使用混合Q学习蜂蜜鱼算法 (QL-HBA) 为物联网网络的节能组优先级MAC协议
Ilayaraja Venkatachalam1, Sirajudeen Ameer John2, Jagadeesan Srinivasan3
1Department of IOT, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India. ilayaraja.v@vit.ac.in.
Scientific reports
|December 29, 2024
概括
本研究介绍了物联网 (IoT) 网络的能效和组优先MAC (EEGP-MAC) 协议,显著减少延迟和能源消耗,同时提高吞吐量.
科学领域:
- 计算机科学 计算机科学
- 网络工程 网络工程
- 人工智能的人工智能
背景情况:
- 选择适当的MAC层协议对于优化基于应用程序特定服务特征的物联网 (IoT) 网络性能至关重要.
- 现有的协议可能无法充分解决物联网应用程序的多样化要求,导致延迟,能源消耗和吞吐量效率低下.
研究的目的:
- 为物联网网络设计了一种新的能源效率和集团优先级MAC (EEGP-MAC) 协议.
- 通过使用混合智能算法优化MAC协议选择来提高网络效率.
主要方法:
- EEGP-MAC协议使用混合Q学习蜂蜜子算法 (QL-HBA) 进行智能MAC协议选择.
- 网络设备由PAN协调员根据位置,能源和流量类型进行分组,并使用组合指标分配优先级.
- QL-HBA算法优化了群体内的争议节点选择,考虑了邻居数和流量负载等因素.
主要成果:
- 在NS3中的模拟表明,EEGP-MAC与QL-DGMAC协议相比实现了11%的较少延迟.
- 拟议的协议显示,能源消耗减少了16%.
- 在各种网络大小的网络中,EEGP-MAC可使吞吐量增加10%.
结论:
- 通过智能地管理MAC层操作,EEGP-MAC协议有效地提高了物联网网络性能.
- 整合QL-HBA提供了一个强大的机制来优化组优先级和资源配置.
- 对现有协议来说,EEGP-MAC是一个显著的改进,使其适合于各种物联网应用.
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