Catalyst Project: Indoor Moving Objects Trajectory Generation and Query Evaluation

催化剂项目支持历史悠久的黑人学院和大学致力于建立教职员工的研究能力，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职员工的研究能力，改善学院的研究和教学，并让本科生参与研究经验。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方面的研究经验来加强他们的教育。这位研究人员与奥本大学的教职员工建立了强有力的合作关系。该项目将导致开发一些室内查询评估机制和从原始的错误的室内跟踪数据中得出室内移动物品的准确位置的技术。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，包括基于室内位置的服务和热点查找。将设计出新颖的室内跟踪数据管理技术。该项目的目标是：1)实现一个仿真工具包和一个原型系统，其中将模拟许多室内环境和部署环境以进行性能评估；2)开发和比较多种基于机器学习的位置推理方法，以精确地生成室内环境中的轨迹；3)针对各种类型的查询，特别是空间查询类型，如范围查询和最近邻查询，设计新颖的室内查询评估算法；以及4)发明一种基于误差模型的室内对象轨迹跟踪方法，这是一种非机器学习方法。这项研究的结果将提高许多基于室内位置的应用程序的性能，这些应用程序将使人们更容易定位，并帮助在紧急情况下引导人们到安全的地方。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.