Citation

Jiang, Zheng and Chua, Fang Fang and Lim, Amy Hui Lan (2026) An Adaptive Grouping PBFT Consensus Algorithm for Enhanced Privacy and Efficiency in IoV. Frontiers in Artificial Intelligence and Applications. ISSN 09226389

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Abstract

Consensus in the Internet of Vehicles (IoV) must tolerate rapidly changing network topologies while protecting vehicular privacy. Standard Practical Byzantine Fault Tolerance (PBFT) scales poorly because its communication overhead grows as O(N2). This paper proposes the Adaptive Grouping PBFT (AGPBFT) algorithm, which selects a subset of nodes per consensus round based on a reputation metric that weighs historical consensus accuracy, response latency, and current computational load. The selected nodes are organized into groups via an iterative stochastic optimization that precomputes multiple candidate configurations. Because group composition changes unpredictably between rounds, external observers cannot determine which nodes will participate next, providing a built-in privacy property without cryptographic cost. At 100 RSUs on Hyperledger Fabric, AG-PBFT reaches 110 TPS (more than double SG-PBFT, 22% above TRUGPBFT) with consensus latency of 420 ms (47% and 28% lower than SG-PBFT and TRUG-PBFT). Setting the participation ratio P = 0.5 cuts theoretical message count by 75%.

Item Type:	Article
Uncontrolled Keywords:	Internet of Vehicles
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	01 Oct 2026 03:34
Last Modified:	01 Oct 2026 03:35
URII:	http://shdl.mmu.edu.my/id/eprint/16769

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