任务分配和路径规划机制基于等级匹配程度和任务相似性在参与式人群感知中
Xiaoxue He1, Yubo Wang2, Xu Zhao2
1School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China.
Sensors (Basel, Switzerland)
|January 26, 2024
概括
本研究介绍了参与式人群感知 (PCS) 的多任务分配和路径规划问题 (MTAPP). 它提出了一种新的算法,通过匹配任务能力和考虑任务相似性来最大限度地提高用户实用性,提高效率和利.
科学领域:
- 计算机科学 计算机科学
- 移动计算 移动计算
- 数据科学数据科学数据科学
背景情况:
- 参与式人群传感 (PCS) 使用移动设备传感器进行数据收集.
- 现有的PCS研究往往忽略了任务关联和用户能力匹配.
- 不高效的任务分配导致数据质量差,资源浪费.
研究的目的:
- 引入一个多任务分配和路径规划问题 (MTAPP),以优化PCS.
- 最大化整体用户效用,定义为利与时间比.
- 改进PCS中的任务分配和路径生成.
主要方法:
- 开发了一个等级匹配度和基于相似性的机制 (GSBM).
- 建立了一个数学模型来分析任务相似性对完成的影响.
- 采用了改进的群优化 (IACO) 算法来实现效用最大化.
主要成果:
- GSBM有效地将用户的能力与任务相匹配,增强收入决定能力.
- 任务相似性对用户任务完成率产生积极影响.
- 在关键性能指标上,IACO算法显著优于其他方法.
结论:
- 拟议的MTAPP框架和GSBM机制提高了PCS的效率.
- IACO算法为优化参与式人群传感提供了一个强大的解决方案.
- 这种方法导致更好的任务覆盖,完成率,利和分配理性.
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