CPS: Medium: A Secure, Trustworthy, and Reliable Air Quality Monitoring System for Smart and Connected Communities

智能技术的一个关键应用是智能、互联和安全的环境监测网络，可以帮助管理人员和研究人员找到更好的方法，将证据和数据纳入与环境相关的公共决策。在该项目中，研究人员将在佛罗里达州奥兰多市及其周边地区使用密集部署的低成本传感器建立一个安全、可信和可靠的空气质量监测网络系统，以更好地为该地区的污染缓解战略提供信息。获取城市规模的空气质量传感器数据和预测可以对环境正义、公共卫生和可持续发展倡议产生积极的社会影响。调查人员将把该项目的成果纳入计算机和网络安全与隐私、移动的计算、环境科学与工程以及社会科学等课程。拟议的工作将提供动手练习，研究和教育机会，本科生，研究生和K-12 students.The objectives of this project including performing remote low-cost sensor calibration，drift and malfunction detection.将开发一种创新的建模方法，用于对低成本PM2.5传感器进行远程校准。将开发一个三传感器系统，采用操作统计方法，每小时交叉评估传感器测量数据，以确定潜在的传感器漂移和故障。项目组将建立一个值得信赖的空气质量监测网络。将开发可信靴子策略，以确保传感器固件在引导时是真实的，执行系统状态的动态分析，将测量结果发送到验证器进行远程认证，并接受来自验证器的命令以对违规行为采取行动。该团队还将创建一个准确的基于深度学习的空气质量预测系统，该系统基于一个新颖的两阶段半监督学习框架，来自嘈杂和混合标记的传感器大数据。团队中的社会科学家将对空气质量监测和预测进行社会行为研究。该项目强调通过开展空气质量教育和数据利用及宣传培训，可持续地增强居民的权能。该项目超越了被动的公民科学，使公民成为他们利益的倡导者，不仅提高外部空气质量，而且提高社区公民的整体生活质量。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

A critical application of smart technologies is a smart, connected, and secured environmental monitoring network that can help administrators and researchers find better ways to incorporate evidence and data into public decision-making related to the environment. In this project, the investigators will establish a secure, trustworthy and reliable air quality monitoring network system using densely deployed low-cost sensors in and around the city of Orlando, Florida, to better inform development of pollution mitigation strategies in the region. Access to the urban-scale air quality sensor data and forecasts can have a positive social impact on environmental justice, public health, and sustainability initiatives. The investigators will incorporate the outcome of the project into courses on computer and network security and privacy, mobile computing, environmental sciences and engineering, and social science. The proposed work will provide hands-on exercises, research, and educational opportunities for undergraduate, graduate students and K-12 students.The objectives of this project include performing remote low-cost sensor calibration, drift and malfunction detection. An innovative modeling method will be developed to perform remote calibration for low-cost PM2.5 sensors. A triple-sensor system will be developed, employing an operational statistical method that cross-evaluates sensor measurement data every hour to identify potential sensor drifts and malfunctions. The project team will build a trustworthy air quality monitoring network. A trusted boot strategy will be developed to ensure the sensor firmware is genuine at bootstrapping, performing dynamic analysis of states of the system, sending the measurement to a verifier for remote attestation, and accepting commands from the verifier to act on violations. The team will also create an accurate deep learning-based air quality prediction system based on a novel two-stage semi-supervised learning framework from noisy and mixed-labeled sensor big data. Social scientists on the team will conduct a social behavioral study of air quality monitoring and prediction. This project emphasizes sustainable empowerment of residents through processes of education on air quality and training on data utilization and advocacy. The project goes beyond passive citizen science to enable citizens to become advocates for their interests to increase not only outside air quality but also the overall quality of life of citizens in the community.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.