SLDSM: sparse location data sharing model based on VN-PBFT for IoV

Location data sharing is a key foundational service for intelligent transportation. However, in sparse location data scenarios, existing data sharing solutions based on Practical Byzantine Fault Tolerance (PBFT) consensus face high latency and even consensus failure issues. In response to this issue, we propose a sparse location data sharing model based on virtual node PBFT consensus, which utilizes virtual nodes to complete node clustering and improves the real-time performance of the model. Simulation experiment results show that compared with PBFT and its improved algorithm Optimized Byzantine Consensus Parallelism Scheme (ParBFT), the average consensus latency of Virtual Nodes-Practical Byzantine Fault Tolerance (VN-PBFT) algorithm is increased by 42.91% and 23.48% respectively.

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Publication Details

Published
2025-05-19
DOI
https://doi.org/10.1117/12.3056950
Primary Topic
Opportunistic and Delay-Tolerant Networks
Type
article
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article

SLDSM: sparse location data sharing model based on VN-PBFT for IoV

Jun Wang, Yi Xin Zhou, Dejun Wang, Qiang Yan
Opportunistic and Delay-Tolerant Networks
article

SLDSM: sparse location data sharing model based on VN-PBFT for IoV

Jun Wang, Yi Xin Zhou, Dejun Wang, Qiang Yan
article en

Abstract

Location data sharing is a key foundational service for intelligent transportation. However, in sparse location data scenarios, existing data sharing solutions based on Practical Byzantine Fault Tolerance (PBFT) consensus face high latency and even consensus failure issues. In response to this issue, we propose a sparse location data sharing model based on virtual node PBFT consensus, which utilizes virtual nodes to complete node clustering and improves the real-time performance of the model. Simulation experiment results show that compared with PBFT and its improved algorithm Optimized Byzantine Consensus Parallelism Scheme (ParBFT), the average consensus latency of Virtual Nodes-Practical Byzantine Fault Tolerance (VN-PBFT) algorithm is increased by 42.91% and 23.48% respectively.

Minzu University of China (CN), Institute of Computing Technology (CN)
Openalex Percentile: Top 89%
Opportunistic and Delay-Tolerant Networks
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