Enhancing Blockchain Communication with Named Data Networking: A Novel Node Model and Information Transmission Mechanism

In recent years, blockchain research has garnered significant attention.However, the traditional TCP/IP-based communication model struggles to support the broadcasting mode required for efficiently transmitting large volumes of data.Deep Reinforcement Learning (DRL) techniques have been employed to optimize blockchain data transmission and network resource utilization to address these limitations.A novel framework is introduced, leveraging DRL to enhance network performance by intelligently managing node operations and resource allocation.Key components of the system include adaptive request aggregation, dynamic cache management, and efficient routing strategies, all optimized through DRL-based decision-making.This approach significantly reduces redundant traffic, accelerates communication, and improves overall network efficiency.Furthermore, a virtual currency application is implemented as an example to demonstrate the practical benefits of this framework.Simulation results showcase the superior performance of the proposed DRL-driven mechanism compared to conventional models, particularly in terms of scalability and data propagation efficiency.Future research directions are discussed, emphasizing the integration of DRL with advanced blockchain technologies to overcome emerging challenges and support next-generation decentralized systems.