Researchers Receive NSF Grant to Develop 'Sparse-Sensing' System for Wireless Sensor Networks

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Jing Yang, University of Arkansas

FAYETTEVILLE, Ark. – Engineering researchers at the University of Arkansas have received a $362,394 grant from the National Science Foundation to develop new sensing technologies that significantly reduce the amount of data collected and processed in sensor networks powered by extremely limited energy resources.

Their energy-aware, “sparse-sensing” system, based on the development of new theories and algorithms, will increase the efficiency of wireless sensor networks that perform critical functions, such as surveillance, environmental monitoring, biomedical sensing and disaster relief and assessment. The new technologies will aid any process that relies on large quantities of data collected by wireless sensor networks.

“The ubiquity of smart phones and the widespread deployment of wireless sensor networks play a critical role in many aspects of our life,” said Jing Yang, assistant professor of electrical engineering. “These applications usually generate astronomical volumes of data, which impose formidable challenges for efficient processing, storage and transmission of data. On the other hand, wireless sensors usually operate under stringent energy constraints, which might not meet the energy demands of data-intensive sensing applications. This project will address these big-data challenges in an energy-constrained environment.”

Yang and Jingxian Wu, associate professor of electrical engineering, are working on a set of algorithms that will lead to the design of networking systems to dynamically and sparsely sample random fields of information by adapting to the energy availability of sensors and the time-varying nature of monitored objects. This diverse and distributed network will bridge the gap between energy supply and energy demand in energy-constrained systems. The ultimate goal of the project is to align energy resources with information demands, so that limited resources can be used to collect the most informative data.

“The sparse sensing is enabled by the fact that some information is more important than other information,” Wu said. “The algorithms will allow us to sample only the most useful information.”

The researchers will also focus on designing systems and techniques that increase efficiency in energy-harvesting devices, such as photovoltaic panels and thermal devices.