Solo Mining Research Articles

Optimizing pool mining performance: A VIKOR‐based model for identifying reputed miners in blockchain networks

AbstractBlockchain networks continue to gain attraction in cutting‐edge applications and mining within these networks has become increasingly popular. To get rewards, miners solve cryptographic puzzles and add new blocks to blockchain networks using the proof‐of‐work (PoW) consensus mechanism. Numerous miners opt to participate in mining pools due to the challenges of solo mining. However, selecting reputed miners for pool mining poses a significant challenge, given the decentralized nature of the blockchain system. This paper addresses this challenge by introducing a new ranking model that evaluates miners' performance and reputation through trust scores. It provides a method for optimizing pool mining performance by identifying highly reputed miners within mining pools, enhancing overall pool profitability. This endeavor necessitates the development of ranking algorithms tailored to the unique dynamics of mining pools. The research offers a meticulously designed ranking model that identifies reputed miners. We extensively evaluate the proposed model using the hyperledger blockchain framework, guaranteeing strong performance across vital metrics like block authorization time, Processing time, block creation time, validation time, and confirmation time.

Scaling Up Security and Efficiency in Financial Transactions and Blockchain Systems

Blockchain, the underlying technology powering the Bitcoin cryptocurrency, is a distributed ledger that creates a distributed consensus on a history of transactions. Cryptocurrency transaction verification takes substantially longer than it does for conventional digital payment systems. Despite blockchain’s appealing benefits, one of its main drawbacks is scalability. Designing a solution that delivers a quicker proof of work is one method for increasing scalability or the rate at which transactions are processed. In this paper, we suggest a solution based on parallel mining rather than solo mining to prevent more than two miners from contributing an equal amount of effort to solving a single block. Moreover, we propose the idea of automatically selecting the optimal manager over all miners by using the particle swarm optimization (PSO) algorithm. This process solves many problems of blockchain scalability and makes the system more scalable by decreasing the waiting time if the manager fails to respond. Additionally, the proposed model includes the process of a reward system and the distribution of work. In this work, we propose the particle swarm optimization proof of work (PSO-POW) model. Three scenarios have been tested including solo mining, parallel mining without using the PSO process, and parallel mining using the PSO process (PSO-POW model) to ensure the power and robustness of the proposed model. This model has been tested using a range of case situations by adjusting the difficulty level and the number of peers. It has been implemented in a test environment that has all the qualities required to perform proof of work for Bitcoin. A comparison between three different scenarios has been constructed against difficulty levels and the number of peers. Local experimental assessments were carried out, and the findings show that the suggested strategy is workable, solves the scalability problems, and enhances the overall performance of the blockchain network.

Analyzing the Solo Mining Profitability of Zcash Cryptocurrency in the United States of America

Zcash is a proof-of-work (PoW) cryptocurrency that has gained attention due to its promise of enhanced user privacy. Notwithstanding, Zcash’s thorough acceptance notably depends on how profitable its mining process can be. To tackle this issue, we propose an analytical model to compute the mining hashrate under solo mining to achieve a liquid revenue equal to the minimum wage in the United States. In the sequence, we then estimate how profitable Zcash solo mining is based on that computed mining hasrate. Our proposed model spans crucial parameters of the whole mining process. In the experiments, we compare Zcash with the popular Bitcoin and also present a competitive analysis encompassing the ten top cryptocurrencies by market capitalization. Final results highlight that: (i) Zcash owns a value of hmin which is about eight orders of magnitude smaller than that of Bitcoin in all investigated scenarios, which refer to the ten least and most expensive american states in terms of electricity tariff; and (ii) Zcash is the second best cryptocurrency for solo mining among the aforementioned ten cryptocurrencies, being the only one whose protocol implements the concept of zero-knowledge proofs. Within this context, our key contribution is to provide the scientific literature with valuable insights to formally pave the way to develop practical analytical models for PoW-cryptocurrency systems, which may be chiefly valuable regarding competitive analyses in general.

Diversification across mining pools: optimal mining strategies under PoW

Abstract Mining is a central operation of all proof-of-work (PoW)-based cryptocurrencies. The vast majority of miners today participate in “mining pools” instead of “solo mining” in order to lower risk and achieve a more steady income. However, this rise of participation in mining pools negatively affects the decentralization levels of most cryptocurrencies. In this work, we look into mining pools from the point of view of a miner: We present an analytical model and implement a computational tool that allows miners to optimally distribute their computational power over multiple pools and PoW cryptocurrencies (i.e. build a mining portfolio), taking into account their risk aversion levels. Our tool allows miners to maximize their risk-adjusted earnings by diversifying across multiple mining pools. Our underlying techniques are drawn from both the areas of financial economy and computer science since we use computer science-based approaches (i.e. optimization techniques) to experimentally prove how parties (and in particular miners) interact with cryptocurrencies in a way of increasing their Sharpe ratio. To showcase our model, we run an experiment in Bitcoin historical data and demonstrate that a miner diversifying over multiple pools, as instructed by our model/tool, receives a higher overall Sharpe ratio (i.e. average excess reward over its standard deviation/volatility).

Energy Efficient Multiprocessing Solo Mining Algorithms for Public Blockchain Systems

Blockchain as a decentralized distributed ledger is revolutionizing the world with a secure design data storage mechanism. In the case of Bitcoin, mining involves a process of packing transactions in a block by calculating a random number termed as a nonce. The nonce calculation is done by special nodes called miners, and all the miners follow the Proof of Work (PoW) mining mechanism to perform the mining task. The transaction verification time in PoW-based blockchain systems, i.e., Bitcoin, is much slower than other digital transaction systems such as PayPal. It needs to be quicker if a system adapts PoW-based blockchain solutions, where there are thousands of transactions being computed at a time. Besides this, PoW mining also consumes a lot of energy to calculate the nonce of a block. Mining pools resulting into aggregated hashpower have been a popular solution to speed up the PoW mining, but they can be attacked by using different types of attacks. Parallel computing can be used to speed up the solo mining methods by utilizing the multiple processes of the contributing processors. In this research, we analyze various consensus mechanisms and see that the PoW-based blockchain systems have the limitations of low transaction confirmation time and high energy consumption. We also analyze various types of consensus layer attacks and their effects on miners and mining pools. To tackle these issues, we propose parallel PoW nonce calculation methods to accelerate the transaction verification process especially in solo mining. We have tested our techniques on different difficulty levels, and our proposed techniques yield better results than the traditional nonce computation mechanisms.

Cryptomining makes noise: Detecting cryptojacking via Machine Learning

Cryptojacking occurs when an adversary illicitly runs crypto-mining software over the devices of unaware users. This novel cybersecurity attack, that is emerging in both the literature and in the wild, has proved to be very effective given the simplicity of running a crypto-client into a target device. Several countermeasures have recently been proposed, with different features and performance, but all characterized by a host-based architecture. The cited solutions, designed to protect the individual user, are not suitable for efficiently protecting a corporate network, especially against insiders. In this paper, we propose a network-based approach to detect and identify crypto-clients activities by solely relying on the network traffic, even when encrypted and mixed with non-malicious traces. First, we provide a detailed analysis of the real network traces generated by three major cryptocurrencies, Bitcoin, Monero, and Bytecoin, considering both the normal traffic and the one shaped by a VPN. Then, we propose Crypto-Aegis, a Machine Learning (ML) based framework built over the results of our investigation, aimed at detecting cryptocurrencies related activities, e.g., pool mining, solo mining, and active full nodes. Our solution achieves a striking 0.96 of F1-score and 0.99 of AUC for the ROC, while enjoying a few other properties, such as device and infrastructure independence. Given the extent and novelty of the addressed threat we believe that our approach, supported by its excellent results, pave the way for further research in this area.

Improving Transaction Speed and Scalability of Blockchain Systems via Parallel Proof of Work

A blockchain is a distributed ledger forming a distributed consensus on a history of transactions, and is the underlying technology for the Bitcoin cryptocurrency. Its applications are far beyond the financial sector. The transaction verification process for cryptocurrencies is much slower than traditional digital transaction systems. One approach to scalability or the speed at which transactions are processed is to design a solution that offers faster Proof of Work. In this paper, we propose a method for accelerating the process of Proof of Work based on parallel mining rather than solo mining. The goal is to ensure that no more than two or more miners put the same effort into solving a specific block. The proposed method includes a process for selection of a manager, distribution of work and a reward system. This method has been implemented in a test environment that contains all the characteristics needed to perform Proof of Work for Bitcoin and has been tested, using a variety of case scenarios, by varying the difficulty level and number of validators. Experimental evaluations were performed locally and in a cloud environment, and experimental results demonstrate the feasibility the proposed method.

Bitcoin price prediction using ARIMA model

Bitcoin is a highly volatile cryptocurrency with rising popularity. It is a turning point in the way currency is seen. Now the currency, rather than being physical is becoming more and more digital. Due to high variance of solo mining, the number of users joining top most famous bitcoin mining pools is increasing due to the fact that users together under a bitcoin pool will have a higher chance of generating next block in the bitcoins blockchain by reducing the variance and earning the mining reward. In this research paper we are doing a survey on the technology lying underneath bitcoin's network and the various machine learning predictive algorithms. We collected the dataset on bitcoin blockchain from 28 April 2013 to 31 July 2017 which is publicly available on https://coinmarketcap.com and applied the ARIMA model for price prediction of bitcoin.

A type of block withholding delay attack and the countermeasure based on type-2 fuzzy inference.

We proposed a new type of bitcoin withholding attack named block withholding delay (BWD). It is different from the traditional withholding attacks which always drop valid blocks. BWD attackers never discard blocks but they delay the submissions of blocks to the pool managers, resulting the pool failed in the mining competitions and loss of rewards. We analyzed the optimum strategy of a BWD attacker who split its computing power into two parts, one was utilized to launch BWD attacks on the victim pools, while the other part was used for solo mining. We present detailed quantitative analysis of the maximum incentive that an attacker can earn by carefully splitting its computing power, and demonstrated that the attacker can obtain higher incentives than its contribution to the network in different conditions. Furthermore, we proposed a countermeasure against BWD based on the interval type-2 Takagi-Sugeno-Kang fuzzy inference system (IT2-TSK-FIS). The principle is to modify the private payoff scheme of pools to increase the risk of losing revenues of the rogue miners who deliberately delay block submissions. The scheme dealing the uncertain cause of block delay using fuzzy inference, and it is so designed that it does not require modifications of public mining protocols or data structures of the bitcoin network, which makes it applicable in practical pools.

Coalition Game-Based Computation Resource Allocation for Wireless Blockchain Networks

Public blockchain network (PBN) has been widely used in wired networks such as bitcoin network, in which proof-of-work (PoW) algorithm is deployed among miners to reach consensus on users data during the mining process. However, the PoW consensus mechanism is computation-consuming which obstacles the application of PBN in wireless mobile networks since most Internet of Things/mobile devices (IMDs) are resource limited. Recently, mobile edge computing (MEC) has been regarded as a promising technology which can allow IMDs to offload their computation tasks to the edge nodes. Although IMDs can offload their computation tasks to the edge nodes, there is still lots of competition among enormous solo mining IMDs when reaching consensus. In this paper, we first formulate the computation resource allocation problem of PBN from the viewpoint of coalition game theory under the MEC environment. Then, we propose a coalition formation game-based algorithm to maximize the system sum utility and take both the individual profit of IMD and coalition profit into consideration. Furthermore, we prove the proposed algorithm converges to a Nash-stable partition in a fast convergence rate and finally reaches the near-optimal solution with low computational complexity. The simulation results demonstrate the optimality and convergence of the proposed algorithm, and the proposed algorithm outperforms other schemes in terms of system sum profit and ratio of rewarded IMDs to overall IMDs.

MINING AND BUSINESS ACTIVITIES: IN SEARCH OF THE BALANCE

The article reveals the content and outlines approaches to the definition of the legal nature of mining. Attention is drawn to the necessity of legal regulation of this activity, which is predetermined by the Federal Program «Digital Economy of the Russian Federation» — a project that provides for normative regulation of the digital environment. In order to support the mission to eliminate digital illiteracy, which is also envisaged in the National Program, the author elucidates the etymology and meaning of the term «mining» and considers various doctrinal interpretations of this concept. The paper presents such analogies of the blochchain technology as the public ledger, DNA, and a layer-cake for a better understanding of the blockchain technology that is associated with mining. Material-technical and organizational foundations of mining are revealed. The author demonstrates advantages and disadvantages of solo mining, pool mining, and cloud mining. The results of comparative monitoring of the attitude to the recognition of cryptocurrency as a means of payment are presented. Attention is drawn to the liberal legal regulation of blockchain technology and mining under the laws of the Republic of Belarus. The paper determines the stages of a law-making process aimed at legal support of mining in Russia. Based on the results of comparison of concepts of entrepreneurial activity and mining, it is concluded that mining represents one of new types of entrepreneurship brought to life due to the needs of digital economy. The author suggests thatmining participants be recognized as self-employed persons. It is noted that the entrepreneurial nature of mining arises questions concerning measures of its state regulation which is difficult within the framework of the existing paradigm, but should be built on the basis of a balance between private and public interests.

Research on the Selection Strategies of Blockchain Mining Pools

Since the increasing popularization of the emerging blockchain technology, blockchain mining has attracted more and more attention. Due to the difficulty of solo mining, typically miners choose to join a mining pool. As there are many mining pools and different mining pools may adopt different reward mechanisms, how to choose the appropriate mining pool has become one of the most important issues faced by miners, since miners can get different rewards in different pools. In practice, there are three commonly used reward mechanisms for the mining pools to distribute the reward among their miners, namely, the proportional mechanism, the pay-per-share mechanism, and the pay-per-last- $N$ -share mechanism. In this paper, we study the pool selection problem faced by the miners, and model it as a risk decision problem since different reward mechanisms have different risks. We establish a pool selection model based on the maximum-likelihood criterion and also study the effect of $N$ on the miners’ optimal pool selection decisions. By utilizing the computational experiments approach, we validate our proposed pool selection strategies. Our results can provide important managerial insights for miners when making their pool selection decisions.

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

The past three years have seen the rapid increase of Bitcoin difficulty, which has led to a substantial variance in solo mining. As a result, miners tend to join a large open pool to get a more stable reward. Nowadays, mining pools take up over 98% of Bitcoins total computation power. In a sense, this is a manifestation of Bitcoin that tends to be centralized. Thus, researchers have shown an increased interest in pool mining payoff and security. The purpose of this paper is to review and summarize recent research in Bitcoin pool mining system. We first introduce several common reward distribution schemes, and analyze their advantages and disadvantages with some improvement mechanisms; In the second section, to address pool security problems, we examined the practical utility of some existing and potential attack strategies. To study those malicious attack in details, several defense methods are collected. Finally, we make an outlook on Bitcoin future.