HEOCP:使用分析建模和深度学习集成的WSNs的混合能源优化集群协议
Yen-Wu Ti1, Rei-Heng Cheng1, Songlin Wei1,2
1School of Information Engineering, Xiamen Ocean Vocational College, Xiamen 361100, China.
Sensors (Basel, Switzerland)
|February 27, 2026
概括
本研究介绍了用于无线传感器网络 (WSN) 的混合能源优化集群协议 (HEOCP). 通过混合分析和深度学习方法,HEOCP通过智能管理能源消耗来提高物联网设备的寿命.
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:
- 无线传感器网络 (WSN) 对物联网 (IoT) 是至关重要的.
- 有限的传感器节点能量显著限制了WSN的运行寿命.
- 有效的能源管理对于可持续的物联网应用是必不可少的.
研究的目的:
- 引入混合能源优化集群协议 (HEOCP),以延长WSN的使用寿命.
- 在物联网部署中提高能源效率和网络性能.
- 为WSNs开发一个可持续的能源管理范式.
主要方法:
- 开发了一个分析框架,以限制距离的集群头 (CH) 资格和最佳集群数量.
- 采用遗传算法 (GA) 进行最佳的CH配置.
- 使用ResNet-50深度网络来训练GA配置和实时CH预测.
主要成果:
- 与基于LEACH和GA的方法相比,HEOCP可以将网络寿命延长高达60%.
- 有效地延迟了第一节点的死亡,并提高了整体的能源效率.
- 混合GA-ResNet框架显示了高的可扩展性和计算效率.
结论:
- 将分析能源建模与深度学习集成为智能WSN能源管理提供了强大的方法.
- 对于大规模的物联网部署,HEOCP提供了一个可持续和有效的解决方案.
- 拟议的协议显著提高了WSN的寿命和效率.
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