一个基于团队的真相发现框架通过移动人群传感数据流
Bayan Hashr Saeed Alamri1,2, Muhammad Mostafa Monowar2, Suhair Alshehri2
1Faculty of Cyber Security and Forensic Computing, Prince Mugrin University, Al-Madinah, Saudi Arabia.
PloS one
|August 26, 2025
概括
本研究为移动人群传感 (MCS) 数据流引入了一种新的基于两层组的真相发现 (TGTD) 框架. 通过改进真相发现,TGTD提高了基于组的MCS应用中的数据准确性.
科学领域:
- 计算机科学
- 数据科学
- 移动计算
背景情况:
- 移动人群传感 (MCS) 面临着由于数据来源不同而导致的数据不一致性挑战.
- 群体行为和社区层面的参与正在越来越多地影响MCS应用.
- 现有的真相发现 (TD) 方法难以应对MCS数据流的规模和实时需求.
研究的目的:
- 为MCS数据流提出一个新的雾辅助集团式真相发现框架.
- 解决实时MCS应用中的数据不一致性和组动态.
- 在大规模,动态的MCS环境中提高真相发现的准确性和效率.
主要方法:
- 开发了一个基于两层组的真相发现 (TGTD) 机制.
- 使用可信度级别进行初始化参与者权重.
- 整合了雾辅助架构,用于实时处理MCS数据流.
主要成果:
- 与现有的流动 TD 方法相比,TGTD 框架显示出更高的真相发现准确性.
- 提出的框架保持了合理的运行时间,证明了它的效率.
- 通过对合成和现实数据集的广泛实验, 验证了该框架的有效性.
结论:
- 基于团队的真相发现框架为MCS中的数据不一致提供了有效的解决方案.
- TGTD成功地处理了群组动态和实时处理要求.
- 为未来的工作建议对流动过程的雾架构模拟进行进一步的研究.
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