通过混合优化和基于深度学习的能源预测,在物联网无线传感器网络中进行节能多路径路由
G A Senthil1, R Prabha2, R Renuka Devi3
1Department of Information Technology, Agni College of Technology, Chennai, India.
概括
本研究介绍了一种混合贝卢加-科蒂优化 (HBWCO) 算法,用于在无线传感器网络 (WSN) 中有效传输数据. 通过考虑多个性能因素,HBWCO提高了路由可靠性和吞吐量.
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:
- 高效的数据传输对于无线传感器网络 (WSN) 至关重要.
- 传统的路由协议通常优先考虑能效,而不是其他性能指标.
- 现有的方法可能无法充分解决WSN中的性能降低因素.
研究的目的:
- 提出一种新的混合贝卢加-科蒂优化 (HBWCO) 算法,以优化WSN中节能数据传输.
- 通过考虑不仅仅是能源的多个因素来提高WSN中的路由可靠性和吞吐量.
- 引入一个强大的路线维护战略,以应对链路断裂.
主要方法:
- 传感器节点和场维度的初始化.
- K-意味着用于节点分组的集群和用于能源水平预测的深度Q-Net.
- 混合贝卢加-科蒂优化 (HBWCO) 基于可靠性,剩余能量,预测能量,吞吐量和流量强度的多路线路由选择.
- 源链断裂警告 (SLBW) 策略用于路线维护.
主要成果:
- 该HBWCO算法实现了0.948.8的可靠性.
- 在HBWCO的方法证明了3496.6的吞吐量.
- 拟议的方法提供了一种全面的方法来提高网络能源效率和数据传输.
结论:
- 在WSN中,HBWCO算法为数据传输和路由可靠性提供了有效的解决方案.
- 这种新的方法在性能上超越了传统方法.
- 优化算法与深度学习的整合提高了WSN的性能.
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