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

The Learning Enhanced Watershed Assessment System (LEWAS) is a unique high-frequency real-time environmental monitoring lab on the campus of Virginia Tech. The LEWAS has the following four stages: 1) data inputs which consist of environmental instruments including an acoustic Doppler current profiler, a water quality Sonde and a weather transmitter taking measurements every 1-3 min., 2) data processing occurring locally on a Raspberry Pi, 3) data storage on a remote server and 4) data visualization through an Online Watershed Learning System (OWLS) (www.lewas.centers.vt.edu/dataviewer/) through which end users access the LEWAS data for research and education. In this paper, we discuss the developmental work that was involved in upgrading the processing (stage 2) and storage components (stage 3) of the LEWAS and its application in various courses at Virginia Tech and in VWCC. The development of this upgraded system started as part of a Research Experience for Undergraduate (REU)/ NSF program during the summer of 2014. A Raspberry Pi computer was adopted as the processing unit primarily because it offers the following advantages: low power consumption, compatibility with multiple programming languages, ability to interface with many sensors and a low purchase price. A python program for each instrument was developed and tested on the Raspberry Pi to collect, parse and store the environmental data locally into a MySQL database. Though the REU work ended at the end of summer, the work is continuing at the time of writing. The aim of this continued development is to make the system more flexible, robust and maintainable in order for the LEWAS to become easily extendable and adaptable to different environments. Redesign of the partially completed upgraded system includes incorporation of the following three new components: 1) refactoring the instrument parsing code, 2) restructuring the database schema that will allow easy integration of new sensors as they are added to the system regardless of the syntactic and semantic structures of the data, and 3) implementing a REST Application Programming Interface (API) to allow for easier user application development. This development will ultimately help to provide smooth operation of user applications like the OWLS. Since 2009, this lab has been utilized in various courses at Virginia Tech and in VWCC, and more than 10,000 students from both these schools have used the LEWAS and/or OWLS to learn about high frequency environmental monitoring and its use continues to grow. The development of the LEWAS into a robust and reliable system will help students/faculty not only from this university but also from various parts of the world use the lab and its data for environmental education and research. [ABSTRACT FROM AUTHOR]