Optimized Block-K Clustering IoT Clustering and Blockchain Sharding Strategy Using Deep Reinforcement Learning

The inherent properties of immutability, decentralization, and enable linear scalability, making sharded blockchain a compelling approach to resolving trust issues in large-scale Internet of Things (IoT) networks. Sharding is a technique in building efficient blockchain architectures. Nevertheless, existing sharding mechanisms encounter two primary challenges. First, systems that require full committee reconstruction can experience substantial time delays during reconfiguration. Second, sharding models that rely on random assignment struggle with security vulnerabilities. To overcome these issues, this paper introduces Block-K Clustering, a secure, incentive-driven, and low-latency sharding blockchain framework utilizing the cuckoo rule. Block-K Clustering incorporates optimized reconstruction processes through the integration of genetic algorithms and cuckoo rules, enabling parallel onboarding of new nodes, which mitigates the issue of prolonged reconfiguration. Additionally, Block-K Clustering incorporates a reputation-based incentive system to identify and flag malicious nodes early, facilitating an even distribution of nodes. The system further refines cluster (shard) numbers and consensus parameters using a Deep Reinforcement Learning (DRL) model. Simulations demonstrate that Block-K Clustering effectively reduces reconstruction time, improves both sharding performance and security, and enhances the scalability of blockchain technology in IoT environments.