Knowledge Enterprise awards funding for transdisciplinary research

MAY 19, 2020 —The Office of the Vice President for Research, Economic Development, and Knowledge Enterprise and Academic Affairs have awarded the FY 2021 Transdisciplinary Teams research awards. Two teams, representing four colleges and five departments collectively, were selected and will be awarded $50,000 each.

UTSA launched the Transdisciplinary Teams Program last year in response to the growing complexity of research and faculty needs. T² supports faculty to engage in scholarly research activities that foster transdisciplinary collaboration by assembling teams of researchers from different disciplines. The program also establishes a foundation for faculty to seek extramural funding, ultimately advancing UTSA’s research strengths and growing new areas of opportunity.

“The idea of ‘smart cities’ is not new, but it’s gathering momentum, particularly with the advent of COVID-19.”

With San Antonio forecast to have significant population growth by 2040, this year’s research concentration was smart and connected cities, with a call for proposals that intersected technology and policy in topic areas that merge social mobility, affordable housing, water, utility and waste management, population health and community wellness, digital connectivity, transportation, e-governance and sustainability.

Earlier this year UTSA became a key collaborative partner in the R&D League, a new research and development program with the City of San Antonio, the Southwest Research Institute and USAA to address local civic challenges to enhance our neighborhoods and communities.

“The idea of ‘smart cities’ is not new, but it’s gathering momentum, particularly with the advent of COVID-19,” explained Bernard Arulanandam, vice president for research, economic development, and knowledge enterprise at UTSA. “The need for interconnected civic infrastructure with thoughtful engagement is critical to the well-being of the community.”

The first team—led by principal investigator Wenbo Wu (management science and statistics) in collaboration with Ying Huang (demography), Thankam Sunil (public health) and Eric Shattuck (public health)—is working on “Integration of Mobile Positioning, Sociodemographic factors, and Household Health for Urban Scale Energy Consumption Simulation and Planning,” integrating expertise from statistics, engineering, public health, and demography.

The team is developing a novel approach to understand how diverse occupant behaviors impact energy consumption, including tracking time use, mobility and health status—elements often overlooked in previous energy-use models.

Over 400 households in the San Antonio neighborhoods of Elm Creek and Jefferson Heights will be surveyed. The communities were chosen for their socioeconomic diversity. Data will be aggregated and combined with mobility data derived from cell phone GPS log files to understand how neighborhood population densities vary over time. The team will then be able to model neighborhood-level occupant behavior to better predict energy use and refine sustainable smart community/city models.

The second project is led by Dakai Zhu (computer science) working with Jeff Prevost (electrical and computer engineering), Keying Ye (management science), Amanda Fernandez (computer science) and Wei Wang (computer science).

The project, “Toward Statistical and Adaptive Learning in Edges for Smart Health Applications in Connected Communities with Security and Privacy Enforcement,” is focused on designing and developing a micro-service-based intelligent Edge computing framework.

The team will use digitized sensing data from IoT-enabled devices (such as smart thermometers and motion sensors) and smart applications (such as assisted living and pandemic flu status) from a large number of mixed-communities to better serve their health needs. The team is also considering the added requirements of ensuring the security and privacy of the data and the devices while providing operational flexibility and usage efficiency.

The team is leveraging statistical and learning models, scheduling algorithms and management schemes in Edge devices to support connected communities. These devices can provide effective protection for the privacy of the sensitive health data collected as well as enabling advanced security features for smart health applications.

The project will also provide education and research opportunities for underrepresented minority students.

Both of these projects align with UTSA’s vision of growing data intelligence across a wide spectrum of fields to address societal challenges and to improve our communities.

— Sarah Hada