On-Chain and Off-Chain Data Management for Blockchain-Internet of Things: A Multi-Agent Deep Reinforcement Learning Approach

Abstract

The emergence of blockchain technology has seen applications increasingly hybridise cloud storage and distributed ledger technology in the Internet of Things (IoT) and cyber-physical systems, complicating data management in decentralised applications (DApps). Because it is inefficient for blockchain technology to handle large amounts of data, effective on-chain and off-chain data management in peer-to-peer networks and cloud storage has drawn considerable attention. Space reservation is a cost-effective approach to managing cloud storage effectively, contrasting with the demand for additional space in real-time. Furthermore, off-chain data replication in the peer-to-peer network can eliminate single points of failure of DApps. However, recent research has rarely discussed optimising on-chain and off-chain data management in the blockchain-enabled IoT (BIoT) environment. In this study, the BIoT environment is modelled, with cloud storage and blockchain orchestrated over the peer-to-peer network. The asynchronous advantage actor-critic algorithm is applied to exploit intelligent agents with the optimal policy for data packing, space reservation, and data replication to achieve an intelligent data management strategy. The experimental analysis reveals that the proposed scheme demonstrates rapid convergence and superior performance in terms of average total reward compared with other typical schemes, resulting in enhanced scalability, security and reliability of blockchain-IoT networks, leading to an intelligent data management strategy.