在医疗监测系统中通过深度强化学习来实现联合能源意识任务卸载和隐私保护
Jingjing Zhang1, Yunyi Hu2, Mengmeng Shao3
1School of Computer Science and Engineering, Central South University, Changsha, China.
Scientific reports
|December 9, 2025
概括
本研究引入了体内健康传感器的深度强化学习框架,优化数据卸载和能源使用,同时保护用户隐私免受无线能源传输风险的影响.
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
- 网络安全 网络安全
背景情况:
- 体内健康传感器 (IBHS) 需要可持续的电力和高效的数据处理,通常依赖于无线射频无线能量传输 (RF-WET).
- 对数据上传和能源采集信号的被动观察带来了重大隐私风险,包括用户活动和位置推断.
研究的目的:
- 开发一个深度强化学习 (DRL) 框架,用于IBHS系统中的任务卸载.
- 共同优化移动边缘计算 (MEC),能源意识和隐私保护.
- 为量化位置隐私泄漏引入一个能感的隐私模型.
主要方法:
- 一个基于DRL的任务卸载框架,集成MEC,能源意识和隐私.
- 一个能源意识的隐私模型来评估隐私风险.
- 通过检测无线通道,MEC资源和能量状态来动态优化数据上传和计算卸载.
主要成果:
- 拟议的框架显示了与现有方法相比的显著改进.
- 在降低延迟和提高能源效率方面取得了进展.
- 提供了针对RF-WET接入点的增强位置隐私保护.
结论:
- 在边缘辅助医疗保健系统中,DRL框架有效平衡了性能和隐私.
- 能源意识的隐私模型准确地量化了隐私泄露.
- 该方法显示了下一代实时医疗监测的巨大潜力.
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