Blockchain and Deep Reinforcement Learning Empowered Data Storage in Internet of Things

With the advancement of Internet of Things (IoT) technology, various applications have greatly enhanced convenience in daily life, but there are still many security issues. Due to centralized cloud storage and limited computational resources, sensitive IoT data face security risks such as malicious tampering, privacy leakage, and data loss. The integration of blockchain and IoT provides an effective solution for ensuring data security. However, existing blockchain systems suffer from high storage demands, long consensus time, and vulnerability to malicious attacks. To address these issues, we propose a secure storage scheme for IoT data based on blockchain and Deep Reinforcement Learning (DRL). Specifically, our solution can be divided into two parts. The first part focuses on data generation, hierarchical encryption transmission, storage and verification. A capability-differentiated identity authentication method is introduced to ensure the trustworthiness of entities in the system. Besides, the integration of blockchain and the InterPlanetary File System (IPFS) ensures reliable and decentralized data storage. The second part introduces a consensus nodes selection mechanism based on DRL. This mechanism optimally selects consensus nodes according to different conditions, achieving a higher consensus success rate and lower consensus latency compared to traditional methods. Simulation results demonstrate that our scheme maintains a low computational overhead of 14.97ms, and obtains an average response delay for a single storage request of 0.42s, which proves its effectiveness in IoT environments.