一个适应式的区块链框架为联合IoMT与强化学习基于共识和资源预测预测
C H V N U Bharathi Murthy1, M Lawanya Shri2
1School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, 632014, India.
Scientific reports
|February 10, 2026
概括
本研究介绍了一种新的框架,将机器学习与区块链集成在一起,以确保医疗物联网 (IoMT) 的安全数据管理. 该系统提高了效率,减少了延迟,并提高了远程医疗保健应用程序的安全性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 医疗物联网 (IoMT) 的快速扩张产生了大量敏感的医疗数据,这给传统架构在管理低延迟,可扩展性和安全性数据流方面带来了挑战.
- 目前的IoMT系统在实时决策方面遇到了困难,原因是系统开销和延迟,影响了关键的医疗保健操作.
研究的目的:
- 提出一种新的框架,将机器学习 (ML) 与基于区块链的物联网 (IoT) 云集成,以实现高效和安全的医疗数据处理.
- 解决传统 IoMT 架构在管理大规模敏感数据流方面的局限性.
主要方法:
- 利用渐变增强机器 (GBM) 进行智能数据存储优化,使用深度Q学习 (DQN) 进行资源管理,以及用于增强隐私和安全的卷积自动编码器.
- 实现了长短期记忆 (LSTM) 网络,用于预测资源利用率,并与强化学习 (RL) 实现了自适应的拜占庭式故障容忍 (ABFT) 共识协议,以提高交易效率.
- 为私有区块链网络利用Hyperledger Fabric,确保框架层之间优化数据的无流动.
主要成果:
- 使用GBM实现了缓存命中率的25%提高和读取延迟的30%降低. DQN优化了资源管理,降低了20%的CPU负载.
- 卷积自编码器提高了异常检测95%,错误阳性减少了10%. 通过LSTM,资源利用率预测提高到90%.
- ABFT-RL的共识协议显示,交易吞吐量有40%的改善,交易延迟有20%的减少,PBFT和Raft的表现分别为43%和31%.
结论:
- 拟议的ML集成区块链框架为在IoMT环境中管理医疗保健数据提供了可扩展,安全和高效的解决方案.
- 该框架显著提高了实时决策能力,减少了系统开销,并改善了远程医疗和远程医疗保健应用的整体性能.
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