一个基于游戏理论的COVID-19近距离接触检测方法与边缘计算协作合作
Yue Shen1,2, Bowen Liu1, Xiaoyu Xia3
1State Key Laboratory for Novel Software Technology, Nanjing University, China.
概括
本研究介绍了一种使用边缘计算的游戏理论方法,以有效地检测COVID-19密切接触者. 采用GCDM方法可以提高感染风险评估,同时保护用户隐私并尽量降低成本.
科学领域:
- 计算机科学 计算机科学
- 公共卫生 公共卫生
- 网络安全 网络安全
背景情况:
- 由于COVID-19的流行,需要有效的方法来识别密切接触者,以制传播.
- 准确和及时评估感染风险仍然是一个重大挑战.
- 传统方法在实时数据处理和隐私问题上扎.
研究的目的:
- 通过游戏理论和边缘计算提出一种用于检测COVID-19密切接触者的新方法.
- 提高感染风险评估的效率和隐私.
- 为了解决接触追踪的计算和存储需求.
主要方法:
- 开发了一种基于游戏理论的方法,称为GCDM (基于游戏理论的COVID-19密切接触检测方法).
- 边缘计算协作已集成,以管理数据处理和存储.
- 该方法利用用户的位置信息进行分散的检测.
主要成果:
- 在游戏平衡时,GCDM方法最大限度地提高了近距离接触检测率.
- 它最大限度地减少了去中心化系统中的评估延迟和成本.
- 理论分析和广泛的实验证实了GCDM的性能优于现有方法.
结论:
- GCDM为COVID-19近距离接触者检测提供了一种高效且保护隐私的解决方案.
- 边缘计算有效地支持拟议方法的计算和存储需求.
- 游戏理论框架确保了分散环境中的最佳性能.
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