BIGDATA: IA: Collaborative Research: Protecting Yourself from Wildfire Smoke: Big Data Driven Adaptive Air Quality Prediction Methodologies

大数据：IA：协作研究：保护自己免受野火烟雾的侵害：大数据驱动的自适应空气质量预测方法

• 批准号: 1838024
• 负责人: Lei Yang
• 金额: $ 68.47万
• 依托单位: Board of Regents, NSHE, obo University of Nevada, Reno
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1838024&HistoricalAwards=false
• 关键词: BIGDATA; IA; Collaborative; Research; Protecting

The objective of this project is to develop a framework to achieve real-time smoke transport prediction and air quality forecasting. Wildfire smoke can transport very fast and pose significant health hazards to communities. State-of-the-art smoke forecasting models typically have infrequent updates and provide predictions with a coarse spatial resolution due to spatiotemporal resolution limitations of input data and the tremendous computational power required to simulate atmospheric conditions. This project will develop real-time smoke transport and air quality prediction methodologies with better spatial resolution for improving the scalability and efficiency of the underlying data processing system to enable timely air quality alerts. While this project is applied towards smoke transport and air quality prediction, this work can be generalized to solve many other big data problems that require such design. The principal investigators will use the materials and topics from this project to enhance education by creating new big data analytics related courses and developing a Big Data Minor program at the University of Nevada, Reno. The project will also provide opportunities to engage more students from underrepresented groups and impact the education of several students, via K-12 outreach and mentoring undergraduate and graduate students.The intellectual merit of this research is in establishing a novel big data driven air quality prediction for wildfire smoke to provide timely and effective health alerts. The planned new prediction methodology will integrate the novel Gaussian Markov Random Field based real-time spatiotemporal prediction with statistical-based long-term spatiotemporal prediction. To tackle the challenge of missing high-resolution data, a data fusion methodology is planned to integrate fine-grained image data collected from camera networks with air pollution monitoring data to increase data resolution. A Deep Neural Network based smoke density detection process will extract air quality information from camera image data. The planned novel signature time-series based prediction methodology will open opportunities to process larger amounts of spatiotemporal data using limited resources. By identifying critical data based on spatiotemporal properties, the project will develop a communication framework that enables efficient camera data transfer. Efficient parallel and distributed data processing is utterly important to support processing large scale data in real time. The planned decomposition-based parallelization methodology and a performance model driven scheduling framework will enable efficient dynamic computing resource management, which is key to the success of this project.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.

该项目的目标是开发一个框架，以实现实时烟雾传输预测和空气质量预测。 野火烟雾可以快速传播，对社区构成重大健康危害。 由于输入数据的时空分辨率限制和模拟大气条件所需的巨大计算能力，最先进的烟雾预报模型通常不经常更新，并提供具有粗略空间分辨率的预测。 该项目将开发具有更高空间分辨率的实时烟雾传输和空气质量预测方法，以提高基础数据处理系统的可扩展性和效率，从而能够及时发出空气质量警报。 虽然该项目被应用于烟雾传输和空气质量预测，但这项工作可以推广到解决许多其他需要这种设计的大数据问题。主要研究人员将使用该项目的材料和主题，通过创建新的大数据分析相关课程并在里诺的内华达州大学开发大数据未成年人计划来加强教育。 该项目还将提供机会，吸引更多来自代表性不足群体的学生，并通过K-12外展和指导本科生和研究生来影响几名学生的教育。这项研究的智力价值在于建立一种新颖的大数据驱动的空气质量预测野火烟雾，以提供及时有效的健康警报。计划中的新预测方法将把新的基于高斯马尔可夫随机场的实时时空预测与基于概率的长期时空预测相结合。为应对高分辨率数据缺失的挑战，计划采用数据融合方法，将从摄像机网络收集的细粒度图像数据与空气污染监测数据相结合，以提高数据分辨率。基于深度神经网络的烟雾密度检测过程将从相机图像数据中提取空气质量信息。计划中的基于时间序列的新特征预测方法将为利用有限资源处理大量时空数据提供机会。通过根据时空特性确定关键数据，该项目将开发一个通信框架，以实现有效的相机数据传输。高效的并行和分布式数据处理对于支持大规模数据的真实的实时处理至关重要。计划分解为基础的并行化方法和性能模型驱动的调度框架将实现高效的动态计算资源管理，这是该项目成功的关键。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

Drifting Streaming Peaks-over-Threshold-Enhanced Self-Evolving Neural Networks for Short-Term Wind Farm Generation Forecast

用于短期风电场发电预测的漂移流峰值超阈值增强型自进化神经网络

• DOI: 10.3390/fi15010017
• 发表时间: 2023
• 期刊: Future Internet
• 影响因子: 3.4
• 作者: Liu, Yunchuan;Ghasemkhani, Amir;Yang, Lei
• 通讯作者: Yang, Lei

AUTOGR Automated Geo-Replication with Fast System Performance and Preserved Application Semantics

AUTOGR 自动异地复制，具有快速的系统性能和保留的应用程序语义

• DOI: 10.14778/3461535.3461541
• 发表时间: 2021
• 期刊: Proceedings of the VLDB Endowment
• 影响因子: 2.5
• 作者: Wang Jiawei;Li Cheng;Ma Kai;Huo Jingze;Yan Feng;Feng Xinyu;Xu Yinlong
• 通讯作者: Xu Yinlong

CEDULE: A Scheduling Framework for Burstable Performance in Cloud Computing

CEDULE：云计算中突发性能的调度框架

• DOI: 10.1109/icac.2018.00024
• 发表时间: 2018
• 期刊: 2018 IEEE International Conference on Autonomic Computing (ICAC
• 作者: Ali, Ahsan;Pinciroli, Riccardo;Yan, Feng;Smirni, Evgenia
• 通讯作者: Smirni, Evgenia

Intelligent Edge: Leveraging Deep Imitation Learning for Mobile Edge Computation Offloading

• DOI: 10.1109/mwc.001.1900232
• 发表时间: 2020-02-01
• 期刊: IEEE WIRELESS COMMUNICATIONS
• 影响因子: 12.9
• 作者: Yu, Shuai;Chen, Xu;Zhang, Junshan
• 通讯作者: Zhang, Junshan

Feature Collusion Attack on PMU Data-driven Event Classification

PMU数据驱动事件分类的特征共谋攻击

• 发表时间: 2024
• 期刊: IEEE
• 作者: Ghasemkhani, Amir;Haridas, Rutuja Sanjeev;Sajjadi Mohammadabadi, Seyed Mahmoud;Yang, Lei
• 通讯作者: Yang, Lei