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Journal articles on the topic 'Bitcoin Simulator'

Author: Grafiati

Published: 28 June 2021

Last updated: 1 February 2022

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1

Conoscenti, Marco, Antonio Vetrò, Juan De Martin, and Federico Spini. "The CLoTH Simulator for HTLC Payment Networks with Introductory Lightning Network Performance Results." Information 9, no. 9 (September 3, 2018): 223. http://dx.doi.org/10.3390/info9090223.

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The Lightning Network (LN) is one of the most promising off-chain scaling solutions for Bitcoin, as it enables off-chain payments which are not subject to the well-known blockchain scalability limit. In this work, we introduce CLoTH, a simulator for HTLC payment networks (of which LN is the best working example). It simulates input-defined payments on an input-defined HTLC network and produces performance measures in terms of payment-related statistics (such as time to complete payments and probability of payment failure). CLoTH helps to predict issues and obstacles that might emerge in the development stages of an HTLC payment network and to estimate the effects of an optimisation action before deploying it. We conducted simulations on a recent snapshot of the HTLC payment network of LN. These simulations allowed us to identify network and payments configurations for which a payment is more likely to fail than to succeed. We proposed viable solutions to avoid such configurations.

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Lagaillardie, Nicolas, Mohamed Aimen Djari, and Önder Gürcan. "A Computational Study on Fairness of the Tendermint Blockchain Protocol." Information 10, no. 12 (November 30, 2019): 378. http://dx.doi.org/10.3390/info10120378.

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Fairness is a crucial property for blockchain systems since it affects the participation: the ones that find the system fair tend to stay or enter, the ones that find the system unfair tend to leave. While current literature mainly focuses on fairness for Bitcoin-like blockchains, little has been done to analyze Tendermint. Tendermint is a blockchain technology that uses a committee-based consensus algorithm, which finds an agreement among a set of block creators (called validators), even if some are malicious. Validators are regularly selected to the committee based on their investments. When a validator does not have enough asset to invest, it can increase it with the help of participants that delegate their assets to the validators (called delegators). In this paper, we implement the default Tendermint model and a Tendermint model for fairness in a multi-agent blockchain simulator where participants are modeled as rational agents who enter or leave the system based on their utility values. We conducted experiments for both models where agents have different investment strategies and with various numbers of delegators. In the light of our experimental evaluation, we observed that while, for both models, the fairness decreases and the system shrinks in the absence of delegators, the fairness increases, and the system expands for the second model in the presence of delegators.

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3

Yu, Jiang, Yue Shang, and Xiafei Li. "Dependence and Risk Spillover among Hedging Assets: Evidence from Bitcoin, Gold, and USD." Discrete Dynamics in Nature and Society 2021 (September 11, 2021): 1–20. http://dx.doi.org/10.1155/2021/2010705.

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Understanding the dependence and risk spillover among hedging assets is crucial for portfolio allocation and regulatory decision making. Using various copula and conditional Value-at-Risk (CoVaR) measures, this paper quantifies the dependence and risk spillover effects between three traditional and emerging hedging assets: Bitcoin, gold, and USD. Furthermore, we investigate these effects at various short- and long-term horizons using a variational model decomposition (VMD) method. The empirical results show that there is strong negative dependence between gold and USD, but Bitcoin and gold are weakly and positively connected. Secondly, risk spillovers exist only between Bitcoin and gold and between gold and USD. The risk spillover effect between Bitcoin and gold are not stable, that is, if Bitcoin or gold faces the downward or upward risk, both the downward and upward risk of another asset have the chance to increase. The negative risk spillover between gold and USD is stable, especially in long-term horizons. Finally, the risk spillover between Bitcoin and gold as well as between gold and USD are asymmetric at downward and upward market environment.

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4

Rakotomarolahy, Patrick. "Predicting the bitcoin return direction with logistic, discriminant analysis and machine learning classification techniques." Model Assisted Statistics and Applications 16, no. 3 (August 27, 2021): 169–76. http://dx.doi.org/10.3233/mas-210530.

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This paper proposes prediction of the bitcoin return direction with logistic, discriminant analysis and machine learning classification techniques. It extends the prediction of the bitcoin return direction using exogenous macroeconomic and financial variables which have been investigated as drivers of bitcoin return. We also use google trends as proxy for investors interest on bitcoin. We consider those variables as predictors for bitcoin return direction. We conduct an in-sample and out-of-sample empirical analysis and achieve a misclassification error around 4% for in-sample evaluation and around 41% in out-of-sample empirical analysis. Ensemble learning trees based outperforms the other methods in both in-sample and out-of-sample analyses.

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Yao, Yuhang, Xiao Zeng, Tianyue Cao, Luoyi Fu, and Xinbing Wang. "APRP: An Anonymous Propagation Method in Bitcoin Network." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 10073–74. http://dx.doi.org/10.1609/aaai.v33i01.330110073.

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Due to little attention given to anonymous protection against eavesdropping attacks in Bitcoin network, this paper initiatively proposes a solution to Bitcoin anonymization based on network structure. We first present a general adversarial network model for formulizing deanonymization attack, then present a novel propagation method APRP(Adaptive PageRank Propagation) that adopts PageRank as propagation delay factor and constantly adjusts PR-value of nodes to adapt to network dynamics. Experiments on both simulated and real Bitcoin networks confirm the superiority of APRP in terms of 20-50% performance enhancement under various deanonymization attacks.

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6

BEZUIDENHOUT, Riaan, Wynand Nel, and Andries Burger. "NONLINEAR PROOF-OF-WORK: IMPROVING THE ENERGY EFFICIENCY OF BITCOIN MINING." Journal of Construction Project Management and Innovation 10, no. 1 (September 30, 2020): 20–32. http://dx.doi.org/10.36615/jcpmi.v10i1.351.

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Bitcoin is probably the most well-known blockchain system in existence. It employs the proof-of-work (PoW) consensus algorithm to add transactions to the blockchain. This process is better known as Bitcoin mining. PoW requires miners to compete in solving a cryptographic puzzle before being allowed to add a block of transactions to the blockchain. This mining process is energy-intensive and results in high energy wastage. The underlying cause of this energy inefficiency is the result of the current implementation of the PoW algorithm. PoW assigns the same cryptographic puzzle to all miners, creating a linear probability of success between the miner’s computational power as a proportion of the total computational power of the network. To address this energy inefficiency of the PoW mining process, the researchers investigated whether a nonlinear probability of success, between the miner’s computation power and its probability of success, will result in better energy usage. A nonlinear proof-of-work (nlPoW) algorithm was constructed by using a design science approach to derive the requirements for and structure of the algorithm. The Bitcoin mining process was tested through statistical simulation, comparing the performance of nlPoW with PoW. Preliminary results, simulating a network of 1000 miners with identical computational power, indicate that nlPoW reduce the number of hash computations, and therefore the energy consumption, required by Bitcoin mining. The findings are significant because nlPoW does not reduce the degree of decentralised consensus, or trade energy usage for some other resource as is the case with many other attempts to address the energy consumption problem in PoW.

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7

Niu, Jianyu, Ziyu Wang, Fangyu Gai, and Chen Feng. "Incentive analysis of Bitcoin-NG, revisited." Performance Evaluation 144 (December 2020): 102144. http://dx.doi.org/10.1016/j.peva.2020.102144.

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8

Atsalakis, George S., Ioanna G. Atsalaki, Fotios Pasiouras, and Constantin Zopounidis. "Bitcoin price forecasting with neuro-fuzzy techniques." European Journal of Operational Research 276, no. 2 (July 2019): 770–80. http://dx.doi.org/10.1016/j.ejor.2019.01.040.

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9

Cocco, Luisanna, and Michele Marchesi. "Modeling and Simulation of the Economics of Mining in the Bitcoin Market." PLOS ONE 11, no. 10 (October 21, 2016): e0164603. http://dx.doi.org/10.1371/journal.pone.0164603.

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10

De Almeida Brito Júnior, Jorge. "USING BLOCKCHAIN TECHNOLOGY FOR IMPLEMENTATION OF AN ANDROID GRAPHICS SIMULATION APPLICATION." International Journal of Innovation Education and Research 7, no. 6 (June 30, 2019): 105–18. http://dx.doi.org/10.31686/ijier.vol7.iss6.1558.

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The Blockchain technology can be used for many purposes on the web, the main one being the financial branch, having in mind the present article clearly presents the technology concepts behind Bitcoin called Blockchain and is shown throughout the work to implementation of a mobile application developed in the Android platform, where it makes use of blockchain to simulate the operation of its own, being a practical guide to blockchain, can be used to teach technology to lay visually, the tool uses principles such as hash function, chain of blocks, consensus algorithm among others that are linked to the technology of crypto coins

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11

Naimy, Viviane Y., and Marianne R. Hayek. "Modelling and predicting the Bitcoin volatility using GARCH models." International Journal of Mathematical Modelling and Numerical Optimisation 8, no. 3 (2018): 197. http://dx.doi.org/10.1504/ijmmno.2018.088994.

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12

Jiang, Chuxuan, Priya Dev, and Ross A. Maller. "A Hypothesis Test Method for Detecting Multifractal Scaling, Applied to Bitcoin Prices." Journal of Risk and Financial Management 13, no. 5 (May 20, 2020): 104. http://dx.doi.org/10.3390/jrfm13050104.

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Multifractal processes reproduce some of the stylised features observed in financial time series, namely heavy tails found in asset returns distributions, and long-memory found in volatility. Multifractal scaling cannot be assumed, it should be established; however, this is not a straightforward task, particularly in the presence of heavy tails. We develop an empirical hypothesis test to identify whether a time series is likely to exhibit multifractal scaling in the presence of heavy tails. The test is constructed by comparing estimated scaling functions of financial time series to simulated scaling functions of both an iid Student t-distributed process and a Brownian Motion in Multifractal Time (BMMT), a multifractal processes constructed in Mandelbrot et al. (1997). Concavity measures of the respective scaling functions are estimated, and it is observed that the concavity measures form different distributions which allow us to construct a hypothesis test. We apply this method to test for multifractal scaling across several financial time series including Bitcoin. We observe that multifractal scaling cannot be ruled out for Bitcoin or the Nasdaq Composite Index, both technology driven assets.

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13

Das, Debasis. "Toward Next Generation of Blockchain Using Improvized Bitcoin-NG." IEEE Transactions on Computational Social Systems 8, no. 2 (April 2021): 512–21. http://dx.doi.org/10.1109/tcss.2021.3049477.

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14

Kodama, Osamu, LukĂˇĹˇ Pichl, and Taisei Kaizoji. "REGIME CHANGE AND TREND PREDICTION FOR BITCOIN TIME SERIES DATA." CBU International Conference Proceedings 5 (September 23, 2017): 384–88. http://dx.doi.org/10.12955/cbup.v5.954.

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Bitcoin time series dataset recording individual transactions denominated in Euro at the COINBASE market between April 23, 2015 and August 15, 2016 is analyzed. Markov switching model is applied to classify the regions of varying volatility represented by three hidden state regimes using univariate autoregressive model and dependent mixture model. Causality extraction and price prediction of daily BTCEUR exchange rates is performed by means of a recurrent neural network using the standard Elman model. Strong correlations is found between the normalized mean squared error of the Elman network (out-of-sample 5-day-ahead prediction) and the realized volatility (sum of minute returns squared throughout the trading day). The present approach is calibrated using simulated regime change in standard econometric models. Our results clearly demonstrate the applicability of recurrent neural networks to causality extraction even in the case of highly volatile cryptocurrency exchange rate time series data.

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15

Elysha Kamaruzaman, Nor, Ihsan Mohd Yassin, Azlee Zabidi, Fadhlan Hafizhelmi Kamaru Zaman, Zairi Ismael Rizman, Rahimi Baharom, and Norfishah Abdul Wahab. "Blockchain Technology for Islamic Marriage Certificate." International Journal of Engineering & Technology 7, no. 4.11 (October 2, 2018): 193. http://dx.doi.org/10.14419/ijet.v7i4.11.20802.

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In recent years, an individual under the pseudonym of Satoshi Nakamoto devised a revolutionary technology called blockchain as the engine behind the first decentralized virtual currency called Bitcoin. A radical concept departing from government-centric controlled currencies, Bitcoin has emerged as a disruptive technology with the power to revolutionize business and its processes. Advantages of the blockchain include decentralized control, immutability, elimination of central authority and solution of concurrency problems in traditional databases. Leveraging on the advantages of blockchain technology defined above, this paper discusses the potential application of blockchain technology for storage of Islamic marriage certificates. Marriage certificates are documents issued to couples to legally recognize their marriage. Due to its paper-based nature, there is significant risk for them to be forged or frauded. These issues can be addressed effective using blockchain. The proposed application was implemented using smart contracts on a simulated Ethereum platform. A smart contract is designed to execute automatically under certain predefined conditions. The use of smart contracts eliminate manipulation by a single party. In addition, the immutable concept of blockchain ensures that data integrity is always preserved, greatly reducing the risk of fraud.

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16

Cheng, Yukun, Zhiqi Xu, and Shuangliang Yao. "The Evolutionary Equilibrium of Block Withholding Attack." Journal of Systems Science and Information 9, no. 3 (June 1, 2021): 266–79. http://dx.doi.org/10.21078/jssi-2021-266-14.

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Abstract Bitcoin is the most famous and the most used cryptocurrency in the world, such that it has received extreme popularity in recent years. However the Bitcoin system is accompanied by different attacks, including the block withholding (BWH) attack. When a miner plays the BWH attack, it will withhold all the blocks newly discovered in the attack pool, damaging the honest miners’ right to obtain the fair reward. In this paper, we consider a setting in which two miners may honestly mine or perform the BWH attack in a mining pool. Different strategy profiles will bring different payoffs, in addition influence the selection of the strategies. Therefore, we establish an evolutionary game model to study the behavior tendency of the miners and the evolutionary stable strategies under different conditions, by formulating the replicator dynamic equations. Through numerical simulations, we further verify the theoretical results on evolutionary stable solutions and discuss the impact of the factors on miners’ strategic choice. Based on these simulation results, we also make some recommendations for the manager and the miners to mitigate the BWH attack and to promote the cooperation between miners in a mining pool.

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17

Uyar, Umut, and Ibrahim Korkmaz Kahraman. "The risk analysis of Bitcoin and major currencies: value at risk approach." Journal of Money Laundering Control 22, no. 1 (January 7, 2019): 38–52. http://dx.doi.org/10.1108/jmlc-01-2018-0005.

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Purpose This study aims to compare investors of major conventional currencies and Bitcoin (BTC) investors by using the value at risk (VaR) method common risk measure. Design/methodology/approach The paper used a risk analysis named as VaR. The analysis has various computations that Historical Simulation and Monte Carlo Simulation methods were used for this paper. Findings Findings of the analysis are assessed in two different aspects of singular currency risk and portfolios built. First, BTC is found to be significantly risky with respect to the major currencies; and it is six times riskier than the singular most risky currency. Second, in terms of inclusion of BTC into a portfolio, which equally weights all currencies, it elevates overall portfolio risk by 98 per cent. Practical implications In spite of the remarkable risk level, it could be considered that investors are desirous of making an investment on BTC could mitigate their overall exposed risk relatively by building a portfolio. Originality/value The paper questions the risk level of Bitcoin, which is a digital currency. BTC, a matter of debate in the contemporary period, is seen as a digital currency free from control or supervision of a regulatory board. With the comparison of major currencies and BTC shows that how could be risky of a financial instrument without regulations. However, there is some advice for investors who would like to invest digital currencies despite the risk level in this study.

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18

Zhou, Wei. "Fuzzy rules based efficient event-driven simulation of blockchain-based applications." Journal of Intelligent & Fuzzy Systems 40, no. 4 (April 12, 2021): 8101–7. http://dx.doi.org/10.3233/jifs-189633.

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Decentralized application (DAPP), replacing traditional business logic and data access layer with block chain, is a new form of Internet service. Testing DAPP requires large-scale distributed systems. Performing experiments in a real system is costly and difficult. This article carefully analyses the process of block generation and synchronization and explains the reasons for the low efficiency of block chain system simulation. We incorporate fuzzy rule based model for enhancing the logging system in blockchain. Rules based on fuzzy are utilized inside system of fuzzy logic to obtain outcome on basis of input variables. The data of Ethereum and Bitcoin proves that the block generation interval conforms to the exponential distribution, and the real PoW calculation can be replaced with random numbers. Both block verification and network propagation processes have latency, which can be simulated with asynchronous messaging. Based on the above analysis, this article proposes a high-performance simulation method based on event-driven model, which is suitable for describing the communication and synchronization behave our of block chain networks. The method can effectively describe the block generation, the synchronization process between nodes, and supports different equity proof forms. Using this method, the performance of the PoW systemis tested. Under the ecs.c6.xlargeinstance,the simulation running speed reaches 782 times of actual system. Further experiments show that this method can be efficiently used in larger-scale networks and is an effective tool for DAPP developing and testing.

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19

Zhou, Yuren, Jiajing Wu, Jiaquan Yao, Mingjie Ma, Zibin Zheng, and Weili Chen. "Dependence structure between bitcoin price and its influence factors." International Journal of Computational Science and Engineering 1, no. 1 (2019): 1. http://dx.doi.org/10.1504/ijcse.2019.10018973.

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20

Chen, Weili, Zibin Zheng, Mingjie Ma, Jiajing Wu, Yuren Zhou, and Jiaquan Yao. "Dependence structure between bitcoin price and its influence factors." International Journal of Computational Science and Engineering 21, no. 3 (2020): 334. http://dx.doi.org/10.1504/ijcse.2020.106058.

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21

Masuhr, Andreas, and Mark Trede. "Bayesian estimation of generalized partition of unity copulas." Dependence Modeling 8, no. 1 (July 4, 2020): 119–31. http://dx.doi.org/10.1515/demo-2020-0007.

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AbstractThis paper proposes a Bayesian estimation algorithm to estimate Generalized Partition of Unity Copulas (GPUC), a class of nonparametric copulas recently introduced by [18]. The first approach is a random walk Metropolis-Hastings (RW-MH) algorithm, the second one is a random blocking random walk Metropolis-Hastings algorithm (RBRW-MH). Both approaches are Markov chain Monte Carlo methods and can cope with ˛at priors. We carry out simulation studies to determine and compare the efficiency of the algorithms. We present an empirical illustration where GPUCs are used to nonparametrically describe the dependence of exchange rate changes of the crypto-currencies Bitcoin and Ethereum.

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22

Frolkova, Maria, and Michel Mandjes. "A Bitcoin-inspired infinite-server model with a random fluid limit." Stochastic Models 35, no. 1 (January 2, 2019): 1–32. http://dx.doi.org/10.1080/15326349.2018.1559739.

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23

Bowden, R., H. P. Keeler, A. E. Krzesinski, and P. G. Taylor. "Modeling and analysis of block arrival times in the Bitcoin blockchain." Stochastic Models 36, no. 4 (July 20, 2020): 602–37. http://dx.doi.org/10.1080/15326349.2020.1786404.

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24

Javier, Kayla, and Brian Fralix. "A further study of some Markovian Bitcoin models from Göbel et al." Stochastic Models 36, no. 2 (April 2, 2020): 223–50. http://dx.doi.org/10.1080/15326349.2020.1761390.

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25

Giudici, Paolo, and Paolo Pagnottoni. "Vector error correction models to measure connectedness of Bitcoin exchange markets." Applied Stochastic Models in Business and Industry 36, no. 1 (July 25, 2019): 95–109. http://dx.doi.org/10.1002/asmb.2478.

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26

Lejun, Zhang, Zhang Zhijie, Wang Weizheng, Waqas Rasheed, Zhao Chunhui, Kim Seokhoon, and Chen Huiling. "A Covert Communication Method Using Special Bitcoin Addresses Generated by Vanitygen." Computers, Materials & Continua 65, no. 1 (2020): 597–616. http://dx.doi.org/10.32604/cmc.2020.011554.

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27

Terentiev, Oleksandr, Tatyana Prosiankina-Zharova, Volodymyr Savastiyanov, Valerii Lakhno, and Vira Kolmakova. "The Features of Building a Portfolio of Trading Strategies Using the SAS OPTMODEL Procedure." Computation 9, no. 7 (July 6, 2021): 77. http://dx.doi.org/10.3390/computation9070077.

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The article describes the original information technology of the algorithmic trading, designed to solve the problem of forming the optimal portfolio of trade strategies. The methodology of robust optimization, using the Ledoit–Wolf shrinkage method for obtaining stable estimates of the covariance matrix of algorithmic strategies, was used for the formation of a portfolio of trade strategies. The corresponding software was implemented by SAS OPTMODEL Procedure. The paper deals with a portfolio of trade strategies built for highly-profitable, but also highly risky financial tools—cryptocurrencies. Available bitcoin assets were divided into a corresponding proportion for each of the recommended portfolio strategies, and during the selected period (one calendar month) were used for this research. The portfolio of trade strategies is rebuilt at the end of the period (every month) based on the results of trade during the period, in accordance with the conditions of risk minimizing or income maximizing. Trading strategies work in parallel, being in a state of waiting for a relevant trading signal. Strategies can be changed by moving the parameters in accordance with the current state of the financial market, removed if ineffective, and replaced where necessary. The efficiency of using a robust decision-making method in the context of uncertainty regarding cryptocurrency trading was confirmed by the results of real trading for the Bitcoin/Dollar pair. Implementation of the offered information technology in electronic trading systems will allow risk reduction as a result of making incorrect decisions or delays in making decisions in a systemic trading.

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28

Memon, Raheel, Jian Li, and Junaid Ahmed. "Simulation Model for Blockchain Systems Using Queuing Theory." Electronics 8, no. 2 (February 19, 2019): 234. http://dx.doi.org/10.3390/electronics8020234.

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In recent years, blockchains have obtained so much attention from researchers, engineers, and institutions; and the implementation of blockchains has started to revive a large number of applications ranging from e-finance, e-healthcare, smart home, Internet of Things, social security, logistics and so forth. In the literature on blockchains, it is found that most articles focused on their engineering implementation, while little attention has been devoted to the exploration of theoretical aspects of the system; however, the existing work is limited to model the mining process only. In this paper, a queuing theory-based model is proposed for understanding the working and theoretical aspects of the blockchain. We validate our proposed model using the actual statistics of two popular cryptocurrencies, Bitcoin and Ethereum, by running simulations for two months of transactions. The obtained performance measures parameters such as the Number of Transactions per block, Mining Time of Each Block, System Throughput, Memorypool count, Waiting Time in Memorypool, Number of Unconfirmed Transactions in the Whole System, Total Number of Transactions, and Number of Generated Blocks; these values are compared with actual statistics. It was found that the results gained from our proposed model are in good agreement with actual statistics. Although the simulation in this paper presents the modeling of blockchain-based cryptocurrencies only, the proposed model can be used to represent a wide range of blockchain-based systems.

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Adu Gyamfi, Daniel, and Albert Kofi Kwansah Ansah. "Enhancing user and transaction privacy in bitcoin with unlinkable coin mixing scheme." International Journal of Computational Science and Engineering 23, no. 4 (2020): 381. http://dx.doi.org/10.1504/ijcse.2020.10035561.

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Ansah, Albert Kofi Kwansah, and Daniel Adu Gyamfi. "Enhancing user and transaction privacy in bitcoin with unlinkable coin mixing scheme." International Journal of Computational Science and Engineering 23, no. 4 (2020): 381. http://dx.doi.org/10.1504/ijcse.2020.113183.

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31

Li, Kejun, Yunan Liu, Hong Wan, and Yining Huang. "A discrete-event simulation model for the Bitcoin blockchain network with strategic miners and mining pool managers." Computers & Operations Research 134 (October 2021): 105365. http://dx.doi.org/10.1016/j.cor.2021.105365.

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32

K. Alkhodhairi, Reem, Shahad R. Aljalhami, Norah K. Rusayni, Jowharah F. Alshobaili, Amal A. Al-Shargabi, and Abdulatif Alabdulatif. "Bitcoin Candlestick Prediction with Deep Neural Networks Based on Real Time Data." Computers, Materials & Continua 68, no. 3 (2021): 3215–33. http://dx.doi.org/10.32604/cmc.2021.016881.

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33

Derbentsev, Vasily D., Vitalii S. Bezkorovainyi, and Iryna V. Luniak. "Application of Deep Learning Methods to Forecasting Changes in Short-Term Currency Trends." Scientific Bulletin of Mukachevo State University Series “Economics” 7, no. 2 (December 28, 2020): 75–86. http://dx.doi.org/10.52566/msu-econ.7(2).2020.75-86.

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This study investigates the issues of forecasting changes in short-term currency trends using deep learning models, which is relevant for both the scientific community and for traders and investors. The purpose of this study is to build a model for forecasting the direction of change in the prices of currency quotes based on deep neural networks. The developed architecture was based on the model of valve recurrent node, which is a modification of the model of “Long Short-Term Memory”, but is simpler in terms of the number of parameters and learning time. The forecast calculations of the dynamics of quotations of the currency pair euro/dollar and the most capitalised cryptocurrency Bitcoin/dollar were performed using daily, four-hour and hourly datasets. The obtained results of binary classification (forecast of the direction of trend change) when applying daily and hourly quotations turned out to be generally better than those of time series models or models of neural networks of other architecture (in particular, multilayer perceptron or “Long Short-Term Memory” models). According to the study results, the highest accuracy of classification was for the model of daily quotations for both euro/dollar – about 72%, and for Bitcoin/ dollar – about 69%. For four-hour and hourly time series, the accuracy of classification decreased, which can be explained both by the increase in the impact of “market noise” and the probable overfitting. Computer simulation has demonstrated that models predict a rising trend better than a declining one. The study confirmed the prospects for the application of deep learning models for short-term forecasting of time series of currency quotes. The use of the developed models proved to be effective for both fiat and cryptocurrencies. The proposed system of models based on deep neural networks can be used as a basis for developing an automated trading system in the foreign exchange market

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Ouaguid, Abdellah, Noreddine Abghour, and Mohammed Ouzzif. "A Novel Security Framework for Managing Android Permissions Using Blockchain Technology." International Journal of Cloud Applications and Computing 8, no. 1 (January 2018): 55–79. http://dx.doi.org/10.4018/ijcac.2018010103.

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This article presents a new framework named ANDROSCANREG (Android Permissions Scan Registry) that allows to extract and analyze the requested permissions in an Android application via a decentralized and distributed system. This framework is based on the emerging technology Blockchain whose potential is approved in the matter of transparency, reliability, security and availability without resorting to a central processing unit judged of trust. ANDROSCANREG consists of two Blockchains, the first one (PERMBC) will handle analysis, validation and preparation of the raw results so that they will persist in the second Blockchain of Bitcoin already existing (BTCBC), which will assume the role of a Registry of recovered permissions and will save the permissions history of each version of the applications being scanned via financial transactions, whose wallet source, recipient wallet and transaction value have a precise meaning. An example of a simulation will be presented to describe the different steps, actors, interactions and messages generated by the different entity of ANDROSCANREG.

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35

Ahn, Jaehong, Mingyu Park, Hyungsik Shin, and Jeongyeup Paek. "A Model for Deriving Trust and Reputation on Blockchain-Based e-Payment System." Applied Sciences 9, no. 24 (December 8, 2019): 5362. http://dx.doi.org/10.3390/app9245362.

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E-commerce has become a crucial part of our life allowing us to buy products, request services, and transfer money easily with a press of a button. As such, establishing immutable trust and reputation of entities that are resilient to manipulation by the malicious are critical in today’s online systems. In this work, we propose a model for calculating trust and reputation using the values stored on blockchain ledger. The model is applied to blockchain-based online payment systems which have a characteristic of immutability by preventing data manipulation. The model normalizes user evaluations based on each user’s personal evaluation criteria that changes over time. In addition, the model derives reputation of, and trust between, users by applying psychological factors. We evaluate our model using not only simulated transaction data but also on real Bitcoin transaction-based dataset to show that our model is able to derive stable values from immutable transactions on blockchain-based online payment systems. Our model has been built into a live commercial blockchain service platform, and new application developments are underway.

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Husaini, Noor Aida, Rozaida Ghazali, Nureize Arbaiy, and Ayodele Lasisi. "MCS-MCMC for Optimising Architectures and Weights of Higher Order Neural Networks." International Journal of Intelligent Systems and Applications 12, no. 5 (October 8, 2020): 52–72. http://dx.doi.org/10.5815/ijisa.2020.05.05.

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The standard method to train the Higher Order Neural Networks (HONN) is the well-known Backpropagation (BP) algorithm. Yet, the current BP algorithm has several limitations including easily stuck into local minima, particularly when dealing with highly non-linear problems and utilise computationally intensive training algorithms. The current BP algorithm is also relying heavily on the initial weight values and other parameters picked. Therefore, in an attempt to overcome the BP drawbacks, we investigate a method called Modified Cuckoo Search-Markov chain Monté Carlo for optimising the weights in HONN and boost the learning process. This method, which lies in the Swarm Intelligence area, is notably successful in optimisation task. We compared the performance with several HONN-based network models and standard Multilayer Perceptron on four (4) time series datasets: Temperature, Ozone, Gold Close Price and Bitcoin Closing Price from various repositories. Simulation results indicate that this swarm-based algorithm outperformed or at least at par with the network models with current BP algorithm in terms of lower error rate.

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Zhao, Wenting, Jun Lv, Xilong Yao, Juanjuan Zhao, Zhixin Jin, Yan Qiang, Zheng Che, and Chunwu Wei. "Consortium Blockchain-Based Microgrid Market Transaction Research." Energies 12, no. 20 (October 9, 2019): 3812. http://dx.doi.org/10.3390/en12203812.

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The microgrid trading market can effectively solve the problem of in-situ consumption of distributed energy and reduce the impact of distributed generation (DG) on the grid. However, the traditional microgrid trading model has some shortcomings, such as high operation cost and poor security. Therefore, in this paper, a microgrid market trading model was developed using consortium blockchain technology and Nash game equilibrium theory. Firstly, blockchain container is used to authenticate the users who want to participate in the transaction. Then, the pricing system collects and integrates transaction requests submitted by users, then formulates transaction pricing strategy of microgrid using Nash equilibrium theory. Finally, the price, trading volume, and user information are submitted to the blockchain container for transaction matching to achieve the transaction. After the transaction is completed, its related information is recorded in the hyperledger and the dispatch system is called. The scene simulation was implemented on Fabric 1.1 platform and the results analyzed. Results show that the trading model proposed in this paper greatly reduces the cost of electricity purchase and improves the benefits of electricity sales. Besides, the model is far more capable of handling transactions than the models based on Bitcoin and Ethereum.

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Zhang, Peijun, Lianhai Wang, Wei Wang, Kunlun Fu, and Jinpeng Wang. "A Blockchain System Based on Quantum-Resistant Digital Signature." Security and Communication Networks 2021 (March 3, 2021): 1–13. http://dx.doi.org/10.1155/2021/6671648.

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Blockchain, which has a distributed structure, has been widely used in many areas. Especially in the area of smart cities, blockchain technology shows great potential. The security issues of blockchain affect the construction of smart cities to varying degrees. With the rapid development of quantum computation, elliptic curves cryptosystems used in blockchain are not secure enough. This paper presents a blockchain system based on lattice cipher, which can resist the attack of quantum computation. The most challenge is that the size of public keys and signatures used by lattice cryptosystems is typically very large. As a result, each block in a blockchain can only accommodate a small number of transactions. It will affect the running speed and performance of the blockchain. For overcoming this problem, we proposed a way that we only put the hash values of public keys and signatures on the blockchain and store the complete content of them on an IPFS (interplanetary file system). In this way, the number of bytes occupied by each transaction is greatly reduced. We design a bitcoin exchange scheme to evaluate the performance of the proposed quantum-resistant blockchain system. The simulation platform is verified to be available and effective.

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Widyantara, I. Made Oka, Muhammad Audy Bazly, and Ngurah Indra ER. "ADAPTIVE STREAMING OVER HTTP (DASH) UNTUK APLIKASI VIDEO STREAMING." Majalah Ilmiah Teknologi Elektro 14, no. 2 (December 30, 2015): 18. http://dx.doi.org/10.24843/mite.2015.v14i02p04.

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This paper aims to analyze Internet-based streaming video service in the communication media with variable bit rates. The proposed scheme on Dynamic Adaptive Streaming over HTTP (DASH) using the internet network that adapts to the protocol Hyper Text Transfer Protocol (HTTP). DASH technology allows a video in the video segmentation into several packages that will distreamingkan. DASH initial stage is to compress the video source to lower the bit rate video codec uses H.26. Video compressed further in the segmentation using MP4Box generates streaming packets with the specified duration. These packages are assembled into packets in a streaming media format Presentation Description (MPD) or known as MPEG-DASH. Streaming video format MPEG-DASH run on a platform with the player bitdash teritegrasi bitcoin. With this scheme, the video will have several variants of the bit rates that gave rise to the concept of scalability of streaming video services on the client side. The main target of the mechanism is smooth the MPEG-DASH streaming video display on the client. The simulation results show that the scheme based scalable video streaming MPEG-DASH able to improve the quality of image display on the client side, where the procedure bufering videos can be made constant and fine for the duration of video views

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Nascimento, Kerolly Kedma Felix do, Fábio Sandro dos Santos, Jader da Silva Jale, and Tiago Alessandro Espínola Ferreira. "Comportamento de agentes financeiros em um mercado artificial desenvolvido com o algoritmo Particle Swarm Optimization." Research, Society and Development 9, no. 7 (May 12, 2020): e285974216. http://dx.doi.org/10.33448/rsd-v9i7.4216.

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Os mercados financeiros são sistemas complexos em que os negociadores interagem usando as mais variadas estratégias. Técnicas computacionais que usam agentes inteligentes podem auxiliar na tomada de decisão com o objetivo de maximizar os ganhos. Neste sentido, o objetivo deste artigo é observar o comportamento dos agentes financeiros participantes de mercados simulados e inferir sobre os ganhos destes agentes. Por meio do algoritmo Particle Swarm Optimization, utilizamos dois grupos distintos de agente inteligentes: um grupo utiliza um grau de crença na previsão dos ativos para o dia seguinte e o outro grupo não utiliza, em que ambos interagem entre si buscando maximizar seus ganhos. Foi realizada uma pesquisa exploratória, com análise de natureza quantitativa sobre os dados. Os resultados mostraram que o grupo que usa a previsão é mais homogêneo, apresentando maiores ganhos de riqueza média, com concentrações de capital e de ações adquiridos variando de acordo com a série histórica de preços utilizada (Bitcoin, Ethereum, Litcoin ou Ripple). Diante disso, o procedimento implementado pode ser aperfeiçoado e utilizado para o desenvolvimento de ambientes que visem a melhor compreensão dos mercados financeiros e auxiliem os agentes participantes dos mercados na definição de estratégias de negociação que possibilitem a minimização de perdas financeiras.

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Feld, Sebastian, Mirco Schönfeld, and Martin Werner. "Traversing Bitcoin's P2P network: insights into the structure of a decentralised currency." International Journal of Computational Science and Engineering 13, no. 2 (2016): 122. http://dx.doi.org/10.1504/ijcse.2016.078441.

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Werner, Martin, Mirco Schönfeld, and Sebastian Feld. "Traversing Bitcoin's P2P network: insights into the structure of a decentralised currency." International Journal of Computational Science and Engineering 13, no. 2 (2016): 122. http://dx.doi.org/10.1504/ijcse.2016.10000107.

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Chinthapalli, Usha Rekha. "A Comparative Analysis on Probability of Volatility Clusters on Cryptocurrencies, and FOREX Currencies." Journal of Risk and Financial Management 14, no. 7 (July 6, 2021): 308. http://dx.doi.org/10.3390/jrfm14070308.

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In recent years, the attention of investors, practitioners and academics has grown in cryptocurrency. Initially, the cryptocurrency was designed as a viable digital currency implementation, and subsequently, numerous derivatives were produced in a range of sectors, including nonmonetary activities, financial transactions, and even capital management. The high volatility of exchange rates is one of the main features of cryptocurrencies. The article presents an interesting way to estimate the probability of cryptocurrency volatility clusters. In this regard, the paper explores exponential hybrid methodologies GARCH (or EGARCH) and through its portrayal as a financial asset, ANN models will provide analytical insight into bitcoin. Meanwhile, more scalable modelling is needed to fit financial variable characteristics such as ANN models because of the dynamic, nonlinear association structure between financial variables. For financial forecasting, BP is contained in the most popular methods of neural network training. The backpropagation method is employed to train the two models to determine which one performs the best in terms of predicting. This architecture consists of one hidden layer and one input layer with N neurons. Recent theoretical work on crypto-asset return behavior and risk management is supported by this research. In comparison with other traditional asset classes, these results give appropriate data on the behavior, allowing them to adopt the suitable investment decision. The study conclusions are based on a comparison between the dynamic features of cryptocurrencies and FOREX Currency’s traditional mass financial asset. Thus, the result illustrates how well the probability clusters show the impact on cryptocurrency and currencies. This research covers the sample period between August 2017 and August 2020, as cryptocurrency became popular around that period. The following methodology was implemented and simulated using Eviews and SPSS software. The performance evaluation of the cryptocurrencies is compared with FOREX currencies for better comparative study respectively.

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KARACA, YELIZ, and DUMITRU BALEANU. "A NOVEL R/S FRACTAL ANALYSIS AND WAVELET ENTROPY CHARACTERIZATION APPROACH FOR ROBUST FORECASTING BASED ON SELF-SIMILAR TIME SERIES MODELING." Fractals 28, no. 08 (July 10, 2020): 2040032. http://dx.doi.org/10.1142/s0218348x20400320.

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It has become vital to effectively characterize the self-similar and regular patterns in time series marked by short-term and long-term memory in various fields in the ever-changing and complex global landscape. Within this framework, attempting to find solutions with adaptive mathematical models emerges as a major endeavor in economics whose complex systems and structures are generally volatile, vulnerable and vague. Thus, analysis of the dynamics of occurrence of time section accurately, efficiently and timely is at the forefront to perform forecasting of volatile states of an economic environment which is a complex system in itself since it includes interrelated elements interacting with one another. To manage data selection effectively and attain robust prediction, characterizing complexity and self-similarity is critical in financial decision-making. Our study aims to obtain analyzes based on two main approaches proposed related to seven recognized indexes belonging to prominent countries (DJI, FCHI, GDAXI, GSPC, GSTPE, N225 and Bitcoin index). The first approach includes the employment of Hurst exponent (HE) as calculated by Rescaled Range ([Formula: see text]) fractal analysis and Wavelet Entropy (WE) in order to enhance the prediction accuracy in the long-term trend in the financial markets. The second approach includes Artificial Neural Network (ANN) algorithms application Feed forward back propagation (FFBP), Cascade Forward Back Propagation (CFBP) and Learning Vector Quantization (LVQ) algorithm for forecasting purposes. The following steps have been administered for the two aforementioned approaches: (i) HE and WE were applied. Consequently, new indicators were calculated for each index. By obtaining the indicators, the new dataset was formed and normalized by min-max normalization method’ (ii) to form the forecasting model, ANN algorithms were applied on the datasets. Based on the experimental results, it has been demonstrated that the new dataset comprised of the HE and WE indicators had a critical and determining direction with a more accurate level of forecasting modeling by the ANN algorithms. Consequently, the proposed novel method with multifarious methodology illustrates a new frontier, which could be employed in the broad field of various applied sciences to analyze pressing real-world problems and propose optimal solutions for critical decision-making processes in nonlinear, complex and dynamic environments.

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Jiang, Shangrong, Yuze Li, Quanying Lu, Yongmiao Hong, Dabo Guan, Yu Xiong, and Shouyang Wang. "Policy assessments for the carbon emission flows and sustainability of Bitcoin blockchain operation in China." Nature Communications 12, no. 1 (April 6, 2021). http://dx.doi.org/10.1038/s41467-021-22256-3.

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AbstractThe growing energy consumption and associated carbon emission of Bitcoin mining could potentially undermine global sustainable efforts. By investigating carbon emission flows of Bitcoin blockchain operation in China with a simulation-based Bitcoin blockchain carbon emission model, we find that without any policy interventions, the annual energy consumption of the Bitcoin blockchain in China is expected to peak in 2024 at 296.59 Twh and generate 130.50 million metric tons of carbon emission correspondingly. Internationally, this emission output would exceed the total annualized greenhouse gas emission output of the Czech Republic and Qatar. Domestically, it ranks in the top 10 among 182 cities and 42 industrial sectors in China. In this work, we show that moving away from the current punitive carbon tax policy to a site regulation policy which induces changes in the energy consumption structure of the mining activities is more effective in limiting carbon emission of Bitcoin blockchain operation.

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ZHOU, QING, QINGQING ZHANG, and QI ZHANG. "Agent-based Simulation Research on Bitcoin Price Fluctuation." DEStech Transactions on Computer Science and Engineering, aiea (October 31, 2017). http://dx.doi.org/10.12783/dtcse/aiea2017/14910.

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Shynkevich, Andrei. "Bitcoin Futures, Technical Analysis and Return Predictability in Bitcoin Prices." Journal of Forecasting, January 14, 2020. http://dx.doi.org/10.1002/for.2656.

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Stavroyiannis, Stavros. "Expected Shortfall via Filtered Historical Simulation for Bitcoin and Ethereum." SSRN Electronic Journal, 2018. http://dx.doi.org/10.2139/ssrn.3162537.

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Stinner, Jona. "Inside the Black Box of Bitcoin Mining: A Theoretical Framework and Simulation Evidence." SSRN Electronic Journal, 2021. http://dx.doi.org/10.2139/ssrn.3853812.

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Brown, Mark, Erol Peköz, and Sheldon Ross. "BLOCKCHAIN DOUBLE-SPEND ATTACK DURATION." Probability in the Engineering and Informational Sciences, May 21, 2020, 1–9. http://dx.doi.org/10.1017/s0269964820000212.

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Many cryptocurrencies including Bitcoin are susceptible to a so-called double-spend attack, where someone dishonestly attempts to reverse a recently confirmed transaction. The duration and likelihood of success of such an attack depends on the recency of the transaction and the computational power of the attacker, and these can be related to the distribution of time for counts from one Poisson process to exceed counts from another by some desired amount. We derive an exact expression for this distribution and show how it can be used to obtain efficient simulation estimators. We also give closed-form analytic approximations and illustrate their accuracy.

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