Thematische Bibliographien / Bitcoin Simulator

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Auswahl der wissenschaftlichen Literatur zum Thema „Bitcoin Simulator“

Autor: Grafiati

Veröffentlicht am 28. Juni 2021

Zuletzt aktualisiert am 1. Februar 2022

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Zeitschriftenartikel zum Thema "Bitcoin Simulator"

Conoscenti, Marco, Antonio Vetrò, Juan De Martin und Federico Spini. „The CLoTH Simulator for HTLC Payment Networks with Introductory Lightning Network Performance Results“. Information 9, Nr. 9 (03.09.2018): 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 und Önder Gürcan. „A Computational Study on Fairness of the Tendermint Blockchain Protocol“. Information 10, Nr. 12 (30.11.2019): 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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Yu, Jiang, Yue Shang und Xiafei Li. „Dependence and Risk Spillover among Hedging Assets: Evidence from Bitcoin, Gold, and USD“. Discrete Dynamics in Nature and Society 2021 (11.09.2021): 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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Rakotomarolahy, Patrick. „Predicting the bitcoin return direction with logistic, discriminant analysis and machine learning classification techniques“. Model Assisted Statistics and Applications 16, Nr. 3 (27.08.2021): 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 und Xinbing Wang. „APRP: An Anonymous Propagation Method in Bitcoin Network“. Proceedings of the AAAI Conference on Artificial Intelligence 33 (17.07.2019): 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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BEZUIDENHOUT, Riaan, Wynand Nel und Andries Burger. „NONLINEAR PROOF-OF-WORK: IMPROVING THE ENERGY EFFICIENCY OF BITCOIN MINING“. Journal of Construction Project Management and Innovation 10, Nr. 1 (30.09.2020): 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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Niu, Jianyu, Ziyu Wang, Fangyu Gai und Chen Feng. „Incentive analysis of Bitcoin-NG, revisited“. Performance Evaluation 144 (Dezember 2020): 102144. http://dx.doi.org/10.1016/j.peva.2020.102144.

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Atsalakis, George S., Ioanna G. Atsalaki, Fotios Pasiouras und Constantin Zopounidis. „Bitcoin price forecasting with neuro-fuzzy techniques“. European Journal of Operational Research 276, Nr. 2 (Juli 2019): 770–80. http://dx.doi.org/10.1016/j.ejor.2019.01.040.

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Cocco, Luisanna, und Michele Marchesi. „Modeling and Simulation of the Economics of Mining in the Bitcoin Market“. PLOS ONE 11, Nr. 10 (21.10.2016): e0164603. http://dx.doi.org/10.1371/journal.pone.0164603.

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De Almeida Brito Júnior, Jorge. „USING BLOCKCHAIN TECHNOLOGY FOR IMPLEMENTATION OF AN ANDROID GRAPHICS SIMULATION APPLICATION“. International Journal of Innovation Education and Research 7, Nr. 6 (30.06.2019): 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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Dissertationen zum Thema "Bitcoin Simulator"

Borčík, Filip. „Testování bezpečnosti a výkonu Proof-of-Stake Protokolů pomocí simulace“. Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-445485.

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This work deals with performance and security testing of blockchain protocols based on the Proof-of-Stake (PoS) consensus model. It describes properties, problems, but also the use of blockchain systems. On theoretical levels, this thesis compares the properties and resistance to various attacks of numerous PoS protocols, specifically Algorand, Casper, Gasper, Snow White, Stellar and Decred. Additionally, this work implements a protocol simulator of Algorand, Casper FFG and Gasper. The simulator is built on top of the Bitcoin Simulator simulation tool, which is based on the NS-3 discrete network event simulator. Then, it compares the properties of the implemented protocols using discrete simulation.

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Mubaslat, Jad S. „Demonstrating the Functionality and Efficacy of Blockchain-based System in Healthcare Using Simulation Tools“. Wright State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=wright1526812918128916.

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Shih, Yun-Rou, und 施雲柔. „Simulation and Analysis of Bitcoin Consensus Algorithm“. Thesis, 2018. http://ndltd.ncl.edu.tw/handle/543774.

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碩士
國立中山大學
資訊工程學系研究所
107
Bitcoin is one of the earliest and most popular applications in the blockchain. It does not need a trusted third-party organizations to trade more efficiently. Bitcoin nodes form a peer-to-peer distributed network to transfer blocks. Moreover, the consensus algorithm confirms that all nodes can reach a consensus and make sure that everyone’s ledger are consistent. Bitcoin’s consensus algorithm is to select the longest chain. After every 6 blocks, the block will be fixed and confirmed. And we propose another consensus method of Ethereum, which uses the tree structure to store the blockchain. Each tree node has a maximum of three branches. If the degree is greater than or equal to 11, the block will be confirmed. We simulated and compared this two methods, and believed that Bitcoin’s consensus algorithm is more suitable and stable than Ethereum’s consensus algorithm.

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Wang, Tien-Hsuan, und 王湉媗. „Blockchain Simulation and Analysis on Bitcoin System“. Thesis, 2018. http://ndltd.ncl.edu.tw/handle/ewmgmd.

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碩士
國立中山大學
資訊工程學系研究所
107
Blockchain derived from Bitcoin is characterized by decentralization. It allows all nodes in the network to manage and maintain the ledger together. Consensus protocol based on Proof-of-Work is impartial and secure. Confirmed transactions also can''t be subjected to modifying. It is said to have a lot of merits. However, there are two system limits: 1MB block size and 10-minute block interval, which restricte the performance of Bitcoin and lead not to handle instant and huge transactions. This study is to implement a blockchain simulation, explore the relation between block interval and propagation delay, analyze the consensus probability of different ratios and block confirmations, and then find reasonable parameters that can improve the performance of Bitcoin.

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Buchteile zum Thema "Bitcoin Simulator"

Ročkai, Petr, und Jiří Barnat. „A Simulator for LLVM Bitcode“. In Formal Methods for Industrial Critical Systems, 127–42. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-27008-7_8.

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Al Shibli, Murad. „Hybrid Artificially Intelligent Multi-Layer Blockchain and Bitcoin Cryptology (AI-MLBBC)“. In Encyclopedia of Criminal Activities and the Deep Web, 1089–111. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-5225-9715-5.ch075.

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This article presents an integrated secured framework of blockchain and bitcoin cryptology with the artificial intelligence of neural networks and machine learning. Recently blockchain has received special attention and been used as a new platform for digital information and to store encrypted data and process secure digital transactions. Furthermore, data on blockchain and bitcoin platforms are assumed to be highly encrypted and secured. Although blockchain and bitcoin databases are encrypted using private keys, many cryptocurrency bitcoin wallets have been reported hacked, which has resulted in losing millions of dollars. The artificial intelligent neural networks (AINN) algorithms possess the capability features of processing and operating encrypted data and will lead to minimal risk as a part of the blockchain to protect personal data and information as third wall defense against hacking. Simulation results and analysis demonstrates the effectiveness of this technique for protecting personal data and financial information.

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Roy, Swagatam, Ahan Chatterjee und Trisha Sinha. „An Econometric Overview on Growth and Impact of Online Crime and Analytics View to Combat Them“. In Advances in Data Mining and Database Management, 115–57. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-4706-9.ch005.

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In this chapter, the authors take a closer look into the economic relation with cybercrime and an analytics method to combat that. At first, they examine whether the increase in the unemployment rate among youths is the prime cause of the growth of cybercrime or not. They proposed a model with the help of the Phillips curve and Okun's law to get hold of the assumptions. A brief discussion of the impact of cybercrime in economic growth is also presented in this paper. Crime pattern detection and the impact of bitcoin in the current digital currency market have also been discussed. They have proposed an analytic method to combat the crime using the concept of game theory. They have tested the vulnerability of the cloud datacenter using game theory where two players will play the game in non-cooperative strategy in the Nash equilibrium state. Through the rational state decisions of the players and implementation MSWA algorithm, they have simulated the results through which they can check the dysfunctionality probabilities of the datacenters.

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Konferenzberichte zum Thema "Bitcoin Simulator"

Alsahan, Lina, Noureddine Lasla und Mohamed Abdallah. „Local Bitcoin Network Simulator for Performance Evaluation using Lightweight Virtualization“. In 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT). IEEE, 2020. http://dx.doi.org/10.1109/iciot48696.2020.9089630.

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Di Francesco Maesa, Damiano, Matteo Franceschi, Barbara Guidi und Laura Ricci. „BITKER: A P2P Kernel Client for Bitcoin“. In 2018 International Conference on High Performance Computing & Simulation (HPCS). IEEE, 2018. http://dx.doi.org/10.1109/hpcs.2018.00035.

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Lee, Vincent, und Haozheng Wei. „Exploratory simulation models for fraudulent detection in Bitcoin system“. In 2016 IEEE 11th Conference on Industrial Electronics and Applications (ICIEA). IEEE, 2016. http://dx.doi.org/10.1109/iciea.2016.7603912.

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Schüssler, Fabian, Pezhman Nasirifard und Hans-Arno Jacobsen. „Attack and Vulnerability Simulation Framework for Bitcoin-like Blockchain Technologies“. In Middleware '18: 19th International Middleware Conference. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3284014.3284017.

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Li, Kejun, Yunan Liu, Hong Wan und Ling Zhang. „Capturing Miner and Mining Pool Decisions In A Bitcoin Blockchain Network: A Two-Layer Simulation Model“. In 2020 Winter Simulation Conference (WSC). IEEE, 2020. http://dx.doi.org/10.1109/wsc48552.2020.9383980.

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Cheng, Zhongqi, Tim Schmidt, Guantao Liu und Rainer Doomer. „Thread- and data-level parallel simulation in SystemC, a Bitcoin miner case study“. In 2017 IEEE International High-Level Design Validation and Test Workshop (HLDVT). IEEE, 2017. http://dx.doi.org/10.1109/hldvt.2017.8167466.

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Neudecker, Till, Philipp Andelfinger und Hannes Hartenstein. „A simulation model for analysis of attacks on the Bitcoin peer-to-peer network“. In 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM). IEEE, 2015. http://dx.doi.org/10.1109/inm.2015.7140490.

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Silveira, Jonathas, Isaías Felzmann, João Fabrício Filho und Lucas Wanner. „RV-Across: An Associative Processing Simulator“. In XXI Simpósio em Sistemas Computacionais de Alto Desempenho. Sociedade Brasileira de Computação, 2020. http://dx.doi.org/10.5753/wscad.2020.14064.

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Associative Processing provides high-performance and energyefﬁcient parallel computation using a Content-Addressable Memory (CAM). Emerging big data applications can be signiﬁcantly sped-up by Associative Processing, but validation and evaluation are key challenges. We present RVAcross, a RISC-V Associative Processing Simulator for testing, validation, and modeling associative operations. RV-Across eases the design of associative and near-memory processing architectures by offering interfaces to both building new operations and providing high-level experimentation. Our simulator records memory and registers states of each associative operation pass, giving the user visibility and control over the simulation. The user can employ the simulation statistics provided by RV-Across to compute performance and energy metrics. RV-Across implements common associative operations and provides a framework to allow for easy extension. We show how the simulator works by experimenting with different scenarios for associative operations with three applications that test the functionality of logic and arithmetic computations: matrix multiply, checksum, and bitcount. Our results highlight the direct relation between the data length and potential performance improvement of associative processing in comparison to regular CPU serial and parallel operation. In case of matrix multiplication, the speed-up increases linearly with matrices dimension, achieving 8X for 200x200 bytes matrices and overcoming parallel execution in an 8-core CPU.

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Ichsani, Yuditha, Resisca Audia Deyani und Rizal Broer Bahaweres. „The Cryptocurrency Simulation using Elliptic Curve Cryptography Algorithm in Mining Process from Normal, Failed, and Fake Bitcoin Transactions“. In 2019 7th International Conference on Cyber and IT Service Management (CITSM). IEEE, 2019. http://dx.doi.org/10.1109/citsm47753.2019.8965370.

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Nieto-Chaupis, Huber. „The Metropolis-Hastings Algorithm To Simulate Fluxes of Bitcoins Volumes in Developing Countries: Are you the winner or loser?“ In 2019 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON). IEEE, 2019. http://dx.doi.org/10.1109/chilecon47746.2019.8988021.

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