Open Access

ARTICLE

FPSblo: A Blockchain Network Transmission Model Utilizing Farthest Point Sampling

Longle Cheng^1,2, Xiru Li¹, Shiyu Fang², Wansu Pan¹, He Zhao^1,*, Haibo Tan¹, Xiaofeng Li^1,2

1 Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China
2 University of Science and Technology of China, Hefei, 230026, China

* Corresponding Author: He Zhao. Email:

Computers, Materials & Continua 2024, 78(2), 2491-2509. https://doi.org/10.32604/cmc.2024.047166

Received 27 October 2023; Accepted 11 December 2023; Issue published 27 February 2024

Abstract

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.

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Keywords

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