CAREER: Towards Reliable and Optimized Data-Driven Cyber-Physical Systems using Human-Centric Sensing

职业：利用以人为本的传感实现可靠且优化的数据驱动的网络物理系统

• 批准号: 1845639
• 负责人: Dong Wang
• 金额: $ 54.4万
• 依托单位: University of Notre Dame
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1845639&HistoricalAwards=false
• 关键词: CAREER; Towards; Reliable; Optimized; Data

Participatory science has opened opportunities for many to participate in data collection for science experiments about the environment, local transportation, disaster response, and public safety where people live. The nature of the collection by non-scientists on a large scale carries inherent risks of sufficient coverage, accuracy and reliability of measurements. This project is motivated by the challenges in data and predictive analytics and in control for participatory science data collection and curation in cyber-physical systems (CPS) experiments.This project focuses on data-driven frameworks to address these challenges in CPS-enabled participatory science that builds on statistics, optimization, control, natural language processing, CPS fundamentals, and coordination of participants, known as crowd steering. This framework, known as DCCDI for Data-driven Crowdsensing CPS Design and Implementation, tightly combines the underlying methods and techniques, especially focusing on physical sensors, mobility, and model-based approaches, to improve efficiency, effectiveness, and accountability. Validation of the DCCDI framework is conducted through simulations, case studies, and on real-world CPS-enabled experiments. This project closely integrates education and training with foundational research and public outreach that enhances interdisciplinary thinking about CPS systems, engages the public through participatory science, and broadens participation in science, technology, engineering, mathematics, and computer science.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.

参与式科学为许多人提供了参与有关环境、当地交通、灾难响应和人们居住的公共安全的科学实验数据收集的机会。 非科学家大规模收集的性质带来了测量覆盖范围、准确性和可靠性的固有风险。 该项目的动机是数据和预测分析方面的挑战，以及网络物理系统 (CPS) 实验中参与式科学数据收集和管理的控制。该项目侧重于数据驱动框架，以解决支持 CPS 的参与式科学中的这些挑战它建立在统计、优化、控制、自然语言处理、CPS 基础知识和参与者协调（称为人群引导）的基础上。该框架被称为数据驱动人群感知 CPS 设计和实施的 DCCDI，紧密结合了底层方法和技术，特别关注物理传感器、移动性和基于模型的方法，以提高效率、有效性和问责制。 DCCDI 框架的验证是通过模拟、案例研究和现实世界中支持 CPS 的实验进行的。该项目将教育和培训与基础研究和公共宣传紧密结合起来，增强对 CPS 系统的跨学科思考，通过参与式科学吸引公众，并扩大对科学、技术、工程、数学和计算机科学的参与。该奖项反映了 NSF 的法定使命通过使用基金会的智力优点和更广泛的影响审查标准进行评估，并被认为值得支持。