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

\bibitem{asee_peer_25103}
  Basu, D., \& Purviance, J. S. G., \& Maczka, D. K., \& Brogan, D. S., \& Lohani, V. K. (2015, June), \emph{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

Debarati Basu, John Stanton Goldstein Purviance, Darren K Maczka, Daniel S Brogan, and Vinod K. Lohani.  "Work-in-Progress: High-Frequency Environmental Monitoring Using a Raspberry Pi-Based System".  2015 ASEE Annual Conference & Exposition, Seattle, Washington, 2015, June.  ASEE Conferences, 2015.  https://strategy.asee.org/25103 Internet.  28 May, 2024

\bibitem{asee_peer_25103}
  Debarati Basu, John Stanton Goldstein Purviance, Darren K Maczka, Daniel S Brogan, and Vinod K. Lohani.
  "Work-in-Progress: High-Frequency Environmental Monitoring Using a Raspberry Pi-Based System".
  \emph{2015 ASEE Annual Conference \& Exposition, Seattle, Washington, 2015, June}.
  ASEE Conferences, 2015.
  https://strategy.asee.org/25103 Internet.  28 May, 2024

@INPROCEEDINGS{asee_peer_25103
author = "Debarati Basu, John Stanton Goldstein Purviance, Darren K Maczka, Daniel S Brogan, and Vinod K. Lohani"
title = "Work-in-Progress: High-Frequency Environmental Monitoring Using a Raspberry Pi-Based System"
booktitle = "2015 ASEE Annual Conference \& Exposition"
year = "2015"
month = "June"
address = "Seattle, Washington"
publisher = "ASEE Conferences"
note = {https://strategy.asee.org/25103}
number = {10.18260/p.25103}
}

TY  - CPAPER
AB  -     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.
AU  - Debarati Basu
AU  - John Stanton Goldstein Purviance
AU  - Darren K Maczka
AU  - Daniel S Brogan
AU  - Vinod K. Lohani
CY  - Seattle, Washington
DA  - 2015/06/14
PB  - ASEE Conferences
TI  - Work-in-Progress: High-Frequency Environmental Monitoring Using a Raspberry Pi-Based System
UR  - https://strategy.asee.org/25103
DO  - 10.18260/p.25103
ER  -