对物联网交易的自适应性冲突解决:基于强化学习的混合验证协议.
Mohammad A Al Khaldy1, Ahmad Nabot2, Ahmad Al-Qerem3
1Business Intelligence & Data Analytics, University of Petra, Amman, Jordan.
Scientific reports
|July 15, 2025
概括
本研究介绍了一种基于强化学习的混合验证协议 (RL-CC),用于物联网 (IoT) 交易. RL-CC显著减少了交易中断,并提高了对时间敏感传感器数据处理的吞吐量.
科学领域:
- 计算机科学 计算机科学
- 分布式系统 分布式系统
- 人工智能的人工智能
背景情况:
- 高效的交易管理对于基于传感器的系统至关重要,特别是在时间敏感的物联网 (IoT) 应用中.
- 维护数据完整性和在时间有效性限制范围内及时执行是一个重大挑战.
研究的目的:
- 引入一种新的基于强化学习的混合验证协议 (RL-CC),用于自适应边缘-云协调.
- 为了最大限度地减少交易中断,并最大限度地提高时间敏感的物联网交易的吞吐量.
主要方法:
- RL-CC协议采用两阶段验证:边缘验证用于初步冲突检测和优先级,云验证用于全球冲突解决.
- 强化学习 (RL) 机制动态调整决策,优先考虑交易和解决冲突,基于考虑绩效参数的奖励函数.
主要成果:
- 在交易中断率方面,RL-CC实现了90%的降低 (5%对2PL的45%).
- 与传统方法相比,该协议显示了3倍的吞吐量 (300 TPS vs. 100 TPS) 和70%的低延迟.
- 观察到并发管理和传感器数据处理效率的显著改善.
结论:
- 在基于传感器的应用中,RL-CC协议提供了一个可扩展和适应的解决方案,用于基于传感器的高同步交易处理.
- 它有效地确保交易在它们的时间有效窗口内执行,这对于物联网网络和实时系统至关重要.
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