vProChain: Efficient Provenance Verification in Industrial Internet of Things (IIoT)

The Industrial Internet of Things (IIoT) has been widely deployed to enable real-time monitoring and automation. Within IIoT-driven production, supply chain management plays a critical role, necessitating verifiable provenance to ensure the authenticity and traceability of goods across multi-stakeholder networks. While blockchain provides a tamper-proof foundation, traditional storage structures suffer from unsecured data integrity, poor query efficiency, and scalability over provenance data. To address these challenges, we propose vProChain, an efficient provenance verification system to support verifiable and parallel queries over graph-structured provenance data. First, we design an Adaptive DAG Verkle Tree (ADVT) that deterministically maps supply chain dependencies into a graph-native authenticated data structure, enabling constant-size proofs and low-overhead verification. Second, we introduce the Merkle Inverted Patricia Trie (MIPT) to facilitate fast, verifiable multi-dimensional Boolean queries. Third, we develop a parallel provenance query algorithm that accelerates multi-hop path retrieval via consistent hashing and weighted bipartite matching. Finally, formal security analysis and extensive empirical evaluations demonstrate that vProChain can provide provable cryptographic guarantees for the soundness of provenance proofs and the completeness of query retrievals, while achieving high query efficiency in a large-scale IIoT environment.