Catalyst Project: Indoor Moving Objects Trajectory Generation and Query Evaluation

Catalyst 项目：室内移动物体轨迹生成和查询评估

• 批准号: 2000348
• 负责人: Yasmeen Rawajfih
• 金额: $ 18.85万
• 依托单位: Tuskegee University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-15 至 2023-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2000348&HistoricalAwards=false
• 关键词: Catalyst; Project; Indoor; Moving; Objects

Catalyst Projects provide support for Historically Black Colleges and Universities to work towards establishing research capacity of faculty to strengthen science, technology, engineering and mathematics undergraduate education and research. It is expected that the award will further the faculty member's research capability, improve research and teaching at the institution, and involve undergraduate students in research experiences. This project at Tuskegee University seeks to develop an indoor data-based tracking framework designed to understand the mathematical and technological foundations of indoor data management. The project provides an opportunity for undergraduate students to enhance their education through research experiences in computer modeling and data management techniques. The researcher has established a strong collaboration with faculty at Auburn University. This project will result in the development of a number of indoor query evaluation mechanisms and the techniques to derive the accurate locations of indoor moving items from raw erroneous indoor tracking data. This will improve the accuracy of indoor spatial queries, which can support many indoor high-level applications, including indoor location-based service and hotspot finding. Novel indoor tracking data management techniques will be devised. The goals of the project are to: 1) implement a simulation toolkit and a prototype system, where many indoor environments and deployment settings will be simulated for performance evaluation; 2) develop and compare a number of machine learning-based location inference methods for accurate trajectory generation in indoor environments; 3) design novel indoor query evaluation algorithms for various types of queries, in particular spatial query types such as range query and nearest neighbor query; and 4) invent an error model-based approach for indoor object trajectory tracking which is a non-machine learning approach. The results of this research will improve the performance of number of indoor location-based application which will make it easy to locate people and help to guide people to safety during emergency situations.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.

催化剂项目为历史上的黑人学院和大学提供支持，努力建立教师的研究能力，以加强科学，技术，工程和数学本科教育和研究。预计该奖项将进一步提高教师的研究能力，改善研究和教学机构，并参与本科生的研究经验。塔斯基吉大学的这个项目旨在开发一个基于室内数据的跟踪框架，旨在了解室内数据管理的数学和技术基础。 该项目为本科生提供了一个机会，通过计算机建模和数据管理技术的研究经验，提高他们的教育。研究人员与奥本大学的教师建立了密切的合作关系。该项目将导致一些室内查询评估机制和技术的发展，以获得室内移动项目的准确位置，从原始错误的室内跟踪数据。这将提高室内空间查询的准确性，可以支持许多室内高级应用，包括室内基于位置的服务和热点发现。 将设计新颖的室内跟踪数据管理技术。该项目的目标是：1）实现一个模拟工具包和一个原型系统，其中将模拟许多室内环境和部署设置以进行性能评估; 2）开发和比较一些基于机器学习的位置推断方法，用于室内环境中的精确轨迹生成; 3）针对不同类型的查询，特别是范围查询和最近邻查询等空间查询类型，设计新颖的室内查询评估算法;以及4）发明了一种用于室内对象轨迹跟踪的基于误差模型的方法，该方法是一种非机器学习方法。这项研究的结果将提高室内定位应用的性能，这将使人们更容易找到人，并有助于引导人们在紧急情况下的安全。这个奖项反映了NSF的法定使命，并已被认为是值得的支持，通过评估使用基金会的知识价值和更广泛的影响审查标准。