This project addresses energy issues in sensor systems. Energy harvesting permits wireless sensor network (WSN) applications to extract and store energy from the environment. Certain evolving application classes, particularly those supporting critical infrastructures like long-term monitoring and surveillance systems, require higher levels of communication and computational performance. Often, systems in this application class must be able to provide robust and flexible responses to unforeseen or emergency situations. Many of these proposed applications will also be required to run autonomously for years or even decades, and therefore may require energy harvesting techniques.

Current methods for WSN-based energy harvesting typically do not provide a coordinated energy management policy for this class of applications. This project addresses this gap in three ways. First, the team is designing and evaluating a set of optimal energy harvesting policies that combine dynamic voltage scaling (changing the CPU speed) with dynamic modulation scaling (changing wireless transmission rates.) The algorithms efficiently support time-critical applications that have a wide range of workloads while maintaining necessary energy reserves in order to deal with emergency situations. Second, is the development of distributed protocols to implement these algorithms. Finally, is the development of prototypes of two energy harvesting hardware testbeds, a flow-based water energy generation system and a solar-based energy harvester used in a hybrid robotic sensor network application. The two prototype testbeds will not only yield critical technical insight but will introduce students of various backgrounds into the issues of networking, device, and robotic design.

The PIs have a multi-step plan to integrate their research and education mission of their proposal. This includes development of new courses, expanding and strengthening the schools effort on energy management, developing week-long training camps for high school instructors through the STEM program, and contributing to the advancement of the GMU robotics and sensor labs.

National Science Foundation (NSF)
Division of Computer and Network Systems (CNS)
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Anita J. LaSalle
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George Mason University
United States
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