DPRP: A Lightweight and Scalable Multi-Layer Blockchain Based on Swarm Reputation for Trustworthy AIoT Collaboration

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Authors: W J Zhang, Miao Du, Xin Guo, Neal N. Xiong
Cited by: 1
References: 45

Abstract

Artificial Intelligence of Things (AIoT)-enabled Edge Collaboration systems (AEC) are characterized by openness, heterogeneity, and constrained device resources, which pose severe challenges to achieving trusted collaboration within AEC. While blockchain provides a trusted foundation for multi-party coordination, establishing robust inter-device trust relationships at low cost and promoting the collaborative participation of heterogeneous devices remain critical challenges that need to be addressed. To address the aforementioned challenges, this paper introduces DPRP, a lightweight and scalable multilayer blockchain model based on delegated proof of swarm reputation with hierarchical Practical Byzantine Fault Tolerance (PBFT). The model introduces a lightweight consensus protocol, DPoRP, which constructs swarm reputation by evaluating node contributions within collaborative swarms, thereby establishing a trust foundation. This enables a swarm-reputation-driven multilayer consensus mechanism, structured around meta-nodes functioning as autonomous units. To effectively incentivize swarm collaboration and constrain node behavior, DPoRP incorporates a hierarchical reward and penalty scheme tailored to swarm collaboration. Moreover, to reduce the participation threshold for heterogeneous devices, DPRP designs a dynamic, redundancy-aware hierarchical storage mechanism that adjusts redundancy factors based on actual storage demands, thus improving storage efficiency and supporting multilayer deployment. Simulation results and analysis demonstrate that DPRP significantly enhances system robustness and scalability while reducing communication complexity.

Fields of Study & Subjects

Blockchain Technology Applications and Security Access Control and Trust Security and Verification in Computing

Publication details

Journal / source
Big Data Mining and Analytics
Publisher
Tsinghua University Press
Volume
9
Issue
3
Pages
896–918
Publication date
26 May 2026
Language
EN