NeTS-NECO: A New Resource Management Paradigm for Sensor Networks with Energy Replenishment
Ohio State University Research Foundation -Do Not Use, Columbus OH
Investigators
Abstract
NeTS-NECO: A New Resource Management Paradigm for Sensor Networks with Energy Replacement (Principal Investigators: Ness B. Shroff, Emre Koksal, and Prasun Sinha) Recent advances in sensor networks have resulted in a unique capability to remotely sense the environment. These networks can be used to sense natural as well as human-created phenomena (e.g., earthquake, fires, troop movements, radioactive substances). These systems could be deployed in remote or hard-to-reach areas, hence, it is critical that such networks operate unattended for long durations. To that end, new developments in the areas of renewable energy sources suggest that this is feasible. However, the design and control of sensor networks with the added dimension of renewable energy makes the problem of managing these networks challenging and substantially different from their non-replenishment counterparts. The goal of the project is to understand the performance limits of sensor networks with replenishment, to develop simple distributed algorithms and protocols that approach these limits, and validate and fine-tune the results based on experimentation. This ambitious endeavor will be accomplished by a team that brings in expertise that spans physical layer communications, distributed algorithms, control theory, resource allocation, sensor networking, and implementation. Broader Impact: Given that sensor nodes with replenishment are an emerging technology, the techniques developed in this project have the potential to make a significant impact on emerging industry sectors. The PIs will actively share their results by giving academic and industrial seminars and facilitating student internships. Students on this project will learn a wide range of theoretical and experimental methodologies to prepare them for the workforce. The PIs will continue their efforts to recruit and train under-represented students.
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