Blockchain-Enhanced UAV Networks: Optimizing Data Storage for Real-Time Efficiency

Due to their flexibility and cost-effectiveness, Autonomous vehicles (UAV) networks are widely employed in complex tasks, such as collaborative search, agricultural monitoring, and communication relaying. However, frequent data transmission in these applications poses significant challenges to network efficiency and security. To address these issues, this article proposes a blockchain-based data storage solution for UAV networks, where UAVs are categorized into task and storage nodes. Task nodes transmit sensed data to the ground station, which processes them into blocks and strategically allocates them to aerial storage nodes. Efficiently managing UAV clustering and data allocation is critical to optimizing network performance. Therefore, three algorithms are proposed. First, a hybrid similarity-based UAV clustering algorithm (HSUCA) efficiently groups task nodes with similar data access preferences. Second, a proximity-aware optimization for storage allocation algorithm (PAOSA) optimizes data distribution across storage nodes based on the characteristics of clusters and their neighboring clusters. Third, to address the coupling between node clustering and data allocation, we introduce a cyclic clustering and data allocation optimization (CCDAO) algorithm. Experimental results demonstrate that, compared to baseline algorithms, the proposed method reduces data access costs by at least 34.8%. Moreover, it achieves an average runtime of 21.8 ms, which is comparable to existing approaches, further confirming its effectiveness and potential for efficient data access in UAV networks.