Deep Reinforcement Learning-based Dynamic Sharding for Blockchain IoT

Abstract

Internet of Things (IoT) and Blockchain integration are having a huge impact on the future of technological progress. IoT has progressed from an emerging notion to a widely used technology, shaping the future of digital connectivity. With billions of networked IoT devices generating large amounts of data, efficient data management becomes critical. Blockchain has been explored extensively to enhance security in IoT networks however, its scalability limitations become evident when handling large-scale deployments. Sharding is recognized as a promising approach to improve blockchain scalability by partitioning the network into multiple independent groups. These groups, called shards, process transactions in parallel, increasing throughput while reducing communication, computation, and storage overhead. Despite its advantages, many existing blockchain sharding models rely on static algorithm, which fail to accommodate the dynamic nature of blockchain networks. Factors such as variable node involvement and possible security concerns present problems that static sharding cannot solve. To address these restrictions, deep learning provides a strong solution for dynamic and multidimensional sharding in blockchain-based IoT systems. Deep learning, with its capacity to understand complex patterns and adapt to changing network circumstances, can improve the efficiency, security, and scalability of blockchain-powered IoT networks. This article proposes a deep reinforcement learningbased dynamic shards in blockchain IoT applications to overcome scalability difficulties.