Such XML-based web services exhibit disadvantages in terms of payload and connectivity in low-bandwidth low energy unreliable networks, such as remote 3G uplinks. Monitoring stations deliver frequent measurements in real-time, but dynamic implementation of measurement frequencies, adapted to certain environmental conditions, are rarely implemented. We describe a responsive integrated hydrological monitoring prototype to calculate rainfall recharge for water management purposes.
When rainfall is observed, a threshold event triggers a reconfiguration task for the soil moisture sensors, using asynchronous, push-based communication implemented with an MQTT queue. A Sensor Planning Service commits that request via MQTT into the wireless sensor network, and updates the measurement frequency of the target sensors to gain higher resolution for the vertical soil water infiltration.
The system integrates a Sensor Observation Service (SOS) including field observations and internet-based environmental data with a rainfall recharge model that allows near-real time calculation of rainfall recharge in the Upper Rangitaiki catchment, Bay of Plenty region in New Zealand.
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| Figure 1: Setup and location of the sensor field site, central North Island, New Zealand |
The prototype site comprises a main station conducting comprehensive measurements of meteorological, hydrological and pedological parameters. For the wireless data transmission within the local site installation XBee-PRO modules from the Digi Company ZigBee IEEE 802.15.4 protocol are used. The main station receives continuous sensor measurements from the attached sensor units, and acts as the gateway to the online SOS and SPS services by providing the communication channel from the local sensor network to the web-enabled data management infrastructure.
The field site has been established in the Upper Rangitaiki catchment (Figure 1) and comprises a field computer (Raspberry Pi) with a direct internet link (GPRS/3G) and a sensor board (Waspmote) that has 12 typical meteorological, hydrological and pedological sensors attached (i.e., wind speed, wind direction, rainfall, 1x groundwater probe, 5x temperature and 3x soil moisture). The Raspberry Pi and Waspmote can be monitored and reprogrammed from an online server.
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| Figure 2: Raw sensor series visualized in a website from a SOS query. |
The site setup allows scaling up to a multitude of low cost, low energy sensor stations throughout the catchment, with only one field computer that serves as data logger for backup. The observations were available in a standardized open format. The website accessed the raw data from the SOS server and plotted data points within 5-10 minutes of field measurement. This website was easily accessible via browsers and smartphones (Figure 2).
The paper was was presented at the 19th AGILE International Conference on Geographic Information Science, 15th of June, in Helsinki, Finlkand.
Kmoch, A., Klug, H., White, P., & Reichel, S. (2016). SensorWeb Semantics on MQTT for responsive Rainfall Recharge Modelling. In 19th AGILE International Conference on Geographic Information Science. Helsinki.
