基于深度强化学习的光纤物联网医疗保健系统,用于混合远程医疗应用的组合约束调度
Abdullah Lakhan1, Jan Nedoma2, Mazin Abed Mohammed3
1Department of Cybersecurity and Computer Science, Dawood University of Engineering and Technology, Karachi City 74800, Sindh, Pakistan; Department of Telecommunications, VSB-Technical University of Ostrava, 70800 Ostrava, Czech Republic; Department of Cybernetics and Biomedical Engineering, VSB-Technical University of Ostrava, 70800 Ostrava, Czech Republic.
Computers in biology and medicine
|June 13, 2024
概括
本研究介绍了一种用于混合远程医疗应用的光纤物联网 (IoT) 系统. 适应性安全深度q学习网络 (ASDQN) 算法通过最小化延迟和最大化奖励来优化性能.
科学领域:
- 医疗保健技术 技术 医疗保健 技术
- 计算机科学 计算机科学
- 网络工程 网络工程
背景情况:
- 远程医疗利用物联网 (IoT) 和光纤来增强数字医疗保健.
- 现有的远程医疗应用的深度强化学习方法在有效性和最佳性方面存在局限性.
- 远程医疗的关键应用包括疾病监测,远程通信和远程手术.
研究的目的:
- 提出光纤物联网医疗保健系统,利用混合远程医疗的深度强化学习.
- 引入自适应性安全深度学习网络 (ASDQN) 算法,用于在服务质量 (QoS) 约束下管理远程医疗应用程序.
- 为了尽量减少延迟,并最大限度地奖励远程医疗应用程序的调度和卸载.
主要方法:
- 适应性安全深度学习网络 (ASDQN) 算法的开发.
- 使用光纤内镜数据集 (图像,视频,数值数据) 进行培训和验证.
- 实施混合远程医疗应用的组合约束调度.
主要成果:
- 拟议的ASDQN算法有效地执行远程医疗应用程序,同时遵守QoS约束 (截止日期,延迟,安全性,资源).
- ASDQN在最小化本地,通信和边缘节点的整体延迟方面表现出卓越的性能.
- 模拟结果表明,ASDQN在执行和调度方面表现优于现有的SARSA和DQN政策.
结论:
- 带有ASDQN的光纤物联网医疗系统为混合远程医疗应用提供了有效的解决方案.
- 在严格的QoS要求下,ASDQN成功地平衡了延迟减少和奖励最大化.
- 这种方法通过先进的人工智能和网络技术提高了数字医疗服务的效率和可靠性.
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