UA researcher receives NSF award to improve deployment of wireless sensor networks

Jing Yang, an assistant professor in the College of Engineering at the University of Arkansas, has received a $500,000 Faculty Early Career Development Program award from the National Science Foundation to continue developing sensing and transmission systems for energy-harvesting, wireless sensor networks.

Energy-harvesting, wireless sensor networks are systems that include collaborating embedded devices, such as sensor nodes, that are capable of sensing, computation and communication. They are often used for application-specific analysis, such as environmental monitoring in homes or factories. The sensors perform long-range communications that are impossible or impractical to implement with the use of wires.

These networks use energy from the ambient environment - including solar power, but also sources such as vibration and wind - to collect and transmit vast amounts of data. However, they struggle to maintain reliable collection, transmission and analysis of data because the energy supply for this process can be random, scarce and inconsistent.

Yang is working on a set of algorithms that will lead to the design of new systems that can dynamically and intelligently allocate scarce energy to collect and transmit the most informative data samples.

She uses two distinct but related approaches, one driven by energy, the other by data:
• For the energy-driven approach, the statistics of the energy-harvesting process are exploited to coordinate sensor data collection in large-scale systems and govern data transmission under stringent delay constraints.
• The data-driven approach utilizes the characteristics of underlying sensing signals and the recent progress on high-dimensional data analysis and machine learning to adaptively allocate scarce energy resources to collect and transmit the most important sensor data.

Yang's work will enable perpetual, large-scale wireless sensor networks that match energy supply and demand in data-intensive applications. The research will improve the design and deployment of sensor networks that perform critical functions related to health care and environmental monitoring, surveillance and disaster relief. The work eventually could be adapted to smart-grid applications and micro-grid technologies with renewable energy sources.