Seattle, Washington
June 14, 2015
June 14, 2015
June 17, 2015
978-0-692-50180-1
2153-5965
Computers in Education
17
26.1767.1 - 26.1767.17
10.18260/p.25103
https://peer.asee.org/25103
686
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.
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.
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.
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.
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.
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
ASEE holds the copyright on this document. It may be read by the public free of charge. Authors may archive their work on personal websites or in institutional repositories with the following citation: © 2015 American Society for Engineering Education. Other scholars may excerpt or quote from these materials with the same citation. When excerpting or quoting from Conference Proceedings, authors should, in addition to noting the ASEE copyright, list all the original authors and their institutions and name the host city of the conference. - Last updated April 1, 2015