Collaborative Research: CPS: Medium: Real-Time Crowd-Sourced Geospatial Digital Twin for Cyber-Physical Systems

This research project focuses on enhancing the way vital information is delivered to smart mobile devices—such as smartphones and tablets. With the advancement of technology, there is a growing necessity for these devices to receive various types of information (like images, videos, and texts) instantly and effectively. One promising approach to achieving this is through the use of Geospatial Digital Twins (GDT), which are digital models of physical environments. GDTs are becoming increasingly important as they allow for real-time updates and interactions, making them invaluable for various applications such as monitoring, maintenance, and emergency response. Traditionally, data for GDTs has been collected through automated systems like distributed sensor devices, satellites and drones. However, these methods have limitations, especially when it comes to updating data quickly and covering hard-to-reach areas. To overcome these challenges, this project will develop a novel approach that involves the community through “human-in-the-loop” strategies. This means using crowd-sourced data, where people provide real-time updates to digital models. This method not only promises to enhance the accuracy and timeliness of the information but also to allow discovery of new information. The project has the potential to revolutionize how we interact with and understand our physical world, potentially making this work a cornerstone for further scientific and educational advancements. The project will also play an important role in education, integrating research findings into university curricula and offering unique learning opportunities for students, including students from underrepresented groups. The goal of this project is to establish an intellectual foundation for building a real-time crowd-sourced GDT. To achieve this goal, we will work toward a fundamental understanding of crowd-sourced multi-modal information collection and processing to account for the underlying human incentives and human-machine integration, which underpin the foundation of crowd-sourced GDT. In this project, we will investigate the design of crowd-sourced GDT to ensure timely, truthful, and unbiased imagery data collection from the crowd. Our efforts will be organized around four tightly integrated research thrusts: 1) ensuring crowd-sourced data freshness for a GDT; 2) integrating crowd-sourced data for real-time GDT updates; 3) guaranteeing truthful reporting in crowd-sourced data collection; and 4) mitigating self-reinforcing bias in crowd-sourced GDT updates. Collectively, this project will result in new tools for optimization and control that directly contribute to real-time crowd-sourced GDTs. 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.