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: 2120276
• 负责人: Yan Wang
• 金额: $ 34万
• 依托单位: Temple University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2120276&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为移动边缘传感和计算带来了新的研究机会，包括活动识别、健康监测、用户认证、人体动态跟踪等。然而，移动边缘传感与计算的研究存在劳动密集型的训练、不现实的实验环境、实际场景中严重的环境干扰等问题。此外，不同的研究小组通常单独进行小规模实验，这使得研究结果和数据难以在同一社区的小组之间共享。美国移动边缘传感和计算社区需要一个实验基础设施来在全国范围内共享数据/模型，并进行实用和可重复的实验。该项目的目标是建立一个大规模的移动边缘传感和计算基础设施，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。基于拟议的基础设施，可以将各个研究小组连接起来，以低成本进行大规模研究。许多跨学科社区也可以聚集在一起，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、人流监测、智能家居和智能城市。研究成果可以为本科/研究生和少数民族学生提供新的研究课题和任务的跨学科课程。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/模型共享，并在全国范围内加入分离的研究小组。特别是，该项目开发了移动传感功能，以支持在移动边缘设备上进行低成本大规模传感数据收集、机器人实验和隐私保护学习方面的引人注目的研究。此外，该项目开发了一种边缘计算功能，集成了远程操作的移动边缘设备和移动开发工具包，以支持低成本移动设备的软硬件协同设计和设备上人工智能学习研究。此外，还开发了一种新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。提供了统一的web门户，允许用户远程使用这些功能。拟议的基础设施为CISE研究重点的前沿研究提供了必要的硬件和软件基础，包括移动边缘传感、硬件和软件协同设计以及具有来自实际环境的可共享大规模数据的分布式计算。该项目的成果，包括独特的集成功能，强大的工具和服务，以及全面的数据集，进一步加强了全国学术界，工业界和政府许多研究小组的研究合作。该奖项反映了美国国家科学基金会的法定使命，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。

项目成果

A Survey of Deep Learning on Mobile Devices: Applications, Optimizations, Challenges, and Research Opportunities

移动设备深度学习调查：应用、优化、挑战和研究机会

• DOI: 10.1109/jproc.2022.3153408
• 发表时间: 2022
• 期刊: Proceedings of the IEEE
• 影响因子: 20.6
• 作者: Zhao, Tianming;Xie, Yucheng;Wang, Yan;Cheng, Jerry;Guo, Xiaonan;Hu, Bin;Chen, Yingying
• 通讯作者: Chen, Yingying

mmFit: Low-Effort Personalized Fitness Monitoring Using Millimeter Wave

• DOI: 10.1109/icccn54977.2022.9868878
• 发表时间: 2022-07
• 期刊: 2022 International Conference on Computer Communications and Networks (ICCCN)
• 作者: Yucheng Xie;Ruizhe Jiang;Xiaonan Guo;Yan Wang;Jerry Q. Cheng;Yingying Chen

WatchID: Wearable Device Authentication via Reprogrammable Vibration

WatchID：通过可重新编程的振动进行可穿戴设备身份验证

• 发表时间: 2021
• 期刊: International Conference on Mobile and Ubiquitous Systems Computing Networking and Services
• 作者: Cheng, J.Q.

Defending against Thru-barrier Stealthy Voice Attacks via Cross-Domain Sensing on Phoneme Sounds

通过音素声音的跨域感知防御穿墙隐形语音攻击

• DOI: 10.1109/icdcs54860.2022.00071
• 发表时间: 2022
• 期刊: 2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS
• 作者: Shi, Cong;Zhao, Tianming;Zhang, Wenjin;Mahdad, Ahmed Tanvir;Ye, Zhengkun;Wang, Yan;Saxena, Nitesh;Chen, Yingying
• 通讯作者: Chen, Yingying

BioCase: Privacy Protection via Acoustic Sensing of Finger Touches on Smartphone Case Mini-Structures

• DOI: 10.1145/3581791.3596841
• 发表时间: 2023-06
• 期刊: Proceedings of the 21st Annual International Conference on Mobile Systems, Applications and Services
• 作者: Yilin Yang;Xin Li;Zhengkun Ye;Yan Wang;Yingying Chen