In an increasingly energy-conscious world, reducing power consumption in any form is extremely important, and to do it without sacrificing performance is critical.
A $400,000 Faculty Early Career Development (CAREER) award from the National Science Foundation will allow West Virginia University’s Dr. David Graham to create computationally efficient electronics to extend the lifetimes of wireless sensor nodes.
“Users of battery-powered devices, such as consumer electronics and implantable biomedical devices, demand increased signal-processing performance and longevity of operation,” said Graham. “These demands have re-awakened interest in analog signal processing for use in ultra-low-power systems. Significant advances in electronics design are expected to be achieved by using analog and digital systems to work cooperatively in a given signal-processing application.”
Graham, an assistant professor in the Lane Department of Computer Science and Electrical Engineering, is working with one of the most severely energy-constrained types of systems, wireless sensor networks. WSNs consist of small electronic sensor nodes that can be placed in large quantities and in various locations in order to monitor the environment around them. The individual nodes communicate with each other in order to build a cohesive understanding of the system they are monitoring.
WSNs hold great promise for use in applications such as environmental monitoring; border security; and monitoring critical infrastructures, like bridges and power grids.
“One such example of where these nodes might come in handy would be in monitoring the ‘health’ of a bridge,” said Graham. “By placing many different types of sensors at various locations on, under and around the bridge, you could detect a specific set of conditions, like unwanted vibrations, and alert officials before a catastrophic incident occurs.”
The problem, according to Graham, is that sensor nodes are often placed in remote locations and in large quantities where changing batteries would be impractical. “These nodes must be able to operate for extended periods of time on very small power sources.”
The focus of Graham’s research uses ultra-low-power analog circuitry to provide additional computational resources at each node while simultaneously reducing the total power consumed. “Analog signal processing will be used to perform pre-processing and compression of the raw sensor data, as well as energy management in the form of a ‘wakeup’ detector for the power-intensive digital portions of the sensor node,” Graham said.
Preliminary results show that analog signal processing can extend the lifetime of a sensor node in a vehicle classification scenario to nine years, as compared to only four months for an all-digital approach.
The CAREER Program offers the NSF’s most prestigious awards in support of junior faculty who exemplify the role of teacher-scholars through outstanding research, excellent education and the integration of education and research within the context of the mission of their organizations.
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CONTACT: Mary C. Dillon