计算机工程2024，Vol.50Issue(1)：50-59,10.DOI:10.19678/j.issn.1000-3428.0066577
面向6G物联网设备协同的区块链动态分片
Dynamic Blockchain Sharding for 6G Internet of Things Devices Collaboration
摘要
Abstract
With the massive deployment of Internet of Things(IoT)applications,numerous devices work together to generate massive,high-value data.If the security of these data is not guaranteed,they are vulnerable to threats such as data abuse,privacy leakage,and data tampering.Therefore,a decentralized,immutable,and secure blockchain sharding network is applied in this scenario,gradually replacing the traditional centralized network.However,the performance of blockchain sharding network is limited by the high proportion of cross-shared collaborative transactions or complex environments.To solve these problems,a dynamic blockchain sharding optimization scheme for 6G IoT device collaboration is proposed.First,the sharding architecture of the system is designed,and throughput,security,and delay models of the system are established.Subsequently,a two-stage sharding optimization strategy is proposed.In the first stage,the nodes are screened using a reputation-based sharing and grading strategy.In the second stage,a dynamic sharding strategy based on Deep Reinforcement Learning(DRL)is used to reduce the proportion of cross-shard collaborative transactions and determine the number of shards.By combining these two stages,the throughput of the entire system can be improved while ensuring safety.The experimental results reveal that in the blockchain sharding scenario with the collaboration of IoT devices,the abovementioned scheme,compared with traditional schemes based on reputation-based grading sharding,uniform sharded,or random sharding strategies,can reduce cross-shard collaborative transaction proportions by over 50%.In this case,the throughput of the system improved.关键词
物联网/区块链分片/委托拜占庭容错/信誉值/跨片协同/深度强化学习Key words
Internet of Things(IoT)/blockchain sharding/Delegated Byzantine Fault Tolerance(DBFT)/reputation value/cross-shard collaboration/Deep Reinforcement Learning(DRL)分类
信息技术与安全科学引用本文复制引用
蔡梓越,谭北海,余荣,黄旭民,王思明..面向6G物联网设备协同的区块链动态分片[J].计算机工程,2024,50(1):50-59,10.基金项目
国家自然科学基金(61971148,U22A2054). （61971148,U22A2054）
