CRII: CSR: RUI: Mobility Coordination of the Crowds in Mobile Crowd Sensing Platforms

移动的人群感知（MCS）试图通过招募已经在目标移动性领域漫游的个体来实现感知任务。人群辅助系统消除了对专用物理基础设施的需要，从而降低了感测成本。此外，将众包资源作为系统的一部分，可以开发新的智能服务和应用程序，这在其他情况下是不可能的。如果监控监的参与完全是自愿的，则任务覆盖范围可能是不可预测的。然而，通过激励，可以改善人类代理的招募。该项目旨在协调参与人类代理的流动性，并优化激励措施，以实现传感目标。在这个项目中，来自优化理论和近似算法的技术将被用来优化协调MCS平台中的任务分配，同时开发有效的路由。这些机制与激励兼容的支付模式相结合，以最大限度地提高代理人的参与度。此外，将开发后端服务以将这些机制集成到协调MCS应用中，以评估它们在真实的人群中的性能。任务/路由被分配给代理的机制，以及代理被补偿一致性的机制，在优化问题的领域中创建了丰富的问题集，图表和数据挖掘，以及人群经济学。该项目有助于推动移动的人群感知技术的发展，以解决更大的问题，对智慧城市计划和物联网解决方案的发展产生重大影响。此外，由于韦尔斯利学院是一所女子学院，该项目直接有助于提高妇女对计算机科学的参与，使多名本科女生能够在该领域进行高级研究。在此项目中开发的所有代码和收集的数据将在项目期间存储在计算机科学部门服务器上专用于该项目的私人目录中。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

Mobile Crowd Sensing (MCS) seeks to achieve sensing tasks by recruiting individuals who are already roaming in the target mobility field. A crowd-assisted system eliminates the need for a dedicated physical infrastructure, thereby reducing the cost of sensing. Moreover, the use of crowd-sourced resources as part of the system allows for the development of new smart services and applications that would not have been possible otherwise. If MCS participation is purely voluntary, task coverage may be unpredictable. However, with incentive, recruitment of human agents may be improved. This project seeks to coordinate the mobility of participating human agents, and to optimize incentives to achieve sensing goals. In this project, techniques from optimization theory and approximation algorithms will be used to optimize task allocation in a Coordinated-MCS platform while developing efficient routing. These mechanisms are coupled with incentive-compatible payment models to maximize agent participation. Moreover, a backend service will be developed to integrate these mechanisms within Coordinated MCS applications, to evaluate their performance on real crowd.Coordinated mobility depends on, and hence requires research in, two areas; the mechanisms via which tasks/routes are assigned to agents, and the mechanisms via which agents are compensated for conformance, creating a rich set of problems in the fields of optimization problems, graph and data mining, and crowd economics. This project contributes towards the advancement of mobile crowd sensing for solving larger problems, with a great impact on smart cities initiatives, and the advancement of Internet of Things solutions. Moreover, as Wellesley College is a women's college, this project contributes directly to increasing the participation of women in computer science, allowing multiple undergraduate female students to perform advanced research in the field. All code developed in this project and collected data will be stored on a private directory dedicated for the project on the Computer Science department server for the duration of the project.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.