WINLAB receives NSF Grant for Community-based Mobile EdgeTestbeds

WINLAB Team receives NSF Grant for Developing Community-based Mobile Edge Sensing and Computing Testbeds

 

A team of researchers led by Professor Yingying Chen (PI) and WINLAB Chief Technologist Ivan Seskar (Co-PI) at WINLAB have received an NSF award for the project titled “Nation-wide Community-based Mobile Edge Sensing and Computing Testbeds.” This three-year $1.5M project is collaborating with three other universities including Indiana University, Temple University, and New York Institute of Technology.

 

 

What is being studied, what is the scope of the research? The advancement of mobile sensing devices and mobile computing technologies have triggered new research opportunities in mobile edge sensing and computing, including human 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. This research project will design and develop an experimental infrastructure to share data/models nationwide and perform practical and repeatable experiments that can benefit many research groups in edge sensing and computing community.

 

What will be designed and developed for community usage?  The goal of this project is to build a large-scale, mobile edge sensing and computing infrastructure that can provide practical experimental environments, rich user tools and services, and data/model sharing to a broad research community. The proposed research infrastructure includes three organically connected functionalities to provide repeatable experimental environments, facilitate data/model-sharing, and connect separated research groups on a national scale. The three functionalities include: 1) the mobile sensing functionality for supporting compelling research in low-effort large-scale sensing data collection, robot-enabled experimenting, and privacy-preserved learning on mobile edge devices; 2) the 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; and 3) the novel data and model sharing functionality that supports a broad spectrum of mobile edge sensing and computing research areas. Furthermore, a uniform web portal will be developed to allow users to use these functionalities remotely. The community-based infrastructure will provide an essential hardware and software foundation that enables cutting-edge research in computer and information sciences, 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, will further enhance the research collaboration of many research groups in academia, industry, and government across the nation.

What are the potential impacts on research going forward? By utilizing this new infrastructure, individual research groups can be connected to conduct large-scale research with low efforts. Many interdisciplinary communities can also be brought together, researching 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.

What are the outreach activities to institutions and community? Besides conducting the research described above, the team plans to build a robust user community via seminars, workshops, tutorials, and invited talks. The team will hold seminars and workshops to develop and nurture a diverse user community from students, researchers, and engineers in mobile edge sensing and computing. The results of the proposed infrastructure will be demonstrated to the conference participants and industrial partners. The team will also provide summer intern opportunities and develop teaching modules for incorporation into high-school and undergraduate student outreach activities. In addition, the research topics of mobile edge sensing and computing and the proposed functionalities and tools will be integrated into the interdisciplinary curricula at Rutgers and other participating institutes.