弗拉明戈果鱼基于搜索优化的路由与深度学习启用了WSN数据通信中的能源预测
Dhanabal Subramanian1, Sangeetha Subramaniam2, Krishnamoorthy Natarajan3
1Department of Computer Science and Engineering, Kongunadu College of Engineering and Technology, Trichy, India.
概括
本研究介绍了无线传感器网络 (WSN) 的新型动态集群和路由模型,以提高能源效率和网络寿命. 弗拉明戈水母搜索优化 (FJSO) 模型优化了能源预测和路线选择,大大提高了网络性能.
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) 由于能源有限而面临重大限制,影响网络的整体寿命.
- 有效的能源管理和有效的路由对于维持WSN运营至关重要.
研究的目的:
- 设计和评估WSN的动态集群和路由模型,以应对能源限制.
- 通过优化能源预测和路线选择来改善网络寿命和性能.
主要方法:
- 使用能源,移动性,信任和链接寿命 (LLT) 模型模拟WSN中的动态聚类.
- 利用深度神经模糊网络 (DNFN) 来预测剩余能量,以及用于动态工作负载平衡的模糊系统.
- 应用Flamingo水母搜索优化 (FJSO) 算法来调整模糊系统重量并确定最佳数据传输路径.
主要成果:
- FJSO模型实现了最大能量为0.657J,最小距离为0.739m,最小延迟为0.649s.
- 该模型的信任分数为0.849,吞吐量为0.885 Mbps.
- 使用MATLAB的实验验证证了拟议的FJSO模型的有效性.
结论:
- 由FJSO优化开发的动态集群和路由模型显著提高了WSN的性能.
- 该方法有效地平衡节点工作负载,预测能源消耗,并选择最佳路线,从而提高网络寿命和效率.
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