A proof-based dynamic bridge model for multi-blockchain interoperability in supply chain network traceability

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Authors: Iken Mohamed, Lahcen Tamym, Lekbir Afraites, Ahmed Nait Sidi Moh, Aissam Hadri
Cited by: 0
References: 42

Abstract

Blockchain technology has emerged as a promising solution for enhancing transparency and traceability of supply chain management (SCM). However, current blockchain solutions are mostly fragmented and lack interoperability as well as standardized communication protocols, which limits the comprehensive end-to-end traceability needed in logistics multi-actor and obstructs effective data sharing across diverse Blockchain platforms. To address these challenges, this paper proposes a novel Proof-Based Dynamic Bridge Model that enables secure, scalable, and verifiable interoperability between heterogeneous blockchains deployed by different actors within a supply chain network (SCN) (DBM-SCN). To this end, DBM-SCN model introduces a dynamic, temporary bridge that facilitates the transfer of cryptographically signed proofs which are digital documents of logistical events between different blockchains without requiring their fusion or consensus alignment. Moreover, DBM-SCN ensures the integrity and authenticity of exchanged data through cryptographic chaining of proofs, enabling reliable end-to-end traceability across autonomous blockchain networks. DBM-SCN achieves end-to-end traceability across heterogeneous, fragmented blockchain networks while preserving actor sovereignty, delivering stable throughput ( T P S ≈ 0 . 55 t x / s ) and quasi-linear scalability (LSF = 1.4286 for 20 actors) with an efficiency of 70%. Latency is dominated by inherent blockchain storage costs ( ≈ 87%), while cryptographic operations contribute less than 2% of total latency and gas consumption scales linearly with the number of actors, enabling predictable deployment costs.

Fields of Study & Subjects

Blockchain Technology Applications and Security Food Supply Chain Traceability Blockchain Technology in Education and Learning

Publication details

Journal / source
Telematics and Informatics Reports
Volume
21
Pages
100305–100305
Publication date
27 February 2026
Language
EN