An Efficient Bitcoin Network Topology Discovery Algorithm for Dynamic Display
This program is tentative and subject to change.
The Bitcoin network comprises numerous nodes, necessitating users to invest significant network requests and time in comprehending its network topology. In this paper, we propose a Bitcoin network topology discovery algorithm that utilizes lightweight probe nodes to facilitate rapid transmission of network protocols. Building upon this, we introduce a node layer clustering algorithm based on filtering stable network nodes, enabling parallel discovery of the network topology. Additionally, we present an adaptive method for dynamically displaying the layered structure of the network topology. Experimental results demonstrate that our proposed method reduces communication overhead by approximately 72.16% when achieving a 95% similarity in network topology. Furthermore, the algorithm is applicable for discovering the network topology in other blockchain networks with similar structures.
This program is tentative and subject to change.
Sat 27 JulDisplayed time zone: Beijing, Chongqing, Hong Kong, Urumqi change
11:00 - 12:15 | Session 4 - Network & ConsensusResearch Track at Main Room Chair(s): Yury Yanovich Skolkovo Institute of Science and Technology; Faculty of Computer Science, HSE University | ||
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11:15 15mPaper | Fault Tolerance Testing and Tuning for Consortium Blockchain Research Track Taiwu Pang East China Normal University, Zheming Ye East China Normal University, Zhao Zhang East China Normal University, Cheqing Jin East China Normal University | ||
11:30 15mPaper | ATBFT-Automatically switch consensus protocol Research Track Yuxuan Lu School of Software, Shandong University, Jinan 250101, PR China, Chang Liu School of Computing Science, Newcastle University, Newcastle NE1 7RU, PR United Kingdom, Lanju Kong Shangdong University, Xiangyu Niu School of Software, Shandong University, Jinan 250101, PR China | ||
11:45 15mPaper | An Efficient Bitcoin Network Topology Discovery Algorithm for Dynamic Display Research Track Zening Zhao Tianjin University of Technology, Jinsong Wang Tianjin University of Technology, Miao Yang Tianjin University of Technology, Haitao Wang Tianjin University of Technology | ||
12:00 15mPaper | Meta Reinforcement Learning Based Dynamic Tuning for Blockchain Systems in Diverse Network Environments Research Track Yue Pei Beihang University, Mengxiao Zhu North China University of Technology, Chen Zhu Beihang University, weihusong Beihang University, Yi Sun Chinese Academy of Sciences, Lei Li Zhongguancun Laboratory, Haogang Zhu Beihang University | ||