GpDB: A Graph-partition Based Storage Strategy for DAG-Blockchain in Edge-cloud IIoT

Abstract

The industrial Internet-of-things (IIoT) has attracted extensive attention due to its real-time and automation characteristics. Edge computing and blockchain technologies facilitate the IIoT in terms of low latency services and data security respectively. However, with the continuous expansion of industrial data and the growth of industrial nodes, traditional blockchain technology has some critical limitations on low transaction throughput and high data storage costs. Directed Acyclic Graph (DAG)-blockchain adopts a graph structure of a single transaction as the basic unit, and it has the characteristics of asynchronous consensus. Some existing studies use DAGblockchain to replace the traditional blockchain to alleviate its low throughput problems like IOTA. However, with the rapid data generation in the IIoT environment, the topology scale of DAG-blockchain will increase sharply, which will aggravate the data storage cost of blockchain nodes. In this article, to reduce the data storage cost of edge servers, we design a Graphpartition based storage strategy for DAG-Blockchain (GpDB), equipped with a graph partition algorithm based on transaction freshness, which can partition DAG-blockchain topology in edge servers into two parts, which will be retained and removed respectively. Simulation shows that, in terms of storage cost, GpDB outperforms LDV and Layerchain by 62% and 74% respectively, and with the increasing number of transactions, GpDB has good scalability in reducing the storage cost, and better transaction throughput than IOTA.