Tianwei Cai;Yi Sun;Chao Wang;Zhaowen Lin;Xueyun Zeng;Xiaohong Cai;Yijing Lin
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引用次数: 0
Abstract
Blockchain technology innovatively realizes data trusted verification and traceability through the chain structure. But meanwhile, the constant growth of blockchain causes a serious state bloat, becoming a critical challenge restricting the scalability of blockchain. The stateless blockchain, eliminating the necessity of storing the entire state by compressing it into a commitment, is a promising solution to address the state bloat problem. However, current stateless blockchains for unspent transaction output (UTXO) model suffer from poor performance and scalability. In this paper, we present TerseChain, a sharding stateless UTXO-based blockchain that can effectively balance storage, computation, and communication overhead. Specifically, TerseChain innovatively decouples the cross-shard transaction processing into local validation, global ordering, and parallel execution by stateless verification, improving scalability while guaranteeing security. More importantly, our enhanced algorithms markedly increase the efficiency of stateless verification within each shard, necessitating only a constant overhead in storage. Through rigorous theoretical analysis and extensive experimental results, we demonstrate that TerseChain provides high processing capacity that scales favorably with the expansion of the network. TerseChain not only addresses the scalability issues of stateless blockchains but also provides a foundation for AI-enhanced blockchain scalability solutions.
期刊介绍:
The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.