无线传感器网络的神经网络集群和集群智能路由协议:机器学习视角
Awatef Salem Balobaid1, Saahira Banu Ahamed1, Shermin Shamsudheen1
1Department of Computer Science, College of Computer Science & Information Technology, Jazan University, Jazan 45142, Saudi Arabia.
Computational intelligence and neuroscience
|August 7, 2023
概括
本研究介绍了用于无线传感器网络 (WSN) 的高效机器学习 (ML) 算法. 拟议的基于ML的集群和路由提高了网络性能和寿命.
科学领域:
- 计算机科学 计算机科学
- 电气工程 电气工程
- 网络安全 网络安全
背景情况:
- 无线传感器网络 (WSN) 为军事行动和应急响应等关键应用提供快速部署.
- 由于WSN的分散性,它们需要强大的路由和入侵检测机制.
- 在WSN节点中,有限的能量和带宽需要高效的数据处理和传输策略.
研究的目的:
- 解决现有的WSN解决方案的局限性,包括可靠性差和网络寿命短.
- 为WSNs开发一个高效的集群和路由算法,利用机器学习 (ML).
- 在资源有限的WSN环境中改进数据处理和能源效率.
主要方法:
- 一个基于机器学习的新算法被设计用于WSN中的集群和路由.
- 进行了模拟,以评估拟议的算法的性能与现有方法相比.
- 分析了关键性能指标,如准确性,特异性和灵敏性.
主要成果:
- 拟议的基于机器学习的算法在与最先进的模型相比显示出更高的性能.
- 在模拟中实现了高精度 (0.93),特异性 (0.93) 和灵敏度 (0.92).
- 在网络效率和数据管理方面取得了显著的改善.
结论:
- 开发的ML算法为提高WSN性能提供了有效的解决方案.
- 该方法解决了WSN中的关键挑战,包括能源限制和数据传输.
- 这项工作有助于为各种应用程序提供更可靠和更有效的WSN.
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