基于边缘计算和联合学习的大规模功率物联网的自适应路由协议
Yong Zhang1, Sixiang Zhang2, Shipeng Li2
1Shandong Electric Power Engineering Consulting Institute Corp., Ltd., Jinan, 250013, Shandong, China. zyxczyxc@163.com.
Scientific reports
|March 5, 2026
概括
本研究介绍了使用边缘计算和联合学习为Power Internet of Things (PIoT) 提供适应性路由协议. 它显著提高了网络吞吐量,减少了延迟,并在大规模的PIOT部署中节省了能源.
科学领域:
- 计算机科学 计算机科学
- 电气工程 电气工程
- 网络工程 网络工程
背景情况:
- 电力物联网 (PIoT) 网络面临由于众多异质设备的路由和能源管理挑战.
- 现有的解决方案在大规模的PIOT部署中难以实现可扩展性和效率.
研究的目的:
- 为PIoT.提出一个新的自适应路由协议,集成边缘计算和联合学习.
- 在异质的PIoT环境中优化能源效率和设备协调.
- 为了提高网络吞吐量并减少端到端延迟.
主要方法:
- 开发了一个带有边缘节点的等级架构,用于分布式处理和本地决策.
- 实现了自适应路由算法,根据实时网络条件,设备规格和能源限制调整参数.
- 利用联合学习进行协作优化,而无需集中数据共享.
主要成果:
- 与传统协议相比,实现了比传统协议高35%至50%的网络吞吐量.
- 减少了40-60%的端到端延迟.
- 已证明节省了45-65%的能源.
结论:
- 拟议的协议显示了卓越的可扩展性和稳定性,保持了高达1万台设备的性能.
- 边缘计算和联合学习有效地解决了PIoT的复杂性.
- 网络性能和能源效率的显著改进得到了验证.
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