A survey: Reward distribution mechanisms and withholding attacks in Bitcoin pool mining

Zhu, Saide; Li, Wei; Li, Hong; Hu, Chunqiang; Cai, Zhipeng

doi:10.3934/mfc.2018020

Cited by 12 publications

(5 citation statements)

References 37 publications

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“…On the other hand, mining pools usually charge membership fees to each participant and allocate rewards to each miner according to their own reward‐sharing mechanisms (Bhaskar & Lee, 2015). Some common reward allocation mechanisms in practice are Pay‐Per‐Last‐N‐Shares (PPLNS) (Qin, Yuan & Wang, 2019) and Full‐Pay‐Per‐Share (Zhu et al, 2019a). One popular public chain is Ethereum (Wood, 2014), which allows users to send not only digital coins, but also smart contracts (Wohrer & Zdun, 2018).…”

Section: Review Of Blockchainmentioning

confidence: 99%

Machine learning in/for blockchain: Future and challenges

et al. 2021

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Machine learning and blockchain are two of the most notable technologies of recent years. The first is the foundation of artificial intelligence and big data analysis, and the second has significantly disrupted the financial industry. Both technologies are data‐driven, and thus there are rapidly growing interests in integrating both for more secure and efficient data sharing and analysis. In this article, we review existing research on combining machine learning and blockchain technologies and demonstrate that they can collaborate efficiently and effectively. In the end, we point out some future directions and expect more research on deeper integration of these two promising technologies.

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Section: Review Of Blockchainmentioning

confidence: 99%

Machine learning in/for blockchain: Future and challenges

et al. 2021

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“…The estimation of computing resources relying on Proof of Work has been studied earlier by [29] and others [30], [31]. In his work, Eyal describes how not only full PoW solutions but also partial solutions can be utilized to ensure members in a mining pool do contribute to the collective mining effort.…”

Section: ) Proof Of Workmentioning

confidence: 99%

“…The utilization of partial proof of works has been previously been proposed in different mechanisms that secure and raise the level of fairness in mining pools [29], [69], [30], [70], [31]. Mining pools utilize various payout systems in order to distribute the mined coins between their miners even if individual nodes have not been able to provide a full PoW solution.…”

Section: B Pow For Online Estimation Of Computational Resourcesmentioning

confidence: 99%

Blockchain-Powered Collaboration in Heterogeneous Swarms of Robots

Queralta,

Westerlund

2019

Preprint

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One of the key challenges in the collaboration within heterogeneous multi-robot systems is the optimization of the amount and type of data to be shared between robots with different sensing capabilities and computational resources. In this paper, we present a novel approach to managing collaboration terms in heterogeneous multi-robot systems with blockchain technology. Leveraging the extensive research of consensus algorithms in the blockchain domain, we exploit key technologies in this field to be integrated for consensus in robotic systems. We propose the utilization of proof of work systems to have an online estimation of the available computational resources at different robots. Furthermore, we define smart contracts that integrate information about the environment from different robots in order to evaluate and rank the quality and accuracy of each of the robots' sensor data. This means that the key parameters involved in heterogeneous robotic collaboration are integrated within the Blockchain and estimated at all robots equally without explicitly sharing information about the robots' hardware or sensors. Trustability is based on the verification of data samples that are submitted to the blockchain within each data exchange transaction, and validated by other robots operating in the same environment. Initial results are reported which show the viability of the concepts presented in this paper.

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“…After completing the mining task, miners report their solutions to the pool operator. If the Bitcoin network accepts the solution, the operator collects the mining rewards and divide it among miners following a specific reward function such as PPS and PPLNS [28] that both parties agreed with on beforehand.…”

Section: Pooled Mining Principlesmentioning

confidence: 99%

Measurement and Analysis of the Bitcoin Networks: A View from Mining Pools

Wang¹,

Chu²,

Qin³

2019

Preprint

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has received much attention from both industry and the academy. Mining pools, the main components of the Bitcoin network, dominate the computing resources and play essential roles in network security and performance aspects. Although many existing measurements of the Bitcoin network are available, little is known about the details of mining pool behaviors (e.g., empty blocks, mining revenue and transaction collection strategies) and their effects on the Bitcoin end users (e.g., transaction fees, transaction delay and transaction acceptance rate). This paper aims to fill this gap with a systematic study of mining pools. We traced over 1.56 hundred thousand blocks (including about 257 million historical transactions) from February 2016 to January 2019 and collected over 120.25 million unconfirmed transactions from March 2018 to January 2019. Then we conducted a board range of measurements on the pool evolutions, labeled transactions (blocks) as well as real-time network traffics, and discovered new interesting observations and features. Specifically, our measurements show the following. 1) A few mining pools entities continuously control most of the computing resources of the Bitcoin network. 2) Mining pools are caught in a prisoner's dilemma where mining pools compete to increase their computing resources even though the unit profit of the computing resource decreases. 3) Mining pools are stuck in a Malthusian trap where there is a stage at which the Bitcoin incentives are inadequate for feeding the exponential growth of the computing resources. 4) The market price and transaction fees are not sensitive to the event of halving block rewards. 5) The block interval of empty blocks is significantly lower than the block interval of non-empty blocks. 6) Feerate plays a dominating role in transaction collection strategy for the top mining pools. Our measurements and analysis help to understand and improve the Bitcoin network.

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A survey: Reward distribution mechanisms and withholding attacks in Bitcoin pool mining

Cited by 12 publications

References 37 publications

Machine learning in/for blockchain: Future and challenges

Machine learning in/for blockchain: Future and challenges

Blockchain-Powered Collaboration in Heterogeneous Swarms of Robots

Measurement and Analysis of the Bitcoin Networks: A View from Mining Pools

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