用人工智能驱动的边缘计算为分布式IoMT网络转变智能医疗保健系统
Maram Fahaad Almufareh1, Mamoona Humayun2, Khalid Haseeb3
1Department of Information System, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Al-Jouf, Saudi Arabia.
Bioengineering (Basel, Switzerland)
|November 27, 2025
概括
这项研究介绍了一个人工智能驱动的边缘计算模型,用于智能医疗保健系统. 这种创新方法增强了医疗物联网 (IoMT) 的网络异常检测,提高了系统的效率和响应能力.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 医疗保健技术 技术 医疗保健 技术
背景情况:
- 医疗物联网 (IoMT) 通过连接的设备和边缘计算实现了智能医疗保健系统 (SHM).
- 现有的 IoMT 解决方案往往忽视了对公共基础设施的动态适应和受限制设备上的高效数据处理.
- 挑战包括负载分布不均和在关键医疗保健场景中减少系统响应能力.
研究的目的:
- 为增强智能医疗保健系统提供轻量级,人工智能驱动的边缘计算模型.
- 改进分布式IoMT网络中的学习能力和异常检测.
- 在动态的医疗环境中解决现有方法的局限性.
主要方法:
- 为IoMT集成边缘计算的AI驱动模型的开发.
- 为分布式环境实施高效的网络异常检测算法.
- 通过使用合成数据的模拟进行验证和测试.
主要成果:
- 与相关解决方案相比,拟议的模型显示了显著的改进.
- 在能源消耗方面实现了53%的降低.
- 延迟降低了46%,数据包丢失率降低了52%,开销降低了48%.
- 增加了56%的网络吞吐量.
结论:
- 由人工智能驱动的边缘计算模型为智能医疗保健提供了创新和高效的解决方案.
- 该模型有效地检测到IoMT中的网络异常,而无需额外的系统开销.
- 结果验证了该模型在改善能源消耗,延迟,数据包丢失,吞吐量和开销方面的有效性.
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