Fast track article: Design of smart sensing components for volcano monitoring

Authors:
Mingsen Xu;Wen-Zhan Song;Renjie Huang;Yang Peng;Behrooz Shirazi;Richard Lahusen;Aaron Kiely;Nina Peterson;Andy Ma;Lohith Anusuya-Rangappa;Michael Miceli;Devin McBride
Affiliations:
Sensorweb Research Laboratory, Washington State University, Vancouver, WA 98686, USA;Sensorweb Research Laboratory, Washington State University, Vancouver, WA 98686, USA;Sensorweb Research Laboratory, Washington State University, Vancouver, WA 98686, USA;Sensorweb Research Laboratory, Washington State University, Vancouver, WA 98686, USA;School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99163, USA;Cascades Volcano Observatory, U.S.Geological Survey, Vancouver, WA, USA;Jet Propulsion Laboratory, California Institute of Technology, Technology, Pasadena, CA 91109, USA;School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99163, USA;Sensorweb Research Laboratory, Washington State University, Vancouver, WA 98686, USA;School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99163, USA;School of Computer Science, Louisiana State University, Baton Rouge, LA 70803, USA;School of Computer Science, Seattle University, Seattle, WA 98122, USA
Venue:
Pervasive and Mobile Computing
Year:
2009

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Abstract

In a volcano monitoring application, various geophysical and geochemical sensors generate continuous high-fidelity data, and there is a compelling need for real-time raw data for volcano eruption prediction research. It requires the network to support network synchronized sampling, online configurable sensing and situation awareness, which pose significant challenges on sensing component design. Ideally, the resource usages shall be driven by the environment and node situations, and the data quality is optimized under resource constraints. In this paper, we present our smart sensing component design, including hybrid time synchronization, configurable sensing, and situation awareness. Both design details and evaluation results are presented to show their efficiency. Although the presented design is for a volcano monitoring application, its design philosophy and framework can also apply to other similar applications and platforms.