使用适应性模糊逻辑和混合优化优化,为物联网支持的医疗保健提供节能集群和路由
Rachit Manchanda1, Sandip Panchal2, Rajendar Sandiri3
1Department of Computer Science Engineering, Chandigarh University, Mohali, 140413, Punjab, India.
Scientific reports
|October 3, 2025
概括
本研究介绍了物联网 (IoT) 医疗保健系统的自适应模型,优化集群头选择和路由. 该模型提高了数据传输效率,这对于实时患者监测和紧急响应至关重要.
科学领域:
- 生物医学工程 生物医学工程
- 计算机科学 计算机科学
- 网络工程 网络工程
背景情况:
- 物联网 (IoT) 技术在医疗保健中的集成,包括无线传感器网络 (WSN) 和下一代网络,对于无医疗设备集成和智能设备交互至关重要.
- 有效的数据传输和通信对于基于物联网的医疗保健系统至关重要,以满足实时监控和应急响应需求.
- 现有系统在优化关键医疗保健应用的网络性能和能源效率方面面临着挑战.
研究的目的:
- 提出一种适应型模型,以优化在支持物联网的医疗保健应用程序中的集群头选择和路由.
- 提高通信可靠性,降低能源消耗,提高整体网络性能.
- 解决传感器节点 (SN) 特性中的不确定性,并适应动态的医疗保健网络条件.
主要方法:
- 适应性模糊逻辑机制用于集群头的选择,考虑能量水平,SN密度,移动性和链路稳定性.
- 一种混合优化方法,将粒子群优化 (PSO) 和遗传算法 (GA) 结合起来,以实现最佳路由路径的发现.
- 用MATLAB和谷歌协作进行了广泛的模拟,用于性能评估.
主要成果:
- 拟议的模型实现了92.5%的数据包交付比率 (PDR).
- 显示平均最小延迟为0.10秒.
- 实现了61.5 bps的吞吐量和9.1 J/bit的能源效率.
- 在关键绩效指标上显示出与现有方法相比的显著改进.
结论:
- 适应型模型有效优化通信可靠性,并降低基于物联网的医疗保健系统的能源消耗.
- 路由路径的混合优化方法提高了网络性能.
- 这些发现强调了该模型通过先进的医疗保健技术改善患者治疗结果的潜力.
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