Collaborative Research: CCRI: New: Nation-wide Community-based Mobile Edge Sensing and Computing Testbeds

合作研究：CCRI：新：全国范围内基于社区的移动边缘传感和计算测试平台

• 批准号: 2304766
• 负责人: Xiaonan Guo
• 金额: $ 23万
• 依托单位: George Mason University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2304766&HistoricalAwards=false
• 关键词: Collaborative; Research; CCRI; New; Nation

The advancement of mobile sensing devices and mobile computing technologies have triggered new research opportunities in mobile edge sensing and computing, including activity recognition, wellbeing monitoring, user authentication, human dynamics tracking, etc. However, research in mobile edge sensing and computing suffers from labor-intensive training, unrealistic experimental environments, heavy environmental interferences in practical scenarios. In addition, different research groups usually conduct small-scale experiments separately, which makes it difficult to share the research results and data among groups in the same community. The U.S. mobile edge sensing and computing community demands an experimental infrastructure to share data/models nationwide and perform practical and repeatable experiments. The goal of this project is to build a large-scale, mobile edge sensing and computing infrastructure to provide practical experimental environments, rich user tools and services, and data/model sharing. Based on the proposed infrastructure, individual research groups can be connected to conduct large-scale research with low efforts. Many interdisciplinary communities can also be brought together, conducting research via the proposed infrastructure, including deep learning-based hardware design, smart healthcare, AR/VR, human flow monitoring, smart home, and smart city. The research results can benefit interdisciplinary curriculums with new research topics and tasks for undergraduate/graduate and minority students.The proposed research infrastructure includes three organically connected functionalities to provide repeatable experimental environments, facilitate data/model-sharing, and join separated research groups on a national scale. In particular, this project develops mobile sensing functionalities for supporting compelling research in low-effort large-scale sensing data collection, robot-enabled experimenting, and privacy-preserved learning on mobile edge devices. Furthermore, this project develops an edge computing functionality integrating remote-operated mobile edge devices and mobile development kits to support research in software and hardware co-design and on-device AI learning for low-cost mobile devices. In addition, a novel data and model sharing functionality is developed to support a broad spectrum of mobile edge sensing and computing research areas. A uniform web portal is provided to allow users to use these functionalities remotely. The proposed infrastructure provides an essential hardware and software foundation that enables cutting-edge research in CISE research focuses, including mobile edge sensing, hardware and software co-design, and distributed computing with sharable large-scale data from practical environments. The outcome from this project, including the unique integrated functionalities, powerful tools and services, and comprehensive datasets, further enhances the research collaboration of many research groups in academia, industry, and government across the nation.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.

移动传感设备和移动计算技术的进步引发了移动边缘感测和计算方面的新研究机会，包括活动识别，福祉监测，用户认证，人体动态跟踪等。但是，移动边缘感测和计算中的研究受到劳动密集型培训，不切实际的实验环境，严重的环境，在实践场景中受到严重的环境干扰。此外，不同的研究小组通常会分别进行小规模的实验，这使得很难在同一社区的组之间共享研究结果和数据。美国移动边缘传感和计算社区要求实验基础架构在全国范围内共享数据/模型，并执行实用且可重复的实验。该项目的目的是建立大规模的移动边缘传感和计算基础架构，以提供实用的实验环境，丰富的用户工具和服务以及数据/模型共享。根据提议的基础设施，可以将个别研究小组连接到以低努力进行大规模研究。许多跨学科的社区也可以通过拟议的基础设施进行研究，包括基于深度学习的硬件设计，智能医疗保健，AR/VR，人类流量监控，智能家居和智能城市。研究结果可以使本科/研究生和少数族裔学生的新研究主题和任务受益。拟议的研究基础设施包括三个有机连接的功能，以提供可重复的实验环境，促进数据/模型分享，并在国际规模上加入分离的研究小组。特别是，该项目开发了移动传感功能，以支持低饮料大规模传感数据收集，支持机器人的实验和在移动边缘设备上保留隐私性学习的引人入胜的研究。此外，该项目开发了一个边缘计算功能，该功能集成了远程操作的移动边缘设备和移动开发套件，以支持低成本移动设备的软件和硬件共同设计和设备AI学习中的研究。此外，开发了一种新颖的数据和模型共享功能，以支持广泛的移动边缘传感和计算研究领域。提供统一的Web门户，以允许用户远程使用这些功能。拟议的基础架构提供了必不可少的硬件和软件基础，​​该基础结构可以在Cise Research的重点进行尖端研究，包括移动边缘传感，硬件和软件共同设计，并通过实用环境中提供可共享的大规模数据分布式计算。该项目的结果，包括独特的综合功能，强大的工具和服务以及全面的数据集，进一步增强了全国许多学术界，行业和政府在全国范围内许多研究小组的研究合作。这项奖项反映了NSF的法定任务，并认为通过基金会的知识分子和更广泛的影响，通过评估值得评估的支持。

项目成果

Universal Targeted Adversarial Attacks Against mmWave-based Human Activity Recognition

• DOI: 10.1109/infocom53939.2023.10228887
• 发表时间: 2023-05
• 期刊: IEEE INFOCOM 2023 - IEEE Conference on Computer Communications
• 作者: Yucheng Xie;Ruizhe Jiang;Xiaonan Guo;Yan Wang;Jerry Q. Cheng;Yingying Chen

Secure and Efficient Mobile DNN Using Trusted Execution Environments

• DOI: 10.1145/3579856.3582820
• 发表时间: 2023-07
• 期刊: Proceedings of the 2023 ACM Asia Conference on Computer and Communications Security
• 作者: B. Hu;Yan Wang;Jerry Q. Cheng;Tianming Zhao;Yucheng Xie;Xiaonan Guo;Ying Chen