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FPSblo:A Blockchain Network Transmission Model Utilizing Farthest Point Sampling

查看全文 作　　者：Longle [1,2]Cheng;Xiru [1]Li;Shiyu [2]Fang;Wansu [1]Pan;He [1]Zhao;Haibo [1]Tan;Xiaofeng [1,2]Li 高影响力作者 机构地区：[1]Hefei Institutes of Physical Science,Chinese Academy of Sciences,Hefei,230031,China;[2]University of Science and Technology of China,Hefei,230026,China高影响力机构 出　　处：《Computers, Materials & Continua》索引2024年第78卷第2期,共19页高影响力期刊 基　　金：This present research work was supported by the National Key R&D Program of China(No.2021YFB2700800);the GHfund B(No.202302024490). 摘　　要：Peer-to-peer(P2P)overlay networks provide message transmission capabilities for blockchain systems.Improving data transmission efficiency in P2P networks can greatly enhance the performance of blockchain systems.However,traditional blockchain P2P networks face a common challenge where there is often a mismatch between the upper-layer traffic requirements and the underlying physical network topology.This mismatch results in redundant data transmission and inefficient routing,severely constraining the scalability of blockchain systems.To address these pressing issues,we propose FPSblo,an efficient transmission method for blockchain networks.Our inspiration for FPSblo stems from the Farthest Point Sampling(FPS)algorithm,a well-established technique widely utilized in point cloud image processing.In this work,we analogize blockchain nodes to points in a point cloud image and select a representative set of nodes to prioritize message forwarding so that messages reach the network edge quickly and are evenly distributed.Moreover,we compare our model with the Kadcast transmission model,which is a classic improvement model for blockchain P2P transmission networks,the experimental findings show that the FPSblo model reduces 34.8%of transmission redundancy and reduces the overload rate by 37.6%.By conducting experimental analysis,the FPS-BT model enhances the transmission capabilities of the P2P network in blockchain. 关 键 词：Blockchain P2P networks SCALABILITY farthest point sampling
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