Towards a Software Framework to Support Deployment of Low Cost End-to-End Hydroclimatological Sensor Network
Abstract:Deployment of environmental sensors assemblies based on cheap platforms such as Raspberry Pi and Arduino have gained much attention over the past few years. While they are more attractive due to their ability to be controlled with a few programming language choices, the configuration task can become quite complex due to the need of having to learn several different proprietary data formats and protocols which constitute a bottleneck for the expansion of sensor network. In response to this rising complexity the Institute of Electrical and Electronics Engineers (IEEE) has sponsored the development of the IEEE 1451 standard in an attempt to introduce a common standard. The most innovative concept of the standard is the Transducer Electronic Data Sheet (TEDS) which enables transducers to self-identify, self-describe, self-calibrate, to exhibit plug-and-play functionality, etc.
We used Python to develop an IEEE 1451.0 platform-independent graphical user interface to generate and provide sufficient information about almost ANY sensor and sensor platforms for sensor programming purposes, automatic calibration of sensors data, incorporation of back-end demands on data management in TEDS for automatic standard-based data storage, search and discovery purposes. These features are paramount to make data management much less onerous in large scale sensor network. Along with the TEDS Creator, we developed a tool namely HydroUnits for three specific purposes: encoding of physical units in the TEDS, dimensional analysis, and on-the-fly conversion of time series allowing users to retrieve data in a desired equivalent unit while accommodating unforeseen and user-defined units. In addition, our back-end data management comprises the Python/Django equivalent of the CUAHSI Observations Data Model (ODM) namely DjangODM that will be hosted by a MongoDB Database Server which offers more convenience for our application. We are also developing a data which will be paired with the data autoloading capability of Django and a TEDS processing script to populate the database with the incoming data. The Python WaterOneFlow Web Services developed by the Texas Water Development Board will be used to publish the data. The software suite is being tested on the Raspberry Pi as end node and a laptop PC as the base station in a wireless setting.