III: Small: GeoCrowd - A Generic Framework for Trustworthy Spatial Crowdsourcing

许多研究预测，智能手机用户的数量、手机的硬件和软件功能以及宽带带宽都将大幅增长。因此，一个变革性的研究领域是利用这个新平台来完成各种任务，其中最有前途的是空间众包。空间众包使个人、群体和社区参与收集、分析和传播城市、社会和其他时空信息。在现实应用中，空间众包成功的两个主要障碍是可伸缩性和信任问题。因此，本项目的第一个目标是研究空间众包中的规模问题。特别是，考虑到任务分配是系统的主要瓶颈，任务的空间方面被利用来降低分配的复杂性。此外，还研究了基于云的分布式方法来解决该问题，以实现更好的横向扩展。第二个目标是通过维护每个工作人员的声誉分数和每个空间任务的置信度来扩展框架以纳入信任。因此，多个工人可以冗余地执行一项任务，以满足其置信度。空间任务分配解决方案被扩展以考虑冗余任务分配和置信度满意度，空间众包在灾难应对、城市规划、智能交通、新闻和智能领域都有应用。作为该项目的一部分，正在开发一个空间众包系统，该系统可用于真实世界的数据收集和评估，以及用于社会研究和教育目的。结果通过网络发布：http://infolab.usc.edu/projects/GeoCrowd/

Many studies foresee significant growth in the number of smart phone users, the phone's hardware and software features, and the broadband bandwidth. Therefore, a transformative area of research is to utilize this new platform for various tasks, among which the most promising is spatial crowdsourcing. Spatial crowdsourcing engages individuals, groups, and communities in the act of collecting, analyzing, and disseminating urban, social, and other spatiotemporal information. Two major impediments to the success of spatial crowdsourcing in real-world applications are scalability and trust issues. Therefore, the first objective of this project is to study the issue of scale in spatial crowdsourcing. In particular, given that the task assignment is the main bottleneck of the system, the spatial aspects of the tasks are exploited to reduce the complexity of assignment. In addition, a cloud-based distributed approach to the problem is investigated for better scale-out. The second objective is to extend the framework to incorporate trust by maintaining a reputation score per worker and a confidence level for every spatial task. Consequently, multiple workers can perform a task redundantly in order to satisfy its confidence level. The spatial task assignment solutions are extended to take redundant task assignments and confidence satisfaction into consideration.Spatial crowdsourcing has applications in disaster-response, urban planning, intelligent transportation, journalism and intelligence. As part of this project a spatial crowdsourcing system is being developed that can be used for real-world data collection and evaluation as well as for social studies and educational purposes.Results are disseminated through the web: http://infolab.usc.edu/projects/GeoCrowd/