DESIGN OF AN EFFICIENT Q-LEARNING BASED BLOCKCHAIN SHARDING MODEL WITH ITERATIVE TRUST ANALYSIS FOR SELECTION OF MINER NODES
Abstract
Abstract: Blockchain technology has become increasingly popular for secure and decentralized data storage and management. However, scalability and energy consumption issues in current blockchain systems have been major challenges. One promising solution is sharding, which involves partitioning the blockchain network into smaller, manageable segments. However, efficient and secure selection of miner nodes for sharding remains a challenging task for different use cases. In this paper, we propose a novel Q-Learning based blockchain sharding model with iterative trust analysis for the selection of miner nodes. Our proposed model combines Q-Learning, a popular reinforcement learning algorithm, and iterative trust analysis to achieve high efficiency and security in the selection of miner nodes for sharding. The Q-Learning algorithm allows the system to learn and optimize the selection of miner nodes based on their previous performance, while the iterative trust analysis enhances the security and reliability of the selected nodes. To evaluate the performance of our proposed model, we conducted extensive simulations and experiments using real-world datasets & scenarios. The results show that our model outperforms existing approaches in terms of efficiency, security, and energy consumption. In addition, we demonstrate the effectiveness of our individual models, showing that Q-Learning achieves high accuracy in the selection of miner nodes, while iterative trust analysis significantly improves the security of the system even under attacks. Thus, our proposed Q-Learning based blockchain sharding model with iterative trust analysis provides an efficient and secure solution for the selection of miner nodes in blockchain sharding sets. This paper contributes to the development of blockchain technology and provides insights into the use of Q-Learning and iterative trust analysis in blockchain systems.
