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Cited by 59Blockchain Technology Applications and SecurityComplex Network Analysis TechniquesPeer-to-Peer Network Technologies

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

Open access
Wang, Canhui, Chu, Xiaowen, Qin, Yang·2020·2020 6th International Conference on Big Data Computing and Communications (BIGCOM)
DOI 10.1109/bigcom51056.2020.00032
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Abstract

Bitcoin network has received much attention from both industry and academy due to Bitcoin's recent success. 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., mining revenue and transaction collection strategy) 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 2.15 hundred thousand blocks from February 2016 to February 2020 and collected over 4.12 TB unconfirmed transactions. 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 showed the following. 1) A few mining pools continuously controlled most of the computing resources of the Bitcoin network. 2) Mining pools were 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 were 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 were not sensitive to the past events of halving block rewards. 5) Feerate played a dominating role in the transaction collection strategy for the top mining pools. Our measurements and analysis helped the Bitcoin community to understand and improve the Bitcoin network.

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  • Bitcoin's fundamental feature could be the halving effect, occurring every four years; these investigations view this event as a structural change and a potential source of price performance.

    “Therefore, Bitcoin's fundamental feature could be the halving effect, occurring every four years. These investigations view this event as a structural change and a potential source of price performance, based on historical data as well as other market-specific influencing factors (Ciaian et al. 2015; Meynkhard 2019; Wang et al. 2020).”

    From An Empirical Examination of Bitcoin’s Halving Effects: Assessing Cryptocurrency Sustainability within the Landscape of Financial Technologies (2024) · cites this paper as Wang et al. 2020

    Section 2.2 confirms that block rewards halve about every four years, and §4.3.2 analyzes the resulting reward transition. But §4.3.2 reports that Bitcoin market prices were not sensitive to the halving events, contrary to attributing price performance to them. (LLM, conf=0.88)

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  • The Bitcoin protocol was designed to reduce the reward by half every 210,000 blocks, which is around every four years.

    “However, the reward is not always the same; the Bitcoin protocol was designed to reduce the reward by half every 210,000 blocks, which, in time, is around every four years.”

    From An Empirical Examination of Bitcoin’s Halving Effects: Assessing Cryptocurrency Sustainability within the Landscape of Financial Technologies (2024) · cites this paper as Wang et al. 2020

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  40. Canhui Wang received his B.E. degree from Shandong University, P.R. China, in 2016. He is currently pursuing his Ph.D. degree with Hong Kong Baptist University. He was a research as- sistant with IRACE research center in Shenzhen. His research interests include GPU comput- ing, distributed storage systems and Blockchain technology. He is a graduate student member of the IEEE.
  41. Xiaowen Chu received the B.E. degree in com- puter science from Tsinghua University, P.R. China, in 1999, and the Ph.D. degree in com- puter science from the Hong Kong University of Science and Technology in 2003. Currently, he is currently a full professor in the Depart- ment of Computer Science, Hong Kong Baptist University. His research interests include parallel and distributed computing, cloud computing and wireless networks. He is serving as an Asso- ciate Editor of IEEE Access and IEEE Internet of Things Journal. He is a senior member of the IEEE.
  42. Qin Yang received her B.Sc in computer sci- ence at Southwest Jiaotong University, China, in 1989, M.S in computer science at Huazhong University of Science & Technology, in 1992, and PhD in computer science, Hong Kong Univer- sity of Science & Technology, Kowloon, Hong Kong at November of 1999. She is an associate professor in the Department of Computer Sci- ence and Technology, Harbin Institute of Tech- nology, Shenzhen Graduate School, China. Her research interest is in the area of wireless networks, mobile computing, crosslayer design, QoS of routing and scheduling, high speed optical networks and so forth. She is a senior member of the IEEE.

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Authors
Wang, C., Chu, X., & Qin, Y.
Affiliations
  • Hong Kong Baptist University (HK)
  • Harbin Institute of Technology (CN)
ORCID
Venue
2020 6th International Conference on Big Data Computing and Communications (BIGCOM)
Publisher
IEEE
Published
2020-07
Pages
180-188
DOI
10.1109/bigcom51056.2020.00032
OpenAlex
W3047886495
Cited by
59
References
4
Citations by year
2025: 7 · 2024: 7 · 2023: 8 · 2022: 12 · 2021: 17 · 2020: 6 · 2019: 1
FWCI
8.15
Type
conference paper
Language
en
Open access
Licence
Locations
  • International Conference on Big Data Computing and Communications (BigCom) (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
  • Physical Sciences › Computer Science › Computer Networks and Communications › Peer-to-Peer Network Technologies
Keywords
Bitcoin, mining pools, network measurement, transaction fees, transaction delay, prisoner's dilemma, Malthusian trap, network traffic

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