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有关环境，当地运输，灾难响应和人们居住的公共安全的科学实验的数据收集的机会。 非科学家在大规模的收集性质具有足够的覆盖范围，准确性和可靠性的固有风险。 该项目是由数据和预测分析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策划控制的挑战所激发的。本项目的重点是数据驱动的框架，以解决基于CPS的参与性科学中这些挑战，这些框架基于统计，统计，最佳，控制，控制，自然语言处理，自然语言处理，CPS基础群体和参与群众，并群众群众群体，群众群众。该框架被称为DCCDI用于数据驱动的CPS设计和实施的DCCDI，将基本方法和技术紧密地结合在一起，尤其是专注于物理传感器，移动性和基于模型的方法，以提高效率，有效性和责任感。 DCCDI框架的验证是通过模拟，案例研究和现实世界中CPS实验进行的。该项目将教育和培训与基础研究和公众宣传紧密相结合，从而增强了有关CPS系统的跨学科思维，通过参与式科学吸引公众，并扩大了参与科学，技术，工程，工程，数学和计算机科学的参与。本奖反映了NSF的立法使命，并认为通过基金会的知识效果和广泛的评估值得通过评估来进行评估。