readme under development, but please go ahead 😄
Open educational resources (OER) for the development of an environmental data acquisition systems — a hardware/software hybrid.
The aim of this project is to provide knowledge in hardware and software development using the example of an environmental data acquisition system. Besides of being a tutorial, a major objective is to elaborate on the requirments of such a measuring system, design decisions and basic knowledge, in order to provide as much support as possible.
Interested individuals and institutions are very much invited to contribute!
Environmental data allow us to measure the conditions of the direct and indirect surrounding we live in. At any moment we affect the surrounding and vice versa, e.g. sealing greenland in urban areas and lower infiltration with higher surface runoff creating floods. Monitoring our environment is key to observe these interactions and the first step for a comprehensive analysis and prediction of future behaviour.
Also, (open) environmental data are getting more and more in the focus of the modern society, as they allow to provide new services to costumers, built on information of their surrounding which, again, is affecting them at any moment.
A comprehensive analysis relies heavily on an adequat spatio-temporal distribution of data sources in order to describe how patterns develop and vary over space and time. In order to address this, a sophisticated environmental data acquisition system is premise.
There are plenty of projects out there that work on similar things like weather stations. For all of which I have seen, the aim is to have a running system that measures the wished quantities.
However, having such a system deployed in the field, without WiFi, Bluetooth or cable, and no access to the power grid, is a very different issue. Also, such projects do not focus on the system maintenance and the importance of data integrity regarding the data analysis. What happens, when you exchange a defect sensor or you observe changes in your local environment (a new building next to wind speed measurement, or recent clear-cutting of your forest). A sophisticated system needs to be able to incorporate these information into the data in order to acount for that during the analysis.
In short: the dedicated focus on data quality and integrity, deployed system maintenance, and the complete chain from the sensor until the graph, make this project different.
- Remote station: Arduino Mega; power supply (photovoltaics), buffering (battery) and management; local data storage (RAM and SD card); time keeping (real-time clock); sensor interfaces (digital, analog, protocol); interrupts; maintenance interface;
- Remote transmission: mobile phone network (or LoRaWAN); login at server; encrypted data transmission; over-the-air-programming;
- Server station: Raspberry Pi3; dynDNS; station management; data base; running scripts for pre-processing; providing data via credentials;
