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

III：小型：GeoCrowd - 值得信赖的空间众包的通用框架

• 批准号: 1320149
• 负责人: Cyrus Shahabi
• 金额: $ 50万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1320149&HistoricalAwards=false
• 关键词: III; Small; GeoCrowd; Generic; Framework

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/

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的显着增长。 因此，研究的变革领域是将这个新平台用于各种任务，其中最有前途的是空间众包。 空间众包吸引个人，群体和社区来收集，分析和传播城市，社会和其他时空信息的行为。在现实世界应用中，空间众包成功的两个主要障碍是可扩展性和信任问题。 因此，该项目的第一个目标是研究空间众包中规模问题。 尤其是，鉴于任务分配是系统的主要瓶颈，因此利用任务的空间方面以减少分配的复杂性。 此外，研究了基于云的分布式方法，以更好地扩展规模。第二个目标是扩展框架以通过维持每个工人的声誉得分和每个空间任务的信心水平来纳入信任。 因此，为了满足其信心水平，多个工人可以冗余地执行任务。 扩展了空间任务分配解决方案，以考虑多种任务分配和信心满意度。作为该项目的一部分，正在开发一种空间众包系统，可用于现实世界中的数据收集和评估以及社会研究和教育目的。通过网络传播回归：http://infolab.usc.usc.edu/project/projects/geocrowd/