-chain variable groups and the attention mechanism, for the prediction model. Experiments with real-world BTC price data and various method setups have proven the proposed framework’s effectiveness in BTC price prediction. The results are promising, with the highest MAE, RMSE, MSE, and MAPE values of 0.3462, 0.5035, 0.2536, and 1.3251, respectively.

In this paper, we explain the Alphabill family of technologies that addresses both unlimited scalability and unrestricted adaptivity. We deliver a sharded blockchain technology with unlimited scalability, called KSI Cash, which is based on a new form of electronic money scheme, the bill scheme. We present performance tests of KSI Cash that we have conducted with the European Central Bank in order to assess the feasibility of a digital euro. We show the system operating with 100 million wallets and 15,000 transactions per second (under simulation of realistic usage); having an estimated carbon footprint of 0.0001g CO2 per transaction. Furthermore, we show the system operating with up to 2 million payment orders per second, an equivalent of more than 300,000 transactions per second (in a laboratory setting with the central components of KSI Cash), scaling linearly in the number of shards. We explain, in detail, the key concepts that unlock this performance (i.e., the concepts of the bill money scheme). The results provide evidence that the scalability of our technology is unlimited in both permissioned and permissionless scenarios, resulting into the Alphabill Money technology. Next, we contribute the architecture of a universal tokenization platform that allows for universal asset tokenization, transfer and exchange as a global medium of exchange, called Alphabill platform. We reveal the crucial conceptual and technical contributions of the platform’s architecture, including the data structures of KSI Cash and Alphabill Money, the dust collection solution of Alphabill Money, and the atomic swap solution of the Alphabill platform.

According to recent estimates, one bitcoin transaction consumes as much energy as 1.5 million Visa transactions. Why is bitcoin using so much energy? Most of the energy is used during the bitcoin mining process, which serves at least two significant purposes: a) distributing new cryptocurrency coins to the cryptoeconomy and b) securing the Bitcoin blockchain ledger. In reality, the comparison of bitcoin transactions to Visa transactions is not that simple. The amount of transactions in the Bitcoin network is not directly connected to the amount of bitcoin mining power nor the energy consumption of those mining devices; for example, it is possible to multiply the number of bitcoin transactions per second without increasing the mining power and the energy consumption. Bitcoin is not only “digital money for hackers”. It has very promising future potential as a global reserve currency and a method to make the World Wide Web (WWW) immune to cyberattacks such as the Distributed Denial-of-Service attacks. This survey approached cryptocurrencies’ various technological and environmental issues from many different perspectives. To make various cryptocurrencies, including bitcoin (BTC) and ether (ETH), greener and more justified, what technological solutions do we have? We found that cryptocurrency mining might be cleaner than is generally expected. There is also a plan to make a vast renewable energy source available by combining Ocean Thermal Energy Conversion and Bitcoin mining. There are plans to use unconventional computing methods (quantum computing, reversible computing, ternary computing, optical computing, analog computing) to solve some of the issues regarding the vast energy consumption of conventional computing (including cryptocurrency mining). We think using spare computing cycles for grid computing efforts is justified. For example, there are billions of smartphones in the world. Many smartphones are being recharged every day. If this daily recharging period of twenty to sixty minutes would be used for grid computing, for example, finding new cures to cancer, it would probably be a significant breakthrough for medical research simulations. We call on the cryptocurrency communities to research and develop grid computing and unconventional computing methods for the most significant cryptocurrencies: bitcoin (BTC)and ether (ETH).

Miners in a blockchain system are typically rewarded in two ways - through a fixed block reward, and by the transaction fees that are voluntarily offered by its users. As the available space inside a block is limited, users must compete against each other by submitting higher fees to obtain this limited resource. In this paper, we model blockchain transaction inclusion as a time-sensitive dynamic game, where users base their fees depending on both what their competitors in the network are offering, and by their own urgency of having their transactions approved. We then investigate the effect that mempool congestion (the aggregate size of transactions waiting to be confirmed) and different block sizes have on the fees users would be willing to pay. Our analysis concludes that miners have no rational reason to artificially limit the block size, which is in direct contrast with previous research findings. Instead, we find that increasing the block size in relation to a growing mempool both lowers the individual fees that users have to pay, and increases the total in fees collected, which raises not only the utility of the miners, but also of the regular users of the blockchain system.

The secure hash algorithm 2 (SHA-2) family, including the SHA-224/256/384/512 hash functions, is widely adopted in many modern domains, ranging from Internet of Things devices to cryptocurrency. SHA-2 functions are often implemented on hardware to optimize performance and power. In addition to the high-performance and low-cost requirements, the hardware for SHA-2 must be highly flexible for many applications. This paper proposes an SHA-2 hardware architecture named the multimode SHA-2 accelerator (MSA), which has high performance and flexibility at the system-on-chip level. To achieve high performance and flexibility, our accelerator applies three optimal techniques. First, a multimode processing element architecture is proposed to enable the accelerator to compute various SHA-2 functions for many applications. Second, a three-stage arithmetic logic unit pipeline architecture is proposed to reduce the critical paths and hardware resources. Finally, nonce generator and nonce validator architectures are proposed to reduce memory access and maximize the performance of the proposed MSA for blockchain mining applications. The MSA accuracy is tested on a real hardware platform (the Xilinx Alveo U280 FPGA). The experimental results on the field programmable gate array (FPGA) prove that the proposed MSA achieves significantly better performance, hardware efficiency, and flexibility than previous works. The evaluation results for energy efficiency show that the proposed MSA achieves up to 38.05 Mhps/W, which is 543.6 and 29 times better than the state-of-the-art Intel i9-10940X CPU and RTX 3090 GPU, respectively.

The use of Blockchain technology has recently become widespread. It has emerged as an essential tool in various academic and industrial fields, such as healthcare, transportation, finance, cybersecurity, and supply chain management. It is regarded as a decentralized, trustworthy, secure, transparent, and immutable solution that innovates data sharing and management. This survey aims to provide a systematic review of Blockchain application to intelligent transportation systems in general and the Internet of Vehicles (IoV) in particular. The survey is divided into four main parts. First, the Blockchain technology including its opportunities, relative taxonomies, and applications is introduced; basic cryptography is also discussed. Next, the evolution of Blockchain is presented, starting from the primary phase of pre-Bitcoin (fundamentally characterized by classic cryptography systems), followed by the Blockchain 1.0 phase, (characterized by Bitcoin implementation and common consensus protocols), and finally, the Blockchain 2.0 phase (characterized by the implementation of smart contracts, Ethereum, and Hyperledger). We compared and identified the strengths and limitations of each of these implementations. Then, the state of the art of Blockchain-based IoV solutions (BIoV) is explored by referring to a large and trusted source database from the Scopus data bank. For a well-structured and clear discussion, the reviewed literature is classified according to the research direction and implemented IoV layer. Useful tables, statistics, and analysis are also presented. Finally, the open problems and future directions in BIoV research are summarized.

Bitcoin is one of the best-known cryptocurrencies, which captivated researchers with its innovative blockchain structure. Examinations of this public blockchain resulted in many proposals for improvement in terms of anonymity and privacy. Generally used methods for improvement include mixing protocols, ring signatures, zero-knowledge proofs, homomorphic commitments, and off-chain storage systems. To the best of our knowledge, in the literature, there is no study examining Bitcoin in terms of differential privacy, which is a privacy notion coming up with some mechanisms that enable running useful statistical queries without identifying any personal information. In this paper, we provide a theoretical examination of differential privacy in Bitcoin. Our motivation arises from the idea that the Bitcoin public blockchain structure can benefit from differential privacy mechanisms for improved privacy, both making anonymization and privacy breaches by direct queries impossible, and preserving the checkability of the integrity of the blockchain. We first examine the current Bitcoin implementation for four query functions using the differential privacy formulation. Then, we present the feasibility of the utilization of two differential privacy mechanisms in Bitcoin; the noise addition to the transaction amounts and the user graph perturbation. We show that these mechanisms decrease the fraction of the cases violating differential privacy, therefore they can be used for improving anonymity and privacy in Bitcoin. Moreover, we showcase the noise addition to transaction amounts by using IBM Differential Privacy Library. We compare four differential privacy mechanisms for varying privacy parameter values and determine the feasible mechanisms and the parameters.

Bitcoin is a decentralized cryptocurrency where all the transactions are saved in a ledger. Blockchains have good features to be used in different finance applications by using smart contract. Also, blockchains are attractive platform for many industrial domains, including the logistics and supply chain industries. Supply chain technology contributes to record every single asset through its flow, tracking orders, receipts, and payments. There are several relay protocols proposed in previous research; these relay protocols are used to connect different types of blockchains. In this paper, we present a Secure Organizational Transactions Framework (SOTF) based on bridge chain to connect private chain or consortium with public chain which reduces interaction with public blockchain and saves the organization details privately in private chain or consortium. The proposed framework makes it possible for smart contract services to acquire means of payment in the consortium and private chain. Moreover, the proposed framework automates the process of updating the payment non-interactively. In this paper, we validate the communication between organizations blockchain and bitcoin, and find out the development cost of the proposed framework which ensures the efficiency and feasibility of the proposed framework. The code of framework implementation is publicly available at GitHub.

Bitcoin introduced a cryptocurrency as a form of public ledger consequently that turned into a most popular security technology, Blockchain. Its integrated mining technology lies the key security mechanism. The system allows forming a pool mining group to solve a particular job and share their revenues to their CPU usage while one of them successfully mines a block. To mine a block, a cryptographic puzzle should be solved, which requires significant compute resources that cause huge energy consumption. On the other hand, recent statistics show that low computational energy-restricted Internet of Things (IoT) devices are increasing exponentially. Although it has low energy and limited computation power, it is large in quantity when it is integrated. So we focus on a stochastic geometry theory, which resolves the issue of block mining computation via utilizing multiple mobile IoT devices, given that these IoT devices are Computation Capable Nodes (CCNs). To further normalize this issue, we propose an efficient mathematical solution that uses smart coordination of Virtual Network Functions (VNFs) for IoT devices to enable their CPU usage efficiently. At the same time, the work and credit point distribution policy is smartly handled through virtual pool mining. The proposal renders Network Function Virtualization technology to configure VNF, and Service Function Chain technology is utilized to enable the network flow of such VNFs. New algorithms are presented to solve multiple issues like node discovery, computation offloading, and work credit point distribution. Our goal is to minimize energy consumption within the given time constraint. Implementation results show that although virtual functions for block mining require extensive computations in IoT devices, dividing computation work into small fractions called tasks embedded with VNF, and offloading them to nearby CCNs, tend to minimize the cost and energy consumption of individual shared miners. The overall mining process is proved efficient and faster.

Bitcoin is the most widely used cryptocurrency for illegal trade in current darknet markets. Owing to the anonymity of its addresses, even though transaction flows are globally visible, Bitcoin clustering remains one of the most challenging and open problems in illegal Bitcoin transaction analysis. In this article, to resolve this problem, we propose a novel multi-layer heuristic algorithm for Bitcoin clustering, which leverages on-chain transactions as well as off-chain application data in the real world. For this purpose, we first explored the unique characteristics of darknet market ecosystems including their trading systems. By conducting an in-depth analysis of the data manually collected for 11 months, we found that some darknet market review data disclosed transactions containing Bitcoin value and item delivery information. We then identified unique Bitcoin addresses associated with the disclosed information, owned by the same darknet providers. Based on address ownership, more accurate market clusters could be created, which have not previously been identified by other clustering algorithms. According to our experimental results, approximately 31.68% of the darknet market review data matched real Bitcoin transactions, and 122 hidden clusters associated with Silk Road 4 were found. This indicates that the proposed algorithm can complement existing clustering methods and significantly reduce the false negative rate by up to 91.7%.

In recent years, Bitcoin and other cryptocurrencies have been increasingly considered investment options for emerging markets. However, Bitcoin’s erratic behavior has discouraged some potential investors. To get insights into its behavior and price fluctuation, past studies have discovered the correlation between Twitter sentiments and Bitcoin behavior. Most of them have exclusively focused on their relationships, instead of the Twitter sentiment analysis itself. Finding the most suitable classification algorithms for sentiment analysis for this kind of data is challenging. For the enormous data in Twitter, the supervised sentiment analysis approach of unlabeled data can be time-consuming and expensive, which has been studied to be superior to unsupervised ones. As such, we propose the HyVADRF (hybrid valence aware dictionary and sentiment reasoner (VADER)–random forest) and gray wolf optimizer (GWO) model. A semantic and rule-based VADER was used to calculate polarity scores and classify sentiments, which overcame the weakness of manual labeling, while a random forest was utilized as its supervised classifier. Furthermore, considering Twitter’s massive size, we collected over 3.6 million tweets and analyzed various dataset sizes as these are related to the model’s learning process. Lastly, GWO parameter tuning was conducted to optimize the classifier’s performance. The results show that 1) the HyVADRF model had an accuracy of 75.29%, precision of 70.22%, recall of 87.70%, and F1-score of 78%. 2) The most ideal dataset size percentage is 90% of the total collected tweets ( $n =1$ ,249,060). 3) The standard deviations are 0.0008 for accuracy and F1-score and 0.0011 for precision and recall. Hence, the HyVADRF model consistently delivers stable results.

A new era awaits the development of cryptocurrency and blockchain technologies. This study highlights the influential and conceptual aspects of blockchain and cryptocurrency literature with bibliometric analysis. There are 1965 related documents from 2015 and 2020, represent various structures and technologies, cryptocurrencies, and blockchain applications. We have deployed “bibliometrix 3.0”, the r-package, and VOS viewer to analyze key literature aspects. IEEE Access is the core journal for cryptocurrency and blockchain publications. The University of Cagliari, Notreported, and Peking University are key affiliations, and the USA, China, and India are core countries for literary research. H. Wang is the core author with the central theme of security of blockchain technologies. It is a set basis for conceptual aspects, core, and future research streams and themes. The study proposes three research streams: the structure of cryptocurrencies and implications of blockchain technologies, privacy, security management of data and information, and development of optimal information systems. The study further segregates themes into highly centralized and motor themes that are also core themes. Topics related to Consensus protocols, proof of work, distribution ledger technology, blockchain, and cryptocurrency procedures and structure comes under core themes. Highly developed and emerging themes are considered as a stepping stone for future directions. Underlying topics are related to fintech, Islamic finance, valuations, and dynamics of cryptocurrencies.

Bitcoin is a decentralized digital currency without a central bank or single administrator sent from user to user on the peer-to-peer bitcoin blockchain network without intermediaries' need. In this Bitcoin trend analysis work, initial attributes are considered from five sectors based on financial, social, token, network, and that count to thirteen attributes. The thirteen attributes considered are price, volume, market cap, a mean dollar invested age, social volume, social dominance, development activity, transaction volume, token age consumed, token velocity, token circulation, market value to realized value, and realized cap. We apply the attribute selection and trend analysis mapped with potential seven attributes: Price, Volume, Market Cap, Social Dominance, Development Activity, Market Value to Realized Value & Realized Cap. We have conducted Nonlinear Autoregressive with External Input analysis considering seven attributes. The work employed three training algorithms to train a neural network as Levenberg-Marquard, Bayesian Regularization, and Scaled Conjugate Gradient algorithm. The Error histogram and regression plots results indicate that the Bayesian Regularized Neural Network is showing good performance and thus provides a better forecast.

In recent years, distributed ledger technologies, and especially blockchain, have gained tremendous interest from academia, government, and industry. Although various blockchain-based solutions were created, the lack of tools to evaluate these complex distributed systems may hinder the development of the field. Many advantages of blockchain systems can be demonstrated only at large scales, e.g., using thousands of nodes. An investigation of different implementations and design choices is complicated and hardly feasible on real systems. Meanwhile, blockchain simulators give the possibility to repeat the complex real-world processes at a low cost. This work provides the first and an up-to-date systematic review and empirical analysis of blockchain simulators. Simulators are easily extensible and can test the performance of distributed ledgers using different settings and parameters on a single computer. The features and limitations of selected simulators are summarized and experimentally validated. Finally, recommendations for potential future research directions in the field are provided.

Bitcoin generates a massive amount of data every day due to its innate transparency and capacity of operating completely decentralised. In this paper, we introduce on-chain metrics derived from data on the bitcoin network that enable us to describe the state and usage of the underlying network. Based on their characteristics, we classify them into user, miner, exchange activities and run a correlation analysis with the price to understand the dynamics of bitcoin's price and its underlying mechanics. Using the correlated data, we develop a deep learning model. However, determining the best values of parameters in a deep learning model can be a very challenging and time-consuming task. Hence, we propose a self-adaptive technique using a jSO optimization algorithm to find the best values of these parameters to accurately predict the price of bitcoin. Compared to traditional LSTM model, our approach is highly accurate and optimised with a minimum error rate.

As the Internet of Things (IoT) paradigm gets more attention from academia and industry, implementation tools of IoT will be explored more and more. One example is the applicability of blockchain systems to provide security and privacy of IoT networks, which is the topic of this article. Blockchain systems are on the rise, as crypto-currency payment systems (such as Bitcoin, Litecoin, etc.) boomed in the last few years due to their attractive de-centralized and anonymous features. As in every transaction, access of the users to IoT systems needs to be controlled. However, these systems are peer-to-peer systems and do not have centralized control, which means that traditional access control techniques will not be optimal. As a result, distributed access control schemes are needed and this paper aims at providing the state of the art in the literature. Thereby, we introduce and discuss the details and applicability of centralized (role-based) and distributed (threshold-signature, reputation, trusted-computing, identity, capability, ACL, group-signature, and hybrid) access control schemes to blockchain systems under the IoT ecosystems. Moreover, permissioned vs. permissionless blockchain systems are also discussed. Finally, challenges and research directions related to the application of all those presented blockchain systems to IoT are discussed.

During the Coincheck incident, which recorded the largest damages in cryptocurrency history in 2018, it was demonstrated that using Mosaic token can have a certain effect. Although it seems attractive to employ tokens as countermeasures for cryptocurrency leakage, Mosaic is a specific token for the New Economy Movement (NEM) cryptocurrency and is not employed for other blockchain systems or cryptocurrencies. Moreover, although some volunteers tracked leaked NEM using Mosaic in the CoinCheck incident, it would be better to verify that the volunteers can be trusted. Simultaneously, if someone (e.g., who stole cryptocurrencies) can identify the volunteers, then that person or organization may be targets of them. In this paper, we propose an anonymous trust-marking scheme on blockchain systems that is universally applicable to any cryptocurrency. In our scheme, entities called token admitters are allowed to generate tokens adding trustworthiness or untrustworthiness to addresses. Anyone can anonymously verify whether these tokens were issued by a token admitter. Simultaneously, only the designated auditor and no one else, including nondesignated auditors, can identify the token admitters. Our scheme is based on accountable ring signatures and commitment, and is implemented on an elliptic curve called Curve25519, and we confirm that both cryptographic tools are efficient. Moreover, we also confirm that our scheme is applicable to Bitcoin, Ethereum, and NEM.

Cryptocurrencies gain trust in users by publicly disclosing the full creation and transaction history. In return, the transaction history faithfully records the whole spectrum of cryptocurrency user behaviors. This article analyzes and summarizes the existing research on knowledge discovery in the cryptocurrency transactions using data mining techniques. Specifically, we classify the existing research into three aspects, i.e., transaction tracings and blockchain address linking, the analyses of collective user behaviors, and the study of individual user behaviors. For each aspect, we present the problems, summarize the methodologies, and discuss major findings in the literature. Furthermore, an enumeration of transaction data parsing and visualization tools and services is also provided. Finally, we outline several gaps and trends for future investigation in this research area.

Since the inception of Bitcoin in 2009, the market of cryptocurrencies has grown beyond the initial expectations, as witnessed by the thousands of tokenised assets available on the market, whose daily trades exceed dozens of USD billions. The pseudonymity features of cryptocurrencies have attracted the attention of cybercriminals, who exploit them to carry out potentially untraceable scams. The wide range of cryptocurrency-based scams observed over the last ten years has fostered the study on their effects, and the development of techniques to counter them. The research in this field is hampered by various factors. First, there exist only a few public data sources about cryptocurrency scams, and they often contain incomplete or misclassified data. Further, there is no standard taxonomy of scams, which leads to ambiguous and incoherent interpretations of their nature. Indeed, the unavailability of reliable datasets makes it difficult to train effective automatic classifiers that can detect and analyse scams. In this paper, we perform an extensive review of the scientific literature on cryptocurrency scams, which we systematise according to a novel taxonomy. By collecting and homogenising data from different public sources, we build a uniform dataset of thousands of cryptocurrency scams. We build upon this dataset to implement a tool that automatically recognises scams and classifies them according to our taxonomy. We assess the effectiveness of our tool through standard performance metrics. We then analyse the results of the classification, providing key insights about the distribution of scam types, and the correlation between different types. Finally, we propose a set of guidelines that policymakers could follow to improve user protection against cryptocurrency scams.

The transaction and market of bitcoin is volatile, meaning it’s uncertain because it changes frequently. There have been a number of research studies that have presented bitcoin price prediction models, but none of them have looked at the controlling variables linked with bitcoin transaction timestamps. It might be that price is not the only key criteria influencing bitcoin transactions, or the available model for bitcoin price prediction is yet to consider timestamp as a determining factor in its transaction. A better and more accurate model would be required to predict how the Timestamp influences changes of bitcoin transactions. That is why this current study utilized a Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the prediction of timestamp influence on Bitcoin value. Bitcoin historical datasets which are converted to a nonlinear regression into a “well-formulated” statistical problem in the manner of a ridge regression are used. Simulation analysis indicates that bitcoin digital currency’s performance variation is highly influenced by its transaction timestamp with the prediction accuracy of 96%. The contributions of this research lies with the fact that specific Bitcoin transaction events repeat themselves over and over again, meaning that the Open-Price, High-Price, Low-Price, and Close-Price of Bitcoin price over timestamp developed a pattern that was predicted by NARX with less That means those involved in the transaction of bitcoin at the wrong timestamp will certainly face the uncertainty negative effect of the bitcoin market.

The controversies surrounding Bitcoin, one of the most frequently used and advertised cryptocurrency, are focused on identifying its qualities, the advantages and disadvantages of using it and, last but not least, its ability to survive over time and become a viable alternative to the traditional currency, taking into account the effects on the environment of the technology used to extract and trade it. Based on such considerations, this article aims to provide an overview of this cryptocurrency, from the perspective of conducting a systematic review of the literature dedicated to the economic and environmental impact of Bitcoin. Using peer-reviewed articles collected from academic databases, we aimed at synthesizing and critically evaluating the points of view in the scientific literature regarding the doctrinal source of the emergence of Bitcoin, the identity of this cryptocurrency from an economic point of view, following its implications on the economic and social environment. Subsequently, this research offers the opportunity of evaluating the level of knowledge considering the impact of Bitcoin mining process on the environment from the perspective of the energy consumption and CO2 emissions, in order to finally analyze Bitcoin regulation and identify possible solutions to reduce the negative impact on the environment and beyond. The findings suggest that, despite high energy consumption and adverse environmental impact, Bitcoin continues to be an instrument used in the economic environment for a variety of purposes. Moreover, the trend of regulating it in various countries shows that the use of Bitcoin is beginning to gain some legitimacy, despite criticism against this cryptocurrency.

The applications that use blockchain are cryptocurrencies, decentralized finance applications, video games, and many others. Most of these applications trust that the blockchain will prevent issues like fraud, thanks to the built-in cryptographic mechanisms provided by the data structure and the consensus protocol. However, blockchains suffer from what is called a 51% attack or majority attack, which is considered a high risk for the integrity of these blockchains, where if a miner, or a group of them, has more than half the computing capability of the network, it can rewrite the blockchain. Even though this attack is possible in theory, it is regarded as hard-achievable in practice, due to the assumption that, with enough active members, it is very complicated to have that much computing power; however, this assumption has not been studied with enough detail. In this work, a detailed characterization of the miners in the Bitcoin and Crypto Ethereum blockchains is presented, with the aim of proving the computing distribution assumption and creating profiles that may allow the detection of anomalous behaviors and prevent 51% attacks. The results of the analysis show that, in the last years, there has been an increasing concentration of hash rate power in a very small set of miners, which generates a real risk for current blockchains. Also, that there is a pattern in mining among the main miners, which makes it possible to identify out-of-normal behavior.

With the exponential increase in the complexity of network management and configuration, Software Defined Networking (SDN) has emerged as a promising network paradigm. SDN aims to efficiently transform network architecture and operations to be agile, and effectively enrich the functionality of underlying network elements, such as routers and switches, by decoupling the control plane from the data plane. In SDN, the network intelligence is centralized in a software entity so-called SDN controller, which enables network administrators to dynamically manage, secure, and optimize network resources and programmatically shape all entire network traffic pattern. Despite the impressive benefits SDN has brought to network architecture, it introduces new security challenges and prompts different implementation strategies to spread attack vectors. This paper comprehensively describes the utilization of Blockchain technology to secure and protect SDN architecture and discusses the feasibility of integrating the revolutionary technologies of SDN and Blockchain to provide confidentiality, integrity, and availability to network infrastructure.

Our transaction history in the current centralized banking system has the ability to reveal a lot of private information for each spender, both to the banking system itself, but also to those entities that surround it (e.g., governments, industry etc). Examples of leaking information constitute the amounts spent, the goods on which the amounts were spent, the spending locations and the users we exchange money with. This knowledge is powerful in the hands of those who have it, and can be used in multiple ways, not always to our benefit. Cryptocurrencies, such as the famous Bitcoin, were proposed as a means to address the limitations of centralized banking systems and to offer its users privacy with regards to their transactional data. In this work, we perform a systematic literature review on the realm of privacy for electronic currencies. We present the development of digital money from electronic cash to cryptocurrencies and focus on the techniques that are employed to enhance user-privacy. Furthermore, we present flaws of the current cryptocurrency systems, which reduce the privacy of the cryptocurrency users. Finally, we describe three research directions to enhance privacy for cryptocurrencies: transaction propagation mechanisms, succinct ZK proof systems without a trusted setup, and specialised trustless zero-knowledge proofs.

The term blockchain has its roots in cryptocurrencies. However, its applications are now more widespread, and in many areas, this technology has become the foundation of the distributed ledger. The blockchain protocol assumes that all the participants of the system are both contributors and safeguards of this ledger, since the lack of a trusted third party requires other security precautions in order to maintain the consistency of transactions. In this work, we investigate whether for the participants of a blockchain-based system that does not require revealing the character explicitly, it can be discovered by other means. In order to verify this, we built and publicly released a dataset of nearly 9,000 addresses of nodes in the most popular cryptocurrency - Bitcoin, and then labelled them. These labels represent the character the nodes have in the network, e.g. miners or exchanges. We then developed a set of features that quantify the behaviour of nodes in the network and used supervised machine learning algorithms to find out whether the character of nodes can be revealed based on these features. Our results demonstrate, due to the F-score reaching over 95% in the best-performing algorithms, that it is hard to hide the role the node has in a blockchain-based network. These results indicate that to build trustworthy blockchain-based systems that fully comply with original blockchain assumptions, specific countermeasures are needed in order to preserve the desired level of anonymity.

Smart contracts, as an added functionality to blockchain, have received increased attention recently. They are executable programs whose instance and state are stored in blockchain. Hence, smart contracts and blockchain enable a trustable, trackable, and irreversible protocol without the need for trusted third parties which generally constitute a single point of failure. If a user creates and distributes a smart contract, others will be able to interact with it while the underlying blockchain ensures a trustable execution. In this paper, we aim to introduce state-of-the-art technologies of the smart contract protocol. We firstly introduce the history of blockchain and smart contracts followed by their step-by-step operations. Then, we introduce the survey results which are classified into four categories based on their purposes: cryptography, access management, social application, and smart contract structure. By presenting the most recent knowledge, this paper will contribute to the advances and proliferation of smart contracts.

As a kind of point-to-point distributed public ledger technology, blockchain has been widely concerned in recent years. The privacy protection of blockchain technology has always been the core issue of people's attention. In this paper, some existing solutions to the current problems of user identity and transaction privacy protection are surveyed, including coin mixing mechanism, zero knowledge proof, ring signature and other technologies. Secondly, five typical applications of privacy protection technology based on blockchain are proposed and analyzed, which are mainly divided into technology applications based on coin mixing protocol, encryption protocol, secure channel protocol and so on. Finally, in view of the shortages of the existing blockchain privacy protection technology, we explore future research challenges that need to be studied in order to preserve privacy in blockchain system, and looks forward to the future development direction.

Bitcoin is the first and most successful Blockchain system so far. In the Bitcoin system, miners use transaction attached fees as a driving force to mine a new block and package transactions, while users compete by bidding transaction fees for faster confirmation. Considering the particularity of Bitcoin trading system, we take time series into consideration to analyze the transaction rules of Bitcoin system from the perspective of multiple cycles and establish a dynamic game model related to time under Generalised Second Price(GSP) mechanism, and also confirm the model's superiority on saving users' fees, compared with the static game model. Also, we propose the quantification of the user experience quantified by calculating the price difference between the transactions uploaded by the same user within adjacent times, making the transaction process of the Bitcoin system no longer the final say of the transaction price. The dynamic game model shows that there is a perfect Bayesian game equilibrium solution in the payment decision, so there is no incentive for users to change the attached fee, and the whole system is maintained stably. In addition, we verify the dynamic game model from computational experiment. Firstly, it is proved that with the help of revenue discount, the cost saving of the dynamic model is generally higher than that of the static model. Then the user's revenue under the dynamic model is showing an upward trend, and the transactions order under the dynamic model is more stable than that under static model, which can be illustrated mathematically and computationally that the proposed dynamic game model in this paper will help all transactions be processed more efficiently in a uniform pipeline.

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.

The majority of previous research on new technology acceptance has been conducted with single-step Structural Equation Modeling (SEM) based methods. The primary purpose of the study is to enhance the new technology acceptance based research with the Artificial Neural Network (ANN) method to enable more precise and in-depth research results as compared to the single-step SEM method. This study measures the relation between technology readiness dimension (optimism, innovativeness, discomfort, insecurity) and the technology acceptance (perceived ease of use and perceived usefulness) - and the intention to use cryptocurrency, such as bitcoin. The contribution of this study include the use of a multi-analytical approach by combining Partial Least Squares- Structural Equation Modeling (PLS-SEM) and Artificial Neural Network (ANN) analysis. First, PLS-SEM was applied to assess which factor has significant influence toward intention to use cryptocurrency. Second, an ANN was employed to rank the relative influence of the significant predictor variables attained from the PLS-SEM. The findings of the two-step PLS-SEM and ANN approach confirm that the use of ANN further verifies the results obtained by the PLS-SEM analysis. Also, ANN is capable of modelling complex linear and non-linear relationships with high predictive accuracy compared to SEM methods. Also, an Importance-Performance Map Analysis (IPMA) of the PLS-SEM results provides a more specific understanding of each factor's importance-performance.

With the emergence of decentralized cryptocurrencies such as Bitcoin, it has become very difficult for law enforcement to detect suspicious activities, identify users and obtain transaction records for criminals who utilize the pseudoanonymity provided by the cryptocurrency system. Address clustering aims to break such pseudoanonymity by linking addresses that are controlled by the same user based on the information available from the blockchain, such as transaction graphs. There are already two widely used heuristics for Bitcoin address clustering. One is based on the multiple input addresses of transactions. The other is based on one-time change addresses. By reconsidering the one-time change address-based heuristic from the perspective of address reuse, we propose a new heuristic that detects one-time change addresses by eliminating addresses that are reused later as non-change addresses. As a result, this heuristic works for transactions whose one-time change addresses cannot be identified by the previous two heuristics. The experimental results for different scales of Bitcoin transaction data show that the proposed heuristic has a 0.33% mean contribution to the ratio of address reduction in addition to the contribution of the multiple input addresses and one-time change address heuristics.

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.

The certified mail is a value-added service that is widely used in the paper world. However, the scientific community has not yet provided a solution for certified e-mail that has achieved widespread acceptance. This lack of a certified e-mail solution is not due to a lack of proposed approaches; because over the past 40 years, more than 100 protocols have been reported in journals and at conferences. The vast majority of these proposed protocols use a trusted third party (TTP) to achieve fairness. The few solutions without a TTP have not been successful due to their high computational and/or communication cost. Blockchain provides a new approach to develop the protocols without a TTP but without the prior drawbacks of the previous solutions without a TTP. Here, we present a new protocol for certified e-mail based on a blockchain without a conventional TTP that is integrated with the conventional e-mail infrastructure. The protocol is secure, efficient, and viable from a practical perspective.