Blockchain sharding is a promising solution for scalability but struggles to reach the expected performance due to the high ratio of cross-shard transactions. Account migration has emerged as a critical approach to optimizing shard performance. However, existing migration solutions suffer from inefficient handling of queued withdrawal transactions from a migrating account and inadequate priority mechanism for migration transaction, resulting in prolonged transaction makespan and reduced system throughput. This paper proposes Caravan, a novel blockchain sharding system for optimizing account migration. First, Caravan proposes a transaction aggregation-based migration scheme to efficiently handle withdrawal congestion post-migration. It incorporates a multi-level Merkle tree and cross-shard synchronization protocol to ensure cross-shard security. Second, Caravan presents an economic incentive-driven priority mechanism that motivates miners to perform transaction aggregation and prioritize migration transactions by increasing the associated revenue. Furthermore, its gas recycling strategy enables users to finance migration costs without awareness or extra expenses. Finally, we develop the Caravan prototype, deploy it on Alibaba Cloud, and experiment with real Ethereum transactions. The results show that compared to the state-of-the-art account migration schemes, Caravan significantly mitigates the transaction surge caused by migration, achieving up to a 3.2× throughput improvement and a 65% reduction in transaction confirmation latency. And users share considerable migration costs without extra expenses, significantly reduce system costs. The code for Caravan is available on GitHub.¹¹Caravan are available at https://github.com/Caravan-project/Caravan.

With the rapid growth of blockchain-based Non-Fungible Tokens (NFTs), data trading has evolved to incorporate NFTs for ownership verification. However, the NFT ecosystem faces significant challenges in copyright protection, particularly when malicious buyers slightly modify the purchased data and remint it as a new NFT, infringing upon the original owner’s rights. In this paper, we propose a copyright-preserving data trading protocol to address this challenge. First, we introduce the Merkle Feature Tree (MFT), an enhanced version of the traditional Merkle Tree that incorporates an AI-powered feature layer above the data layer. Second, we design a copyright challenge phase during the trading process, which recognizes the data owner with highly similar feature vectors and earlier on-chain timestamp as the legitimate owner. Furthermore, to achieve efficient and low-gas feature vector similarity computation on blockchain, we employ Locality-Sensitive Hashing (LSH) to compress high-dimensional floating-point feature vectors into single uint256 integers. Experiments across multiple image feature extraction models show that LSH maintains a high F1 score after compression, effectively supporting similarity-based copyright challenges. Experimental results on the Ethereum Sepolia testnet demonstrate NMFT’s scalability with sublinear growth in gas consumption while maintaining stable latency.