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Work-in-Progress: High-Frequency Environmental Monitoring Using a Raspberry Pi-Based System

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Conference

2015 ASEE Annual Conference & Exposition

Location

Seattle, Washington

Publication Date

June 14, 2015

Start Date

June 14, 2015

End Date

June 17, 2015

ISBN

978-0-692-50180-1

ISSN

2153-5965

Conference Session

Computers in Education Engineering Division Poster Session

Tagged Division

Computers in Education

Page Count

17

Page Numbers

26.1767.1 - 26.1767.17

DOI

10.18260/p.25103

Permanent URL

https://peer.asee.org/25103

Download Count

177

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Paper Authors

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Debarati Basu Virginia Tech

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Debarati Basu is a second year Ph.D. student, advised by Dr. Vinod Lohani, in Engineering Education at Virginia Tech. She has B.Tech and M.Tech degrees in Computer Science & Engineering. She is engaged in developing a system with Raspberry Pi for high frequency real-time environmental monitoring in the LEWAS Lab. She has mentored an undergraduate student who was assisting her is developing the system. She has experience in organizing NSF/REU site for interdisciplinary water sciences and engineering and has also helped in developing a project with LEWAS data into a freshman-level course in Virginia Tech.

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John Stanton Goldstein Purviance Virginia Tech

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John S.G. Purviance is a B.S. student in Computer Science at Virginia Tech. He has been working at the Learning Enhanced Watershed Assessment System (LEWAS) Lab for the past two years as an undergraduate research intern. During summer 2014, he worked as an REU fellow at the LEWAS lab, which hosts the REU site. He has a background in python programming.

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Darren K Maczka Virginia Tech Orcid 16x16 orcid.org/0000-0001-5966-5670

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Darren Maczka is a M.S. student in Electrical and Computer Engineering. His background is in control systems engineering and information systems design and he received his B.S. in Computer Systems Engineering from The University of Massachusetts at Amherst. He has several years of experience teaching and developing curricula in the department of Electrical and Computer Engineering at Virginia Tech. He is presently assisting in developing the high frequency real-time environmental monitoring system and upgrading the power distribution system in the LEWAS Lab.

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Daniel S Brogan VIrginia Tech

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Daniel S. Brogan is a Ph.D. student, advised by Dr. Vinod Lohani, in Engineering Education with B.S. and M.S. degrees in Electrical Engineering. He has completed several graduate courses in engineering education pertinent to this research. He is the key developer of the OWLS and leads the LEWAS lab development and implementation work. He has mentored two NSF/REU Site students in the LEWAS lab. He assisted in the development and implementation of curricula for introducing the LEWAS at VWCC including the development of pre-test and post-test assessment questions. Additionally, he has a background in remote sensing, data analysis and signal processing from the University of New Hampshire.

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Vinod K Lohani Virginia Tech

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Dr. Vinod K. Lohani is a Professor of Engineering Education and an adjunct faculty member in Civil & Environmental Engineering at Virginia Tech. He is director of an interdisciplinary lab called Learning Enhanced Watershed Assessment System (LEWAS) at VT. He received a Ph.D. in civil engineering from VT. His research interests are in the areas of computer-supported research and learning systems, hydrology, engineering education, and international collaboration. He has led several interdisciplinary research and curriculum reform projects, funded by the National Science Foundation, and has participated in research and curriculum development projects with $4.5 million funding from external sources. He has been directing/co-directing an NSF/Research Experiences for Undergraduates (REU) Site on interdisciplinary water sciences and engineering at VT since 2007. This site has 66 alumni to date. Dr. Lohani collaborated with his colleagues to implement a study abroad project (2007-12), funded under the US-Brazil Higher Education Program of the U.S. Department of Education, at VT. He has published over 70 papers in peer-reviewed journals and conferences.

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Abstract

Work-in-Progress: High-Frequency Environmental Monitoring Using a Raspberry Pi-Based SystemThe Learning Enhanced Watershed Assessment System (LEWAS) is a unique high-frequencyreal-time environmental monitoring lab on the campus of Virginia Tech. The high-frequencydata collection and storage process is described in the following stages: 1) data collection byenvironmental sensors including an acoustic doppler current profiler, a water quality sonde and aweather station taking measurements every 1-3 min., 2) data processing by a local processor foranalysis and storage, 3) data archival in a remote server, and 4) data sharing through an OnlineWatershed Learning System (OWLS) (www.lewas.centers.vt.edu/dataviewer) through which endusers access the LEWAS data for research and education. In this paper, we discuss the work thatwas undertaken as a part of an NSF/REU Site, hosted at the LEWAS lab, in summer 2014 andinvolved upgrading the local processor (i.e., stage 2 described earlier) of the LEWAS. ARaspberry Pi computer was adopted because it offers the following advantages: lower powerconsumption, compatibility with multiple programming languages, expandability to moresensors and a lower purchase price. A python program for each sensor was developed and testedon the Raspberry Pi to collect, parse and store the environmental sensor data into a local MySQLdatabase. While the REU work ended at the end of summer, the work continues at the time ofthis writing to improve the reliability and modularity of the python programs. In order toimprove the efficiency of data processing work, the local database on the Raspberry Pi is beingmigrated to a remote server. This new database design is based on a relational data model and isinformed by existing database design of systems with similar goals in which tables aresystematically organized in a way that allows easy expandability, maintenance, cross-dimensional data retrieval and analysis. This new design will allow easy integration of newsensors as they are added to the system regardless of the syntactic and semantic structures of thedata. An application programming Interface (API) is under development so that both the sensorcode and user interface can access data in a consistent way. This will provide easy access ofhigh-frequency real-time LEWAS data to the users of the OWLS for education and researchpurposes. It may be noted that 5000+ engineering freshmen, 60+ civil engineering seniors atVirginia Tech and 300+ engineering freshmen at Virginia Western Community College haveused the LEWAS and/or OWLS for water sustainability education and its use continues to growboth within and outside VT.

Basu, D., & Purviance, J. S. G., & Maczka, D. K., & Brogan, D. S., & Lohani, V. K. (2015, June), Work-in-Progress: High-Frequency Environmental Monitoring Using a Raspberry Pi-Based System Paper presented at 2015 ASEE Annual Conference & Exposition, Seattle, Washington. 10.18260/p.25103

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