Node Efficient Consensus Algorithm for Topology-Heterogeneous Large-Scale IIoT Based on Multi-Chain DAG

To address the challenges in Industrial Internet of Things (IIoT) systems—stemming from heterogeneous device computing capabilities and diverse network topologies, which often result in excessive resource consumption and inefficiencies in traditional blockchain consensus algorithms—this paper proposes a Node-Efficient Consensus Algorithm for topology-heterogeneous large-scale IIoT Systems based on a Multichain Directed Acyclic Graph (NECA-MDAG). First, we design a blockchain architecture based on multichain directed acyclic graph (DAG) with a hierarchical structure comprising five layers: device, edge, network, consensus, and application. This design facilitates optimized data sharing between multiple factories or organizational departments. Next, taking into account heterogeneous network topologies—including tree, mesh, and hybrid structures—we propose a lightweight multichain DAG model based on regional division. The nodes construct local slave DAG chains alongside a master DAG chain, thus enhancing the overall efficiency of the consensus process. We then propose an efficient consensus algorithm that combines local slave chain consensus with master chain consensus, enabling parallel consensus across distributed regions and substantially improving transaction throughput. Additionally, we develop a bidirectional cross-chain anchoring mechanism to facilitate secure, cross-regional sharing of industrial data, including inventory and order information. Experimental results show that, irrespective of node scale or network latency, NECA-MDAG consistently enhances transaction throughput, reduces latency and communication overhead, and surpasses CDBFT, LNLCA, and Avalanche in overall performance within IIoT environments.